Abstract
Stock market is an important capital mobilization channel for economy. However, the market has potential loss due to fluctuations of stock prices to reflect uncertain events such as political news, supply and demand of daily trading volume. There are many approaches to reduce risk such as portfolio construction and optimization, hedging strategies. Hence, it is critical to leverage time series prediction techniques to achieve higher performance in stock market. Recently, Vietnam stock markets have gained more and more attention as their performance and capitalization improvement. In this work, we use market data from Vietnam’s two stock market to develop an incorporated model that combines Sequence to Sequence with Long-Short Term Memory model of deep learning and structural models time series. We choose 21 most traded stocks with over 500 trading days from VN-Index of Ho Chi Minh Stock Exchange and HNX-Index of Hanoi Stock Exchange (Vietnam) to perform the proposed model and compare their performance with pure structural models and Sequence to Sequence. For back testing, we use our model to decide long or short position to trade VN30F1M (VN30 Index Futures contract settle within one month) that are traded on HNX exchange. Results suggest that the Sequence to Sequence with LSTM model of deep learning and structural models time series achieve higher performance with lower prediction errors in terms of mean absolute error than existing models for stock price prediction and positive profit for derivative trading. This work significantly contribute to literature of time series prediction as our approach can relax heavy assumptions of existing methodologies such as Auto-regressive–moving-average model, Generalized Auto-regressive Conditional Heteroskedasticity. In practical, investors from Vietnam stock market can use the proposed model to develop trading strategies.
Highlights
Describing the behavior of the observed time series plays a critical role to understand the past and predict the future in many disciplines
Results suggest that the Sequence to Sequence with Long Short-Term Memory (LSTM) model of deep learning and structural models time series achieve higher performance with lower prediction errors in terms of mean absolute error than existing models for stock price prediction and positive profit for derivative trading
We develop a Sequence to Sequence with LSTM architecture
Summary
Describing the behavior of the observed time series plays a critical role to understand the past and predict the future in many disciplines. Creating high accuracy prediction of time series with low error is not an easy job, due to high fluctuations of stock market. From this perspective, there have been many methods that were proposed to study historical patterns of time series data to crate high quality of stock price prediction. Time series prediction: A combination of Long Short-Term Memory and structural time series models. Neural network like Long-Short Term Memory of deep learning can link current event to previous events while Structural Time Series Models only depends on previous event. We use proposed model to automatically trade VN30F1M futures contract on Ha Noi Stock Exchange for back testing
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