Stock market prediction is a classical problem in the intersection of finance and computer science. For this problem, the famous efficient market hypothesis (EMH) gives a pessimistic view and implies that financial market is efficient [Fama, 1965], which maintains that technical analysis or fundamental analysis (or any analysis) would not yield any consistent over-average profit to investors. However, many researchers disagree with EMH [Malkiel, 2003]. Some studies are trying to measure the different efficiency levels for mature and emerging markets, while other studies are trying to build effective prediction models for stock markets, which is also the scope of this survey. The effort starts with the stories of fundamental analysis and technical analysis. Fundamental analysis evaluates the stock price based on its intrinsic value,i.e., fair value, while technical analysis only relies on the basis of charts and trends. The technical indicators from experience can be further used as handcrafted input features for machine learning and deep learning models. Afterwards, linear models are introduced as the solutions for stock market prediction, which include autoregressive integrated moving average (ARIMA) [Hyndman & Athanasopoulos, 2018] and generalized autoregressive conditional heteroskedasticity (GARCH) [Bollerslev, 1986]. With the development of machine learning models, they are also applied for stock market prediction, e.g., Logistic regression and support vector machine [Alpaydin, 2014]. Our focus in this survey would be the latest emerging deep learning, which is represents by various structures of deep neural networks [Goodfellow et al., 2016]. Powered by the collection of big data from the Web, the parallel processing ability of graphics processing units (GPUs), and the new convolutional neural network family, deep learning has achieved a tremendous success in the past few years, for many different applications including image classification [Rawat & Wang, 2017; Jiang & Zhang, 2020], object detection [Zhao et al., 2019], time series prediction [Brownlee, 2018; Jiang & Zhang, 2018], etc. With a strong ability of dealing with big data and learning the nonlinear relationship between input features and prediction target, deep learning models have shown a better performance than both linear and machine learning models on the tasks that include stock market prediction.

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