投资组合的风险因子调整——投资组合分析（EAP.portfolio_analysis）
2024-05-18 20:59:05
实证资产定价（Empirical asset pricing）已经发布于Github和Pypi. 包的具体用法(Documentation)博主将会陆续在CSDN中详细介绍，也可以通过Pypi直接查看。
Pypi: pip install --upgrade EAP
Github: GitHub - whyecofiliter/EAP: empirical asset pricing
投资组合的超额收益的主要来源有两种：承担风险因子和错误定价。对投资组合的超额收益进行风险因子调整是排除已知主要风险因子对超额收益的影响。主要的风险因子模型包括几个经典的定价模型（asset pricing model），CAPM、Fama-French 3 factors model、Cohart 4 factors model、 Fama-French 5 factors model. 如果经过这些定价模型调整后，投资组合的依然具有显著的超额收益，那么这说明投资组合超额收益的来源排除了通过承担这几个主要风险因子带来的风险回报。
这个Demo对诸多投资组合进行了风险因子调整，并且只报告因子调整前后投资组合超额收益的情况，具体的数据预处理可以参考之前的因子Demo。
投资组合：价值因子（HML）
# Independent-sort Bivariate Analysis
bi_1 = Bivariate(np.array(test_data_1), number=4)
bi_1.average_by_time()
bi_1.summary_and_test()
bi_1.print_summary_by_time()
bi_1.print_summary()
===============================================================
+-------+--------+--------+--------+--------+--------+--------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+--------+--------+--------+--------+--------+--------+
| 1 | 0.019 | 0.017 | 0.013 | 0.013 | 0.01 | -0.009 |
| | 3.024 | 2.693 | 2.003 | 1.89 | 1.472 | -3.551 |
| 2 | 0.014 | 0.015 | 0.011 | 0.011 | 0.009 | -0.005 |
| | 2.25 | 2.415 | 1.779 | 1.658 | 1.374 | -1.947 |
| 3 | 0.014 | 0.01 | 0.009 | 0.009 | 0.005 | -0.009 |
| | 2.298 | 1.648 | 1.478 | 1.36 | 0.778 | -3.19 |
| 4 | 0.011 | 0.011 | 0.009 | 0.009 | 0.005 | -0.006 |
| | 1.901 | 1.871 | 1.507 | 1.431 | 0.799 | -1.98 |
| 5 | 0.013 | 0.009 | 0.007 | 0.006 | 0.002 | -0.011 |
| | 2.204 | 1.678 | 1.258 | 0.988 | 0.331 | -2.984 |
| Diff | -0.007 | -0.008 | -0.006 | -0.007 | -0.008 | -0.001 |
| | -1.876 | -2.628 | -1.819 | -2.117 | -2.389 | -0.446 |
+-------+--------+--------+--------+--------+--------+--------+# Risk-adjustment Using Market factor
risk_model = risk_premium['MKT']
risk_model = risk_model['2000':'2019']
bi_1.factor_adjustment(risk_model)
bi_1.print_summary()
===============================================================
+-------+--------+--------+--------+--------+--------+--------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+--------+--------+--------+--------+--------+--------+
| 1 | 0.019 | 0.017 | 0.013 | 0.013 | 0.01 | -0.009 |
| | 3.024 | 2.693 | 2.003 | 1.89 | 1.472 | -3.551 |
| alpha | 0.017 | 0.015 | 0.011 | 0.01 | 0.008 | -0.009 |
| | 2.53 | 2.294 | 1.723 | 1.579 | 1.064 | -3.467 |
| 2 | 0.014 | 0.015 | 0.011 | 0.011 | 0.009 | -0.005 |
| | 2.25 | 2.415 | 1.779 | 1.658 | 1.374 | -1.947 |
| alpha | 0.012 | 0.013 | 0.009 | 0.009 | 0.007 | -0.004 |
| | 1.888 | 1.955 | 1.356 | 1.358 | 1.031 | -2.071 |
| 3 | 0.014 | 0.01 | 0.009 | 0.009 | 0.005 | -0.009 |
| | 2.298 | 1.648 | 1.478 | 1.36 | 0.778 | -3.19 |
| alpha | 0.012 | 0.008 | 0.007 | 0.006 | 0.003 | -0.009 |
| | 1.824 | 1.293 | 1.145 | 1.044 | 0.444 | -3.508 |
| 4 | 0.011 | 0.011 | 0.009 | 0.009 | 0.005 | -0.006 |
| | 1.901 | 1.871 | 1.507 | 1.431 | 0.799 | -1.98 |
| alpha | 0.01 | 0.009 | 0.007 | 0.007 | 0.004 | -0.006 |
| | 1.476 | 1.421 | 1.124 | 1.067 | 0.599 | -2.197 |
| 5 | 0.013 | 0.009 | 0.007 | 0.006 | 0.002 | -0.011 |
| | 2.204 | 1.678 | 1.258 | 0.988 | 0.331 | -2.984 |
| alpha | 0.011 | 0.008 | 0.005 | 0.004 | 0.0 | -0.011 |
| | 1.726 | 1.252 | 0.873 | 0.68 | 0.042 | -3.074 |
| Diff | -0.007 | -0.008 | -0.006 | -0.007 | -0.008 | -0.001 |
| | -1.876 | -2.628 | -1.819 | -2.117 | -2.389 | -0.446 |
| alpha | -0.005 | -0.007 | -0.005 | -0.006 | -0.007 | -0.002 |
| | -1.847 | -2.257 | -1.383 | -1.603 | -1.851 | -0.792 |
+-------+--------+--------+--------+--------+--------+--------+
===============================================================
投资组合：盈利因子（RMW）
# Bivariate analysis
bi_1 = Bivariate(np.array(test_data_1), number=4)
bi_1.average_by_time()
bi_1.summary_and_test()
bi_1.print_summary_by_time()
bi_1.print_summary()
===============================================================
+-------+--------+--------+--------+--------+--------+-------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+--------+--------+--------+--------+--------+-------+
| 1 | 0.014 | 0.017 | 0.021 | 0.023 | 0.02 | 0.006 |
| | 1.683 | 2.197 | 2.712 | 3.026 | 2.519 | 2.308 |
| 2 | 0.014 | 0.015 | 0.017 | 0.018 | 0.019 | 0.005 |
| | 1.702 | 1.895 | 2.228 | 2.604 | 2.567 | 1.699 |
| 3 | 0.008 | 0.011 | 0.012 | 0.016 | 0.018 | 0.01 |
| | 1.091 | 1.373 | 1.712 | 2.246 | 2.624 | 3.775 |
| 4 | 0.008 | 0.011 | 0.01 | 0.014 | 0.016 | 0.008 |
| | 1.002 | 1.442 | 1.468 | 2.109 | 2.305 | 2.41 |
| 5 | 0.004 | 0.009 | 0.008 | 0.01 | 0.016 | 0.012 |
| | 0.565 | 1.271 | 1.257 | 1.613 | 2.417 | 3.234 |
| Diff | -0.009 | -0.008 | -0.012 | -0.013 | -0.004 | 0.005 |
| | -2.446 | -2.151 | -3.025 | -3.231 | -0.917 | 1.502 |
