├── photos ├── pls.png └── factors.png ├── Firm Characteristics and Chinese Stocks.pdf ├── README.md ├── pca.py ├── fm.py ├── fc.py ├── pls.py ├── factor_test_monthly.py ├── LICENSE └── fivefactor_monthly.csv /photos/pls.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cheng-zi-ya/Firm-Characteristics-and-Chinese-Stock-Market/HEAD/photos/pls.png -------------------------------------------------------------------------------- /photos/factors.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cheng-zi-ya/Firm-Characteristics-and-Chinese-Stock-Market/HEAD/photos/factors.png -------------------------------------------------------------------------------- /Firm Characteristics and Chinese Stocks.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cheng-zi-ya/Firm-Characteristics-and-Chinese-Stock-Market/HEAD/Firm Characteristics and Chinese Stocks.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Firm-Characteristics-and-Chinese-Stock-Market 2 | 3 | It is a project that want to accomplish several interesting algorithms to combine features and make predictions. The main reference in the beginning is the paper 'Firm Characteristics and Chinese Stock Market'. 4 | 5 | I am still working to add more tools in this project. 6 | 7 | 8 | ## Introduction 9 | 10 | This project tried several algorithms to combine 75 stock market factors based on companies and financial features. Also, it provided tools to check the performance of stock factors. 11 | 12 | Among all files, the pdf file is the corresponding paper, and the data.csv provides a data sample. Moreover, factor_test_monthly.py includes backtesting on single factors. fm.py, pca.py, pls.py and fc.py provides 4 algorithms. 13 | 14 | We could find that pls and fc have better performance on Chinese stock market. It could achieved about 2.60% monthly return during 2003-06 ~ 2016-12. 15 | 16 | 17 | ## Results 18 | 19 | Two results pictures: 20 | 21 | ![image](https://raw.githubusercontent.com/cheng-zi-ya/Firm-Characteristics-and-Chinese-Stock-Market/main/photos/factors.png) 22 | 23 | ![image](https://raw.githubusercontent.com/cheng-zi-ya/Firm-Characteristics-and-Chinese-Stock-Market/main/photos/pls.png) 24 | 25 | 26 | ## Usage 27 | 28 | factor_test_monthly.py： backtesting and evaluation on single factors. 29 | fm.py, pca.py, pls.py和fc.py： algorithms to combine factors. 30 | 31 | -------------------------------------------------------------------------------- /pca.py: -------------------------------------------------------------------------------- 1 | 2 | """ 3 | 由于并未达到原文中的数值，所以之后会重新检查，看看算式能否进一步提升，因此，并未写出很集成的模块。 4 | 5 | """ 6 | import pandas as pd 7 | import numpy as np 8 | from sklearn.decomposition import IncrementalPCA 9 | 10 | from factor_test_monthly import (compute_num_months, compute_factor_return_series, compute_return_T_test, 11 | compute_5_factor_model) 12 | 13 | from fm import process_bar 14 | 15 | import warnings 16 | warnings.filterwarnings("ignore") 17 | 18 | if __name__ == "__main__": 19 | data = pd.read_csv('./data.csv') 20 | begin_month = 200203 21 | time_length = 190 22 | 23 | months = compute_num_months(begin_month, time_length) 24 | 25 | # 转为按时间排序 26 | data = data.sort_values(by = "TRDMNT") 27 | data = data.reset_index(drop = True) 28 | 29 | # 然后对于每一个时间节点，对于74个因子计算一次，得到其参数， 30 | # 在这里，每个时间对应的PCA，但在计算回报的时候应该用上一个时间点的数值 31 | for i in range(time_length): 