Using a Deep Learning Model to Simulate Human Stock Trader's Methods of Chart Analysis
This work addresses the problem of improving stock trading returns for investors by applying deep learning to chart analysis, though it is incremental as it adapts existing models to financial data.
The paper tackles stock price prediction by simulating professional technical analysts using a deep learning model, achieving returns of 75.36% with a Sharpe ratio of 1.57 in the Korea market and 27.17% with a Sharpe ratio of 0.61 in the US market, outperforming market benchmarks.
Despite the efficient market hypothesis, many studies suggest the existence of inefficiencies in the stock market leading to the development of techniques to gain above-market returns. Systematic trading has undergone significant advances in recent decades with deep learning schemes emerging as a powerful tool for analyzing and predicting market behavior. In this paper, a method is proposed that is inspired by how professional technical analysts trade. This scheme looks at stock prices of the previous 600 days and predicts whether the stock price will rise or fall 10% or 20% within the next D days. The proposed method uses the Resnet's (a deep learning model) skip connections and logits to increase the probability of the prediction. The model was trained and tested using historical data from both the Korea and US stock markets. The backtest is done using the data from 2020 to 2022. Using the proposed method for the Korea market it gave return of 75.36% having Sharpe ratio of 1.57, which far exceeds the market return by 36% and 0.61, respectively. On the US market it gives total return of 27.17% with Sharpe ratio of 0.61, which outperforms other benchmarks such as NASDAQ, S&P500, DOW JONES index by 17.69% and 0.27, respectively.