From 81813acb619bc2bd17f228abeb0474ef5e72a389 Mon Sep 17 00:00:00 2001
From: Marcel Weschke <marcel.weschke@directbox.de>
Date: Sun, 14 Apr 2024 22:25:21 +0200
Subject: [PATCH] Delete Machine Learning for Economics and Finance/Problem Set
 1/Problem_Set_1.html

---
 .../Problem Set 1/Problem_Set_1.html          | 9105 -----------------
 1 file changed, 9105 deletions(-)
 delete mode 100644 Machine Learning for Economics and Finance/Problem Set 1/Problem_Set_1.html
diff --git a/Machine Learning for Economics and Finance/Problem Set 1/Problem_Set_1.html b/Machine Learning for Economics and Finance/Problem Set 1/Problem_Set_1.html
deleted file mode 100644
index d505ac1..0000000
--- a/Machine Learning for Economics and Finance/Problem Set 1/Problem_Set_1.html	
+++ /dev/null
@@ -1,9105 +0,0 @@
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-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
-
-.jp-Collapse {
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-}
-
-.jp-Collapse-header {
-  padding: 1px 12px;
-  background-color: var(--jp-layout-color1);
-  border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
-  color: var(--jp-ui-font-color1);
-  cursor: pointer;
-  display: flex;
-  align-items: center;
-  font-size: var(--jp-ui-font-size0);
-  font-weight: 600;
-  text-transform: uppercase;
-  user-select: none;
-}
-
-.jp-Collapser-icon {
-  height: 16px;
-}
-
-.jp-Collapse-header-collapsed .jp-Collapser-icon {
-  transform: rotate(-90deg);
-  margin: auto 0;
-}
-
-.jp-Collapser-title {
-  line-height: 25px;
-}
-
-.jp-Collapse-contents {
-  padding: 0 12px;
-  background-color: var(--jp-layout-color1);
-  color: var(--jp-ui-font-color1);
-  overflow: auto;
-}
-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
-
-/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */
-
-/**
- * (DEPRECATED) Support for consuming icons as CSS background images
- */
-
-/* Icons urls */
-
-:root {
-  --jp-icon-add-above: url(data:image/svg+xml;base64,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);
-  --jp-icon-add-below: url(data:image/svg+xml;base64,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);
-  --jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
-  --jp-icon-bell: url(data:image/svg+xml;base64,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);
-  --jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
-  --jp-icon-bug: url(data:image/svg+xml;base64,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);
-  --jp-icon-build: url(data:image/svg+xml;base64,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);
-  --jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
-  --jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
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-  --jp-icon-react: url(data:image/svg+xml;base64,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);
-  --jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
-  --jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
-  --jp-icon-regex: url(data:image/svg+xml;base64,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);
-  --jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
-  --jp-icon-running: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDUxMiA1MTIiPgogIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICA8cGF0aCBkPSJNMjU2IDhDMTE5IDggOCAxMTkgOCAyNTZzMTExIDI0OCAyNDggMjQ4IDI0OC0xMTEgMjQ4LTI0OFMzOTMgOCAyNTYgOHptOTYgMzI4YzAgOC44LTcuMiAxNi0xNiAxNkgxNzZjLTguOCAwLTE2LTcuMi0xNi0xNlYxNzZjMC04LjggNy4yLTE2IDE2LTE2aDE2MGM4LjggMCAxNiA3LjIgMTYgMTZ2MTYweiIvPgogIDwvZz4KPC9zdmc+Cg==);
-  --jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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-  --jp-icon-trusted: url(data:image/svg+xml;base64,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);
-  --jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
-  --jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
-  --jp-icon-users: url(data:image/svg+xml;base64,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);
-  --jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
-  --jp-icon-word: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KIDxnIGNsYXNzPSJqcC1pY29uMiIgZmlsbD0iIzQxNDE0MSI+CiAgPHJlY3QgeD0iMiIgeT0iMiIgd2lkdGg9IjE2IiBoZWlnaHQ9IjE2Ii8+CiA8L2c+CiA8ZyBjbGFzcz0ianAtaWNvbi1hY2NlbnQyIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSguNDMgLjA0MDEpIiBmaWxsPSIjZmZmIj4KICA8cGF0aCBkPSJtNC4xNCA4Ljc2cTAuMDY4Mi0xLjg5IDIuNDItMS44OSAxLjE2IDAgMS42OCAwLjQyIDAuNTY3IDAuNDEgMC41NjcgMS4xNnYzLjQ3cTAgMC40NjIgMC41MTQgMC40NjIgMC4xMDMgMCAwLjItMC4wMjMxdjAuNzE0cS0wLjM5OSAwLjEwMy0wLjY1MSAwLjEwMy0wLjQ1MiAwLTAuNjkzLTAuMjItMC4yMzEtMC4yLTAuMjg0LTAuNjYyLTAuOTU2IDAuODcyLTIgMC44NzItMC45MDMgMC0xLjQ3LTAuNDcyLTAuNTI1LTAuNDcyLTAuNTI1LTEuMjYgMC0wLjI2MiAwLjA0NTItMC40NzIgMC4wNTY3LTAuMjIgMC4xMTYtMC4zNzggMC4wNjgyLTAuMTY4IDAuMjMxLTAuMzA0IDAuMTU4LTAuMTQ3IDAuMjYyLTAuMjQyIDAuMTE2LTAuMDkxNCAwLjM2OC0wLjE2OCAwLjI2Mi0wLjA5MTQgMC4zOTktMC4xMjYgMC4xMzYtMC4wNDUyIDAuNDcyLTAuMTAzIDAuMzM2LTAuMDU3OCAwLjUwNC0wLjA3OTggMC4xNTgtMC4wMjMxIDAuNTY3LTAuMDc5OCAwLjU1Ni0wLjA2ODIgMC43NzctMC4yMjEgMC4yMi0wLjE1MiAwLjIyLTAuNDQxdi0wLjI1MnEwLTAuNDMtMC4zNTctMC42NjItMC4zMzYtMC4yMzEtMC45NzYtMC4yMzEtMC42NjIgMC0wLjk5OCAwLjI2Mi0wLjMzNiAwLjI1Mi0wLjM5OSAwLjc5OHptMS44OSAzLjY4cTAuNzg4IDAgMS4yNi0wLjQxIDAuNTA0LTAuNDIgMC41MDQtMC45MDN2LTEuMDVxLTAuMjg0IDAuMTM2LTAuODYxIDAuMjMxLTAuNTY3IDAuMDkxNC0wLjk4NyAwLjE1OC0wLjQyIDAuMDY4Mi0wLjc2NiAwLjMyNi0wLjMzNiAwLjI1Mi0wLjMzNiAwLjcwNHQwLjMwNCAwLjcwNCAwLjg2MSAwLjI1MnoiIHN0cm9rZS13aWR0aD0iMS4wNSIvPgogIDxwYXRoIGQ9Im0xMCA0LjU2aDAuOTQ1djMuMTVxMC42NTEtMC45NzYgMS44OS0wLjk3NiAxLjE2IDAgMS44OSAwLjg0IDAuNjgyIDAuODQgMC42ODIgMi4zMSAwIDEuNDctMC43MDQgMi40Mi0wLjcwNCAwLjg4Mi0xLjg5IDAuODgyLTEuMjYgMC0xLjg5LTEuMDJ2MC43NjZoLTAuODV6bTIuNjIgMy4wNHEtMC43NDYgMC0xLjE2IDAuNjQtMC40NTIgMC42My0wLjQ1MiAxLjY4IDAgMS4wNSAwLjQ1MiAxLjY4dDEuMTYgMC42M3EwLjc3NyAwIDEuMjYtMC42MyAwLjQ5NC0wLjY0IDAuNDk0LTEuNjggMC0xLjA1LTAuNDcyLTEuNjgtMC40NjItMC42NC0xLjI2LTAuNjR6IiBzdHJva2Utd2lkdGg9IjEuMDUiLz4KICA8cGF0aCBkPSJtMi43MyAxNS44IDEzLjYgMC4wMDgxYzAuMDA2OSAwIDAtMi42IDAtMi42IDAtMC4wMDc4LTEuMTUgMC0xLjE1IDAtMC4wMDY5IDAtMC4wMDgzIDEuNS0wLjAwODMgMS41LTJlLTMgLTAuMDAxNC0xMS4zLTAuMDAxNC0xMS4zLTAuMDAxNGwtMC4wMDU5Mi0xLjVjMC0wLjAwNzgtMS4xNyAwLjAwMTMtMS4xNyAwLjAwMTN6IiBzdHJva2Utd2lkdGg9Ii45NzUiLz4KIDwvZz4KPC9zdmc+Cg==);
-  --jp-icon-yaml: url(data:image/svg+xml;base64,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);
-}
-
-/* Icon CSS class declarations */
-
-.jp-AddAboveIcon {
-  background-image: var(--jp-icon-add-above);
-}
-
-.jp-AddBelowIcon {
-  background-image: var(--jp-icon-add-below);
-}
-
-.jp-AddIcon {
-  background-image: var(--jp-icon-add);
-}
-
-.jp-BellIcon {
-  background-image: var(--jp-icon-bell);
-}
-
-.jp-BugDotIcon {
-  background-image: var(--jp-icon-bug-dot);
-}
-
-.jp-BugIcon {
-  background-image: var(--jp-icon-bug);
-}
-
-.jp-BuildIcon {
-  background-image: var(--jp-icon-build);
-}
-
-.jp-CaretDownEmptyIcon {
-  background-image: var(--jp-icon-caret-down-empty);
-}
-
-.jp-CaretDownEmptyThinIcon {
-  background-image: var(--jp-icon-caret-down-empty-thin);
-}
-
-.jp-CaretDownIcon {
-  background-image: var(--jp-icon-caret-down);
-}
-
-.jp-CaretLeftIcon {
-  background-image: var(--jp-icon-caret-left);
-}
-
-.jp-CaretRightIcon {
-  background-image: var(--jp-icon-caret-right);
-}
-
-.jp-CaretUpEmptyThinIcon {
-  background-image: var(--jp-icon-caret-up-empty-thin);
-}
-
-.jp-CaretUpIcon {
-  background-image: var(--jp-icon-caret-up);
-}
-
-.jp-CaseSensitiveIcon {
-  background-image: var(--jp-icon-case-sensitive);
-}
-
-.jp-CheckIcon {
-  background-image: var(--jp-icon-check);
-}
-
-.jp-CircleEmptyIcon {
-  background-image: var(--jp-icon-circle-empty);
-}
-
-.jp-CircleIcon {
-  background-image: var(--jp-icon-circle);
-}
-
-.jp-ClearIcon {
-  background-image: var(--jp-icon-clear);
-}
-
-.jp-CloseIcon {
-  background-image: var(--jp-icon-close);
-}
-
-.jp-CodeCheckIcon {
-  background-image: var(--jp-icon-code-check);
-}
-
-.jp-CodeIcon {
-  background-image: var(--jp-icon-code);
-}
-
-.jp-CollapseAllIcon {
-  background-image: var(--jp-icon-collapse-all);
-}
-
-.jp-ConsoleIcon {
-  background-image: var(--jp-icon-console);
-}
-
-.jp-CopyIcon {
-  background-image: var(--jp-icon-copy);
-}
-
-.jp-CopyrightIcon {
-  background-image: var(--jp-icon-copyright);
-}
-
-.jp-CutIcon {
-  background-image: var(--jp-icon-cut);
-}
-
-.jp-DeleteIcon {
-  background-image: var(--jp-icon-delete);
-}
-
-.jp-DownloadIcon {
-  background-image: var(--jp-icon-download);
-}
-
-.jp-DuplicateIcon {
-  background-image: var(--jp-icon-duplicate);
-}
-
-.jp-EditIcon {
-  background-image: var(--jp-icon-edit);
-}
-
-.jp-EllipsesIcon {
-  background-image: var(--jp-icon-ellipses);
-}
-
-.jp-ErrorIcon {
-  background-image: var(--jp-icon-error);
-}
-
-.jp-ExpandAllIcon {
-  background-image: var(--jp-icon-expand-all);
-}
-
-.jp-ExtensionIcon {
-  background-image: var(--jp-icon-extension);
-}
-
-.jp-FastForwardIcon {
-  background-image: var(--jp-icon-fast-forward);
-}
-
-.jp-FileIcon {
-  background-image: var(--jp-icon-file);
-}
-
-.jp-FileUploadIcon {
-  background-image: var(--jp-icon-file-upload);
-}
-
-.jp-FilterDotIcon {
-  background-image: var(--jp-icon-filter-dot);
-}
-
-.jp-FilterIcon {
-  background-image: var(--jp-icon-filter);
-}
-
-.jp-FilterListIcon {
-  background-image: var(--jp-icon-filter-list);
-}
-
-.jp-FolderFavoriteIcon {
-  background-image: var(--jp-icon-folder-favorite);
-}
-
-.jp-FolderIcon {
-  background-image: var(--jp-icon-folder);
-}
-
-.jp-HomeIcon {
-  background-image: var(--jp-icon-home);
-}
-
-.jp-Html5Icon {
-  background-image: var(--jp-icon-html5);
-}
-
-.jp-ImageIcon {
-  background-image: var(--jp-icon-image);
-}
-
-.jp-InfoIcon {
-  background-image: var(--jp-icon-info);
-}
-
-.jp-InspectorIcon {
-  background-image: var(--jp-icon-inspector);
-}
-
-.jp-JsonIcon {
-  background-image: var(--jp-icon-json);
-}
-
-.jp-JuliaIcon {
-  background-image: var(--jp-icon-julia);
-}
-
-.jp-JupyterFaviconIcon {
-  background-image: var(--jp-icon-jupyter-favicon);
-}
-
-.jp-JupyterIcon {
-  background-image: var(--jp-icon-jupyter);
-}
-
-.jp-JupyterlabWordmarkIcon {
-  background-image: var(--jp-icon-jupyterlab-wordmark);
-}
-
-.jp-KernelIcon {
-  background-image: var(--jp-icon-kernel);
-}
-
-.jp-KeyboardIcon {
-  background-image: var(--jp-icon-keyboard);
-}
-
-.jp-LaunchIcon {
-  background-image: var(--jp-icon-launch);
-}
-
-.jp-LauncherIcon {
-  background-image: var(--jp-icon-launcher);
-}
-
-.jp-LineFormIcon {
-  background-image: var(--jp-icon-line-form);
-}
-
-.jp-LinkIcon {
-  background-image: var(--jp-icon-link);
-}
-
-.jp-ListIcon {
-  background-image: var(--jp-icon-list);
-}
-
-.jp-MarkdownIcon {
