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.

由于对冲成本，制定对冲策略以降低投资组合中给定股票组合的损失风险是一项艰巨的任务。在越南股市，交叉对冲涉及对冲股票的多头头寸，因为该股票没有看跌期权。此外，河内证券交易所仅交易 VN30 股指期货合约。受最近深度强化学习成就的启发，我们探索了通过在强化学习技术中利用协作多代理自动构建对冲策略的可行性，而无需高级领域知识。在这项工作中，我们使用胡志明证券交易所的 10 支热门股票，以及 VN30F1M（VN30 指数期货合约在一个月内结算）开发了一个股票市场模拟器（包括交易费、税金、和交易的结算日期）用于强化学习代理培训。我们使用每日回报作为训练过程的输入数据。结果表明，智能体可以学习交易和对冲策略来获利和减少损失。此外，我们还发现我们的代理可以在市场系统性崩溃的情况下保护投资组合并获得正利润。在实践中，这项工作可以帮助越南股市投资者提高业绩并减少交易损失，尤其是在波动性无法控制的情况下。我们还发现，如果市场系统性崩溃，我们的代理人可以保护投资组合并获得正利润。在实践中，这项工作可以帮助越南股市投资者提高业绩并减少交易损失，尤其是在波动性无法控制的情况下。我们还发现，如果市场系统性崩溃，我们的代理人可以保护投资组合并获得正利润。在实践中，这项工作可以帮助越南股市投资者提高业绩并减少交易损失，尤其是在波动性无法控制的情况下。