{"id":29339,"date":"2024-10-27T16:17:57","date_gmt":"2024-10-27T16:17:57","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29339"},"modified":"2024-11-26T06:53:01","modified_gmt":"2024-11-26T06:53:01","slug":"%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d-%eb%b0%8f-%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%95%8c%ea%b3%a0%eb%a6%ac%ec%a6%98-%ed%8a%b8%eb%a0%88%ec%9d%b4%eb%94%a9-%ec%b5%9c%ec%a0%81-%ed%8f%ac%ed%8a%b8%ed%8f%b4","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29339\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \ucd5c\uc801 \ud3ec\ud2b8\ud3f4\ub9ac\uc624\ub97c \uc704\ud55c \uacc4\uce35\uc801 \uad70\uc9d1\ud654"},"content":{"rendered":"<p><body><\/p>\n<p>\ubcf8 \uac15\uc88c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \ud65c\uc6a9\ud558\uc5ec \uc790\ub3d9 \ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\uace0, \ucd5c\uc801\uc758 \ud3ec\ud2b8\ud3f4\ub9ac\uc624\ub97c \uad6c\ud604\ud558\uae30 \uc704\ud55c \uacc4\uce35\uc801 \uad70\uc9d1\ud654 \uae30\ubc95\uc5d0 \ub300\ud574 \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4. \uae08\uc735\uc2dc\uc7a5\uc5d0\uc11c\ub294 \ub354\uc6b1 \ubcf5\uc7a1\ud574\uc9c0\ub294 \ub370\uc774\ud130 \ud658\uacbd\uacfc \uc608\uce21\uc758 \uc5b4\ub824\uc6c0 \uc18d\uc5d0\uc11c, \ud6a8\uacfc\uc801\uc778 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uacfc \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \uad00\ub9ac \ubc29\ubc95\ub860\uc774 \uc808\uc2e4\ud788 \uc694\uad6c\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \uc774\ub7ec\ud55c \uc694\uad6c\uc5d0 \uc801\ud569\ud55c \ubc29\ubc95\ub860\uacfc \uad6c\ud604 \ubc29\ubc95\uc744 \uc0c1\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>1. \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c \uc774\ud574<\/h2>\n<p>\uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ucc3e\uc544\ub0b4\uc5b4 \uc608\uce21 \ubaa8\ub378\uc744 \ub9cc\ub4dc\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uc740 \uc8fc\ub85c \ud2b9\uc815 \uc54c\uace0\ub9ac\uc998\uc744 \ud1b5\ud574 \ub370\uc774\ud130\ub97c \ud559\uc2b5\ud558\uace0, \ub525\ub7ec\ub2dd\uc740 \uc2e0\uacbd\ub9dd\uc744 \uc774\uc6a9\ud558\uc5ec \ub354\uc6b1 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \ubaa8\ub378\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uae08\uc735 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \uc774 \ub450 \uae30\uc220\uc740 \ub370\uc774\ud130\uc758 \uc5ed\uc0ac\uc801 \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \ud2b8\ub808\uc774\ub529 \uc2e0\ud638\ub97c \uc0dd\uc131\ud558\ub294 \ub370\uc5d0 \ud544\uc218\uc801\uc785\ub2c8\ub2e4.<\/p>\n<h3>1.1 \uba38\uc2e0\ub7ec\ub2dd \uae30\ucd08<\/h3>\n<ul>\n<li><strong>\ud68c\uadc0(Regression)<\/strong>: \uc5f0\uc18d\uc801\uc778 \uac12\uc744 \uc608\uce21\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc0c1\uad00\uad00\uacc4\ub97c \ubaa8\ub378\ub9c1\ud560 \ub54c \uc720\uc6a9\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ubd84\ub958(Classification)<\/strong>: \uc774\uc9c4 \ub610\ub294 \ub2e4\uc911 \ud074\ub798\uc2a4 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc2dc\uc7a5\uc758 \uc0c1\uc2b9\uc774\ub098 \ud558\ub77d \ub4f1\uc744 \uc608\uce21\ud560 \ub54c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ud074\ub7ec\uc2a4\ud130\ub9c1(Clustering)<\/strong>: \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\ub97c \uc720\uc0ac\ud55c \ud2b9\uc131\uc5d0 \ub530\ub77c \uadf8\ub8f9\ud654\ud569\ub2c8\ub2e4. \ud3ec\ud2b8\ud3f4\ub9ac\uc624\uc758 \uc790\uc0b0\uad70\uc744 \ub098\ub204\ub294 \ub370 \uc720\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h3>1.2 \ub525\ub7ec\ub2dd \uae30\ucd08<\/h3>\n<p>\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud558\ub294 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud558\uc704 \ubd84\uc57c\uc785\ub2c8\ub2e4. \ub2e4\uc218\uc758 \uc740\ub2c9\uce35\uc744 \uac00\uc9c4 \uc2e0\uacbd\ub9dd\uc744 \ud1b5\ud574 \ube44\uc120\ud615 \uad00\uacc4\ub97c \ubaa8\ub378\ub9c1 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uae08\uc735 \ub370\uc774\ud130\ub294 \uc77c\ubc18\uc801\uc73c\ub85c \ube44\uc120\ud615\uc801\uc774\uae30 \ub54c\ubb38\uc5d0, \ub525\ub7ec\ub2dd\uc740 \uc774\ub7ec\ud55c \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \uac15\ub825\ud55c \ub3c4\uad6c\uac00 \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>2. \ub370\uc774\ud130 \uc900\ube44 \ubc0f \uc804\ucc98\ub9ac<\/h2>\n<p>\ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uc218\ub9bd\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0 \uc774\ub97c \uc804\ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ub294 \uc6d0\uc2dc \ub370\uc774\ud130\ub97c \ubaa8\ub378\uc774 \uc774\ud574\ud560 \uc218 \uc788\ub294 \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4.<\/p>\n<h3>2.1 \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n<p>\uae08\uc735 \ub370\uc774\ud130\ub294 \ub2e4\uc591\ud55c \ucd9c\ucc98\uc5d0\uc11c \uc218\uc9d1\ud560 \uc218 \uc788\uc73c\uba70, \uc8fc\uc2dd \uac00\uaca9, \uac70\ub798\ub7c9, \uae30\uc220 \uc9c0\ud45c \ub4f1\uc744 \ud3ec\ud568\ud574\uc57c \ud569\ub2c8\ub2e4. API\ub97c \ud1b5\ud574 \uc2e4\uc2dc\uac04 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uac70\ub098, \uae30\uc874\uc758 \uc5ed\uc0ac\uc801 \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>2.2 \ub370\uc774\ud130 \uc815\uc81c<\/h3>\n<p>\uc218\uc9d1\ub41c \ub370\uc774\ud130\ub294 \uacb0\uce21\uce58, \uc774\uc0c1\uce58, \ub178\uc774\uc988 \ub4f1\uc744 \ud3ec\ud568\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \uc815\uc81c\ud558\ub294 \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, NaN \uac12\uc744 \uc81c\uac70\ud558\uac70\ub098 \ub300\uccb4\ud558\uace0, \ube44\uc815\uc0c1\uc801\uc778 \uac12\uc744 \uc2b9\uaca9\uc2dc\ud0a4\ub294 \ub4f1\uc758 \uc791\uc5c5\uc744 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>2.3 \ud2b9\uc131 \uc0dd\uc131 \ubc0f \uc120\ud0dd<\/h3>\n<p>\uc8fc\uac00\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce58\ub294 \ub2e4\uc591\ud55c \uc694\uc778\ub4e4(\uac70\ub798\ub7c9, \ub2e4\uc6b0 \uc774\ud3c9\uc120, RSI \ub4f1)\uc744 \uae30\ubc18\uc73c\ub85c \uc0c8\ub85c\uc6b4 \ud2b9\uc131\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4. \uc0dd\uc131\ub41c \ud2b9\uc131 \uc911 \uac00\uc7a5 \uac00\uce58 \uc788\ub294 \uac83\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc774 \uc911\uc694\ud558\uba70, \uc774\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud06c\uac8c \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \uc694\uc18c\uc785\ub2c8\ub2e4.