32 | month = months[i] 33 | data_atmonth = data[data.TRDMNT == month] 34 | # 线性回归 35 | X = data_atmonth.iloc[:, 18:92].values 36 | model = IncrementalPCA(n_components=1).fit(X) 37 | pca_point = model.components_ 38 | if i == 0: 39 | pca_matrix = pca_point 40 | else: 41 | pca_matrix = np.vstack((pca_matrix, pca_point)) # 该矩阵和时间的对应关系为: 时间对应的那一行用到了下一个月的回报， 42 | # 所以应该移动 43 | 44 | # 对于 data 表，对每一个时间节点的每一个股票计算对应 45 | T = 12 46 | dates = data.TRDMNT.tolist() 47 | data_matrix = data.iloc[:, 18:92].values 48 | pca_data = [] 49 | for i in range(len(data)): 50 | date = dates[i] 51 | if date >= months[T]: # 因为需要前T个时间点的系数进行计算 52 | now_pos = int((date - 200200)/100) * 12 + date%100- 3 53 | pca_params = np.sum(pca_matrix[now_pos - T:now_pos, :], axis=0) / T 54 | pca_point = np.sum(pca_params * data_matrix[i, :]) 55 | pca_data.append(pca_point) 56 | else: 57 | pca_data.append(0) 58 | process_bar(i, len(data)) 59 | 60 | data["pca"] = pca_data 61 | 62 | new_panel = data.loc[:, ['stkid', 'TRDMNT', 'retx', 'pca']] 63 | # new_panel.to_csv('./pca.csv', mode='w', header=True) 64 | 65 | FACTOR = 'pca' 66 | begin_month = 200303 # 200203 67 | time_length = 178 # 从200306 到 201612 68 | 69 | months = compute_num_months(begin_month, time_length) 70 | # 计算该因子对应的多空组合回报率表格 71 | result = compute_factor_return_series(new_panel, FACTOR, begin_month, time_length) 72 | 73 | print("Factor Name:", FACTOR) 74 | the_return, t, Minus = compute_return_T_test(result) 75 | 76 | the_return2, t2 = compute_5_factor_model(Minus, months) 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | -------------------------------------------------------------------------------- /fm.py: -------------------------------------------------------------------------------- 1 | 2 | """ 3 | 由于并未达到原文中的数值，所以之后会重新检查，看看算式能否进一步提升，因此，并未写出很集成的模块。 4 | 5 | """ 6 | import pandas as pd 7 | import numpy as np 8 | import statsmodels.api as sm 9 | 10 | from factor_test_monthly import compute_num_months, compute_factor_return_series, compute_return_T_test, compute_5_factor_model 11 | 12 | import warnings 13 | warnings.filterwarnings("ignore") 14 | 15 | def process_bar(current_state, total_state, bar_length=20): 16 | current_bar = int(current_state / total_state * bar_length) 17 | bar = ['['] + ['#'] * current_bar + ['-'] * (bar_length - current_bar) + [']'] 18 | bar_show = ''.join(bar) 19 | print('\r{}%d%%'.format(bar_show) % ((current_state + 1) / total_state * 100), end='') 20 | if current_state == total_state - 1: 21 | bar = ['['] + ['#'] * bar_length + [']'] 22 | bar_show = ''.join(bar) 23 | print('\r{}%d%%'.format(bar_show) % 100, end='') 24 | print('\r') 25 | 26 | if __name__ == "__main__": 27 | data = pd.read_csv('./data.csv') 28 | begin_month = 200203 29 | time_length = 190 30 | 31 | months = compute_num_months(begin_month, time_length) 32 | 33 | # 转为按时间排序 34 | data = data.sort_values(by = "TRDMNT") 35 | data = data.reset_index(drop = True) 36 | 37 | total_months = data.TRDMNT.values 38 | num_stocks = pd.DataFrame({"Date":months, "num":[sum((total_months == months[i]) * 1) for i in range(len(months))]}) 39 | 40 | # 然后对于每一个时间节点，对于75个因子计算一次，得到其参数， 41 | # 在这里，每个时间对应的PCA，但在计算回报的时候应该用上一个时间点的数值 42 | for i in range(time_length): 43 | month = months[i] 44 | data_atmonth = data[data.TRDMNT == month] 45 | # 线性回归 46 | X = data_atmonth.iloc[:, 18:92].values 47 | X = np.column_stack((np.ones(X.shape[0]), X)) 