+-------+--------+--------+--------+--------+--------+-------+
===============================================================# Risk adjustment
risk_model = risk_premium[['MKT', 'SMB', 'HML']]
risk_model = risk_model['2004':'2019']
bi_1.factor_adjustment(risk_model)
bi_1.print_summary()
==============================================================
+-------+--------+--------+--------+--------+--------+-------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+--------+--------+--------+--------+--------+-------+
| 1 | 0.014 | 0.017 | 0.021 | 0.023 | 0.02 | 0.006 |
| | 1.683 | 2.197 | 2.712 | 3.026 | 2.519 | 2.308 |
| alpha | 0.013 | 0.016 | 0.019 | 0.022 | 0.02 | 0.007 |
| | 1.546 | 2.033 | 2.438 | 2.492 | 2.237 | 2.902 |
| 2 | 0.014 | 0.015 | 0.017 | 0.018 | 0.019 | 0.005 |
| | 1.702 | 1.895 | 2.228 | 2.604 | 2.567 | 1.699 |
| alpha | 0.013 | 0.014 | 0.017 | 0.018 | 0.018 | 0.004 |
| | 1.517 | 1.709 | 2.014 | 2.326 | 2.36 | 1.449 |
| 3 | 0.008 | 0.011 | 0.012 | 0.016 | 0.018 | 0.01 |
| | 1.091 | 1.373 | 1.712 | 2.246 | 2.624 | 3.775 |
| alpha | 0.008 | 0.01 | 0.012 | 0.015 | 0.017 | 0.009 |
| | 0.958 | 1.19 | 1.461 | 1.997 | 2.162 | 3.132 |
| 4 | 0.008 | 0.011 | 0.01 | 0.014 | 0.016 | 0.008 |
| | 1.002 | 1.442 | 1.468 | 2.109 | 2.305 | 2.41 |
| alpha | 0.009 | 0.01 | 0.009 | 0.015 | 0.015 | 0.006 |
| | 0.913 | 1.339 | 1.162 | 1.937 | 1.833 | 2.256 |
| 5 | 0.004 | 0.009 | 0.008 | 0.01 | 0.016 | 0.012 |
| | 0.565 | 1.271 | 1.257 | 1.613 | 2.417 | 3.234 |
| alpha | 0.004 | 0.008 | 0.009 | 0.009 | 0.015 | 0.011 |
| | 0.444 | 0.909 | 1.106 | 1.215 | 1.803 | 2.872 |
| Diff | -0.009 | -0.008 | -0.012 | -0.013 | -0.004 | 0.005 |
| | -2.446 | -2.151 | -3.025 | -3.231 | -0.917 | 1.502 |
| alpha | -0.008 | -0.008 | -0.01 | -0.012 | -0.005 | 0.004 |
| | -1.86 | -2.15 | -2.315 | -2.652 | -0.856 | 0.91 |
+-------+--------+--------+--------+--------+--------+-------+
==============================================================
投资组合：投资因子（CMA）
# Bivariate analysis
bi_1 = Bivariate(np.array(test_data_1), number=4)
bi_1.average_by_time()
bi_1.summary_and_test()
bi_1.print_summary_by_time()
bi_1.print_summary()
==============================================================
+-------+--------+--------+--------+--------+--------+-------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+--------+--------+--------+--------+--------+-------+
| 1 | 0.015 | 0.017 | 0.018 | 0.018 | 0.02 | 0.005 |
| | 1.848 | 2.119 | 2.336 | 2.404 | 2.482 | 1.985 |
| 2 | 0.012 | 0.014 | 0.017 | 0.015 | 0.019 | 0.007 |
| | 1.509 | 1.784 | 2.301 | 1.984 | 2.434 | 2.8 |
| 3 | 0.01 | 0.012 | 0.015 | 0.014 | 0.014 | 0.004 |
| | 1.314 | 1.695 | 2.026 | 1.884 | 1.912 | 1.862 |
| 4 | 0.009 | 0.01 | 0.011 | 0.013 | 0.015 | 0.006 |
| | 1.194 | 1.507 | 1.579 | 1.831 | 2.009 | 2.45 |
| 5 | 0.007 | 0.01 | 0.011 | 0.014 | 0.012 | 0.005 |
| | 1.03 | 1.517 | 1.685 | 2.106 | 1.749 | 1.7 |
| Diff | -0.008 | -0.007 | -0.008 | -0.005 | -0.007 | 0.0 |
| | -1.902 | -1.646 | -1.897 | -1.213 | -1.771 | 0.088 |
+-------+--------+--------+--------+--------+--------+-------+
==============================================================# Factor adjustment
# Fama French 3 factors model adjust
# risk factor model
risk_model = risk_factor['2004':'2019'][['MKT', 'SMB','HML']]
bi_1.factor_adjustment(risk_model)
bi_1.print_summary()
=============================================================================================================================
+----------+--------------------+--------------------+--------------------+--------------------+--------------------+-------+
| Group | asset_growth_rate1 | asset_growth_rate2 | asset_growth_rate3 | asset_growth_rate4 | asset_growth_rate5 | Diff |
+----------+--------------------+--------------------+--------------------+--------------------+--------------------+-------+
| Msmvttl1 | 0.015 | 0.017 | 0.018 | 0.018 | 0.02 | 0.005 |
| | 1.848 | 2.119 | 2.336 | 2.404 | 2.482 | 1.985 |
| alpha | 0.014 | 0.016 | 0.017 | 0.017 | 0.019 | 0.005 |
| | 1.655 | 1.951 | 2.203 | 2.159 | 2.157 | 1.959 |
| Msmvttl2 | 0.012 | 0.014 | 0.017 | 0.015 | 0.019 | 0.007 |
| | 1.509 | 1.784 | 2.301 | 1.984 | 2.434 | 2.8 |
| alpha | 0.011 | 0.014 | 0.017 | 0.015 | 0.017 | 0.006 |
| | 1.309 | 1.671 | 2.117 | 1.902 | 1.996 | 3.213 |
| Msmvttl3 | 0.01 | 0.012 | 0.015 | 0.014 | 0.014 | 0.004 |
| | 1.314 | 1.695 | 2.026 | 1.884 | 1.912 | 1.862 |
| alpha | 0.009 | 0.011 | 0.015 | 0.013 | 0.013 | 0.004 |
| | 1.134 | 1.451 | 1.814 | 1.713 | 1.54 | 1.349 |
| Msmvttl4 | 0.009 | 0.01 | 0.011 | 0.013 | 0.015 | 0.006 |
| | 1.194 | 1.507 | 1.579 | 1.831 | 2.009 | 2.45 |
| alpha | 0.009 | 0.01 | 0.01 | 0.012 | 0.014 | 0.005 |