<\/p>\n<h2>3. \uacc4\uce35\uc801 \uad70\uc9d1\ud654(Hierarchical Clustering)<\/h2>\n<p>\uacc4\uce35\uc801 \uad70\uc9d1\ud654\ub294 \ub370\uc774\ud130\ub97c \uacc4\uce35\uc801\uc778 \ud615\ud0dc\ub85c \uadf8\ub8f9\ud654\ud558\uc5ec \uad6c\uc870\ub97c \ud30c\uc545\ud558\uace0, \uac01 \uadf8\ub8f9\uc758 \uc608\uce21\ub825\uc744 \ubd84\uc11d\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uc774\ub294 \uc790\uc0b0 \uac04\uc758 \uc720\uc0ac\uc131\uc744 \ud30c\uc545\ud558\uace0 \ud3ec\ud2b8\ud3f4\ub9ac\uc624\ub97c \ucd5c\uc801\ud654\ud558\ub294 \ub370 \uc720\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n<h3>3.1 \uacc4\uce35\uc801 \uad70\uc9d1\ud654\uc758 \uc6d0\ub9ac<\/h3>\n<p>\uacc4\uce35\uc801 \uad70\uc9d1\ud654\ub294 \uc720\uc0ac\ub3c4 \uae30\ubc18\uc73c\ub85c \ub370\uc774\ud130\ub97c \uadf8\ub8f9\ud654\ud558\uba70, \ub450 \uac00\uc9c0 \ubc29\ubc95\uc73c\ub85c \ub098\ub20c \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>\ubcd1\ud569\uc801 \uad70\uc9d1\ud654(Agglomerative Clustering)<\/strong>: \ubaa8\ub4e0 \ub370\uc774\ud130\ub97c \uac01\uae30 \ud558\ub098\uc758 \uad70\uc9d1\uc73c\ub85c \uc2dc\uc791\ud574, \uac00\uc7a5 \uc720\uc0ac\ud55c \ub450 \uad70\uc9d1\uc744 \ubc18\ubcf5\uc801\uc73c\ub85c \ubcd1\ud569\ud574 \ub098\uac00\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\ubd84\ud560\uc801 \uad70\uc9d1\ud654(Divisive Clustering)<\/strong>: \ub2e8\uc77c \uad70\uc9d1\uc5d0\uc11c \uc2dc\uc791\ud574, \uac00\uc7a5 \ubd88\uc77c\uce58\ud55c \uad70\uc9d1\uc744 \ubc18\ubcf5\uc801\uc73c\ub85c \ubd84\ud560\ud574 \ub098\uac00\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h3>3.2 \uad70\uc9d1\ud654 \uacfc\uc815<\/h3>\n<p>\uad70\uc9d1\ud654 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc774 \uc9c4\ud589\ub429\ub2c8\ub2e4:<\/p>\n<ol>\n<li>\ub370\uc774\ud130\uc758 \uac70\ub9ac \ud589\ub82c\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n<li>\uc720\uc0ac\ub3c4\uc5d0 \uae30\ubc18\ud558\uc5ec \uad70\uc9d1\uc744 \ubcd1\ud569\ud569\ub2c8\ub2e4.<\/li>\n<li>\uacb0\uacfc\ub97c \ub374\ub4dc\ub85c\uadf8\ub7a8(dendrogram)\uc73c\ub85c \uc2dc\uac01\ud654\ud558\uc5ec \uacc4\uce35 \uad6c\uc870\ub97c \ud655\uc778\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n<h3>3.3 Python\uc744 \uc774\uc6a9\ud55c \uacc4\uce35\uc801 \uad70\uc9d1\ud654 \uad6c\ud604<\/h3>\n<pre><code>\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom scipy.cluster.hierarchy import dendrogram, linkage\nfrom sklearn.preprocessing import StandardScaler\n\n# \ub370\uc774\ud130 \uc900\ube44\ndata = pd.read_csv('financial_data.csv')\nfeatures = data[['feature_1', 'feature_2', 'feature_3']]\n\n# \ub370\uc774\ud130 \uc815\uaddc\ud654\nscaler = StandardScaler()\nscaled_data = scaler.fit_transform(features)\n\n# \uacc4\uce35\uc801 \uad70\uc9d1\ud654 \uc218\ud589\nlinked = linkage(scaled_data, method='ward')\n\n# \ub374\ub4dc\ub85c\uadf8\ub7a8 \uc2dc\uac01\ud654\nplt.figure(figsize=(10, 7))\ndendrogram(linked, orientation='top', labels=data['stock_ticker'].values)\nplt.title('Hierarchical