Abstract
It is widely assumed that the stock market is a key aspect of the financial market and stock price forecasting has become a popular topic of an in-depth study by financial technologists. With the advent of financial markets, stock prices face several analytical and forecasting challenges. In this paper, the stock price trend forecast analysis is carried out and the survey object was the transaction data of HSBC from 2010 to 2019. Linear regression, LSTM model and ARIMA model are used to forecast stock price trends. The models’ prediction accuracy is demonstrated after the cross-validation process by combining error indicators and trading performance. As a consequence of the analysis, we discover that LSTM has the lowest error value, which implies that it is ideally suited to stock market forecasting. On the contrary, linear regression holds the largest mean square error, implying that this method has the weakest fitting for the stock market.
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