| | 1.097 | 1.37 | 1.327 | 1.512 | 1.674 | 3.089 |
| Msmvttl5 | 0.007 | 0.01 | 0.011 | 0.014 | 0.012 | 0.005 |
| | 1.03 | 1.517 | 1.685 | 2.106 | 1.749 | 1.7 |
| alpha | 0.007 | 0.009 | 0.01 | 0.013 | 0.012 | 0.005 |
| | 0.871 | 1.241 | 1.281 | 1.599 | 1.382 | 1.714 |
| Diff | -0.008 | -0.007 | -0.008 | -0.005 | -0.007 | 0.0 |
| | -1.902 | -1.646 | -1.897 | -1.213 | -1.771 | 0.088 |
| alpha | -0.007 | -0.006 | -0.007 | -0.004 | -0.007 | 0.0 |
| | -1.97 | -1.449 | -1.592 | -0.834 | -1.414 | 0.12 |
+----------+--------------------+--------------------+--------------------+--------------------+--------------------+-------+
=============================================================================================================================
# Bivariate analysis
bi_1 = Bivariate(np.array(test_data_1), number=4)
bi_1.average_by_time()
bi_1.summary_and_test()
bi_1.print_summary_by_time()
bi_1.print_summary()
==============================================================
+-------+--------+--------+--------+--------+--------+-------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+--------+--------+--------+--------+--------+-------+
| 1 | 0.015 | 0.017 | 0.018 | 0.018 | 0.02 | 0.005 |
| | 1.848 | 2.119 | 2.336 | 2.404 | 2.482 | 1.985 |
| 2 | 0.012 | 0.014 | 0.017 | 0.015 | 0.019 | 0.007 |
| | 1.509 | 1.784 | 2.301 | 1.984 | 2.434 | 2.8 |
| 3 | 0.01 | 0.012 | 0.015 | 0.014 | 0.014 | 0.004 |
| | 1.314 | 1.695 | 2.026 | 1.884 | 1.912 | 1.862 |
| 4 | 0.009 | 0.01 | 0.011 | 0.013 | 0.015 | 0.006 |
| | 1.194 | 1.507 | 1.579 | 1.831 | 2.009 | 2.45 |
| 5 | 0.007 | 0.01 | 0.011 | 0.014 | 0.012 | 0.005 |
| | 1.03 | 1.517 | 1.685 | 2.106 | 1.749 | 1.7 |
| Diff | -0.008 | -0.007 | -0.008 | -0.005 | -0.007 | 0.0 |
| | -1.902 | -1.646 | -1.897 | -1.213 | -1.771 | 0.088 |
+-------+--------+--------+--------+--------+--------+-------+
==============================================================# Factor adjustment
# Fama French 3 factors model adjust
# risk factor model
risk_model = risk_factor['2004':'2019'][['MKT', 'SMB','HML']]
bi_1.factor_adjustment(risk_model)
bi_1.print_summary()
=============================================================================================================================
+----------+--------------------+--------------------+--------------------+--------------------+--------------------+-------+
| Group | asset_growth_rate1 | asset_growth_rate2 | asset_growth_rate3 | asset_growth_rate4 | asset_growth_rate5 | Diff |
+----------+--------------------+--------------------+--------------------+--------------------+--------------------+-------+
| Msmvttl1 | 0.015 | 0.017 | 0.018 | 0.018 | 0.02 | 0.005 |
| | 1.848 | 2.119 | 2.336 | 2.404 | 2.482 | 1.985 |
| alpha | 0.014 | 0.016 | 0.017 | 0.017 | 0.019 | 0.005 |
| | 1.655 | 1.951 | 2.203 | 2.159 | 2.157 | 1.959 |
| Msmvttl2 | 0.012 | 0.014 | 0.017 | 0.015 | 0.019 | 0.007 |
| | 1.509 | 1.784 | 2.301 | 1.984 | 2.434 | 2.8 |
| alpha | 0.011 | 0.014 | 0.017 | 0.015 | 0.017 | 0.006 |
| | 1.309 | 1.671 | 2.117 | 1.902 | 1.996 | 3.213 |
| Msmvttl3 | 0.01 | 0.012 | 0.015 | 0.014 | 0.014 | 0.004 |
| | 1.314 | 1.695 | 2.026 | 1.884 | 1.912 | 1.862 |
| alpha | 0.009 | 0.011 | 0.015 | 0.013 | 0.013 | 0.004 |
| | 1.134 | 1.451 | 1.814 | 1.713 | 1.54 | 1.349 |
| Msmvttl4 | 0.009 | 0.01 | 0.011 | 0.013 | 0.015 | 0.006 |
| | 1.194 | 1.507 | 1.579 | 1.831 | 2.009 | 2.45 |
| alpha | 0.009 | 0.01 | 0.01 | 0.012 | 0.014 | 0.005 |
| | 1.097 | 1.37 | 1.327 | 1.512 | 1.674 | 3.089 |
| Msmvttl5 | 0.007 | 0.01 | 0.011 | 0.014 | 0.012 | 0.005 |
| | 1.03 | 1.517 | 1.685 | 2.106 | 1.749 | 1.7 |
| alpha | 0.007 | 0.009 | 0.01 | 0.013 | 0.012 | 0.005 |
| | 0.871 | 1.241 | 1.281 | 1.599 | 1.382 | 1.714 |
| Diff | -0.008 | -0.007 | -0.008 | -0.005 | -0.007 | 0.0 |
| | -1.902 | -1.646 | -1.897 | -1.213 | -1.771 | 0.088 |
| alpha | -0.007 | -0.006 | -0.007 | -0.004 | -0.007 | 0.0 |
| | -1.97 | -1.449 | -1.592 | -0.834 | -1.414 | 0.12 |
+----------+--------------------+--------------------+--------------------+--------------------+--------------------+-------+
=============================================================================================================================
投资组合：动量因子（MOM）
# Bivariate analysis
bi_1 = Bivariate(np.array(test_data_1), number=4)
bi_1.average_by_time()
bi_1.summary_and_test()
bi_1.print_summary_by_time()
bi_1.print_summary()
==============================================================
+-------+--------+--------+--------+--------+--------+-------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+--------+--------+--------+--------+--------+-------+
| 1 | 0.012 | 0.015 | 0.014 | 0.015 | 0.013 | 0.001 |