48 | y = data_atmonth.retx.values 49 | results = sm.OLS(y, X).fit() 50 | fm_point = results.params 51 | if i == 0: 52 | fm_matrix = fm_point 53 | else: 54 | fm_matrix = np.vstack((fm_matrix, fm_point)) # 该矩阵和时间的对应关系为: 时间对应的那一行用到了下一个月的回报， 55 | 56 | T = 12 57 | dates = data.TRDMNT.tolist() 58 | data_matrix = data.iloc[:, 18:92].values 59 | fm_data = [] 60 | # 对于 data 表，对每一个时间节点的每一个股票计算对应的值 61 | for i in range(len(data)): 62 | date = dates[i] 63 | if date >= months[T]: # 因为需要前T个时间点的系数进行计算 64 | now_pos = int((date - 200200)/100) * 12 + date%100- 3 65 | fm_params = np.sum(fm_matrix[now_pos - T:now_pos, :], axis=0) / T 66 | fm_point = np.sum(fm_params * np.array([1] + list(data_matrix[i, :]))) 67 | fm_data.append(fm_point) 68 | else: 69 | fm_data.append(0) 70 | process_bar(i, len(data)) 71 | 72 | data["fm"] = fm_data 73 | 74 | new_panel = data.loc[:, ['stkid', 'TRDMNT', 'retx', 'fm']] 75 | # new_panel.to_csv('./fm.csv', mode='w', header=True) 76 | 77 | FACTOR = 'fm' 78 | begin_month = 200303 79 | time_length = 178 80 | 81 | months = compute_num_months(begin_month, time_length) 82 | # 计算该因子对应的多空组合回报率表格 83 | result = compute_factor_return_series(new_panel, FACTOR, begin_month, time_length) 84 | 85 | print("Factor Name:", FACTOR) 86 | the_return, t, Minus = compute_return_T_test(result) 87 | 88 | the_return2, t2 = compute_5_factor_model(Minus, months) 89 | 90 | -------------------------------------------------------------------------------- /fc.py: -------------------------------------------------------------------------------- 1 | 2 | """ 3 | 由于并未达到原文中的数值，所以之后会重新检查，看看算式能否进一步提升，因此，并未写出很集成的模块。 4 | """ 5 | import pandas as pd 6 | import numpy as np 7 | import statsmodels.api as sm 8 | from scipy import stats 9 | 10 | from factor_test_monthly import compute_num_months, compute_factor_return_series, compute_return_T_test, compute_5_factor_model 11 | 12 | from fm import process_bar 13 | 14 | import warnings 15 | warnings.filterwarnings("ignore") 16 | 17 | 18 | def forecast_combination(X, y): 19 | fc_params = [] 20 | for i in range(X.shape[1]): 21 | if i == 0: # 对于常数项 22 | # result = sm.OLS(y, X[:, i]).fit() 23 | # fc_params.append(result.params[0]) 24 | fc_params.append(stats.linregress(y, X[:, 1])[1]) 25 | # fc_params.append(stats.linregress(X[:, 1], y)[1]) 26 | else: 27 | fc_params.append(stats.linregress(y, X[:, i])[0]) 28 | # fc_params.append(stats.linregress(X[:, i], y)[0]) 29 | return fc_params 30 | 31 | if __name__ == "__main__": 32 | data = pd.read_csv('./data.csv') 33 | begin_month = 200203 34 | time_length = 190 35 | 36 | months = compute_num_months(begin_month, time_length) 37 | 38 | # 转为按时间排序 39 | data = data.sort_values(by = "TRDMNT") 40 | data = data.reset_index(drop = True) 41 | 42 | # 然后对于每一个时间节点，对于75个因子计算一次，得到其参数， 43 | for i in range(time_length): 44 | month = months[i] 45 | data_atmonth = data[data.TRDMNT == month] 46 | # 线性回归 47 | X = data_atmonth.iloc[:, 18:92].values 48 | # X = data_atmonth.iloc[:, 92:166].values 49 | X = np.column_stack((np.ones(X.shape[0]), X)) #先加上常数看看 50 | # y = data_atmonth.retx.values 51 | y = data_atmonth.reta.values 52 | fc_point = forecast_combination(X, y) 53 | if i == 0: 54 | fc_matrix = fc_point 55 | else: 56 | fc_matrix = np.vstack((fc_matrix, fc_point)) # 该矩阵和时间的对应关系为: 时间对应的那一行用到了下一个月的回报， 57 | # 所以应该移动 58 | T = 12 59 | dates = data.TRDMNT.tolist() 60 | data_matrix = data.iloc[:, 18:92].values 61 | fc_data = [] 62 | for i in range(len(data)): 63 | date = dates[i] 64 | if date >= months[T]: # 因为需要前T个时间点的系数进行计算 65 | now_pos = int((date - 200200)/100) * 12 + date%100- 3 66 | fc_params = np.sum(fc_matrix[now_pos - T:now_pos, :], axis=0) / T 67 | fc_point = np.sum(fc_params * np.array([1] + list(data_matrix[i, :]))) 68 | # fc_point = np.sum(np.array(list(fc_params[0, i] for i in range(74))) * np.array(data.iloc[i, 18:92].tolist())) 69 | # fc_point = np.sum(np.array(list(fc_params[0, i] for i in range(74))) * np.array(data.iloc[i, 92:166].tolist())) 70 | fc_data.append(fc_point) 71 | else: 72 | fc_data.append(0) 73 | process_bar(i, len(data)) 74 | 75 | data["fc"] = fc_data 76 | 77 | new_panel = data.loc[:, ['stkid', 'TRDMNT', 'retx', 'fc']] 78 | # new_panel.to_csv('./fc.csv', mode='w', header=True) 79 | 80 | FACTOR = 'fc' 81 | begin_month = 200303 # 200203 82 | time_length = 178 # 从200306 到 201612 83 | 84 | months = compute_num_months(begin_month, time_length) 85 | # 计算该因子对应的多空组合回报率表格 86 | result = compute_factor_return_series(new_panel, FACTOR, begin_month, time_length) 87 | 88 | print("Factor Name:", FACTOR) 89 | the_return, t, Minus = compute_return_T_test(result) 90 | 91 | the_return2, t2 = compute_5_factor_model(Minus, months) 92 | 93 | 94 | 95 | 96 | 97 | 98 | -------------------------------------------------------------------------------- /pls.py: -------------------------------------------------------------------------------- 1 | 2 | """ 3 | 由于并未达到原文中的数值，所以之后会重新检查，看看算式能否进一步提升，因此，并未写出很集成的模块。 4 | 回归计算参考 : https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Linear_Regression_Methods.ipynb 5 | 6 | """ 7 | import pandas as pd 8 | import numpy as np 9 | from scipy import stats 10 | 11 | from factor_test_monthly import compute_num_months, compute_factor_return_series, compute_return_T_test, compute_5_factor_model 12 | 13 | from fm import process_bar 14 | 15 | import time 16 | 17 | import warnings 18 | warnings.filterwarnings("ignore") 19 | 20 | def forecast_combination(X, y): 21 | fc_params = [] 22 | for i in range(X.shape[1]): 23 | if i == 0: # 对于常数项 24 | # result = sm.OLS(y, X[:, i]).fit() 25 | # fc_params.append(result.params[0]) 26 | fc_params.append(stats.linregress(y, X[:, 1])[1]) 27 | # fc_params.append(stats.linregress(X[:, 1], y)[1]) 28 | else: 29 | fc_params.append(stats.linregress(y, X[:, i])[0]) 30 | # fc_params.append(stats.linregress(X[:, i], y)[0]) 31 | return fc_params 32 | 33 | if __name__ == "__main__": 34 | data = pd.read_csv('./data.csv') 35 | begin_month = 200203 36 | time_length = 190 37 | 38 | months = compute_num_months(begin_month, time_length) 39 | 40 | # 转为按时间排序 41 | data = data.sort_values(by = "TRDMNT") 42 | data = data.reset_index(drop = True) 43 | 44 | # 然后对于每一个时间节点，对于74个因子计算一次，得到其参数， 45 | # 在这里，每个时间对应的PCA，但在计算回报的时候应该用上一个时间点的数值 46 | for i in range(time_length): 47 | month = months[i] 48 | data_atmonth = data[data.TRDMNT == month] 49 | X = data_atmonth.iloc[:, 18:92].values 50 | # X = data_atmonth.iloc[:, 92:166].values 51 | X = np.column_stack((np.ones(X.shape[0]), X)) #先加上常数看看 52 | # y = data_atmonth.retx.values 53 | y = data_atmonth.reta.values 54 | pls_point = forecast_combination(X, y) 55 | if i == 0: 56 | pls_matrix = pls_point 57 | else: 58 | pls_matrix = np.vstack((pls_matrix, pls_point)) # 该矩阵和时间的对应关系为: 时间对应的那一行用到了下一个月的回报， 59 | # 所以应该移动 60 | 61 | T = 12 62 | dates = data.TRDMNT.tolist() 63 | data_matrix = data.iloc[:, 18:92].values 64 | # data_matrix = data.iloc[:, 92:166].values 65 | pls_data = [] 66 | for i in range(int(data_matrix.shape[0])): 67 | date = dates[i] 68 | if date >= months[T]: 69 | now_pos = int((date - 200200)/100) * 12 + date%100- 3 70 | pls_params = np.sum(pls_matrix[now_pos - T:now_pos, :], axis=0) / T 71 | X = pls_params.T 72 | y = np.array([1] + data_matrix[i,:].tolist()) 73 | pls_point = stats.linregress(y, X)[0] 74 | # pls_point = stats.linregress(X, y)[0] 75 | pls_data.append(pls_point) 76 | else: 77 | pls_data.append(0) 78 | process_bar(i, data_matrix.shape[0]) 79 | 80 | data["pls"] = pls_data 81 | new_panel = data.loc[:, ['stkid', 'TRDMNT', 'retx', 'pls']] 82 | # new_panel.to_csv('./pls.csv', mode='w', header=True) 83 | 84 | FACTOR = 'pls' 85 | begin_month = 200203 # 200203 178; 200306, 163；200406， 151；200506， 139； 200506， 139 86 | time_length = 178 87 | 88 | months = compute_num_months(begin_month, time_length) 89 | # 计算该因子对应的多空组合回报率表格 90 | result = compute_factor_return_series(new_panel, FACTOR, begin_month, time_length) 91 | 92 | print("Factor Name:", FACTOR) 93 | the_return, t, Minus = compute_return_T_test(result) 94 | 95 | the_return2, t2 = compute_5_factor_model(Minus, months) 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | -------------------------------------------------------------------------------- /factor_test_monthly.py: -------------------------------------------------------------------------------- 1 | 2 | import pandas as pd 3 | import numpy as np 4 | import matplotlib.pyplot as plt 5 | from scipy import stats 6 | import statsmodels.api as sm 7 | 8 | import time 9 | 10 | def compute_num_months(begin_month, time_length): 11 | """输入起始时间和时间长度，输出起始之后的一段月度的时间序列""" 12 | begin = begin_month 13 | months = [begin] 14 | for i in range(time_length - 1): 15 | if (begin % 100) % 12 != 0: begin = begin + 1 16 | else: begin = begin + 89 17 | months.append(begin) 18 | return months 19 | 20 | def compute_factor_return_series(data, FACTOR, begin_month, time_length): 21 | months = compute_num_months(begin_month, time_length) 22 | 23 | # 计算价值加权，可以直接用PRC字段，因为其就是t-1的股票价格 24 | result = pd.DataFrame({'Date': months, 'High': np.zeros(time_length), 'Low': np.zeros(time_length), 'Minus': np.zeros(time_length)}) 25 | 26 | for i in range(time_length): 27 | date = months[i] 28 | factor, weights, returnrate = [], [], [] 29 | # 对于横截面进行计算（对所有股票计算因子，再按照因子排序进行分组，归并回报） 30 | data_atdate = data[data.TRDMNT == date] 31 | factor_value = data_atdate[FACTOR].values 32 | returns = data_atdate.retx.values 33 | # returns = data_atdate.reta.values 34 | for j in range(len(data_atdate)): 35 | factor.append(factor_value[j]) 36 | weights.append(1) # 如果是value-weighted,这里是PRC;如果是Equal-weighted，这里是1 37 | returnrate.append(returns[j]) 38 | 39 | factor_table = {'factor': factor, 'weights': weights, 'returnrate': returnrate} 40 | factor_table = pd.DataFrame(factor_table) 41 | factor_table = factor_table.sort_values(['factor'], ascending=False) 42 | 43 | len_factor_table = len(factor_table) 44 | len_group = int(np.floor(len_factor_table / 10)) 45 | 46 | group_return, group_weight, final_returns = [], [], [] 47 | for j in range(len_factor_table): 48 | if (j + 1) % len_group == 0: 49 | final_returns.append(np.sum(np.array(group_return) * np.array(group_weight)) / np.sum(group_weight)) 50 | group_return, group_weight = [], [] 51 | else: 52 | group_return.append(factor_table.iloc[j].returnrate) 53 | group_weight.append(factor_table.iloc[j].weights) 54 | 55 | result.loc[i, 'High'] = final_returns[9] 56 | result.loc[i, 'Low'] = final_returns[0] 57 | result.loc[i, 'Minus'] = final_returns[0] - final_returns[9] 58 | 59 | return result 60 | 61 | def compute_return_T_test(result_panel): 62 | # 计算平均回报率，进行t检验。