-  background-image: var(--jp-icon-markdown);
-}
-
-.jp-MoveDownIcon {
-  background-image: var(--jp-icon-move-down);
-}
-
-.jp-MoveUpIcon {
-  background-image: var(--jp-icon-move-up);
-}
-
-.jp-NewFolderIcon {
-  background-image: var(--jp-icon-new-folder);
-}
-
-.jp-NotTrustedIcon {
-  background-image: var(--jp-icon-not-trusted);
-}
-
-.jp-NotebookIcon {
-  background-image: var(--jp-icon-notebook);
-}
-
-.jp-NumberingIcon {
-  background-image: var(--jp-icon-numbering);
-}
-
-.jp-OfflineBoltIcon {
-  background-image: var(--jp-icon-offline-bolt);
-}
-
-.jp-PaletteIcon {
-  background-image: var(--jp-icon-palette);
-}
-
-.jp-PasteIcon {
-  background-image: var(--jp-icon-paste);
-}
-
-.jp-PdfIcon {
-  background-image: var(--jp-icon-pdf);
-}
-
-.jp-PythonIcon {
-  background-image: var(--jp-icon-python);
-}
-
-.jp-RKernelIcon {
-  background-image: var(--jp-icon-r-kernel);
-}
-
-.jp-ReactIcon {
-  background-image: var(--jp-icon-react);
-}
-
-.jp-RedoIcon {
-  background-image: var(--jp-icon-redo);
-}
-
-.jp-RefreshIcon {
-  background-image: var(--jp-icon-refresh);
-}
-
-.jp-RegexIcon {
-  background-image: var(--jp-icon-regex);
-}
-
-.jp-RunIcon {
-  background-image: var(--jp-icon-run);
-}
-
-.jp-RunningIcon {
-  background-image: var(--jp-icon-running);
-}
-
-.jp-SaveIcon {
-  background-image: var(--jp-icon-save);
-}
-
-.jp-SearchIcon {
-  background-image: var(--jp-icon-search);
-}
-
-.jp-SettingsIcon {
-  background-image: var(--jp-icon-settings);
-}
-
-.jp-ShareIcon {
-  background-image: var(--jp-icon-share);
-}
-
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-/*-----------------------------------------------------------------------------
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-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
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-/*-----------------------------------------------------------------------------
-| Printing
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-/*
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-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
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-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
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-/*-----------------------------------------------------------------------------
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-:root {
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-/*-----------------------------------------------------------------------------
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-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
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-/*-----------------------------------------------------------------------------
-| Variables
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-/*-----------------------------------------------------------------------------
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-/*-----------------------------------------------------------------------------
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-/* For devices that support hovering, hide footer until hover */
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-  .jp-Notebook-footer:focus,
-  .jp-Notebook-footer:hover {
-    opacity: 1;
-  }
-}
-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
-
-/*-----------------------------------------------------------------------------
-| Imports
-|----------------------------------------------------------------------------*/
-
-/*-----------------------------------------------------------------------------
-| CSS variables
-|----------------------------------------------------------------------------*/
-
-:root {
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-/*-----------------------------------------------------------------------------
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-/* stylelint-disable selector-max-class */
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-/*-----------------------------------------------------------------------------
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-/*-----------------------------------------------------------------------------
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-
-/*-----------------------------------------------------------------------------
-| Cell toolbar
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-.jp-NotebookTools {
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-/*-----------------------------------------------------------------------------
-| Presentation Mode (.jp-mod-presentationMode)
-|----------------------------------------------------------------------------*/
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-/*-----------------------------------------------------------------------------
-| Side-by-side Mode (.jp-mod-sideBySide)
-|----------------------------------------------------------------------------*/
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-.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
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-.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
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-.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
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-  content: '';
-  display: block;
-  background: var(--jp-border-color2);
-  height: 100%;
-  width: 5px;
-}
-
-.jp-mod-sideBySide.jp-Notebook
-  .jp-CodeCell.jp-mod-resizedCell
-  .jp-CellResizeHandle::after {
-  background: var(--jp-border-color0);
-}
-
-.jp-CellResizeHandle {
-  display: none;
-}
-
-/*-----------------------------------------------------------------------------
-| Placeholder
-|----------------------------------------------------------------------------*/
-
-.jp-Cell-Placeholder {
-  padding-left: 55px;
-}
-
-.jp-Cell-Placeholder-wrapper {
-  background: #fff;
-  border: 1px solid;
-  border-color: #e5e6e9 #dfe0e4 #d0d1d5;
-  border-radius: 4px;
-  -webkit-border-radius: 4px;
-  margin: 10px 15px;
-}
-
-.jp-Cell-Placeholder-wrapper-inner {
-  padding: 15px;
-  position: relative;
-}
-
-.jp-Cell-Placeholder-wrapper-body {
-  background-repeat: repeat;
-  background-size: 50% auto;
-}
-
-.jp-Cell-Placeholder-wrapper-body div {
-  background: #f6f7f8;
-  background-image: -webkit-linear-gradient(
-    left,
-    #f6f7f8 0%,
-    #edeef1 20%,
-    #f6f7f8 40%,
-    #f6f7f8 100%
-  );
-  background-repeat: no-repeat;
-  background-size: 800px 104px;
-  height: 104px;
-  position: absolute;
-  right: 15px;
-  left: 15px;
-  top: 15px;
-}
-
-div.jp-Cell-Placeholder-h1 {
-  top: 20px;
-  height: 20px;
-  left: 15px;
-  width: 150px;
-}
-
-div.jp-Cell-Placeholder-h2 {
-  left: 15px;
-  top: 50px;
-  height: 10px;
-  width: 100px;
-}
-
-div.jp-Cell-Placeholder-content-1,
-div.jp-Cell-Placeholder-content-2,
-div.jp-Cell-Placeholder-content-3 {
-  left: 15px;
-  right: 15px;
-  height: 10px;
-}
-
-div.jp-Cell-Placeholder-content-1 {
-  top: 100px;
-}
-
-div.jp-Cell-Placeholder-content-2 {
-  top: 120px;
-}
-
-div.jp-Cell-Placeholder-content-3 {
-  top: 140px;
-}
-
-</style>
-<style type="text/css">
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
-
-/*
-The following CSS variables define the main, public API for styling JupyterLab.
-These variables should be used by all plugins wherever possible. In other
-words, plugins should not define custom colors, sizes, etc unless absolutely
-necessary. This enables users to change the visual theme of JupyterLab
-by changing these variables.
-
-Many variables appear in an ordered sequence (0,1,2,3). These sequences
-are designed to work well together, so for example, `--jp-border-color1` should
-be used with `--jp-layout-color1`. The numbers have the following meanings:
-
-* 0: super-primary, reserved for special emphasis
-* 1: primary, most important under normal situations
-* 2: secondary, next most important under normal situations
-* 3: tertiary, next most important under normal situations
-
-Throughout JupyterLab, we are mostly following principles from Google's
-Material Design when selecting colors. We are not, however, following
-all of MD as it is not optimized for dense, information rich UIs.
-*/
-
-:root {
-  /* Elevation
-   *
-   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
-   *
-   * https://github.com/material-components/material-components-web
-   * https://material-components-web.appspot.com/elevation.html
-   */
-
-  --jp-shadow-base-lightness: 0;
-  --jp-shadow-umbra-color: rgba(
-    var(--jp-shadow-base-lightness),
-    var(--jp-shadow-base-lightness),
-    var(--jp-shadow-base-lightness),
-    0.2
-  );
-  --jp-shadow-penumbra-color: rgba(
-    var(--jp-shadow-base-lightness),
-    var(--jp-shadow-base-lightness),
-    var(--jp-shadow-base-lightness),
-    0.14
-  );
-  --jp-shadow-ambient-color: rgba(
-    var(--jp-shadow-base-lightness),
-    var(--jp-shadow-base-lightness),
-    var(--jp-shadow-base-lightness),
-    0.12
-  );
-  --jp-elevation-z0: none;
-  --jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
-    0 1px 1px 0 var(--jp-shadow-penumbra-color),
-    0 1px 3px 0 var(--jp-shadow-ambient-color);
-  --jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
-    0 2px 2px 0 var(--jp-shadow-penumbra-color),
-    0 1px 5px 0 var(--jp-shadow-ambient-color);
-  --jp-elevation-z4: 0 2px 4px -1px var(--jp-shadow-umbra-color),
-    0 4px 5px 0 var(--jp-shadow-penumbra-color),
-    0 1px 10px 0 var(--jp-shadow-ambient-color);
-  --jp-elevation-z6: 0 3px 5px -1px var(--jp-shadow-umbra-color),
-    0 6px 10px 0 var(--jp-shadow-penumbra-color),
-    0 1px 18px 0 var(--jp-shadow-ambient-color);
-  --jp-elevation-z8: 0 5px 5px -3px var(--jp-shadow-umbra-color),
-    0 8px 10px 1px var(--jp-shadow-penumbra-color),
-    0 3px 14px 2px var(--jp-shadow-ambient-color);
-  --jp-elevation-z12: 0 7px 8px -4px var(--jp-shadow-umbra-color),
-    0 12px 17px 2px var(--jp-shadow-penumbra-color),
-    0 5px 22px 4px var(--jp-shadow-ambient-color);
-  --jp-elevation-z16: 0 8px 10px -5px var(--jp-shadow-umbra-color),
-    0 16px 24px 2px var(--jp-shadow-penumbra-color),
-    0 6px 30px 5px var(--jp-shadow-ambient-color);
-  --jp-elevation-z20: 0 10px 13px -6px var(--jp-shadow-umbra-color),
-    0 20px 31px 3px var(--jp-shadow-penumbra-color),
-    0 8px 38px 7px var(--jp-shadow-ambient-color);
-  --jp-elevation-z24: 0 11px 15px -7px var(--jp-shadow-umbra-color),
-    0 24px 38px 3px var(--jp-shadow-penumbra-color),
-    0 9px 46px 8px var(--jp-shadow-ambient-color);
-
-  /* Borders
-   *
-   * The following variables, specify the visual styling of borders in JupyterLab.
