Stationarity analysis of the stock market data and its application to mechanical trading

Abstract

This study proposes a scheme for stationarity analysis of stock price fluctuations based on KM_2O-Langevin theory. Using this scheme, we classify the time-series data of stock price fluctuations into three periods: stationary, non-stationary, and intermediate. We then suggest an example of a low-risk stock trading strategy to demonstrate the usefulness of our scheme by using actual stock index data. Our strategy uses a trend-based indicator, moving averages, for stationary periods and an oscillator-based indicator, psychological lines, for non-stationary periods to make trading decisions. Finally, we confirm that our strategy is a safe trading strategy with small maximum drawdown by back testing on the Nikkei Stock Average. Our study, the first to apply the stationarity analysis of KM_2O-Langevin theory to actual mechanical trading, opens up new avenues for stock price prediction.

Tasks

Reproductions