\ucd5c\uadfc \uba87 \ub144\uac04 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\uc220\uc774 \uc810\uc810 \ub354 \ub9ce\uc774 \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \ud6a8\uacfc\uc801\uc778 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc744 \uc704\ud574 \uc120\ud615 \uc694\uc778 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc0c1\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc120\ud615 \uc694\uc778 \ubaa8\ub378\uc740 \uc790\uc0b0 \uc218\uc775\ub960\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce58\ub294 \uc5ec\ub7ec \uc694\uc778\ub4e4\uc744 \uace0\ub824\ud558\uc5ec \ud22c\uc790 \uacb0\uc815\uc744 \ub3d5\ub294 \ub370 \uc720\uc6a9\ud569\ub2c8\ub2e4. \ub610\ud55c, \uc774 \ubaa8\ub378\uc740 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\ubc95\uc744 \ud1b5\ud574 \ucd5c\uc801\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd\uc774\ub780 \ucef4\ud4e8\ud130\uac00 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud559\uc2b5\ud558\uc5ec \uc790\ub3d9\uc73c\ub85c \uc131\ub2a5\uc744 \uac1c\uc120\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc758 \uc9d1\ud569\uc785\ub2c8\ub2e4. \ubc18\uba74, \ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \uc778\uc2dd\ud558\uace0 \uc608\uce21\ud558\ub294 \ub370 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc785\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e4\uc591\ud55c \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150\uc740 \uc77c\ubc18\ud654(generalization), \uacfc\uc801\ud569(overfitting), \ud559\uc2b5(training)\uacfc \ud14c\uc2a4\ud2b8(test) \ub370\uc774\ud130 \uc14b \uad6c\ubd84 \ub4f1\uc785\ub2c8\ub2e4. \ud6a8\uacfc\uc801\uc778 \ubaa8\ub378\uc744 \ub9cc\ub4e4\uae30 \uc704\ud574\uc11c\ub294 \ub2e4\uc74c \ub2e8\uacc4\ub97c \uace0\ub824\ud574\uc57c \ud569\ub2c8\ub2e4:<\/p>\n
\uc120\ud615 \uc694\uc778 \ubaa8\ub378\uc740 \uc790\uc0b0 \uc218\uc775\ub960\uc774 \uc5ec\ub7ec \uc694\uc778\uc758 \uc120\ud615 \uc870\ud569\uc73c\ub85c \uc124\uba85\ub420 \uc218 \uc788\ub2e4\ub294 \uac00\uc815\uc5d0 \uae30\ubc18\ud55c \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uc218\uc2dd\uc744 \ub530\ub985\ub2c8\ub2e4:<\/p>\n
\n R_i = \u03b1 + \u03b2_1F_1 + \u03b2_2F_2 + ... + \u03b2_kF_k + \u03b5_i\n <\/pre>\n\uc5ec\uae30\uc11c:<\/p>\n
\uc120\ud615 \uc694\uc778 \ubaa8\ub378\uc758 \uc7a5\uc810\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\ub2e8\uc810\uc73c\ub85c\ub294 \uacfc\uac70 \ub370\uc774\ud130\uc5d0 \uc758\uc874\ud558\uae30 \ub54c\ubb38\uc5d0, \ubcc0\ud654\ud558\ub294 \uc2dc\uc7a5 \ud658\uacbd\uc5d0\uc11c\uc758 \uc801\uc751\ub825\uc774 \ub5a8\uc5b4\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ud6a8\uacfc\uc801\uc778 \uc120\ud615 \uc694\uc778 \ubaa8\ub378 \uc0dd\uc131\uc744 \uc704\ud574\uc11c\ub294 \ub370\uc774\ud130 \uc218\uc9d1\uc774 \uac00\uc7a5 \uc911\uc694\ud569\ub2c8\ub2e4. \uc8fc\uc694 \ub370\uc774\ud130 \uc18c\uc2a4\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\ub370\uc774\ud130 \uc218\uc9d1\uc774 \uc644\ub8cc\ub418\uba74, \ub370\uc774\ud130 \uc804\ucc98\ub9ac \ub2e8\uacc4\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ub294 \ub2e4\uc74c \ub2e8\uacc4\ub97c \ud3ec\ud568\ud569\ub2c8\ub2e4:<\/p>\n
\n import pandas as pd\n\n # \ub370\uc774\ud130 \ub85c\ub4dc\n data = pd.read_csv('data.csv')\n\n # \uacb0\uce21\uce58 \ucc98\ub9ac\n data.fillna(method='ffill', inplace=True)\n\n # \uc815\uaddc\ud654\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n normalized_data = scaler.fit_transform(data)\n\n # \uc0c8\ub85c\uc6b4 DataFrame\uc73c\ub85c \ubcc0\ud658\n normalized_df = pd.DataFrame(normalized_data, columns=data.columns)\n <\/pre>\n4. \uc120\ud615 \uc694\uc778 \ubaa8\ub378 \uad6c\ucd95<\/h2>\n
\uc120\ud615 \uc694\uc778 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574 \uac01 \uc694\uc778\uacfc \uc790\uc0b0 \uac04\uc758 \uad00\uacc4\ub97c \ubd84\uc11d\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774 \ub2e8\uacc4\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uc808\ucc28\ub85c \uc9c4\ud589\ub429\ub2c8\ub2e4:<\/p>\n