-   */
-
-  --jp-border-width: 1px;
-  --jp-border-color0: var(--md-grey-400);
-  --jp-border-color1: var(--md-grey-400);
-  --jp-border-color2: var(--md-grey-300);
-  --jp-border-color3: var(--md-grey-200);
-  --jp-inverse-border-color: var(--md-grey-600);
-  --jp-border-radius: 2px;
-
-  /* UI Fonts
-   *
-   * The UI font CSS variables are used for the typography all of the JupyterLab
-   * user interface elements that are not directly user generated content.
-   *
-   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
-   * is applied to a parent element. When children elements, such as headings, are sized
-   * in em all things will be computed relative to that body size.
-   */
-
-  --jp-ui-font-scale-factor: 1.2;
-  --jp-ui-font-size0: 0.83333em;
-  --jp-ui-font-size1: 13px; /* Base font size */
-  --jp-ui-font-size2: 1.2em;
-  --jp-ui-font-size3: 1.44em;
-  --jp-ui-font-family: system-ui, -apple-system, blinkmacsystemfont, 'Segoe UI',
-    helvetica, arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
-    'Segoe UI Symbol';
-
-  /*
-   * Use these font colors against the corresponding main layout colors.
-   * In a light theme, these go from dark to light.
-   */
-
-  /* Defaults use Material Design specification */
-  --jp-ui-font-color0: rgba(0, 0, 0, 1);
-  --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
-  --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
-  --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
-
-  /*
-   * Use these against the brand/accent/warn/error colors.
-   * These will typically go from light to darker, in both a dark and light theme.
-   */
-
-  --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
-  --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
-  --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
-  --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
-
-  /* Content Fonts
-   *
-   * Content font variables are used for typography of user generated content.
-   *
-   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
-   * is applied to a parent element. When children elements, such as headings, are sized
-   * in em all things will be computed relative to that body size.
-   */
-
-  --jp-content-line-height: 1.6;
-  --jp-content-font-scale-factor: 1.2;
-  --jp-content-font-size0: 0.83333em;
-  --jp-content-font-size1: 14px; /* Base font size */
-  --jp-content-font-size2: 1.2em;
-  --jp-content-font-size3: 1.44em;
-  --jp-content-font-size4: 1.728em;
-  --jp-content-font-size5: 2.0736em;
-
-  /* This gives a magnification of about 125% in presentation mode over normal. */
-  --jp-content-presentation-font-size1: 17px;
-  --jp-content-heading-line-height: 1;
-  --jp-content-heading-margin-top: 1.2em;
-  --jp-content-heading-margin-bottom: 0.8em;
-  --jp-content-heading-font-weight: 500;
-
-  /* Defaults use Material Design specification */
-  --jp-content-font-color0: rgba(0, 0, 0, 1);
-  --jp-content-font-color1: rgba(0, 0, 0, 0.87);
-  --jp-content-font-color2: rgba(0, 0, 0, 0.54);
-  --jp-content-font-color3: rgba(0, 0, 0, 0.38);
-  --jp-content-link-color: var(--md-blue-900);
-  --jp-content-font-family: system-ui, -apple-system, blinkmacsystemfont,
-    'Segoe UI', helvetica, arial, sans-serif, 'Apple Color Emoji',
-    'Segoe UI Emoji', 'Segoe UI Symbol';
-
-  /*
-   * Code Fonts
-   *
-   * Code font variables are used for typography of code and other monospaces content.
-   */
-
-  --jp-code-font-size: 13px;
-  --jp-code-line-height: 1.3077; /* 17px for 13px base */
-  --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
-  --jp-code-font-family-default: menlo, consolas, 'DejaVu Sans Mono', monospace;
-  --jp-code-font-family: var(--jp-code-font-family-default);
-
-  /* This gives a magnification of about 125% in presentation mode over normal. */
-  --jp-code-presentation-font-size: 16px;
-
-  /* may need to tweak cursor width if you change font size */
-  --jp-code-cursor-width0: 1.4px;
-  --jp-code-cursor-width1: 2px;
-  --jp-code-cursor-width2: 4px;
-
-  /* Layout
-   *
-   * The following are the main layout colors use in JupyterLab. In a light
-   * theme these would go from light to dark.
-   */
-
-  --jp-layout-color0: white;
-  --jp-layout-color1: white;
-  --jp-layout-color2: var(--md-grey-200);
-  --jp-layout-color3: var(--md-grey-400);
-  --jp-layout-color4: var(--md-grey-600);
-
-  /* Inverse Layout
-   *
-   * The following are the inverse layout colors use in JupyterLab. In a light
-   * theme these would go from dark to light.
-   */
-
-  --jp-inverse-layout-color0: #111;
-  --jp-inverse-layout-color1: var(--md-grey-900);
-  --jp-inverse-layout-color2: var(--md-grey-800);
-  --jp-inverse-layout-color3: var(--md-grey-700);
-  --jp-inverse-layout-color4: var(--md-grey-600);
-
-  /* Brand/accent */
-
-  --jp-brand-color0: var(--md-blue-900);
-  --jp-brand-color1: var(--md-blue-700);
-  --jp-brand-color2: var(--md-blue-300);
-  --jp-brand-color3: var(--md-blue-100);
-  --jp-brand-color4: var(--md-blue-50);
-  --jp-accent-color0: var(--md-green-900);
-  --jp-accent-color1: var(--md-green-700);
-  --jp-accent-color2: var(--md-green-300);
-  --jp-accent-color3: var(--md-green-100);
-
-  /* State colors (warn, error, success, info) */
-
-  --jp-warn-color0: var(--md-orange-900);
-  --jp-warn-color1: var(--md-orange-700);
-  --jp-warn-color2: var(--md-orange-300);
-  --jp-warn-color3: var(--md-orange-100);
-  --jp-error-color0: var(--md-red-900);
-  --jp-error-color1: var(--md-red-700);
-  --jp-error-color2: var(--md-red-300);
-  --jp-error-color3: var(--md-red-100);
-  --jp-success-color0: var(--md-green-900);
-  --jp-success-color1: var(--md-green-700);
-  --jp-success-color2: var(--md-green-300);
-  --jp-success-color3: var(--md-green-100);
-  --jp-info-color0: var(--md-cyan-900);
-  --jp-info-color1: var(--md-cyan-700);
-  --jp-info-color2: var(--md-cyan-300);
-  --jp-info-color3: var(--md-cyan-100);
-
-  /* Cell specific styles */
-
-  --jp-cell-padding: 5px;
-  --jp-cell-collapser-width: 8px;
-  --jp-cell-collapser-min-height: 20px;
-  --jp-cell-collapser-not-active-hover-opacity: 0.6;
-  --jp-cell-editor-background: var(--md-grey-100);
-  --jp-cell-editor-border-color: var(--md-grey-300);
-  --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
-  --jp-cell-editor-active-background: var(--jp-layout-color0);
-  --jp-cell-editor-active-border-color: var(--jp-brand-color1);
-  --jp-cell-prompt-width: 64px;
-  --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
-  --jp-cell-prompt-letter-spacing: 0;
-  --jp-cell-prompt-opacity: 1;
-  --jp-cell-prompt-not-active-opacity: 0.5;
-  --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
-
-  /* A custom blend of MD grey and blue 600
-   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
-  --jp-cell-inprompt-font-color: #307fc1;
-
-  /* A custom blend of MD grey and orange 600
-   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
-  --jp-cell-outprompt-font-color: #bf5b3d;
-
-  /* Notebook specific styles */
-
-  --jp-notebook-padding: 10px;
-  --jp-notebook-select-background: var(--jp-layout-color1);
-  --jp-notebook-multiselected-color: var(--md-blue-50);
-
-  /* The scroll padding is calculated to fill enough space at the bottom of the
-  notebook to show one single-line cell (with appropriate padding) at the top
-  when the notebook is scrolled all the way to the bottom. We also subtract one
-  pixel so that no scrollbar appears if we have just one single-line cell in the
-  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
-  */
-  --jp-notebook-scroll-padding: calc(
-    100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
-      var(--jp-code-padding) - var(--jp-cell-padding) - 1px
-  );
-
-  /* Rendermime styles */
-
-  --jp-rendermime-error-background: #fdd;
-  --jp-rendermime-table-row-background: var(--md-grey-100);
-  --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
-
-  /* Dialog specific styles */
-
-  --jp-dialog-background: rgba(0, 0, 0, 0.25);
-
-  /* Console specific styles */
-
-  --jp-console-padding: 10px;
-
-  /* Toolbar specific styles */
-
-  --jp-toolbar-border-color: var(--jp-border-color1);
-  --jp-toolbar-micro-height: 8px;
-  --jp-toolbar-background: var(--jp-layout-color1);
-  --jp-toolbar-box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.24);
-  --jp-toolbar-header-margin: 4px 4px 0 4px;
-  --jp-toolbar-active-background: var(--md-grey-300);
-
-  /* Statusbar specific styles */
-
-  --jp-statusbar-height: 24px;
-
-  /* Input field styles */
-
-  --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
-  --jp-input-active-background: var(--jp-layout-color1);
-  --jp-input-hover-background: var(--jp-layout-color1);
-  --jp-input-background: var(--md-grey-100);
-  --jp-input-border-color: var(--jp-inverse-border-color);
-  --jp-input-active-border-color: var(--jp-brand-color1);
-  --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
-
-  /* General editor styles */
-
-  --jp-editor-selected-background: #d9d9d9;
-  --jp-editor-selected-focused-background: #d7d4f0;
-  --jp-editor-cursor-color: var(--jp-ui-font-color0);
-
-  /* Code mirror specific styles */
-
-  --jp-mirror-editor-keyword-color: #008000;
-  --jp-mirror-editor-atom-color: #88f;
-  --jp-mirror-editor-number-color: #080;
-  --jp-mirror-editor-def-color: #00f;
-  --jp-mirror-editor-variable-color: var(--md-grey-900);
-  --jp-mirror-editor-variable-2-color: rgb(0, 54, 109);
-  --jp-mirror-editor-variable-3-color: #085;
-  --jp-mirror-editor-punctuation-color: #05a;
-  --jp-mirror-editor-property-color: #05a;
-  --jp-mirror-editor-operator-color: #a2f;
-  --jp-mirror-editor-comment-color: #408080;
-  --jp-mirror-editor-string-color: #ba2121;
-  --jp-mirror-editor-string-2-color: #708;
-  --jp-mirror-editor-meta-color: #a2f;
-  --jp-mirror-editor-qualifier-color: #555;
-  --jp-mirror-editor-builtin-color: #008000;
-  --jp-mirror-editor-bracket-color: #997;
-  --jp-mirror-editor-tag-color: #170;
-  --jp-mirror-editor-attribute-color: #00c;
-  --jp-mirror-editor-header-color: blue;
-  --jp-mirror-editor-quote-color: #090;
-  --jp-mirror-editor-link-color: #00c;
-  --jp-mirror-editor-error-color: #f00;
-  --jp-mirror-editor-hr-color: #999;
-
-  /*
-    RTC user specific colors.
