Original paper
Abstract
This paper examines whether insider trades reflect the insiders' superior knowledge of future cash flow realizations, as proxied by the firm's future return and earnings performance. We find strong evidence that insider trades are positively associated with the firm's future earnings performance. This relation is shown to be incremental to the book-to-market and past return relations documented in Rozeff and Zaman (1998), suggesting that insiders trade on both transitory security misvaluation and private information about future cash-flow payoffs. We find that insider trading behavior within each book-to-market portfolio varies with the horizon of the subsequent earnings news. Insider purchases are significantly positively related to next year's earnings news across all book-to-market portfolios, while the sign of the relation between insider purchases and contemporaneous earnings is negative (positive) for glamour (value) firms. Finally, we show that the relation between insider trades and future earnings performance is amplified (attenuated) as the likely ex ante benefits (costs) to trading on financial performance information increase.
Keywords:Â Insider trading, Informativeness of Earnings, Capital Markets
Trading rules
- Investment scope: NYSE, AMEX, NASDAQ stocks over $2 (excluding closed-end funds, REITs, ADRs)
- Compute Net Purchase Ratio (NPR) for each stock over prior 6 months at end of April
- NPR = (Total insider purchases - Total insider sells) / Total transactions
- Order stocks into deciles based on NPR
- Go long on top 10% (highest NPR) and short on bottom 10% (lowest NPR)
- Hold portfolio for one year, then rebalance
Python code
Backtrader
import backtrader as bt
import pandas as pd
import requests
class InsiderStrategy(bt.Strategy):
def __init__(self):
self.insider_data = None
def next(self):
if self.datetime.date().month == 4 and self.datetime.date().day == 30:
self.rebalance()
def rebalance(self):
self.rank_stocks()
long_stocks = self.insider_data.nlargest(10, 'NPR')
short_stocks = self.insider_data.nsmallest(10, 'NPR')
for stock in long_stocks['symbol']:
data = self.getdatabyname(stock)
size = self.broker.get_cash() * 0.1 / data.close[0]
self.buy(data=data, size=size)
for stock in short_stocks['symbol']:
data = self.getdatabyname(stock)
size = self.broker.get_cash() * 0.1 / data.close[0]
self.sell(data=data, size=size)
def rank_stocks(self):
self.insider_data = self.get_insider_data()
self.insider_data['NPR'] = (self.insider_data['purchases'] - self.insider_data['sells']) / self.insider_data['transactions']
self.insider_data.sort_values(by='NPR', ascending=False, inplace=True)
def get_insider_data(self):
# Implement data fetching from external sources and pre-processing as needed
pass
if __name__ == '__main__':
cerebro = bt.Cerebro()
# Implement data loading and filtering based on the specified criteria
# Replace 'filtered_data' with the actual data source
for symbol, data in filtered_data.items():
cerebro.adddata(data, name=symbol)
cerebro.addstrategy(InsiderStrategy)
cerebro.broker.set_cash(100000)
cerebro.broker.setcommission(commission=0.001)
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe')
results = cerebro.run()
final_value = results[0].broker.get_value()
print(f'Final Portfolio Value: {final_value}')
print('Sharpe Ratio:', results[0].analyzers.sharpe.get_analysis()['sharperatio'])Note: This code is a template for the given strategy. You will need to implement the get_insider_data function to fetch the insider trading data from an external source and preprocess it to calculate the NPR. Additionally, you will need to load and filter the stock data based on the specified criteria, and replace filtered_data with the actual data source.