Developing a hedging strategy to reduce risk of losses for a given set of stocks in a portfolio is a difficult task due to cost of the hedge. In Vietnam stock market, cross-hedge is involved hedging a long position of a stock because there is no put option for the stock. In addition, only VN30 stock index futures contracts are traded on Hanoi Stock Exchange. Inspired by recently achievement of deep reinforcement learning, we explore feasibility to construct a hedging strategy automatically by leveraging cooperative multi-agent in reinforcement learning techniques without advanced domain knowledge. In this work, we use 10 popular stocks on Ho Chi Minh Stock Exchange, and VN30F1M (VN30 Index Futures contracts within one month settlement) to develop a stock market simulator (including transaction fee, tax, and settlement date of transactions) for reinforcement learning agent training. We use daily return as input data for training process. Results suggest that the agent can learn trading and hedging policy to make profit and reduce losses. Furthermore, we also find that our agent can protect portfolios and make positive profit in case market collapses systematically. In practice, this work can help Vietnam’s stock market investors to improve performance and reduce losses in trading, especially when the volatility cannot be controlled.

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.