-    These colors are used for the cursor, username in the editor,
-    and the icon of the user.
-  */
-
-  --jp-collaborator-color1: #ffad8e;
-  --jp-collaborator-color2: #dac83d;
-  --jp-collaborator-color3: #72dd76;
-  --jp-collaborator-color4: #00e4d0;
-  --jp-collaborator-color5: #45d4ff;
-  --jp-collaborator-color6: #e2b1ff;
-  --jp-collaborator-color7: #ff9de6;
-
-  /* Vega extension styles */
-
-  --jp-vega-background: white;
-
-  /* Sidebar-related styles */
-
-  --jp-sidebar-min-width: 250px;
-
-  /* Search-related styles */
-
-  --jp-search-toggle-off-opacity: 0.5;
-  --jp-search-toggle-hover-opacity: 0.8;
-  --jp-search-toggle-on-opacity: 1;
-  --jp-search-selected-match-background-color: rgb(245, 200, 0);
-  --jp-search-selected-match-color: black;
-  --jp-search-unselected-match-background-color: var(
-    --jp-inverse-layout-color0
-  );
-  --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
-
-  /* Icon colors that work well with light or dark backgrounds */
-  --jp-icon-contrast-color0: var(--md-purple-600);
-  --jp-icon-contrast-color1: var(--md-green-600);
-  --jp-icon-contrast-color2: var(--md-pink-600);
-  --jp-icon-contrast-color3: var(--md-blue-600);
-
-  /* Button colors */
-  --jp-accept-color-normal: var(--md-blue-700);
-  --jp-accept-color-hover: var(--md-blue-800);
-  --jp-accept-color-active: var(--md-blue-900);
-  --jp-warn-color-normal: var(--md-red-700);
-  --jp-warn-color-hover: var(--md-red-800);
-  --jp-warn-color-active: var(--md-red-900);
-  --jp-reject-color-normal: var(--md-grey-600);
-  --jp-reject-color-hover: var(--md-grey-700);
-  --jp-reject-color-active: var(--md-grey-800);
-
-  /* File or activity icons and switch semantic variables */
-  --jp-jupyter-icon-color: #f37626;
-  --jp-notebook-icon-color: #f37626;
-  --jp-json-icon-color: var(--md-orange-700);
-  --jp-console-icon-background-color: var(--md-blue-700);
-  --jp-console-icon-color: white;
-  --jp-terminal-icon-background-color: var(--md-grey-800);
-  --jp-terminal-icon-color: var(--md-grey-200);
-  --jp-text-editor-icon-color: var(--md-grey-700);
-  --jp-inspector-icon-color: var(--md-grey-700);
-  --jp-switch-color: var(--md-grey-400);
-  --jp-switch-true-position-color: var(--md-orange-900);
-}
-</style>
-<style type="text/css">
-/* Force rendering true colors when outputing to pdf */
-* {
-  -webkit-print-color-adjust: exact;
-}
-
-/* Misc */
-a.anchor-link {
-  display: none;
-}
-
-/* Input area styling */
-.jp-InputArea {
-  overflow: hidden;
-}
-
-.jp-InputArea-editor {
-  overflow: hidden;
-}
-
-.cm-editor.cm-s-jupyter .highlight pre {
-/* weird, but --jp-code-padding defined to be 5px but 4px horizontal padding is hardcoded for pre.cm-line */
-  padding: var(--jp-code-padding) 4px;
-  margin: 0;
-
-  font-family: inherit;
-  font-size: inherit;
-  line-height: inherit;
-  color: inherit;
-
-}
-
-.jp-OutputArea-output pre {
-  line-height: inherit;
-  font-family: inherit;
-}
-
-.jp-RenderedText pre {
-  color: var(--jp-content-font-color1);
-  font-size: var(--jp-code-font-size);
-}
-
-/* Hiding the collapser by default */
-.jp-Collapser {
-  display: none;
-}
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h1><center>Maschinelles Lernen für Wirtschaft und Finanzen<br/>Universität Hamburg</center></h1>
-<h2><center><b>Aufgabenserie 1</b></center></h2>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=66280676-a99a-4e20-98a7-c727bacc0dcd">
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-<h2 id="Inhaltsverzeichnis:">Inhaltsverzeichnis:<a class="anchor-link" href="#Inhaltsverzeichnis:">¶</a></h2><ul>
-<li><a href="#Vorab">Vorab</a></li>
-<li><a href="#Aufbau">Aufbau</a></li>
-<li><a href="#Aufgabe-1">Aufgabe 1</a></li>
-<li><a href="#Aufgabe-2">Aufgabe 2</a></li>
-<li><a href="#Aufgabe-3">Aufgabe 3</a></li>
-<li><a href="#Anhang">Anhang</a></li>
-<li><a href="#Literatur">Literatur</a></li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=040dc2a4-910e-4cf5-9d1e-62fe7d0a8efd">
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-<h3 id="Vorab--">Vorab  <a class="anchor" id="Vorab"></a><a class="anchor-link" href="#Vorab--">¶</a></h3><ul>
-<li>Ziel dieses Tutorials ist es, einige der wichtigsten Konzepte zu üben, die in den ersten Wochen des ML Kurses behandelt werden.</li>
-</ul>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=baac6966-d67a-4a66-acec-8ef6411c4f66">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h3 id="Aufbau-">Aufbau <a class="anchor" id="Aufbau"></a><a class="anchor-link" href="#Aufbau-">¶</a></h3><p><strong>Die Hauptaufgabe dieses Problemsets besteht darin, die Rendite des US-Aktienmarktes vorherzusagen.</strong> Dazu verwenden wir den Datensatz <code>stockmarketdata.RDS</code> von Welch und Goyal (2007), der auf <em>OpenOlat</em> verfügbar ist. Der Datensatz enthält vierteljährliche Renditen des US-Aktienmarktes ($ret$) sowie mehrere andere Variablen, die von Finanzforschern vorgeschlagen wurden, um Aktienrenditen vorherzusagen. Eine Liste aller Variablen zusammen mit einer Beschreibung finden Sie im Anhang. Für das erste Quartal 1999 (<em>date = 19991</em>) enthält er beispielsweise Variablen wie die Rendite des Aktienmarktes ($ret_{t}$), das Dividenden-zu-Preis-Verhältnis ($DP_{t}$), den Kreditspread ($CS_{t}$) und so weiter. Da das Ziel darin besteht, Renditen im folgenden Quartal vorherzusagen, interessieren uns Modelle der Form</p>
-<p>$ret_{t+1} = f (DP_{t}, CS_{t},  ...) + ϵ_{t+1}$</p>
-<p>Angenommen, Sie sind ein Vermögensverwalter und es ist Ende 1994, dh Sie haben alle Daten vor 1995 zur Verfügung, um Ihr Modell zu trainieren und zu validieren. Ihr Ziel ist es, ein Modell zu entwickeln, das nicht nur in der Stichprobe funktioniert, sondern auch zukünftige Renditen vorhersagen kann (nach 1995).</p>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=87902d82-5336-456b-bec8-403530c75f00">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="Aufgabe-1:-Vorbereitung-und-Analyse-der-Daten-">Aufgabe 1: Vorbereitung und Analyse der Daten <a class="anchor" id="Aufgabe-1"></a><a class="anchor-link" href="#Aufgabe-1:-Vorbereitung-und-Analyse-der-Daten-">¶</a></h2><ol>
-<li><p>Zuerst müssen wir die Daten so ausrichten, dass eine Zeile, die die Merkmale für das Datum t enthält, die Rendite für das Datum $t + 1$ enthält (anstelle der Rendite für das Datum t, wie es derzeit der Fall ist). Dadurch wird sichergestellt, dass wir tatsächlich die Renditen für das nächste Quartal vorhersagen. Hierfür müssen wir die Zeitreihe der Rendite um einen Zeitraum nach vorne verschieben. (Hinweis: Verwenden Sie die Funktion <code>shift()</code>, um eine neue Variable zum Dataframe hinzuzufügen, die die Rendite um einen Zeitraum verschiebt. Entfernen Sie anschließend die alte Zeitreihe der Rendite aus dem Dataframe.)</p>
-</li>
-<li><p>Entfernen Sie alle Zeilen, die fehlende Werte im Datensatz enthalten. Google bietet viele verschiedene Möglichkeiten, wie Sie dies tun können. Wenn Sie Schwierigkeiten mit dieser Übung haben, tun Sie Folgendes: Verwenden Sie die kombination der Funktionen <code>.isna().sum()</code> auf das DataFrame, um die Summe aller Zeilen zu bestimmen, die fehlende Werte enthalten. Finden Sie die fehlenden Werte für diese Variablen durch Augeninspektion. Beginnen und beenden Sie die Stichprobe so, dass diese Zeilen mit fehlenden Werten nicht enthalten sind. Verwenden Sie anschließend erneut die Funktion <code>.isna().sum()</code>, um sicherzustellen, dass Sie alle fehlenden Werte entfernt haben.</p>
-</li>
-<li><p>Teilen Sie die Stichprobe in zwei Teile auf. Daten vor 1995 für das Training und die Validierung und Daten nach und einschließlich 1995 für das Out-of-Sample-Testen.</p>
-</li>
-<li><p>Berechnen Sie die durchschnittliche vierteljährliche Rendite und ihre Standardabweichung in den Trainings- und Testdaten. Gibt es etwas, das erwähnenswert ist?</p>
-</li>
-<li><p>Berechnen Sie die Korrelationsmatrix für die Trainingsdaten (einschließlich sowohl der Ergebnisse als auch der Merkmale). Gibt es etwas, das erwähnenswert ist?</p>
-</li>
-</ol>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=c53eedac-cd76-4649-aebc-dc0c0d26c63e">
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-<h3 id="Vorbereitung:">Vorbereitung:<a class="anchor-link" href="#Vorbereitung:">¶</a></h3><ul>
-<li>Einlesen und Grundanpassungen der <code>stockmarketdata.rds</code> Daten</li>
-</ul>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=44ad3d11-abe5-4366-91dc-ac319197b93c">
-<div class="jp-Cell-inputWrapper" tabindex="0">
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-</div>
-<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span> <span class="c1"># Paket zum Unterdrücken von "warnings"</span>
-<span class="kn">import</span> <span class="nn">pyreadr</span>  <span class="c1"># Paket zum einlesen von RDS Datein - https://github.com/ofajardo/pyreadr</span>
-
-<span class="n">df</span> <span class="o">=</span> <span class="n">pyreadr</span><span class="o">.</span><span class="n">read_r</span><span class="p">(</span><span class="s1">'stockmarketdata.rds'</span><span class="p">)</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="c1"># Extrahieren des verfügbaren Pandas-DataFrame Objekts.</span>
-
-<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span> <span class="c1"># Zeige die ersten 5 Zeilen des DataFrames an.</span>
-</pre></div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[1]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
-<div>
-<style scoped="">