Clustering Dendrogram')\nplt.xlabel('Stock Ticker')\nplt.ylabel('Euclidean distances')\nplt.show()\n<\/code><\/pre>\n<h2>4. \ucd5c\uc801 \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \uad6c\uc131<\/h2>\n<p>\ud3ec\ud2b8\ud3f4\ub9ac\uc624\uc758 \ucd5c\uc801 \uad6c\uc131\uc740 \uc704\ud5d8\uc744 \ucd5c\uc18c\ud654\ud558\uace0 \uc218\uc775\uc744 \uadf9\ub300\ud654\ud558\ub294 \uac83\uc744 \ubaa9\ud45c\ub85c \ud569\ub2c8\ub2e4. \uacc4\uce35\uc801 \uad70\uc9d1\ud654 \uae30\ubc95\uc744 \ud1b5\ud574 \uc720\uc0ac\ud55c \uc790\uc0b0\ub4e4\ub07c\ub9ac \uadf8\ub8f9\ud654\ud558\uc5ec \ud3ec\ud2b8\ud3f4\ub9ac\uc624\uc758 \ub2e4\uc591\uc131\uc744 \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>4.1 \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \uc774\ub860<\/h3>\n<p>\ud3ec\ud2b8\ud3f4\ub9ac\uc624 \uc774\ub860\uc740 \uac01 \uc790\uc0b0\uc758 \uc704\ud5d8\uacfc \uae30\ub300 \uc218\uc775\ub960\uc744 \ubc14\ud0d5\uc73c\ub85c \ucd5c\uc801\uc758 \uc790\uc0b0 \ubc30\ubd84\uc744 \uacb0\uc815\ud569\ub2c8\ub2e4. \uc790\uc0b0 \uac04\uc758 \uc0c1\uad00\uad00\uacc4\ub97c \uc774\ud574\ud558\ub294 \uac83\uc774 \uc911\uc694\ud558\uba70, \ud074\ub7ec\uc2a4\ud130\ub97c \uae30\ubc18\uc73c\ub85c \uc790\uc0b0\uc744 \ubd84\uc0b0 \ud22c\uc790\ud558\ub294 \uc804\ub7b5\uc774 \uc720\ud6a8\ud569\ub2c8\ub2e4.<\/p>\n<h3>4.2 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998<\/h3>\n<p>\ucd5c\uc801\uc758 \uc790\uc0b0 \ube44\uc911\uc744 \uacc4\uc0b0\ud558\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, Mean-Variance Optimization\uc774\ub098 Genetic Algorithm \ub4f1\uc744 \uc801\uc6a9\ud558\uc5ec \uc790\uc0b0 \ube44\uc911\uc744 \ucd5c\uc801\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>4.3 \ud30c\uc774\uc36c\uc744 \uc774\uc6a9\ud55c \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \ucd5c\uc801\ud654<\/h3>\n<pre><code>\nimport cvxpy as cp\n\n# \uc790\uc0b0 \uc218\uc775\ub960 \ub370\uc774\ud130\nreturns = pd.DataFrame(np.random.randn(100, 4), columns=['Stock_A', 'Stock_B', 'Stock_C', 'Stock_D'])\n\n# \ud3c9\uade0 \uc218\uc775\ub960 \ubc0f \uacf5\ubd84\uc0b0 \ud589\ub82c \uacc4\uc0b0\nmean_returns = returns.mean()\ncov_matrix = returns.cov()\n\n# \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \ube44\uc911 \ubcc0\uc218 \uc124\uc815\nweights = cp.Variable(len(mean_returns))\n\n# \ubaa9\uc801 \ud568\uc218 \uc124\uc815: \uae30\ub300 \uc218\uc775\ub960 \ucd5c\ub300\ud654\nportfolio_return = mean_returns.T @ weights\n\n# \uc81c\uc57d \uc870\uac74 \uc124\uc815: \ube44\uc911 \ud569\uc774 1\nconstraints = [cp.sum(weights) == 1, weights &gt;= 0]\n\n# \ucd5c\uc801\ud654 \ubb38\uc81c \uc815\uc758\nproblem = cp.Problem(cp.Maximize(portfolio_return), constraints)\nproblem.solve()\n\n# \ucd5c\uc801\uc758 \ube44\uc911\noptimal_weights = weights.value\nprint('Optimal portfolio weights:', optimal_weights)\n<\/code><\/pre>\n<h2>5. \uacb0\ub860 \ubc0f \uc720\uc758\uc0ac\ud56d<\/h2>\n<p>\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud558\uc5ec \uacc4\uce35\uc801 \uad70\uc9d1\ud654\ub97c \ud1b5\ud574 \ucd5c\uc801 \ud3ec\ud2b8\ud3f4\ub9ac\uc624\ub97c \uad6c\uc131\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \ub370\uc774\ud130\uc758 \uc911\uc694\uc131\uc744 \ub2e4\uc2dc \ud55c\ubc88 \uac15\uc870\ud558\uba70, robust\ud55c \ubaa8\ub378 \uad6c\ucd95\uc744 \uc704\ud574 \uc9c0\uc18d\uc801\uc778 \ub370\uc774\ud130 \ubd84\uc11d\uacfc \ud2b9\uc131 \uacf5\ud559\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \ub610\ud55c, \uc2e4\uc81c \uac70\ub798\uc5d0 \uc801\uc6a9\ud558\uae30 \uc804, \ucda9\ubd84\ud55c \ubc31\ud14c\uc2a4\ud2b8\uc640 \uc2e4\ud5d8\uc744 \ud1b5\ud574 \uc2e0\ub8b0\uc131\uc744 \ud655\ubcf4\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<h3>5.1 \ud5a5\ud6c4 \uacfc\uc81c<\/h3>\n<p>\uc55e\uc73c\ub85c\ub294 \ub354\uc6b1 \ubcf5\uc7a1\ud55c \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\uac70\ub098, \ub2e4\ub978 \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uacfc\uc758 \ud1b5\ud569\uc744 \ud1b5\ud574 \uc131\uacfc\ub97c \ud55c\uce35 \uac1c\uc120\ud560 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4. \uae08\uc735\uc2dc\uc7a5\uc740 \ud56d\uc0c1 \ubcc0\ud558\uae30 \ub54c\ubb38\uc5d0, \ubcc0\ud654\uc5d0 \uc801\uc751\ud560 \uc218 \uc788\ub294 \uc720\uc5f0\uc131\uc744 \uc720\uc9c0\ud558\ub294 \uac83\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4.<\/p>\n<h2>\ucc38\uace0 \ubb38\ud5cc<\/h2>\n<ul>\n<li>Markowitz, H. (1952). &#8220;Portfolio Selection&#8221;. The Journal of Finance.<\/li>\n<li>Pratt, W. (2018). &#8220;Machine Learning for Asset Managers&#8221;. CFA Institute Research Foundation.<\/li>\n<li>Yao, J., &amp; Xu, Y. (2019). &#8220;Deep Learning in Finance: Overview and Applications&#8221;. Journal of Financial Data Science.<\/li>\n<\/ul>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ubcf8 \uac15\uc88c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \ud65c\uc6a9\ud558\uc5ec \uc790\ub3d9 \ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\uace0, \ucd5c\uc801\uc758 \ud3ec\ud2b8\ud3f4\ub9ac\uc624\ub97c \uad6c\ud604\ud558\uae30 \uc704\ud55c \uacc4\uce35\uc801 \uad70\uc9d1\ud654 \uae30\ubc95\uc5d0 \ub300\ud574 \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4. \uae08\uc735\uc2dc\uc7a5\uc5d0\uc11c\ub294 \ub354\uc6b1 \ubcf5\uc7a1\ud574\uc9c0\ub294 \ub370\uc774\ud130 \ud658\uacbd\uacfc \uc608\uce21\uc758 \uc5b4\ub824\uc6c0 \uc18d\uc5d0\uc11c, \ud6a8\uacfc\uc801\uc778 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uacfc \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \uad00\ub9ac \ubc29\ubc95\ub860\uc774 \uc808\uc2e4\ud788 \uc694\uad6c\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \uc774\ub7ec\ud55c \uc694\uad6c\uc5d0 \uc801\ud569\ud55c \ubc29\ubc95\ub860\uacfc \uad6c\ud604 \ubc29\ubc95\uc744 \uc0c1\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c \uc774\ud574 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc740 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/29339\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \ucd5c\uc801 \ud3ec\ud2b8\ud3f4\ub9ac\uc624\ub97c \uc704\ud55c \uacc4\uce35\uc801 \uad70\uc9d1\ud654&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[29],"tags":[],"class_list":["post-29339","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - 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