| | 1.781 | 2.256 | 2.24 | 2.234 | 1.916 | 0.467 |
| 2 | 0.01 | 0.013 | 0.012 | 0.012 | 0.011 | 0.0 |
| | 1.599 | 2.074 | 1.873 | 1.836 | 1.613 | 0.14 |
| 3 | 0.009 | 0.009 | 0.009 | 0.011 | 0.01 | 0.001 |
| | 1.42 | 1.514 | 1.447 | 1.711 | 1.58 | 0.329 |
| 4 | 0.007 | 0.008 | 0.01 | 0.011 | 0.008 | 0.002 |
| | 1.064 | 1.401 | 1.735 | 1.831 | 1.378 | 0.492 |
| 5 | 0.006 | 0.008 | 0.009 | 0.008 | 0.009 | 0.003 |
| | 1.002 | 1.395 | 1.58 | 1.403 | 1.67 | 0.99 |
| Diff | -0.006 | -0.007 | -0.006 | -0.007 | -0.004 | 0.002 |
| | -1.875 | -2.071 | -1.829 | -1.895 | -1.016 | 0.584 |
+-------+--------+--------+--------+--------+--------+-------+# Risk Adjustment
risk_model = risk_premium[['MKT', 'SMB', 'HML']]
risk_model = risk_model['2000':'2019']
bi_1.factor_adjustment(risk_model)
bi_1.print_summary()
===============================================================
+-------+--------+--------+--------+--------+--------+--------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+--------+--------+--------+--------+--------+--------+
| 1 | 0.012 | 0.015 | 0.014 | 0.015 | 0.013 | 0.001 |
| | 1.781 | 2.256 | 2.24 | 2.234 | 1.916 | 0.467 |
| alpha | 0.012 | 0.014 | 0.013 | 0.014 | 0.013 | 0.002 |
| | 1.645 | 1.907 | 2.051 | 1.991 | 1.656 | 0.512 |
| 2 | 0.01 | 0.013 | 0.012 | 0.012 | 0.011 | 0.0 |
| | 1.599 | 2.074 | 1.873 | 1.836 | 1.613 | 0.14 |
| alpha | 0.01 | 0.013 | 0.012 | 0.011 | 0.01 | -0.0 |
| | 1.478 | 1.773 | 1.736 | 1.543 | 1.372 | -0.108 |
| 3 | 0.009 | 0.009 | 0.009 | 0.011 | 0.01 | 0.001 |
| | 1.42 | 1.514 | 1.447 | 1.711 | 1.58 | 0.329 |
| alpha | 0.009 | 0.008 | 0.008 | 0.01 | 0.01 | 0.001 |
| | 1.302 | 1.236 | 1.192 | 1.47 | 1.511 | 0.44 |
| 4 | 0.007 | 0.008 | 0.01 | 0.011 | 0.008 | 0.002 |
| | 1.064 | 1.401 | 1.735 | 1.831 | 1.378 | 0.492 |
| alpha | 0.006 | 0.008 | 0.01 | 0.011 | 0.009 | 0.003 |
| | 0.942 | 1.168 | 1.419 | 1.536 | 1.395 | 1.01 |
| 5 | 0.006 | 0.008 | 0.009 | 0.008 | 0.009 | 0.003 |
| | 1.002 | 1.395 | 1.58 | 1.403 | 1.67 | 0.99 |
| alpha | 0.005 | 0.007 | 0.009 | 0.008 | 0.01 | 0.005 |
| | 0.659 | 1.022 | 1.388 | 1.152 | 1.573 | 1.39 |
| Diff | -0.006 | -0.007 | -0.006 | -0.007 | -0.004 | 0.002 |
| | -1.875 | -2.071 | -1.829 | -1.895 | -1.016 | 0.584 |
| alpha | -0.007 | -0.006 | -0.004 | -0.007 | -0.003 | 0.004 |
| | -2.139 | -1.876 | -1.244 | -1.586 | -0.629 | 0.993 |
+-------+--------+--------+--------+--------+--------+--------+投资组合：换手率因子（Turnover）
# Bivariate analysis
bi_1 = Bivariate(np.array(test_data_1), number=4)
bi_1.average_by_time()
bi_1.summary_and_test()
bi_1.print_summary_by_time()
bi_1.print_summary()
================================================================
+-------+--------+--------+--------+--------+--------+--------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+--------+--------+--------+--------+--------+--------+
| 1 | 0.014 | 0.016 | 0.015 | 0.014 | 0.009 | -0.004 |
| | 2.153 | 2.447 | 2.307 | 2.139 | 1.412 | -1.827 |
| 2 | 0.011 | 0.013 | 0.013 | 0.011 | 0.009 | -0.002 |
| | 1.878 | 2.081 | 2.029 | 1.661 | 1.364 | -0.882 |
| 3 | 0.01 | 0.01 | 0.011 | 0.01 | 0.005 | -0.005 |
| | 1.732 | 1.628 | 1.768 | 1.665 | 0.842 | -1.928 |
| 4 | 0.009 | 0.011 | 0.01 | 0.009 | 0.007 | -0.002 |
| | 1.656 | 1.857 | 1.679 | 1.587 | 1.13 | -0.723 |
| 5 | 0.008 | 0.011 | 0.01 | 0.009 | 0.005 | -0.003 |
| | 1.515 | 2.045 | 1.935 | 1.63 | 0.854 | -1.275 |
| Diff | -0.005 | -0.005 | -0.004 | -0.005 | -0.005 | 0.001 |
| | -1.561 | -1.536 | -1.264 | -1.361 | -1.333 | 0.367 |
+-------+--------+--------+--------+--------+--------+--------+
================================================================# Risk Adjustment
risk_model = risk_premium[['MKT', 'SMB', 'HML']]
risk_model = risk_model['2000':'2019']
bi_1.factor_adjustment(risk_model)
bi_1.print_summary()
===============================================================
+-------+--------+--------+--------+--------+--------+--------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+--------+--------+--------+--------+--------+--------+
| 1 | 0.014 | 0.016 | 0.015 | 0.014 | 0.009 | -0.004 |
| | 2.153 | 2.447 | 2.307 | 2.139 | 1.412 | -1.827 |
| alpha | 0.013 | 0.016 | 0.014 | 0.014 | 0.008 | -0.005 |
| | 1.947 | 2.153 | 1.983 | 2.019 | 1.183 | -2.608 |
| 2 | 0.011 | 0.013 | 0.013 | 0.011 | 0.009 | -0.002 |
| | 1.878 | 2.081 | 2.029 | 1.661 | 1.364 | -0.882 |
| alpha | 0.012 | 0.013 | 0.012 | 0.01 | 0.009 | -0.003 |
| | 1.597 | 1.925 | 1.849 | 1.531 | 1.173 | -1.764 |
| 3 | 0.01 | 0.01 | 0.011 | 0.01 | 0.005 | -0.005 |
| | 1.732 | 1.628 | 1.768 | 1.665 | 0.842 | -1.928 |
| alpha | 0.011 | 0.009 | 0.01 | 0.009 | 0.004 | -0.006 |
| | 1.56 | 1.429 | 1.591 | 1.475 | 0.634 | -3.213 |
| 4 | 0.009 | 0.011 | 0.01 | 0.009 | 0.007 | -0.002 |
