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

TL;DR

This paper introduces a stationarity analysis scheme based on KM$_2$O-Langevin theory to classify stock data and develop a low-risk trading strategy, validated on the Nikkei Stock Average.

Contribution

It is the first to apply KM$_2$O-Langevin stationarity analysis to actual mechanical trading, enhancing stock prediction methods.

Findings

The trading strategy achieves small maximum drawdown.

Stationarity classification improves trading decision accuracy.

Back testing confirms strategy safety and effectiveness.

Abstract

This study proposes a scheme for stationarity analysis of stock price fluctuations based on KM$_2$O-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$_2$O-Langevin theory to actual mechanical trading, opens up new avenues for stock price prediction.