事件研究法python代码

 #事件研究法

    
  
    
 import pandas as pd
    
 import tushare as ts
    
 import matplotlib.pyplot as plt
    
 from sklearn.linear_model import LinearRegression #线性回归
    
 import csv
    
 import numpy as np
    
  
    
 plt.rcParams['font.sans-serif']=['SimHei'] #中文
    
 plt.rcParams['axes.unicode_minus'] = False
    
 pro = ts.pro_api('这里用你的token替换')
    
 plt.style.use('fivethirtyeight')
    
  
    
  
    
 #市场收益率Rm
    
 df = pro.index_daily(ts_code='000001.SH', start_date='20140101', end_date='20191231')
    
 df_index = df[['trade_date', 'pct_chg']].copy()
    
 df_index['trade_date'] = pd.to_datetime(df_index['trade_date'])
    
 df_index = df_index.sort_values("trade_date")
    
 df_index = df_index.reset_index().drop("index", axis=1)
    
 df_index['pct_chg'] = df_index['pct_chg'] / 100
    
  
    
  
    
 #企业收益率Ri
    
 ts_code='601988.SH'
    
 d2= pro.daily(ts_code=ts_code, start_date='20140101', end_date='20191231')
    
 d0 = d2[['trade_date', 'pct_chg']].copy()
    
 d0.columns = ['trade_date', 'return']
    
 d0['trade_date'] = pd.to_datetime(d0['trade_date'])
    
 d0 = d0.sort_values("trade_date")
    
 d0 = d0.reset_index().drop("index", axis=1)
    
 d0['return'] = d0['return'] / 100
    
  
    
  
    
 #合并Ri和Rm
    
 df_final = d0.merge(df_index, on='trade_date', how='left')
    
 df_final.to_excel("整理数据.xlsx")
    
  
    
  
    
 #计算预期收益率
    
 def get_OLS(X, y, pre_X):
    
     linear_m = LinearRegression().fit(X, y)
    
     r_2 = linear_m.score(X, y) #值越接近1拟合优度越好
    
     pre_y=linear_m.predict(X)
    
     Residual = sum((y - pre_y)**2)
    
     L_xx = len(X) * np.var(X)
    
     sigma = np.sqrt(Residual / (len(X)-2))
    
     t = linear_m.coef_ * np.sqrt(L_xx) / sigma
    
     t=round(float(t),4)
    
  
    
     print(f"构建模型，拟合优度为{round(r_2*100, 2)}%")
    
     print(f"Ri = {round(linear_m.intercept_,3)} + {round(linear_m.coef_[0],3)}Rm + e")
    
  
    
     if 1.65<=abs(t)<1.96:
    
     print(f'回归模型的t值为{t}，回归系数在10%的置信水平下显著')
    
     elif 1.96<=abs(t)<2.58:
    
     print(f'回归模型的t值为{t}，回归系数在5%的置信水平下显著')
    
     elif abs(t)>=2.58:
    
     print(f'回归模型的t值为{t}，回归系数在1%的置信水平下显著')
    
     else:
    
     print(f'回归模型的t值为{t}，回归系数不显著')
    
  
    
     return linear_m.predict(pre_X)
    
  
    
  
    
 #计算AR,CAR
    
 def get_data(event):
    
     print("事件日为: ", event)
    
     q,h = df_final[df_final['trade_date'] == event].index[0]-15, df_final[df_final['trade_date'] == event].index[0]+15 #事件窗口[-15,15]
    
     target = df_final.loc[q:h].copy()
    
     estimate = df_final.loc[q-195:q-6].copy() #估计窗口[-210,-21]
    
     X = estimate[['pct_chg']] #估计期市场回报率
    
     y = estimate['return'] #估计期企业回报率
    
     predict_X = target[['pct_chg']] #窗口期市场回报率
    
     target['E(Rt)'] = get_OLS(X, y, predict_X) #企业预期收益率
    
     target['ARt'] = target['return']-target['E(Rt)'] #企业异常收益率
    
     target['CARt'] =  target['ARt'].cumsum() #累计异常收益率 = 异常收益率在窗口期的求和
    
     return target
    
  
    
  
    
 #绘制图像
    
 def main(e):
    
     a = get_data(e)
    
     print(a)
    
     a.set_index('trade_date')[['ARt', 'CARt']].plot()
    
  
    
 #结果
    
 events = ['2015-08-11']
    
 for e in events:
    
     main(e)
    
  
    
 plt.title('中国银行')
    
 plt.savefig('.\中国银行.jpg',bbox_inches='tight')