\n import statsmodels.api as sm\n\n # \uc885\uc18d\ubcc0\uc218\uc640 \ub3c5\ub9bd\ubcc0\uc218 \uc815\uc758\n Y = normalized_df['Stock_Return']\n X = normalized_df[['Factor1', 'Factor2', 'Factor3']]\n X = sm.add_constant(X) # \uc0c1\uc218 \ucd94\uac00\n\n # \ud68c\uadc0 \ubaa8\ub378 \ud559\uc2b5\n model = sm.OLS(Y, X).fit()\n \n # \ubaa8\ub378 \uc694\uc57d\n print(model.summary())\n <\/pre>\n5. \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud1b5\ud55c \uc120\ud615 \uc694\uc778 \ubaa8\ub378 \uac1c\uc120<\/h2>\n
\uae30\uc874 \uc120\ud615 \uc694\uc778 \ubaa8\ub378\uc744 \uac1c\uc120\ud558\uae30 \uc704\ud574 \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \ud65c\uc6a9\ud55c \ubc29\ubc95\uc744 \uace0\ub824\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8, \uadf8\ub77c\ub514\uc5b8\ud2b8 \ubd80\uc2a4\ud305, \uadf8\ub9ac\uace0 \ub525\ub7ec\ub2dd \ub4f1\uc758 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ub370\uc774\ud130\uc5d0\uc11c \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \uc608\uce21 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
5.1 \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8\ub97c \ud65c\uc6a9\ud55c \ubaa8\ub378 \uac1c\uc120 \uc608\uc81c<\/h3>\n
\n from sklearn.ensemble import RandomForestRegressor\n\n # \ub370\uc774\ud130 \uc900\ube44\n X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n\n # \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8 \ubaa8\ub378 \ud559\uc2b5\n rf_model = RandomForestRegressor(n_estimators=100)\n rf_model.fit(X_train, y_train)\n\n # \uc131\ub2a5 \ud3c9\uac00\n predictions = rf_model.predict(X_test)\n from sklearn.metrics import mean_squared_error\n mse = mean_squared_error(y_test, predictions)\n print('MSE:', mse)\n <\/pre>\n6. \ub525\ub7ec\ub2dd\uc744 \ud1b5\ud55c \uc120\ud615 \uc694\uc778 \ubaa8\ub378 \ubc1c\uc804<\/h2>\n
\ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ubaa8\ub378 \uad6c\ucd95\uc740 \ub354\uc6b1 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \uc778\uc2dd\ud560 \uc218 \uc788\ub3c4\ub85d \ud574\uc90d\ub2c8\ub2e4. \ud150\uc11c\ud50c\ub85c\uc6b0(TensorFlow) \ub610\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\uc640 \uac19\uc740 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \ubaa8\ub378\ub9c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
6.1 \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud55c \uc2e0\uacbd\ub9dd \uad6c\ucd95 \uc608\uc81c<\/h3>\n
\n import torch\n import torch.nn as nn\n import torch.optim as optim\n\n # \uc2e0\uacbd\ub9dd \uad6c\uc870 \uc815\uc758\n class RegressionNN(nn.Module):\n def __init__(self):\n super(RegressionNN, self).__init__()\n self.fc1 = nn.Linear(input_size, hidden_size)\n self.fc2 = nn.Linear(hidden_size, output_size)\n\n def forward(self, x):\n x = torch.relu(self.fc1(x))\n x = self.fc2(x)\n return x\n\n # \ubaa8\ub378 \ucd08\uae30\ud654 \ubc0f \uc190\uc2e4 \ud568\uc218, optimizer \uc124\uc815\n model = RegressionNN()\n criterion = nn.MSELoss()\n optimizer = optim.Adam(model.parameters(), lr=0.01)\n\n # \ud6c8\ub828 \ub8e8\ud504\n for epoch in range(num_epochs):\n optimizer.zero_grad()\n outputs = model(X_train)\n loss = criterion(outputs, y_train)\n loss.backward()\n optimizer.step()\n <\/pre>\n7. \ubaa8\ub378 \uc131\ub2a5 \ud3c9\uac00<\/h2>\n
\ubaa8\ub378\uc774 \ud6c8\ub828\uc774 \uc644\ub8cc\ub418\uba74 \uc131\ub2a5 \ud3c9\uac00\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \ud3c9\uac00 \uc9c0\ud45c\ub85c\ub294 \ub2e4\uc74c\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n
- MSE(Mean Squared Error)<\/li>\n
- R\u00b2(\uacb0\uc815\uacc4\uc218)<\/li>\n
- MAE(Mean Absolute Error)<\/li>\n<\/ul>\n
8. \uc2e4\uc804 \uc801\uc6a9 \ubc29\ubc95<\/h2>\n
\uac1c\ubc1c\ub41c \uc120\ud615 \uc694\uc778 \ubaa8\ub378\uc740 \uc2e4\uc81c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc73c\ub85c \uc804\ud658\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uacfc \uac19\uc740 \uc791\uc5c5\uc774 \ud544\uc694\ud569\ub2c8\ub2e4:<\/p>\n