Yaming Cao, Jingzhen Hu, Qingzhong Liang, Arafatur Rahman, A K M Rokonuzzaman Sonet


The motivation of our projects is to build a model to predict the future return/trend of a basket of stocks so that financial companies can use it to optimize the portfolios (e.g. balance between the return and risk) and even develop the trading strategies. We used the opening price data between 06/02/2012 and 06/02/2022 of the five stocks (AAPL,TSLA, AMD, SBUX, FB) from Yahoo to tune the LSTM model parameters and test on the most recent data. The five stocks we picked are from different sectors, which allow us to train a general model the fits a large variety of stocks. Testing data consists of the last 90 days while the price of the previous dates form the training set. The resulting plots shows that our
predictions provide the correct trend with a lagging time in general. For relatively new stocks like TSLA, the volatility(beta) is relatively high. For stocks with relatively high (higher than the mean of the basket) historical beta, the prediction of the future 14 days (time-step)

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