原本是High - Low, 而如果收益反转，则 Low - High 63 | Minus = np.array((list(result_panel.High)) - np.array(list(result_panel.Low))) 64 | Minus = Minus * ((np.average(Minus) > 0) *1 *2 -1) 65 | print("monthly average raw returns(%)", np.average(Minus) * 100) 66 | print("T-test", stats.ttest_1samp(Minus, 0)[0]) 67 | return np.average(Minus) * 100, stats.ttest_1samp(Minus, 0)[0], Minus 68 | 69 | def compute_5_factor_model(Minus, months): 70 | # 对于五因子模型计算超额收益率，并计算其t检验值 71 | fivefac = pd.read_csv("./fivefactor_monthly.csv") 72 | 73 | # 然后利用date确定Fama中的对应值 74 | SMB, HML, MKT, RMW, CMA = [], [], [], [], [] 75 | 76 | for i in range(len(fivefac)): 77 | if fivefac.trdmn[i] in months: 78 | SMB.append(fivefac.smb[i]) 79 | HML.append(fivefac.hml[i]) 80 | MKT.append(fivefac.mkt_rf[i]) 81 | RMW.append(fivefac.rmw[i]) 82 | CMA.append(fivefac.cma[i]) 83 | 84 | X = np.column_stack((np.ones(len(MKT)), np.array(SMB), np.array(HML), np.array(MKT), np.array(RMW), np.array(CMA))) 85 | y = list(Minus) 86 | 87 | model = sm.OLS(y, X) 88 | results = model.fit() 89 | 90 | print(results.params[0] * 100) 91 | print(results.tvalues[0]) 92 | # np.set_printoptions(suppress=True) 93 | # print(results.summary()) 94 | return results.params[0] * 100, results.tvalues[0] 95 | 96 | if __name__ == "__main__": 97 | # 输入量 98 | data = pd.read_csv('./data.csv') 99 | # FACTOR = 'roaq' 100 | FACTOR = 'BM' 101 | begin_month = 200203 102 | time_length = 190 103 | 104 | months = compute_num_months(begin_month, time_length) 105 | # 计算该因子对应的多空组合回报率表格 106 | result = compute_factor_return_series(data, FACTOR, begin_month, time_length) 107 | 108 | print("Factor Name:", FACTOR) 109 | the_return, t, Minus = compute_return_T_test(result) 110 | 111 | the_return2, t2 = compute_5_factor_model(Minus, months) 112 | 113 | 114 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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-------------------------------------------------------------------------------- 1 | trdmn,mkt_rf,smb,hml,umd,rf,smb_equal,hml_equal,umd_equal,rmw,cma,rmw_equal,cma_equal 2 | 199402,-0.005733448,-0.00979825,-0.030990519,-0.026590251,0.008719,-0.027549453,-0.046225405,-0.036616959,0.0096924566,0.0048795538,0.0427227805,0.0053676741 3 | 199403,-0.140417362,0.1011218906,0.0445735689,0.1881391873,0.008719,0.1009953872,0.0621829457,0.1367931461,-0.15574424,-0.070236096,-0.104735895,-0.03121357 4 | 199404,-0.168803863,0.0528664209,0.0374895121,0.0083276498,0.008719,0.0494970947,0.0323197487,-0.022157113,0.0116578088,-0.009700877,-0.005095,0.0162540851 5 | 199405,-0.050044268,0.0033944821,-0.007816317,-0.016820707,0.008719,0.0221255641,0.0146680578,0.0333559842,0.0545802258,-0.036817085,0.0234531577,-0.028613077 6 | 199406,-0.1576434,-0.013240454,0.0164682341,0.0263821754,0.008719,-0.009669261,0.0219911751,0.0442008029,0.0261935851,0.0188870606,0.0149113073,0.0202822004 7 | 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