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>date</th>
-<th>ret</th>
-<th>DP</th>
-<th>CS</th>
-<th>ntis</th>
-<th>cay</th>
-<th>TS</th>
-<th>svar</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>0</th>
-<td>19291.0</td>
-<td>0.050490</td>
-<td>-3.367688</td>
-<td>0.010357</td>
-<td>0.079805</td>
-<td>NaN</td>
-<td>-0.0083</td>
-<td>0.007982</td>
-</tr>
-<tr>
-<th>1</th>
-<td>19292.0</td>
-<td>0.087235</td>
-<td>-3.412851</td>
-<td>0.011105</td>
-<td>0.116197</td>
-<td>NaN</td>
-<td>-0.0113</td>
-<td>0.008405</td>
-</tr>
-<tr>
-<th>2</th>
-<td>19293.0</td>
-<td>0.091067</td>
-<td>-3.468392</td>
-<td>0.012517</td>
-<td>0.121390</td>
-<td>NaN</td>
-<td>-0.0083</td>
-<td>0.008056</td>
-</tr>
-<tr>
-<th>3</th>
-<td>19294.0</td>
-<td>-0.268418</td>
-<td>-3.096184</td>
-<td>0.012155</td>
-<td>0.163522</td>
-<td>NaN</td>
-<td>0.0037</td>
-<td>0.100171</td>
-</tr>
-<tr>
-<th>4</th>
-<td>19301.0</td>
-<td>0.165884</td>
-<td>-3.252345</td>
-<td>0.010554</td>
-<td>0.145496</td>
-<td>NaN</td>
-<td>0.0040</td>
-<td>0.004662</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=6d3ccd77-5c88-4a7a-9225-efd36768d36d">
-<div class="jp-Cell-inputWrapper" tabindex="0">
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-</div>
-<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-
-<span class="c1"># Definierung einer Funktion, um die numerische Darstellung in das Format 'JJJJ-QX' zu erreichen.</span>
-<span class="k">def</span> <span class="nf">convert_to_quarterly_date</span><span class="p">(</span><span class="n">numeric_date</span><span class="p">):</span>
-    <span class="n">year</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">numeric_date</span><span class="p">)</span> <span class="o">//</span> <span class="mi">10</span>  <span class="c1"># Ziehen der Information des Jahres</span>
-    <span class="n">quarter</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">numeric_date</span><span class="p">)</span> <span class="o">%</span> <span class="mi">10</span>  <span class="c1"># Ziehen der Information des Quartals</span>
-    <span class="n">quarter_str</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'Q</span><span class="si">{</span><span class="n">quarter</span><span class="si">}</span><span class="s1">'</span>  <span class="c1"># Umwandling der "float" Quartal Daten zu "string" Format</span>
-    <span class="k">return</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">year</span><span class="si">}</span><span class="s1">-Q</span><span class="si">{</span><span class="n">quarter</span><span class="si">}</span><span class="s1">'</span>  <span class="c1"># Rückgabe der überschriebenen Schreibweise</span>
-
-<span class="c1"># Anwendung der Fuktion auf die Variable "date".</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">'date'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'date'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">convert_to_quarterly_date</span><span class="p">)</span>
-
-<span class="n">df</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[2]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
-<div>
-<style scoped="">
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>date</th>
-<th>ret</th>
-<th>DP</th>
-<th>CS</th>
-<th>ntis</th>
-<th>cay</th>
-<th>TS</th>
-<th>svar</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>360</th>
-<td>2019-Q1</td>
-<td>0.137489</td>
-<td>-3.943400</td>
-<td>0.010258</td>
-<td>-0.023230</td>
-<td>-0.039336</td>
-<td>0.0017</td>
-<td>0.004651</td>
-</tr>
-<tr>
-<th>361</th>
-<td>2019-Q2</td>
-<td>0.042688</td>
-<td>-3.960033</td>
-<td>0.010006</td>
-<td>-0.012562</td>
-<td>-0.033844</td>
-<td>-0.0010</td>
-<td>0.003271</td>
-</tr>
-<tr>
-<th>362</th>
-<td>2019-Q3</td>
-<td>0.017042</td>
-<td>-3.951689</td>
-<td>0.008505</td>
-<td>-0.010862</td>
-<td>-0.029529</td>
-<td>-0.0019</td>
-<td>0.005517</td>
-</tr>
-<tr>
-<th>363</th>
-<td>2019-Q4</td>
-<td>0.090143</td>
-<td>-4.015896</td>
-<td>0.008410</td>
-<td>-0.007222</td>
-<td>-0.033609</td>
-<td>0.0032</td>
-<td>0.002319</td>
-</tr>
-<tr>
-<th>364</th>
-<td>2020-Q1</td>
-<td>-0.193794</td>
-<td>-3.769992</td>
-<td>0.012252</td>
-<td>-0.007731</td>
-<td>-0.050141</td>
-<td>0.0058</td>
-<td>0.079049</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f36b947b-7ab0-46f5-82d6-e8df53ae591f">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h3 id="Aufgabe-1.1:">Aufgabe 1.1:<a class="anchor-link" href="#Aufgabe-1.1:">¶</a></h3><ul>
-<li>Anpassung der Rendite Zeitdaten</li>
-</ul>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=f3725bbe-1708-4559-b7b9-fa975a09083f">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Verschiebung der Spalte Rendite um einen Zeitraum t+1.</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'ret'</span><span class="p">]</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-
-<span class="c1"># Entfernen der Spalte "ret" aus dem DataFrame.</span>
-<span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">'ret'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="n">df</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[3]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
-<div>
-<style scoped="">
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>date</th>
-<th>DP</th>
-<th>CS</th>
-<th>ntis</th>
-<th>cay</th>
-<th>TS</th>
-<th>svar</th>
-<th>ret_next</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>360</th>
-<td>2019-Q1</td>
-<td>-3.943400</td>
-<td>0.010258</td>
-<td>-0.023230</td>
-<td>-0.039336</td>
-<td>0.0017</td>
-<td>0.004651</td>
-<td>0.042688</td>
-</tr>
-<tr>
-<th>361</th>
-<td>2019-Q2</td>
-<td>-3.960033</td>
-<td>0.010006</td>
-<td>-0.012562</td>
-<td>-0.033844</td>
-<td>-0.0010</td>
-<td>0.003271</td>
-<td>0.017042</td>
-</tr>
-<tr>
-<th>362</th>
-<td>2019-Q3</td>
-<td>-3.951689</td>
-<td>0.008505</td>
-<td>-0.010862</td>
-<td>-0.029529</td>
-<td>-0.0019</td>
-<td>0.005517</td>
-<td>0.090143</td>
-</tr>
-<tr>
-<th>363</th>
-<td>2019-Q4</td>
-<td>-4.015896</td>
-<td>0.008410</td>
-<td>-0.007222</td>
-<td>-0.033609</td>
-<td>0.0032</td>
-<td>0.002319</td>
-<td>-0.193794</td>
-</tr>
-<tr>
-<th>364</th>
-<td>2020-Q1</td>
-<td>-3.769992</td>
-<td>0.012252</td>
-<td>-0.007731</td>
-<td>-0.050141</td>
-<td>0.0058</td>
-<td>0.079049</td>
-<td>NaN</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=73330b81-0e43-43ac-911f-4086a9f9788f">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h3 id="Aufgabe-1.2:">Aufgabe 1.2:<a class="anchor-link" href="#Aufgabe-1.2:">¶</a></h3><ul>
-<li>Anpassung fehlender Datenwerte im DataFrame</li>
-</ul>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=5c083b5f-f0d4-4fe5-8824-604a073c1215">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Aufzählung aller NaN's (je Variable) des DataFrame.</span>
-<span class="n">df</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>date         0
-DP           0
-CS           0
-ntis         0
-cay         92
-TS           0
-svar         0
-ret_next     1
-dtype: int64</pre>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=2c0b17c8-a060-4687-8047-83abcf22ae46">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="c1"># Verwerfen aller Zeilen, bei denen Variablen eine NaN Zelle besitzen.</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
-<span class="n">df</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>date        0
-DP          0
-CS          0
-ntis        0
-cay         0
-TS          0
-svar        0
-ret_next    0
-dtype: int64</pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=80e4160e-374a-43e1-a159-45077703658e">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h3 id="Aufgabe-1.3:">Aufgabe 1.3:<a class="anchor-link" href="#Aufgabe-1.3:">¶</a></h3><ul>
-<li>Aufteilung des Datensatzes in zwei Teile. Training Datensatz mit Daten vor 1995 und Validierung Datensatz mit den Daten nach 1995 als Out-of-Sample Datensatz.</li>
-</ul>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=b27a4ab6-fb98-4d05-ad9e-340731f68d68">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Erstellen von Variablen mit der Information "1994-Q4" und der Position, welche als Schnittpunkt dienen.</span>
-<span class="n">split_date</span> <span class="o">=</span> <span class="s1">'1994-Q4'</span>
-<span class="n">split_ind</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'date'</span><span class="p">]</span> <span class="o">==</span> <span class="n">split_date</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-
-<span class="c1"># Aufteilung der Daten zu "train_data" und "test_data".</span>
-<span class="n">train_data</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:</span><span class="n">split_ind</span><span class="p">]</span>     <span class="c1"># für in-sample tests</span>
-<span class="n">test_data</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">split_ind</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:]</span>  <span class="c1"># für out-of-sample tests</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"train_data besitzt </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">train_data</span><span class="p">)</span><span class="si">}</span><span class="s2"> Beobachtung."</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"test_data besitzt </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">test_data</span><span class="p">)</span><span class="si">}</span><span class="s2"> Beobachtung."</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>train_data besitzt 172 Beobachtung.
-test_data besitzt 100 Beobachtung.