| | 1.656 | 1.857 | 1.679 | 1.587 | 1.13 | -0.723 |
| alpha | 0.01 | 0.011 | 0.009 | 0.008 | 0.007 | -0.003 |
| | 1.404 | 1.606 | 1.458 | 1.334 | 0.964 | -1.437 |
| 5 | 0.008 | 0.011 | 0.01 | 0.009 | 0.005 | -0.003 |
| | 1.515 | 2.045 | 1.935 | 1.63 | 0.854 | -1.275 |
| alpha | 0.009 | 0.011 | 0.011 | 0.009 | 0.005 | -0.004 |
| | 1.23 | 1.704 | 1.651 | 1.361 | 0.713 | -1.914 |
| Diff | -0.005 | -0.005 | -0.004 | -0.005 | -0.005 | 0.001 |
| | -1.561 | -1.536 | -1.264 | -1.361 | -1.333 | 0.367 |
| alpha | -0.004 | -0.005 | -0.004 | -0.005 | -0.003 | 0.001 |
| | -1.057 | -1.349 | -0.986 | -1.143 | -0.832 | 0.537 |
+-------+--------+--------+--------+--------+--------+--------+
===============================================================投资组合：流动性因子（Liquidity）
# Independent-sort Bivariate analysis
bi_1 = Bivariate(np.array(test_data_1), number=3)
bi_1.average_by_time()
bi_1.summary_and_test()
bi_1.print_summary_by_time()
bi_1.print_summary()
====================================================
+-------+-------+-------+--------+--------+--------+
| Group | 1 | 2 | 3 | 4 | Diff |
+-------+-------+-------+--------+--------+--------+
| 1 | 0.004 | 0.007 | 0.011 | 0.018 | 0.013 |
| | 0.675 | 1.12 | 1.609 | 2.66 | 5.671 |
| 2 | 0.006 | 0.008 | 0.01 | 0.014 | 0.008 |
| | 0.926 | 1.188 | 1.627 | 2.386 | 3.765 |
| 3 | 0.004 | 0.008 | 0.011 | 0.012 | 0.007 |
| | 0.711 | 1.318 | 1.805 | 2.079 | 3.194 |
| 4 | 0.008 | 0.01 | 0.01 | 0.01 | 0.002 |
| | 1.459 | 1.711 | 1.83 | 1.644 | 0.484 |
| Diff | 0.003 | 0.002 | -0.001 | -0.008 | -0.012 |
| | 1.026 | 0.715 | -0.26 | -2.397 | -3.497 |
+-------+-------+-------+--------+--------+--------+
====================================================# Risk Adjustment
risk_model = risk_premium[['MKT', 'SMB', 'HML']]
risk_model = risk_model['2000':'2019']
bi_1.factor_adjustment(risk_model)
bi_1.print_summary()
====================================================
+-------+-------+-------+--------+--------+--------+
| Group | 1 | 2 | 3 | 4 | Diff |
+-------+-------+-------+--------+--------+--------+
| 1 | 0.004 | 0.007 | 0.011 | 0.018 | 0.013 |
| | 0.675 | 1.12 | 1.609 | 2.66 | 5.671 |
| alpha | 0.003 | 0.006 | 0.01 | 0.018 | 0.015 |
| | 0.385 | 0.852 | 1.387 | 2.499 | 7.72 |
| 2 | 0.006 | 0.008 | 0.01 | 0.014 | 0.008 |
| | 0.926 | 1.188 | 1.627 | 2.386 | 3.765 |
| alpha | 0.004 | 0.007 | 0.01 | 0.015 | 0.011 |
| | 0.608 | 0.947 | 1.482 | 2.198 | 6.722 |
| 3 | 0.004 | 0.008 | 0.011 | 0.012 | 0.007 |
| | 0.711 | 1.318 | 1.805 | 2.079 | 3.194 |
| alpha | 0.004 | 0.007 | 0.01 | 0.013 | 0.009 |
| | 0.572 | 1.119 | 1.53 | 1.959 | 4.325 |
| 4 | 0.008 | 0.01 | 0.01 | 0.01 | 0.002 |
| | 1.459 | 1.711 | 1.83 | 1.644 | 0.484 |
| alpha | 0.008 | 0.009 | 0.01 | 0.011 | 0.003 |
| | 1.179 | 1.393 | 1.622 | 1.569 | 0.865 |
| Diff | 0.003 | 0.002 | -0.001 | -0.008 | -0.012 |
| | 1.026 | 0.715 | -0.26 | -2.397 | -3.497 |
| alpha | 0.005 | 0.003 | 0.001 | -0.006 | -0.011 |
| | 1.404 | 1.009 | 0.164 | -1.97 | -3.135 |
+-------+-------+-------+--------+--------+--------+
====================================================投资组合：偏度因子（skewness）
Total Skewness
# Bivariate analysis
bi_1 = Bivariate(np.array(test_data_1), number=4)
bi_1.average_by_time()
bi_1.summary_and_test()
bi_1.print_summary_by_time()
bi_1.print_summary()
====================================================================
+-------+---------+---------+---------+---------+---------+--------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+---------+---------+---------+---------+---------+--------+
| 1 | 0.024 | 0.022 | 0.021 | 0.023 | 0.025 | 0.001 |
| | 14.565 | 13.84 | 13.484 | 14.584 | 14.149 | 0.323 |
| 2 | 0.008 | 0.013 | 0.01 | 0.012 | 0.012 | 0.004 |
| | 5.627 | 9.234 | 6.737 | 8.375 | 7.731 | 1.911 |
| 3 | 0.007 | 0.006 | 0.007 | 0.009 | 0.008 | 0.0 |
| | 4.488 | 4.209 | 4.475 | 6.156 | 5.395 | 0.111 |
| 4 | 0.004 | 0.006 | 0.006 | 0.004 | 0.006 | 0.001 |
| | 2.697 | 3.714 | 3.905 | 2.497 | 3.58 | 0.535 |
| 5 | -0.003 | -0.004 | -0.003 | -0.003 | -0.002 | 0.0 |
| | -1.843 | -2.713 | -1.823 | -2.029 | -1.567 | 0.228 |
| Diff | -0.027 | -0.026 | -0.024 | -0.026 | -0.027 | -0.0 |
| | -11.849 | -11.725 | -10.724 | -11.715 | -12.268 | -0.094 |
+-------+---------+---------+---------+---------+---------+--------+
====================================================================# Risk Adjustment
risk_model = risk_premium[['MKT', 'SMB', 'HML']]
risk_model = risk_model['2000':'2019']
bi_1.factor_adjustment(risk_model)
bi_1.print_summary()
===================================================================
+-------+---------+---------+--------+---------+---------+--------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+---------+---------+--------+---------+---------+--------+
