Performance Analysis of Deep Learning and Statistical Models on Enhancing Stock Market Portfolio

Abstract

: Time series data is considered very useful in the domains of business, finance and economics. Stock market data specifically is generated at high volumes and excessively used for forecasting purposes for gaining wealth. The problem is challenging due to the dynamic nature of stock market fluctuations. Conventional techniques for prediction of next lag of time series data have been successful to an extent with statistical algorithms such as Exponential Smoothing and Autoregressive Integrated Moving Average (ARIMA). With the advent of deep learning architectures and advanced computational processors, we analyze the performance of such techniques for stock market forecasting. The paper presents performance comparison of Exponential Smoothing, ARIMA, Vanilla LSTMs and Stacked LSTM models. The empirical analysis concludes the superior performance of deep learning techniques with RMSE score as low as 3.208 on daily closing price stock data for a period of ten years. Furthermore, we also propose a portfolio optimization method to calculate returns and maintain profits while trading in stock market. The development process should go through relevant data selection, data preprocessing to eliminate noise and missing values to create the prediction model. Study of the right algorithm, accompanied by model assessment. The study presented in this paper uses the LSTM to forecast the stock market exchange activity. The findings indicate that advanced versions of LSTM appear to provide more detailed results than standard algorithms. It can be shown that this paradigm is efficient for both private traders and corporate investors. They will obtain the potential actions of the movement of consumer rates and take the correct decision to make a profit. Different characteristics and facets of the industry should be addressed in future work to make forecasts more reliable. We also plan to use consumer feedback on the product to forecast the market shift.