-</pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=03d19235-25ee-4c3b-b7bf-97cdf27d41b2">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h3 id="Aufgabe-1.4:">Aufgabe 1.4:<a class="anchor-link" href="#Aufgabe-1.4:">¶</a></h3><ul>
-<li>Berechnung der durchschnittlichen vierteljährlichen Rendite und ihre Standardabweichung in den Trainings- und Testdaten.</li>
-</ul>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=a6833298-ab95-4596-85cd-5c4d9666037c">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">train_mean_ret</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="c1"># durchschnittliche vierteljährlichen Rendite (train_data)</span>
-<span class="n">train_std_ret</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>   <span class="c1"># Standardabweichung der vierteljährlichen Rendite (train_data)</span>
-
-<span class="n">test_mean_ret</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="c1"># durchschnittliche vierteljährlichen Rendite (test_data)</span>
-<span class="n">test_std_ret</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>   <span class="c1"># Standardabweichung der vierteljährlichen Rendite (test_data)</span>
-
-<span class="c1"># Ausgabe der Ergebnisse</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">"Trainingsdaten:"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Durchschnittliche Rendite: </span><span class="si">{</span><span class="n">train_mean_ret</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Standardabweichung der Rendite: </span><span class="si">{</span><span class="n">train_std_ret</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Testdaten:"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Durchschnittliche Rendite: </span><span class="si">{</span><span class="n">test_mean_ret</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Standardabweichung der Rendite: </span><span class="si">{</span><span class="n">test_std_ret</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
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-<pre>Trainingsdaten:
-Durchschnittliche Rendite: 0.0306
-Standardabweichung der Rendite: 0.0763
-
-Testdaten:
-Durchschnittliche Rendite: 0.0252
-Standardabweichung der Rendite: 0.0823
-</pre>
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-<h3 id="Aufgabe-1.5:">Aufgabe 1.5:<a class="anchor-link" href="#Aufgabe-1.5:">¶</a></h3><ul>
-<li>Berechnung der Korrelationsmatrix für die Trainingsdaten (einschließlich sowohl der Ergebnisse als auch der Merkmale).</li>
-</ul>
-</div>
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-</div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Berechnung der Korrelationsmatrix für die Trainingsdaten</span>
-<span class="n">train_cor_matrix</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">train_data</span><span class="o">.</span><span class="n">columns</span> <span class="o">!=</span> <span class="s1">'date'</span><span class="p">]</span><span class="o">.</span><span class="n">corr</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">'pearson'</span><span class="p">)</span> <span class="c1"># "date" Spalte ausgelassen (Nur numerische Spalten)</span>
-
-<span class="c1"># Ausgabe der Korrelationsmatrix. Werte auf zwei Nachkommastellen aufgerundet.</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">"Korrelationsmatrix für Trainingsdaten:"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">train_cor_matrix</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
-</pre></div>
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-<pre>Korrelationsmatrix für Trainingsdaten:
-            DP    CS  ntis   cay    TS  svar  ret_next
-DP        1.00  0.38 -0.12 -0.21 -0.14  0.11      0.23
-CS        0.38  1.00 -0.31 -0.02  0.21  0.22      0.18
-ntis     -0.12 -0.31  1.00 -0.40 -0.07 -0.12     -0.19
-cay      -0.21 -0.02 -0.40  1.00  0.46  0.04      0.17
-TS       -0.14  0.21 -0.07  0.46  1.00  0.08      0.16
-svar      0.11  0.22 -0.12  0.04  0.08  1.00      0.13
-ret_next  0.23  0.18 -0.19  0.17  0.16  0.13      1.00
-</pre>
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-<ul>
-<li><strong>Zusatz</strong>: Grafische Abbildung der Korrelationsmatrix</li>
-</ul>
-</div>
-</div>
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=e2727ae0-ab97-4ae4-b7cb-8b3e957ccda5">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span> 
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> 
-
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
-<span class="n">s</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">train_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">train_data</span><span class="o">.</span><span class="n">columns</span> <span class="o">!=</span> <span class="s1">'date'</span><span class="p">]</span><span class="o">.</span><span class="n">corr</span><span class="p">(),</span>
-                <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> 
-                <span class="n">center</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                <span class="n">linewidths</span><span class="o">=</span><span class="mf">.5</span><span class="p">,</span> 
-                <span class="n">square</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                <span class="n">vmin</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">vmax</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> 
-                <span class="n">xticklabels</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">,</span>
-                <span class="n">yticklabels</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">,</span> 
-                <span class="n">fmt</span><span class="o">=</span><span class="s1">'0.2f'</span><span class="p">,</span>
-                <span class="n">cmap</span><span class="o">=</span><span class="s2">"coolwarm"</span><span class="p">)</span>
-<span class="n">s</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Korrelationsmatrix der trainingsdaten Variablen'</span><span class="p">)</span>
-<span class="n">s</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="s1">'Variablen'</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s1">'Variablen'</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
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"/>
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-<h2 id="Aufgabe-2:-Vorhersage-von-Renditen-">Aufgabe 2: Vorhersage von Renditen <a class="anchor" id="Aufgabe-2"></a><a class="anchor-link" href="#Aufgabe-2:-Vorhersage-von-Renditen-">¶</a></h2><p>Nachdem die Daten bereinigt wurden, sind Sie bereit, das erste Modell zur Vorhersage von Renditen zu erstellen.</p>
-<ol>
-<li><p>Verwenden Sie die Trainingsdaten, um ein lineares Modell unter Verwendung aller Variablen (stellen Sie sicher, dass Sie die Datumsvariable ausschließen) anzupassen. Welche Merkmale sind nützlich für die Vorhersage von Renditen?</p>
-</li>
-<li><p>Berechnen Sie das R² in der Stichprobe sowie den mittleren quadratischen Fehler. Glauben Sie, dass vierteljährliche Renditen leicht vorhergesagt werden können?</p>
-</li>
-<li><p>Verwenden Sie eine 5-fache Kreuzvalidierung, um eine Schätzung für den mittleren quadratischen Fehler außerhalb der Stichprobe zu erhalten. Vergleichen Sie diese Schätzung mit dem mittleren quadratischen Fehler in der Stichprobe aus "Aufgabe 2.2".</p>
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-<li><p>Basierend auf Ihren Ergebnissen aus "Aufgabe 2.1" wählen Sie nur eine Teilmenge der Merkmale aus, um Ihr Modell zu verbessern. Welche Merkmale wählen Sie aus und warum? Berechnen Sie das R² in der Stichprobe sowie den mittleren quadratischen Fehler für dieses Modell und verwenden Sie eine 5-fache Kreuzvalidierung, um eine Schätzung für den mittleren quadratischen Fehler außerhalb der Stichprobe zu erhalten. Vergleichen Sie Ihre Ergebnisse mit dem Modell unter Verwendung aller Merkmale.</p>
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-<li><p>Angenommen, Sie verwenden die beiden Modelle, die Sie erstellt haben, um vierteljährliche Renditen in den nächsten 25 Jahren vorherzusagen. Berechnen Sie die mittleren quadratischen Fehler außerhalb der Stichprobe für die Testdaten. Vergleichen Sie diese Fehler mit den Schätzungen für die außerhalb der Stichprobe erhaltenen Fehler aus der k-fachen Kreuzvalidierung. Interpretieren Sie.</p>
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-<h3 id="Aufgabe-2.1:">Aufgabe 2.1:<a class="anchor-link" href="#Aufgabe-2.1:">¶</a></h3><ul>
-<li>MLR unter Verwendung aller Variablen des Trainingdatensatzes</li>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="k">as</span> <span class="nn">sm</span>
-<span class="c1"># Alle nutzen außer - entfernen der "Unabhängige Variablen" und der "qualitativen variablen /bzw. level Variablen" - hier aber die "date" Variable!</span>
-
-<span class="c1"># Möglichkeit 1 - doch ohne weitere Anpassung problematisch:</span>
-<span class="c1">#X = train_data.drop(columns=['date','ret_next'])</span>
-<span class="c1"># DOCH dann fehlt aber der Intercept in der summary !!</span>
-
-<span class="c1"># Möglichkeit 2:</span>
-<span class="c1">#X = pd.DataFrame({'intercept': np.ones(train_data.shape[0]), </span>
-<span class="c1">#                  'DP': train_data['DP'],</span>
-<span class="c1">#                  'CS': train_data['CS'],</span>
-<span class="c1">#                  'ntis': train_data['ntis'],</span>
-<span class="c1">#                  'cay': train_data['cay'],</span>
-<span class="c1">#                  'TS': train_data['TS'],</span>
-<span class="c1">#                  'svar': train_data['svar']</span>
-<span class="c1">#                  })</span>
-
-<span class="c1"># Möglichkeit 3: finale Lösung - Füllung der Exogenen Variable</span>
-<span class="c1"># Erstellen der Modell-Matrix:</span>
-<span class="n">X</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'date'</span><span class="p">,</span><span class="s1">'ret_next'</span><span class="p">])</span> <span class="c1"># Entfernen der endogenen und qualitativen Variablen</span>
-<span class="n">X</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="s1">'intercept'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">train_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-
-<span class="n">y</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span> <span class="c1"># Setzen der Endogene Variable</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span> <span class="c1"># OLS Modell füllen.</span>
-<span class="n">fit_lm</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span> <span class="c1"># Fit des univariaten linearen Regressionsmodells.</span>
-<span class="n">fit_lm</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>     <span class="c1"># Ausgabe der Modellstatistik</span>
-</pre></div>
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-<table class="simpletable">
-<caption>OLS Regression Results</caption>
-<tr>
-<th>Dep. Variable:</th> <td>ret_next</td> <th>  R-squared:         </th> <td>   0.129</td>
-</tr>
-<tr>
-<th>Model:</th> <td>OLS</td> <th>  Adj. R-squared:    </th> <td>   0.097</td>
-</tr>
-<tr>
-<th>Method:</th> <td>Least Squares</td> <th>  F-statistic:       </th> <td>   4.063</td>
-</tr>
-<tr>
-<th>Date:</th> <td>Sun, 14 Apr 2024</td> <th>  Prob (F-statistic):</th> <td>0.000793</td>
-</tr>
-<tr>
-<th>Time:</th> <td>20:47:06</td> <th>  Log-Likelihood:    </th> <td>  210.80</td>
-</tr>
-<tr>
-<th>No. Observations:</th> <td>   172</td> <th>  AIC:               </th> <td>  -407.6</td>
-</tr>
-<tr>
-<th>Df Residuals:</th> <td>   165</td> <th>  BIC:               </th> <td>  -385.6</td>
-</tr>
-<tr>
-<th>Df Model:</th> <td>     6</td> <th> </th> <td> </td>
-</tr>
-<tr>
-<th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td>
-</tr>
-</table>
-<table class="simpletable">
-<tr>
-<td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P&gt;|t|</th> <th>[0.025</th> <th>0.975]</th>
-</tr>
-<tr>
-<th>intercept</th> <td>    0.2968</td> <td>    0.097</td> <td>    3.063</td> <td> 0.003</td> <td>    0.106</td> <td>    0.488</td>
-</tr>
-<tr>
-<th>DP</th> <td>    0.0839</td> <td>    0.028</td> <td>    3.031</td> <td> 0.003</td> <td>    0.029</td> <td>    0.139</td>
-</tr>
-<tr>
-<th>CS</th> <td>    0.4750</td> <td>    1.739</td> <td>    0.273</td> <td> 0.785</td> <td>   -2.958</td> <td>    3.908</td>
-</tr>
-<tr>
-<th>ntis</th> <td>   -0.3945</td> <td>    0.410</td> <td>   -0.961</td> <td> 0.338</td> <td>   -1.205</td> <td>    0.416</td>
-</tr>
-<tr>
-<th>cay</th> <td>    0.4215</td> <td>    0.306</td> <td>    1.379</td> <td> 0.170</td> <td>   -0.182</td> <td>    1.025</td>
-</tr>
-<tr>
-<th>TS</th> <td>    0.6310</td> <td>    0.482</td> <td>    1.309</td> <td> 0.192</td> <td>   -0.320</td> <td>    1.583</td>
-</tr>
-<tr>
-<th>svar</th> <td>    0.8027</td> <td>    0.828</td> <td>    0.969</td> <td> 0.334</td> <td>   -0.832</td> <td>    2.438</td>
-</tr>
-</table>
-<table class="simpletable">
-<tr>
-<th>Omnibus:</th> <td>26.211</td> <th>  Durbin-Watson:     </th> <td>   1.810</td>
-</tr>
-<tr>
-<th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  40.178</td>
-</tr>
-<tr>
-<th>Skew:</th> <td>-0.823</td> <th>  Prob(JB):          </th> <td>1.89e-09</td>
-</tr>
-<tr>
-<th>Kurtosis:</th> <td> 4.701</td> <th>  Cond. No.          </th> <td>1.10e+03</td>
-</tr>
-</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.1e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems.
