International Journal of Electronic Engineering and Computer Science
Articles Information
International Journal of Electronic Engineering and Computer Science, Vol.5, No.3, Sep. 2020, Pub. Date: Dec. 11, 2020
"Stock Prediction Using Deep Learning with Long-Short-Term-Memory Networks "
Pages: 22-32 Views: 1241 Downloads: 383
[01] Carol Anne Hargreaves, Department of Statistics and Applied Probability, National University of Singapore, Singapore.
[02] Chen Leran, Department of Statistics and Applied Probability, National University of Singapore, Singapore.
Background: Stock market prediction is one of the most challenging tasks since the financial time series is highly volatile, noisy, non-linear and dynamic in nature. Long-Short-Term-Memory (LSTM) is a deep learning technique that focuses on sequential learning. Researchers have shown that the LSTM makes good prediction in the S&P 500 stocks, the Hong Kong stocks and the US intra-day stock data. However, there is limited research in the Australian stock market. Objective: This study aims to apply the LSTM technique to predict the stock price movement in the Australian Stock Market and to identify which stocks to buy for a profitable portfolio. Methodology: We analyzed 400 stocks and selected the top 5 stocks to buy and trade, based on the predictions of the LSTM, Regression Tree (CART) and the Auto Regressive Integrated Moving Average (ARIMA) techniques. Results: The results showed that the LSTM, a deep learning neural network algorithm, outperformed the CART and ARIMA-Time Series algorithms by achieving a return rate of thirty-five percent, a Sharpe ratio of 2.13 and a maximum drawdown of 0.34. The LSTM portfolio had a higher overall return rate of 35% versus the Australian market index of 21%. Conclusion: The LSTM networks made more accurate predictions on stock prices than the ARIMA time series and regression trees (CART). This may be because the LSTM networks is good at processing sequential data, extracting useful information and dropping unnecessary information. Further, the LSTM had a relatively more stable return compared to the ARIMA model. This is because a deep learning model is more capable of extracting the non-linear relationship in the data. In addition, the LSTM model stock portfolio outperformed the stock market index and generated profits over three consecutive time periods.
Deep Learning, Deep Neural Network, Long-Short-Term-Memory (LSTM), ARIMA-Time Series, Classification and Regression Tree (CART), Stock Portfolio, Stock Prediction
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