Stock market is one of the attractive domains for researchers as well as academicians. It represents highly complex non-linear fluctuating market behaviours where traders, investors, and organizers look forward to reliable future predictions of the market indices. Such prediction problems can be computationally addressed using various machine learning, deep learning, sentiment analysis, as well as mining approaches. However, the internal parameters configuration can play an important role in the prediction performance; also, feature selection is a crucial task. Therefore, to optimize such approaches, the evolutionary computation-based algorithms can be integrated in several ways. In this article, we systematically conduct a focused survey on genetic algorithm (GA) and its applications for stock market prediction; GAs are known for their parallel search mechanism to solve complex real-world problems; various genetic perspectives are also integrated with machine learning and deep learning methods to address financial forecasting. Thus, we aim to analyse the potential extensibility and adaptability of GAs for stock market prediction. We review stock price and stock trend prediction, as well as portfolio optimization, approaches over the recent years (2013–2022) to signify the state-of-the-art of GA-based optimization in financial markets. We broaden our discussion by briefly reviewing other genetic perspectives and their applications for stock market forecasting. We balance our survey with the consideration of competitiveness and complementation of GAs, followed by highlighting the challenges and potential future research directions of applying GAs for stock market prediction.

中文翻译：

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遗传算法在股市预测中的适用性：过去十年的系统调查


股票市场是对研究人员和学者有吸引力的领域之一。它代表了高度复杂的非线性波动市场行为，交易者、投资者和组织者期望对市场指数的未来进行可靠的预测。此类预测问题可以使用各种机器学习、深度学习、情感分析以及挖掘方法来计算解决。然而，内部参数配置可以对预测性能发挥重要作用；此外，特征选择也是一项至关重要的任务。因此，为了优化此类方法，可以通过多种方式集成基于进化计算的算法。在本文中，我们系统地对遗传算法（GA）及其在股市预测中的应用进行了重点调查；遗传算法以其并行搜索机制来解决复杂的现实世界问题而闻名；各种遗传观点还与机器学习和深度学习方法相结合，以解决财务预测问题。因此，我们的目的是分析遗传算法在股票市场预测方面的潜在可扩展性和适应性。我们回顾了近年来（2013-2022）的股票价格和股票趋势预测以及投资组合优化方法，以表明金融市场中基于 GA 的优化的最新技术。我们通过简要回顾其他遗传学观点及其在股市预测中的应用来扩大我们的讨论。我们平衡了我们的调查与遗传算法的竞争力和互补性的考虑，然后强调了应用遗传算法进行股票市场预测的挑战和潜在的未来研究方向。