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-<h3 id="Aufgabe-2.2:">Aufgabe 2.2:<a class="anchor-link" href="#Aufgabe-2.2:">¶</a></h3><ul>
-<li>$R^{2}$-Wert des Trainingdatatensatzes.</li>
-</ul>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Das train_data R^2 beträgt: </span><span class="si">{</span><span class="n">fit_lm</span><span class="o">.</span><span class="n">rsquared</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-</pre></div>
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-<pre>Das train_data R^2 beträgt: 0.1287
-</pre>
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-<ul>
-<li>Bestimmung des in-sample MSE Wertes.</li>
-</ul>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="c1">#from sklearn.metrics import mean_squared_error  # kann benutzt werden</span>
-<span class="c1">#Oder wie hier, selbst definiert werden:</span>
-
-<span class="c1">##### Computing the MSE for the Auto Data: #####</span>
-<span class="c1"># Compute predicted values y_head for training data</span>
-<span class="n">y_head</span> <span class="o">=</span> <span class="n">fit_lm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-
-<span class="c1"># Function to compute the mean squared error (MSE)</span>
-<span class="k">def</span> <span class="nf">MSE</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_head</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">y</span> <span class="o">-</span> <span class="n">y_head</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
-
-<span class="c1"># Compute the mean squared error in the training data</span>
-<span class="n">MSE_train_data</span> <span class="o">=</span> <span class="n">MSE</span><span class="p">(</span><span class="n">train_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">],</span> <span class="n">y_head</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"in-sample (MSE): </span><span class="si">{</span><span class="n">MSE_train_data</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-</pre></div>
-</div>
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-<pre>in-sample (MSE): 0.0050
-</pre>
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-<h3 id="Aufgabe-2.3:">Aufgabe 2.3:<a class="anchor-link" href="#Aufgabe-2.3:">¶</a></h3><ul>
-<li>Vergleich des out-of-sample MSE's mit dem 5-fach Kreuzvalidierung MSE's:</li>
-</ul>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
-<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="c1"># Separate features and target variables</span>
-<span class="n">X_train</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">,</span><span class="s1">'date'</span><span class="p">])</span>
-<span class="n">y_train</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span>
-<span class="n">X_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">,</span><span class="s1">'date'</span><span class="p">])</span>
-<span class="n">y_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span>
-
-<span class="c1"># Train the model on the training data</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
-<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
-
-<span class="c1"># Predict on the test data</span>
-<span class="n">y_pred_test</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-
-<span class="c1"># Calculate the mean squared error (MSE) on the test data</span>
-<span class="n">mse_test</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred_test</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Out-of-sample MSE: </span><span class="si">{</span><span class="n">mse_test</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-
-<span class="c1"># Perform 5-fold cross-validation and calculate the mean of MSEs</span>
-<span class="n">mse_cv</span> <span class="o">=</span> <span class="o">-</span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s1">'neg_mean_squared_error'</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Cross-validated MSE: </span><span class="si">{</span><span class="n">mse_cv</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-</pre></div>
-</div>
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-<pre>Out-of-sample MSE: 0.0086
-Cross-validated MSE: 0.0081
-</pre>
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-<h3 id="Aufgabe-2.4:">Aufgabe 2.4:<a class="anchor-link" href="#Aufgabe-2.4:">¶</a></h3><ul>
-<li>Bestimmung des out-of-sample MSE's und mittel 5-fache Kreuzvalidierung den Cross-validation MSE<ul>
-<li>Auswertung der Modellgüte mittels verschiedener variablen Kombinationen.</li>
-</ul>
-</li>
-</ul>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
-<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span><span class="p">,</span> <span class="n">r2_score</span>
-
-<span class="c1"># Select subset of features</span>
-<span class="n">selected_features</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'DP'</span><span class="p">]</span>
-
-<span class="c1"># Separate features and target variables for selected features</span>
-<span class="n">X_train_selected</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="n">selected_features</span><span class="p">]</span>
-<span class="n">X_test_selected</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="n">selected_features</span><span class="p">]</span>
-<span class="n">y_train</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span>
-<span class="n">y_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span>
-
-<span class="c1"># Train the model on the training data using selected features</span>
-<span class="n">model_selected</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
-<span class="n">model_selected</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_selected</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
-
-<span class="c1"># Predict on the training data</span>
-<span class="n">y_pred_train_selected</span> <span class="o">=</span> <span class="n">model_selected</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train_selected</span><span class="p">)</span>
-
-<span class="c1"># Compute in-sample R^2</span>
-<span class="n">r2_in_sample_selected</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_pred_train_selected</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"In-sample R^2 mit der Variable (DP): </span><span class="si">{</span><span class="n">r2_in_sample_selected</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-
-<span class="c1"># Compute mean squared error (MSE) for the training data</span>
-<span class="n">mse_train_selected</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_pred_train_selected</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"In-sample MSE mit der Variable (DP): </span><span class="si">{</span><span class="n">mse_train_selected</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-
-<span class="c1"># Perform 5-fold cross-validation and compute out-of-sample MSE for the selected features</span>
-<span class="n">mse_cv_selected</span> <span class="o">=</span> <span class="o">-</span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">model_selected</span><span class="p">,</span> <span class="n">X_train_selected</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s1">'neg_mean_squared_error'</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Out-of-sample MSE bei 5-fold cross-validation mit der Variable (DP): </span><span class="si">{</span><span class="n">mse_cv_selected</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</div>
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-<pre>In-sample R^2 mit der Variable (DP): 0.0541
-In-sample MSE mit der Variable (DP): 0.0055
-Out-of-sample MSE bei 5-fold cross-validation mit der Variable (DP): 0.0056
-</pre>
-</div>
-</div>
-</div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
-<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span><span class="p">,</span> <span class="n">r2_score</span>
-
-<span class="c1"># Select subset of features</span>
-<span class="n">selected_features</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'DP'</span><span class="p">,</span> <span class="s1">'cay'</span><span class="p">]</span>
-
-<span class="c1"># Separate features and target variables for selected features</span>
-<span class="n">X_train_selected</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="n">selected_features</span><span class="p">]</span>
-<span class="n">X_test_selected</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="n">selected_features</span><span class="p">]</span>
-<span class="n">y_train</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span>
-<span class="n">y_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span>
-
-<span class="c1"># Train the model on the training data using selected features</span>
-<span class="n">model_selected</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
-<span class="n">model_selected</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_selected</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
-
-<span class="c1"># Predict on the training data</span>
-<span class="n">y_pred_train_selected</span> <span class="o">=</span> <span class="n">model_selected</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train_selected</span><span class="p">)</span>
-
-<span class="c1"># Compute in-sample R^2</span>
-<span class="n">r2_in_sample_selected</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_pred_train_selected</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"In-sample R^2 mit den Variablen (DP+cay): </span><span class="si">{</span><span class="n">r2_in_sample_selected</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-
-<span class="c1"># Compute mean squared error (MSE) for the training data</span>
-<span class="n">mse_train_selected</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_pred_train_selected</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"In-sample MSE mit den Variablen (DP+cay): </span><span class="si">{</span><span class="n">mse_train_selected</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-
-<span class="c1"># Perform 5-fold cross-validation and compute out-of-sample MSE for the selected features</span>
-<span class="n">mse_cv_selected</span> <span class="o">=</span> <span class="o">-</span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">model_selected</span><span class="p">,</span> <span class="n">X_train_selected</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s1">'neg_mean_squared_error'</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Out-of-sample MSE bei 5-fold cross-validation mit den Variablen (DP+cay): </span><span class="si">{</span><span class="n">mse_cv_selected</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</div>
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-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>In-sample R^2 mit den Variablen (DP+cay): 0.1040
-In-sample MSE mit den Variablen (DP+cay): 0.0052
-Out-of-sample MSE bei 5-fold cross-validation mit den Variablen (DP+cay): 0.0053
-</pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=df4f7f10-2779-43ab-a7b0-3bd1b3f15b0c">
-<div class="jp-Cell-inputWrapper" tabindex="0">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="Aufgabe-3:-Vorhersage-der-Richtung-des-Aktienmarktes-">Aufgabe 3: Vorhersage der Richtung des Aktienmarktes <a class="anchor" id="Aufgabe-3"></a><a class="anchor-link" href="#Aufgabe-3:-Vorhersage-der-Richtung-des-Aktienmarktes-">¶</a></h2><p>Statt Renditen quantitativ vorherzusagen, nehmen Sie nun an, dass Sie die Richtung des Aktienmarktes vorhersagen möchten, d. h. ob die Aktien steigen oder fallen. Basierend auf diesen Vorhersagen möchten Sie entweder in Aktien investieren oder nicht.</p>
-<ol>
-<li><p>Erstellen Sie eine neue Variable sowohl in den Trainings- als auch in den Testdaten, die 1 ist, wenn die Rendite größer als Null ist, und 0 sonst.</p>
-</li>
-<li><p>Berechnen Sie den Anteil positiver Aktienrenditen sowohl in den Trainings- als auch in den Testdaten.</p>
-</li>
-<li><p>Passen Sie eine logistische Regression an die Trainingsdaten an, um die Richtung des Aktienmarktes vorherzusagen (stellen Sie sicher, dass Sie die Datumsvariable und die alte quantitative Renditevariable ausschließen). Welche Merkmale sind nützliche Prädiktoren? Berechnen Sie die In-Sample-Genauigkeit und die Fehlerquote. Glauben Sie, dass Sie ein gutes Modell zur Vorhersage der Richtung des Aktienmarktes erstellt haben?</p>
-</li>
-<li><p>Angenommen, Sie verwenden das von Ihnen erstellte Modell, um die Richtung des Aktienmarktes in den nächsten 25 Jahren vorherzusagen. Berechnen Sie die außerhalb der Stichprobe liegende Genauigkeit und Fehlerquote für die Testdaten. Vergleichen Sie diese Ergebnisse mit den In-Sample-Statistiken. Glauben Sie, dass Ihr Modell gut außerhalb der Stichprobe funktioniert? Interpretieren Sie die Ergebnisse.</p>
-</li>
-</ol>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=309698e2-9577-43ac-84c8-12edad93f8aa">
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-<h3 id="Aufgabe-3.1:">Aufgabe 3.1:<a class="anchor-link" href="#Aufgabe-3.1:">¶</a></h3><ul>
