Utilizing Machine Learning to Reassess the Predictability of Bank Stocks

Hera Antonopoulou, Leonidas Theodorakopoulos, Constantinos Halkiopoulos, Vicky Mamalougkou


Objectives: Accurate prediction of stock market returns is a very challenging task due to the volatile and non-linear nature of the financial stock markets. In this work, we consider conventional time series analysis techniques with additional information from the Google Trend website to predict stock price returns. We further utilize a machine learning algorithm, namely Random Forest, to predict the next day closing price of four Greek systemic banks. Methods/Analysis: The financial data considered in this work comprise Open, Close prices of stocks and Trading Volume. In the context of our analysis, these data are further used to create new variables that serve as additional inputs to the proposed machine learning based model. Specifically, we consider variables for each of the banks in the dataset, such as 7 DAYS MA,14 DAYS MA, 21 DAYS MA, 7 DAYS STD DEV and Volume. One step ahead out of sample prediction following the rolling window approach has been applied. Performance evaluation of the proposed model has been done using standard strategic indicators: RMSE and MAPE. Findings: Our results depict that the proposed models effectively predict the stock market prices, providing insight about the applicability of the proposed methodology scheme to various stock market price predictions. Novelty /Improvement: The originality of this study is that Machine Learning Methods highlighted by the Random Forest Technique were used to forecast the closing price of each stock in the Banking Sector for the following trading session.


Doi: 10.28991/ESJ-2023-07-03-04

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Machine Learning; Random Forest; Predictability; Bank Stocks; Google Trends.


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DOI: 10.28991/ESJ-2023-07-03-04


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