Comparitive Study of Time Series and Deep Learning Algorithms for Stock Price Prediction

Abstract

Stock Price Prediction has always been an intriguing research problem in financial domain. In the past decade, various methodologies based on classical time series, machine learning, deep learning and hybrid models which constitute the combinations of algorithms have been proposed with reasonable effectiveness in predicting the stock price. There is also considerable research work in comparing the performances of these models. However, from literature review, stems a concern, that is, lack of formal methodology that allows comparison of performances of the different models. For example, the lack of guidance on the generalizability of the time series models and optimised deep learning models is concerning. In addition, there is also a lack of guidance on general fitment of models, which can vary in accordance with forecasting requirement of stock price. This study is aimed at establishing a formal methodology of comparing different types of time series forecasting models based on like for like paradigm. The effectiveness of Deep Learning and Time-Series models have been evaluated by predicting the close prices of three banking stocks. The characteristics of the models in terms of generalizability are compared. The impact of the forecasting period on performance for various models are evaluated on a common metric. In most of the previous studies, the forecasting was done for the periods of 1 day, 5 days or 31 days. To keep the impact of volatility in the stock market due to various political and economic shocks both at international and domestic domains to the minimum, the forecasting periods of up 2 days for short term and 5 days for long term are considered. It has been evidenced that the deep learning models have outperformed time series models in terms of generalisability as well as short- and long-term forecasts.