Designing a hybrid model for stock marketing prediction based on LSTM and transfer learning

One of the most complex and controversial issues in financial markets is the prediction of price and stock returns which is always a matter of interest to shareholders. The stock market is vulnerable to various factors that affect the price fluctuations in the stock market.  The development of a strong stock market algorithm that can accurately predict stock behaviour is important to maximize profits and minimize the loss of investors. Although in addition to the history of each share, other psychological factors affect the value of each share, in this research, an artificial intelligence model is proposed based on long short-term memory and text embedding. In addition to being paid to the stock market in the form of time series data; In order to investigate the psychological force of the market, features are also extracted from news sites. And finally, based on the combination of features extracted from news sites and time-series data, predicts the future of the stock market. The results of the evaluations show the proposed model can predict the market future truly.