Intraday trading strategy based on time series and machine learning for Chinese stock market

TL;DR

This paper proposes an intraday trading strategy for the Chinese stock market using Markowitz optimization and MLP-based price prediction, demonstrating its profitability and feasibility through empirical analysis.

Contribution

It introduces a novel combination of Markowitz optimization and MLP for intraday trading, validated with real stock data from Chinese exchanges.

Findings

Markowitz optimization improves portfolio profitability

MLP effectively predicts intraday stock prices

Combined strategy proves feasible and profitable

Abstract

This article comes up with an intraday trading strategy under T+1 using Markowitz optimization and Multilayer Perceptron (MLP) with published stock data obtained from the Shenzhen Stock Exchange and Shanghai Stock Exchange. The empirical results reveal the profitability of Markowitz portfolio optimization and validate the intraday stock price prediction using MLP. The findings further combine the Markowitz optimization, an MLP with the trading strategy, to clarify this strategy's feasibility.