| 1 | 0.026 | 0.023 | 0.022 | 0.026 | 0.026 | 0.0 |
| | 12.803 | 14.141 | 13.638 | 16.104 | 15.155 | 0.183 |
| alpha | 0.026 | 0.023 | 0.022 | 0.025 | 0.026 | 0.0 |
| | 10.87 | 10.593 | 10.481 | 11.664 | 11.215 | 0.099 |
| 2 | 0.01 | 0.013 | 0.011 | 0.012 | 0.013 | 0.003 |
| | 6.505 | 9.595 | 6.935 | 8.557 | 8.474 | 1.51 |
| alpha | 0.01 | 0.013 | 0.011 | 0.012 | 0.013 | 0.003 |
| | 5.658 | 8.328 | 6.28 | 8.797 | 7.819 | 1.078 |
| 3 | 0.008 | 0.007 | 0.007 | 0.01 | 0.008 | 0.0 |
| | 4.625 | 4.855 | 4.591 | 6.799 | 5.666 | 0.123 |
| alpha | 0.007 | 0.007 | 0.007 | 0.009 | 0.008 | 0.001 |
| | 4.946 | 4.67 | 6.211 | 5.793 | 6.825 | 0.448 |
| 4 | 0.004 | 0.006 | 0.007 | 0.003 | 0.005 | 0.001 |
| | 2.596 | 4.007 | 4.413 | 2.268 | 3.239 | 0.413 |
| alpha | 0.004 | 0.007 | 0.007 | 0.004 | 0.006 | 0.002 |
| | 2.351 | 4.104 | 3.081 | 2.18 | 3.105 | 0.781 |
| 5 | -0.003 | -0.005 | -0.003 | -0.003 | -0.003 | 0.0 |
| | -1.882 | -3.172 | -1.897 | -2.287 | -2.015 | 0.007 |
| alpha | -0.003 | -0.005 | -0.003 | -0.004 | -0.003 | -0.0 |
| | -1.799 | -2.836 | -1.467 | -3.032 | -1.838 | -0.008 |
| Diff | -0.029 | -0.028 | -0.025 | -0.029 | -0.029 | -0.0 |
| | -11.138 | -12.246 | -10.82 | -13.018 | -13.407 | -0.142 |
| alpha | -0.029 | -0.028 | -0.025 | -0.029 | -0.029 | -0.0 |
| | -10.215 | -10.218 | -7.54 | -12.935 | -9.892 | -0.111 |
+-------+---------+---------+--------+---------+---------+--------+
===================================================================Coskewness
# Bivariate analysis
bi_2 = Bivariate(np.array(test_data_2), number=4)
bi_2.average_by_time()
bi_2.summary_and_test()
bi_2.print_summary_by_time()
bi_2.print_summary()
==================================================================
+-------+---------+---------+--------+--------+---------+--------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+---------+---------+--------+--------+---------+--------+
| 1 | 0.022 | 0.022 | 0.023 | 0.022 | 0.025 | 0.003 |
| | 14.748 | 13.599 | 15.127 | 13.191 | 13.98 | 1.465 |
| 2 | 0.009 | 0.012 | 0.009 | 0.011 | 0.013 | 0.004 |
| | 6.551 | 8.696 | 5.983 | 7.881 | 9.526 | 1.958 |
| 3 | 0.007 | 0.008 | 0.007 | 0.008 | 0.006 | -0.001 |
| | 4.559 | 4.966 | 4.612 | 5.373 | 4.528 | -0.26 |
| 4 | 0.006 | 0.005 | 0.004 | 0.005 | 0.006 | 0.0 |
| | 3.778 | 2.985 | 2.629 | 3.513 | 3.853 | 0.02 |
| 5 | -0.002 | -0.004 | -0.002 | -0.004 | -0.002 | 0.0 |
| | -1.129 | -2.603 | -1.222 | -2.493 | -1.143 | 0.011 |
| Diff | -0.024 | -0.026 | -0.025 | -0.026 | -0.027 | -0.003 |
| | -11.291 | -11.861 | -11.57 | -11.34 | -11.015 | -1.08 |
+-------+---------+---------+--------+--------+---------+--------+
==================================================================# Risk Adjustment
risk_model = risk_premium[['MKT', 'SMB', 'HML']]
risk_model = risk_model['2000':'2019']
bi_2.factor_adjustment(risk_model)
bi_2.print_summary()
==================================================================
+-------+---------+---------+--------+--------+---------+--------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+---------+---------+--------+--------+---------+--------+
| 1 | 0.022 | 0.022 | 0.023 | 0.022 | 0.025 | 0.003 |
| | 14.748 | 13.599 | 15.127 | 13.191 | 13.98 | 1.465 |
| alpha | 0.022 | 0.022 | 0.023 | 0.022 | 0.026 | 0.003 |
| | 11.225 | 11.137 | 10.08 | 9.801 | 9.568 | 1.311 |
| 2 | 0.009 | 0.012 | 0.009 | 0.011 | 0.013 | 0.004 |
| | 6.551 | 8.696 | 5.983 | 7.881 | 9.526 | 1.958 |
| alpha | 0.01 | 0.012 | 0.009 | 0.01 | 0.013 | 0.003 |
| | 7.241 | 8.351 | 5.363 | 8.371 | 8.967 | 1.73 |
| 3 | 0.007 | 0.008 | 0.007 | 0.008 | 0.006 | -0.001 |
| | 4.559 | 4.966 | 4.612 | 5.373 | 4.528 | -0.26 |
| alpha | 0.008 | 0.007 | 0.006 | 0.007 | 0.007 | -0.001 |
| | 4.378 | 5.254 | 4.569 | 4.778 | 3.691 | -0.335 |
| 4 | 0.006 | 0.005 | 0.004 | 0.005 | 0.006 | 0.0 |
| | 3.778 | 2.985 | 2.629 | 3.513 | 3.853 | 0.02 |
| alpha | 0.007 | 0.005 | 0.004 | 0.004 | 0.007 | 0.001 |
| | 3.74 | 2.782 | 2.635 | 3.258 | 3.772 | 0.382 |
| 5 | -0.002 | -0.004 | -0.002 | -0.004 | -0.002 | 0.0 |
| | -1.129 | -2.603 | -1.222 | -2.493 | -1.143 | 0.011 |
| alpha | -0.001 | -0.004 | -0.002 | -0.005 | -0.001 | 0.0 |
| | -0.81 | -2.895 | -1.374 | -2.329 | -0.753 | 0.099 |
| Diff | -0.024 | -0.026 | -0.025 | -0.026 | -0.027 | -0.003 |
| | -11.291 | -11.861 | -11.57 | -11.34 | -11.015 | -1.08 |
| alpha | -0.023 | -0.026 | -0.025 | -0.027 | -0.027 | -0.003 |
| | -8.296 | -11.797 | -8.175 | -9.631 | -8.631 | -1.096 |
+-------+---------+---------+--------+--------+---------+--------+
==================================================================Idiosycratic Skewness
