Multi-Modal Data Driven Algorithm for Efficient Trade Market Prediction

Financial market prediction is an attractive research area for the researchers as it helps the market participators to make decisions accordingly. However, the forecasting of financial market is not an easy task as the movement of financial market is stochastic in nature and is affected by several controllable and uncontrollable factors. In this research, S&P 500 index and NASDAQ is predicted using five machine learning models including support vector regression, random forest, linear regression, k nearest neighbour and LSTM. Three different datasets are used for the forecasting of daily closing price of S&P 500 index and NASDAQ in order to check the sensitivity of the market towards different factors. Firstly, historical data along with macroeconomic factors is used to design a model. Second dataset is sentiment features extracted from web news. Lastly, a hybrid data is developed by combining the first two datasets. LSTM model outperformed other machine learning models for the prediction of both financial markets. It is also observed that our developed dataset is the most efficient one as the models based on this dataset gives the minimum RMSE.