-<li>Erstelle eine neue Variable, die 1 ist, wenn die Rendite größer als Null ist, und 0 sonst.</li>
-</ul>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=b28e213d-9ca8-4e33-a15f-feae07d73a18">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s1">'ignore'</span><span class="p">)</span> <span class="c1"># to hide the warning message - it's fixed here.</span>
-<span class="c1"># Erstelle eine neue Variable, die 1 ist, wenn die Rendite größer als Null ist, und 0 sonst</span>
-
-<span class="c1"># raises a Warning message: A value is trying to be set on a copy of a slice from a DataFrame.</span>
-<span class="c1">#train_data['return_positive'] = train_data['ret_next'].apply(lambda x: 1 if x &gt; 0 else 0)</span>
-<span class="c1">#test_data['return_positive'] = test_data['ret_next'].apply(lambda x: 1 if x &gt; 0 else 0)</span>
-
-<span class="c1"># .iloc solution to fix the warning:</span>
-<span class="n">train_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s1">'return_positive'</span><span class="p">]</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
-<span class="n">test_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s1">'return_positive'</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
-
-<span class="n">train_data</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
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-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[16]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
-<div>
-<style scoped="">
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>date</th>
-<th>DP</th>
-<th>CS</th>
-<th>ntis</th>
-<th>cay</th>
-<th>TS</th>
-<th>svar</th>
-<th>ret_next</th>
-<th>return_positive</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>92</th>
-<td>1952-Q1</td>
-<td>-2.842696</td>
-<td>0.005328</td>
-<td>0.032094</td>
-<td>-0.010595</td>
-<td>0.0104</td>
-<td>0.002102</td>
-<td>0.038275</td>
-<td>1</td>
-</tr>
-<tr>
-<th>93</th>
-<td>1952-Q2</td>
-<td>-2.845711</td>
-<td>0.005425</td>
-<td>0.027731</td>
-<td>0.000055</td>
-<td>0.0089</td>
-<td>0.001660</td>
-<td>-0.004980</td>
-<td>0</td>
-</tr>
-<tr>
-<th>94</th>
-<td>1952-Q3</td>
-<td>-2.828741</td>
-<td>0.005521</td>
-<td>0.031038</td>
-<td>-0.000695</td>
-<td>0.0106</td>
-<td>0.001076</td>
-<td>0.102295</td>
-<td>1</td>
-</tr>
-<tr>
-<th>95</th>
-<td>1952-Q4</td>
-<td>-2.936193</td>
-<td>0.005231</td>
-<td>0.026535</td>
-<td>-0.015950</td>
-<td>0.0070</td>
-<td>0.001753</td>
-<td>-0.035683</td>
-<td>0</td>
-</tr>
-<tr>
-<th>96</th>
-<td>1953-Q1</td>
-<td>-2.886819</td>
-<td>0.004354</td>
-<td>0.024013</td>
-<td>-0.019021</td>
-<td>0.0093</td>
-<td>0.001574</td>
-<td>-0.032102</td>
-<td>0</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=e2c9d767-2c2a-4937-85f4-823ff387e11f">
-<div class="jp-Cell-inputWrapper" tabindex="0">
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-<h3 id="Aufgabe-3.2:">Aufgabe 3.2:<a class="anchor-link" href="#Aufgabe-3.2:">¶</a></h3><ul>
-<li>Anteil der positiven Aktienrenditen in den Trainingsdaten &amp; Testdaten</li>
-</ul>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=340c54c5-8db6-4fce-ab35-8a782ad501c7">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Berechne den Anteil der positiven Aktienrenditen in den Trainingsdaten</span>
-<span class="n">positive_proportion_train</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'return_positive'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Anteil der positiven Aktienrenditen in den Trainingsdaten: </span><span class="si">{</span><span class="n">positive_proportion_train</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-
-<span class="c1"># Berechne den Anteil der positiven Aktienrenditen in den Testdaten</span>
-<span class="n">positive_proportion_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s1">'return_positive'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Anteil der positiven Aktienrenditen in den Testdaten: </span><span class="si">{</span><span class="n">positive_proportion_test</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-</pre></div>
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-<pre>Anteil der positiven Aktienrenditen in den Trainingsdaten: 0.69
-Anteil der positiven Aktienrenditen in den Testdaten: 0.73
-</pre>
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-<h3 id="Aufgabe-3.3:">Aufgabe 3.3:<a class="anchor-link" href="#Aufgabe-3.3:">¶</a></h3><ul>
-<li>Logistische Regression über die Trainingsdaten, um die Richtung des Aktienmarktes vorherzusagen.</li>
-</ul>
-</div>
-</div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">confusion_matrix</span>
-
-<span class="c1"># Separate features and target variables</span>
-<span class="n">X_train</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'return_positive'</span><span class="p">,</span> <span class="s1">'date'</span><span class="p">,</span> <span class="s1">'ret_next'</span><span class="p">])</span>
-<span class="n">y_train</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'return_positive'</span><span class="p">]</span>
-
-<span class="c1"># Train the logistic regression model</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
-<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
-
-<span class="c1"># Predict on the training data</span>
-<span class="n">y_pred_train</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
-
-<span class="c1"># Compute in-sample accuracy and error rate</span>
-<span class="n">accuracy</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_pred_train</span><span class="p">)</span>
-<span class="n">error_rate</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">accuracy</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"In-sample accuracy: </span><span class="si">{</span><span class="n">accuracy</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"In-sample error rate: </span><span class="si">{</span><span class="n">error_rate</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-
-<span class="c1"># Compute useful predictors</span>
-<span class="n">coefficients</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">coef_</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="n">useful_predictors</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="n">coefficients</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">"Useful predictors:"</span><span class="p">,</span> <span class="n">useful_predictors</span><span class="p">)</span>  <span class="c1"># Variablen die nicht-null Koeffizienten sind, sind nützlich.</span>
-</pre></div>
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-<pre>In-sample accuracy: 0.69
-In-sample error rate: 0.31
-Useful predictors: Index(['DP', 'CS', 'ntis', 'cay', 'TS', 'svar'], dtype='object')
-</pre>
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-<h3 id="Aufgabe-3.4:">Aufgabe 3.4:<a class="anchor-link" href="#Aufgabe-3.4:">¶</a></h3><ul>
-<li>Äquivalent hierzu werden nun die Testdaten genutzt um Out-of-sample accuracy und error rate zu bestimmen und anschließend zu vergleichen mit den Ergebnissen aus Aufgabe 3.3.</li>
-</ul>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Separate features and target variables for test data</span>
-<span class="n">X_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'return_positive'</span><span class="p">,</span> <span class="s1">'date'</span><span class="p">,</span> <span class="s1">'ret_next'</span><span class="p">])</span>
-<span class="n">y_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s1">'return_positive'</span><span class="p">]</span>
-
-<span class="c1"># Predict on the test data</span>
-<span class="n">y_pred_test</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-
-<span class="c1"># Compute out-of-sample accuracy and error rate</span>
-<span class="n">accuracy_test</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred_test</span><span class="p">)</span>
-<span class="n">error_rate_test</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">accuracy_test</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Out-of-sample accuracy: </span><span class="si">{</span><span class="n">accuracy_test</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Out-of-sample error rate: </span><span class="si">{</span><span class="n">error_rate_test</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-
-<span class="c1"># Compare with in-sample statistics</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Comparison with in-sample statistics:"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"In-sample accuracy: </span><span class="si">{</span><span class="n">accuracy</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"In-sample error rate: </span><span class="si">{</span><span class="n">error_rate</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-</pre></div>
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-<pre>Out-of-sample accuracy: 0.73
-Out-of-sample error rate: 0.27
-
-Comparison with in-sample statistics:
-In-sample accuracy: 0.69
-In-sample error rate: 0.31
-</pre>
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-<h2 id="Anhang-">Anhang <a class="anchor" id="Anhang"></a><a class="anchor-link" href="#Anhang-">¶</a></h2><p>Der Datensatz enthält die folgenden Variablen:</p>
-<ul>
-<li><strong>ret</strong>: Die vierteljährliche Rendite des US-Aktienmarktes (eine Zahl von 0,01 entspricht einer Rendite von 1% pro Quartal)
-date: Das Datum im Format JJJJQ (19941 bedeutet das erste Quartal 1994)</li>
-<li><strong>DP</strong>: Das Dividenden-zu-Preis-Verhältnis des Aktienmarktes (eine Bewertungsmessung, ob die Preise im Verhältnis zu den gezahlten Dividenden hoch oder niedrig sind)</li>
-<li><strong>CS</strong>: Der Kreditspread definiert als die Differenz der Renditen zwischen hoch bewerteten Unternehmensanleihen (sichere Anlagen) und niedrig bewerteten Unternehmensanleihen (Unternehmen, die möglicherweise bankrott gehen). CS misst die zusätzliche Rendite, die Investoren für Investitionen in riskante Unternehmen im Vergleich zu etablierten Unternehmen mit geringeren Risiken verlangen.</li>
-<li><strong>ntis</strong>: Ein Maß für die Aktivität bei der Ausgabe von Unternehmensanleihen (IPOs, Rückkäufe von Aktien,...)</li>
-<li><strong>cay</strong>: Ein Maß für das Verhältnis von Vermögen zu Verbrauch (wie viel wird im Verhältnis zum Gesamtvermögen verbraucht)</li>
-<li><strong>TS</strong>: Der Term Spread ist die Differenz zwischen der langfristigen Rendite von Staatsanleihen und kurzfristigen Renditen.</li>
-<li><strong>svar</strong>: Ein Maß für die Varianz des Aktienmarktes
-Für eine vollständige Beschreibung der Daten siehe Welch und Goyal (2007). Google ist auch sehr hilfreich, wenn Sie mehr über die Variablen erfahren möchten.</li>
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-<h2 id="Literatur-">Literatur <a class="anchor" id="Literatur"></a><a class="anchor-link" href="#Literatur-">¶</a></h2><p>Welch, I. and A. Goyal (2007, 03). A Comprehensive Look at The Empirical Performance of Equity
-Premium Prediction. The Review of Financial Studies 21 (4), 1455–1508.</p>
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	date	ret	DP	CS	ntis	cay	TS	svar
0	19291.0	0.050490	-3.367688	0.010357	0.079805	NaN	-0.0083	0.007982
1	19292.0	0.087235	-3.412851	0.011105	0.116197	NaN	-0.0113	0.008405
2	19293.0	0.091067	-3.468392	0.012517	0.121390	NaN	-0.0083	0.008056
3	19294.0	-0.268418	-3.096184	0.012155	0.163522	NaN	0.0037	0.100171
4	19301.0	0.165884	-3.252345	0.010554	0.145496	NaN	0.0040	0.004662
	date	ret	DP	CS	ntis	cay	TS	svar
360	2019-Q1	0.137489	-3.943400	0.010258	-0.023230	-0.039336	0.0017	0.004651
361	2019-Q2	0.042688	-3.960033	0.010006	-0.012562	-0.033844	-0.0010	0.003271
362	2019-Q3	0.017042	-3.951689	0.008505	-0.010862	-0.029529	-0.0019	0.005517
363	2019-Q4	0.090143	-4.015896	0.008410	-0.007222	-0.033609	0.0032	0.002319
364	2020-Q1	-0.193794	-3.769992	0.012252	-0.007731	-0.050141	0.0058	0.079049
Dep. Variable:	ret_next	R-squared:	0.129
Model:	OLS	Adj. R-squared:	0.097
Method:	Least Squares	F-statistic:	4.063
Date:	Sun, 14 Apr 2024	Prob (F-statistic):	0.000793
Time:	20:47:06	Log-Likelihood:	210.80
No. Observations:	172	AIC:	-407.6
Df Residuals:	165	BIC:	-385.6
Df Model:	6
Covariance Type:	nonrobust
	coef	std err	t	P>\|t\|	[0.025	0.975]
intercept	0.2968	0.097	3.063	0.003	0.106	0.488
DP	0.0839	0.028	3.031	0.003	0.029	0.139
CS	0.4750	1.739	0.273	0.785	-2.958	3.908
ntis	-0.3945	0.410	-0.961	0.338	-1.205	0.416
cay	0.4215	0.306	1.379	0.170	-0.182	1.025
TS	0.6310	0.482	1.309	0.192	-0.320	1.583
svar	0.8027	0.828	0.969	0.334	-0.832	2.438
Omnibus:	26.211	Durbin-Watson:	1.810
Prob(Omnibus):	0.000	Jarque-Bera (JB):	40.178
Skew:	-0.823	Prob(JB):	1.89e-09
Kurtosis:	4.701	Cond. No.	1.10e+03
	date	DP	CS	ntis	cay	TS	svar	ret_next	return_positive
92	1952-Q1	-2.842696	0.005328	0.032094	-0.010595	0.0104	0.002102	0.038275	1
93	1952-Q2	-2.845711	0.005425	0.027731	0.000055	0.0089	0.001660	-0.004980	0
94	1952-Q3	-2.828741	0.005521	0.031038	-0.000695	0.0106	0.001076	0.102295	1
95	1952-Q4	-2.936193	0.005231	0.026535	-0.015950	0.0070	0.001753	-0.035683	0
96	1953-Q1	-2.886819	0.004354	0.024013	-0.019021	0.0093	0.001574	-0.032102	0