# Bivariate analysis
bi_3 = Bivariate(np.array(test_data_3), number=4)
bi_3.average_by_time()
bi_3.summary_and_test()
bi_3.print_summary_by_time()
bi_3.print_summary()
=====================================================================
+-------+---------+---------+---------+---------+---------+--------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+---------+---------+---------+---------+---------+--------+
| 1 | 0.023 | 0.021 | 0.026 | 0.02 | 0.026 | 0.003 |
| | 12.798 | 13.936 | 15.498 | 13.077 | 13.947 | 1.32 |
| 2 | 0.012 | 0.011 | 0.012 | 0.01 | 0.01 | -0.002 |
| | 7.916 | 7.402 | 8.251 | 7.194 | 6.609 | -1.04 |
| 3 | 0.008 | 0.009 | 0.008 | 0.004 | 0.008 | -0.0 |
| | 5.345 | 6.356 | 4.972 | 2.965 | 5.339 | -0.032 |
| 4 | 0.005 | 0.003 | 0.006 | 0.007 | 0.005 | 0.0 |
| | 3.067 | 2.279 | 3.414 | 4.156 | 3.488 | 0.205 |
| 5 | -0.003 | -0.002 | -0.002 | -0.003 | -0.003 | -0.0 |
| | -2.029 | -1.535 | -1.256 | -1.865 | -2.183 | -0.138 |
| Diff | -0.026 | -0.023 | -0.028 | -0.023 | -0.029 | -0.003 |
| | -10.992 | -10.301 | -12.114 | -10.841 | -12.072 | -1.123 |
+-------+---------+---------+---------+---------+---------+--------+# Risk Adjustment
risk_model = risk_premium[['MKT', 'SMB', 'HML']]
risk_model = risk_model['2000':'2019']
bi_3.factor_adjustment(risk_model)
bi_3.print_summary()
====================================================================
+-------+---------+---------+---------+---------+---------+--------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+---------+---------+---------+---------+---------+--------+
| 1 | 0.023 | 0.021 | 0.026 | 0.02 | 0.026 | 0.003 |
| | 12.798 | 13.936 | 15.498 | 13.077 | 13.947 | 1.32 |
| alpha | 0.023 | 0.02 | 0.026 | 0.02 | 0.026 | 0.003 |
| | 9.487 | 13.54 | 12.17 | 10.378 | 8.92 | 1.256 |
| 2 | 0.012 | 0.011 | 0.012 | 0.01 | 0.01 | -0.002 |
| | 7.916 | 7.402 | 8.251 | 7.194 | 6.609 | -1.04 |
| alpha | 0.012 | 0.011 | 0.012 | 0.011 | 0.009 | -0.002 |
| | 7.981 | 6.271 | 7.674 | 5.908 | 6.248 | -1.234 |
| 3 | 0.008 | 0.009 | 0.008 | 0.004 | 0.008 | -0.0 |
| | 5.345 | 6.356 | 4.972 | 2.965 | 5.339 | -0.032 |
| alpha | 0.008 | 0.009 | 0.007 | 0.005 | 0.007 | -0.001 |
| | 4.75 | 6.149 | 4.293 | 3.236 | 5.673 | -0.338 |
| 4 | 0.005 | 0.003 | 0.006 | 0.007 | 0.005 | 0.0 |
| | 3.067 | 2.279 | 3.414 | 4.156 | 3.488 | 0.205 |
| alpha | 0.005 | 0.004 | 0.006 | 0.006 | 0.006 | 0.001 |
| | 2.598 | 2.4 | 3.689 | 2.833 | 4.091 | 0.538 |
| 5 | -0.003 | -0.002 | -0.002 | -0.003 | -0.003 | -0.0 |
| | -2.029 | -1.535 | -1.256 | -1.865 | -2.183 | -0.138 |
| alpha | -0.004 | -0.002 | -0.002 | -0.003 | -0.003 | 0.0 |
| | -2.699 | -1.615 | -1.436 | -1.45 | -2.196 | 0.156 |
| Diff | -0.026 | -0.023 | -0.028 | -0.023 | -0.029 | -0.003 |
| | -10.992 | -10.301 | -12.114 | -10.841 | -12.072 | -1.123 |
| alpha | -0.027 | -0.023 | -0.028 | -0.023 | -0.029 | -0.002 |
| | -9.37 | -9.835 | -11.551 | -8.126 | -7.987 | -0.823 |
+-------+---------+---------+---------+---------+---------+--------+
====================================================================投资组合：波动率因子（volatility）
Total volatility
# Bivariate analysis
bi_1 = Bivariate(np.array(test_data_1), number=4)
bi_1.average_by_time()
bi_1.summary_and_test()
bi_1.print_summary_by_time()
bi_1.print_summary()
=================================================================
+-------+--------+--------+---------+--------+---------+--------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+--------+--------+---------+--------+---------+--------+
| 1 | 0.024 | 0.026 | 0.021 | 0.022 | 0.024 | -0.0 |
| | 13.854 | 14.019 | 13.417 | 13.479 | 14.148 | -0.181 |
| 2 | 0.01 | 0.01 | 0.013 | 0.011 | 0.011 | 0.001 |
| | 7.093 | 6.576 | 9.397 | 7.681 | 7.068 | 0.416 |
| 3 | 0.006 | 0.009 | 0.008 | 0.005 | 0.007 | 0.001 |
| | 4.114 | 6.551 | 5.156 | 3.951 | 5.56 | 0.411 |
| 4 | 0.004 | 0.003 | 0.005 | 0.006 | 0.008 | 0.004 |
| | 2.283 | 2.044 | 3.007 | 3.804 | 4.639 | 1.377 |
| 5 | -0.001 | -0.006 | -0.005 | -0.0 | -0.003 | -0.002 |
| | -0.501 | -3.827 | -3.517 | -0.287 | -1.802 | -0.884 |
| Diff | -0.025 | -0.032 | -0.026 | -0.022 | -0.026 | -0.001 |
| | -10.79 | -13.72 | -12.341 | -9.892 | -12.785 | -0.487 |
+-------+--------+--------+---------+--------+---------+--------+
=================================================================# Risk Adjustment
risk_model = risk_premium[['MKT', 'SMB', 'HML']]
risk_model = risk_model.loc[test_data_1['Date_merge'].unique()]
bi_1.factor_adjustment(risk_model)
bi_1.print_summary()
==================================================================
+-------+--------+---------+---------+--------+---------+--------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+--------+---------+---------+--------+---------+--------+
| 1 | 0.024 | 0.026 | 0.021 | 0.022 | 0.024 | -0.0 |
| | 13.854 | 14.019 | 13.417 | 13.479 | 14.148 | -0.181 |
| alpha | 0.024 | 0.025 | 0.021 | 0.022 | 0.024 | -0.0 |
| | 10.259 | 8.958 | 9.653 | 11.34 | 12.171 | -0.189 |
| 2 | 0.01 | 0.01 | 0.013 | 0.011 | 0.011 | 0.001 |
| | 7.093 | 6.576 | 9.397 | 7.681 | 7.068 | 0.416 |
| alpha | 0.009 | 0.01 | 0.013 | 0.011 | 0.011 | 0.001 |
| | 6.632 | 5.461 | 7.061 | 6.417 | 6.671 | 0.537 |
| 3 | 0.006 | 0.009 | 0.008 | 0.005 | 0.007 | 0.001 |
| | 4.114 | 6.551 | 5.156 | 3.951 | 5.56 | 0.411 |
| alpha | 0.006 | 0.009 | 0.007 | 0.006 | 0.007 | 0.001 |
| | 3.57 | 7.651 | 4.941 | 4.278 | 4.117 | 0.355 |
| 4 | 0.004 | 0.003 | 0.005 | 0.006 | 0.008 | 0.004 |
| | 2.283 | 2.044 | 3.007 | 3.804 | 4.639 | 1.377 |
| alpha | 0.005 | 0.003 | 0.005 | 0.006 | 0.008 | 0.003 |
| | 2.02 | 2.316 | 2.984 | 3.791 | 3.951 | 1.399 |
| 5 | -0.001 | -0.006 | -0.005 | -0.0 | -0.003 | -0.002 |
| | -0.501 | -3.827 | -3.517 | -0.287 | -1.802 | -0.884 |
| alpha | -0.001 | -0.007 | -0.005 | 0.0 | -0.003 | -0.002 |
| | -0.542 | -3.953 | -3.285 | 0.046 | -2.47 | -1.071 |
| Diff | -0.025 | -0.032 | -0.026 | -0.022 | -0.026 | -0.001 |
| | -10.79 | -13.72 | -12.341 | -9.892 | -12.785 | -0.487 |
| alpha | -0.025 | -0.032 | -0.026 | -0.022 | -0.026 | -0.001 |
| | -8.186 | -10.372 | -8.567 | -8.355 | -12.049 | -0.471 |
+-------+--------+---------+---------+--------+---------+--------+
==================================================================Idiosycratic Voaltility
# Bivariate analysis
bi_2 = Bivariate(np.array(test_data_2), number=4)
bi_2.average_by_time()
bi_2.summary_and_test()
bi_2.print_summary_by_time()
bi_2.print_summary()
==================================================================
+-------+---------+--------+---------+--------+---------+--------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+---------+--------+---------+--------+---------+--------+
| 1 | 0.025 | 0.021 | 0.023 | 0.022 | 0.025 | -0.001 |
| | 13.38 | 12.289 | 12.749 | 12.311 | 15.666 | -0.34 |
| 2 | 0.01 | 0.009 | 0.012 | 0.011 | 0.011 | 0.001 |
| | 7.286 | 6.693 | 8.082 | 8.541 | 8.022 | 0.514 |
| 3 | 0.006 | 0.006 | 0.007 | 0.008 | 0.008 | 0.002 |
| | 4.009 | 4.115 | 5.047 | 5.496 | 5.562 | 0.928 |
| 4 | 0.005 | 0.008 | 0.005 | 0.003 | 0.006 | 0.002 |
| | 3.072 | 4.216 | 2.919 | 1.984 | 4.165 | 0.852 |
| 5 | -0.003 | -0.003 | -0.004 | -0.001 | -0.003 | -0.0 |
| | -2.12 | -1.627 | -2.343 | -0.898 | -2.162 | -0.012 |
| Diff | -0.028 | -0.024 | -0.027 | -0.023 | -0.028 | 0.001 |
| | -11.514 | -9.464 | -11.654 | -9.987 | -13.271 | 0.25 |
+-------+---------+--------+---------+--------+---------+--------+
==================================================================# Risk Adjustment
risk_model = risk_premium[['MKT', 'SMB', 'HML']]
risk_model = risk_model.loc[test_data_2['Date_merge'].unique()]
bi_2.factor_adjustment(risk_model)
bi_2.print_summary()
==================================================================
+-------+---------+--------+---------+--------+---------+--------+
| Group | 1 | 2 | 3 | 4 | 5 | Diff |
+-------+---------+--------+---------+--------+---------+--------+
| 1 | 0.025 | 0.021 | 0.023 | 0.022 | 0.025 | -0.001 |
| | 13.38 | 12.289 | 12.749 | 12.311 | 15.666 | -0.34 |
| alpha | 0.025 | 0.022 | 0.023 | 0.021 | 0.025 | -0.0 |
| | 9.763 | 9.203 | 8.447 | 11.555 | 10.599 | -0.056 |
| 2 | 0.01 | 0.009 | 0.012 | 0.011 | 0.011 | 0.001 |
| | 7.286 | 6.693 | 8.082 | 8.541 | 8.022 | 0.514 |
| alpha | 0.009 | 0.009 | 0.013 | 0.011 | 0.011 | 0.002 |
| | 6.647 | 5.971 | 7.189 | 8.442 | 10.108 | 1.312 |
| 3 | 0.006 | 0.006 | 0.007 | 0.008 | 0.008 | 0.002 |
| | 4.009 | 4.115 | 5.047 | 5.496 | 5.562 | 0.928 |
| alpha | 0.006 | 0.005 | 0.007 | 0.009 | 0.008 | 0.002 |
| | 4.377 | 4.991 | 4.854 | 6.411 | 5.54 | 1.084 |
| 4 | 0.005 | 0.008 | 0.005 | 0.003 | 0.006 | 0.002 |
| | 3.072 | 4.216 | 2.919 | 1.984 | 4.165 | 0.852 |
| alpha | 0.005 | 0.008 | 0.004 | 0.003 | 0.007 | 0.002 |
| | 2.796 | 3.946 | 2.612 | 1.684 | 3.852 | 1.027 |
| 5 | -0.003 | -0.003 | -0.004 | -0.001 | -0.003 | -0.0 |
| | -2.12 | -1.627 | -2.343 | -0.898 | -2.162 | -0.012 |
| alpha | -0.003 | -0.003 | -0.003 | -0.001 | -0.004 | -0.0 |
| | -2.782 | -1.515 | -1.966 | -0.759 | -2.726 | -0.234 |
| Diff | -0.028 | -0.024 | -0.027 | -0.023 | -0.028 | 0.001 |
| | -11.514 | -9.464 | -11.654 | -9.987 | -13.271 | 0.25 |
| alpha | -0.028 | -0.025 | -0.026 | -0.023 | -0.029 | -0.0 |
| | -9.507 | -6.88 | -8.776 | -11.24 | -10.372 | -0.069 |
+-------+---------+--------+---------+--------+---------+--------+
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