Version 1
: Received: 28 July 2022 / Approved: 29 July 2022 / Online: 29 July 2022 (13:07:42 CEST)
How to cite:
Antonopoulou, H.; Theodorakopoulos, L.; Halkiopoulos, C.; Mamalougkou, V. On the Predictability of Greek Systemic Bank Stocks using Machine Learning Techniques. Preprints2022, 2022070462. https://doi.org/10.20944/preprints202207.0462.v1
Antonopoulou, H.; Theodorakopoulos, L.; Halkiopoulos, C.; Mamalougkou, V. On the Predictability of Greek Systemic Bank Stocks using Machine Learning Techniques. Preprints 2022, 2022070462. https://doi.org/10.20944/preprints202207.0462.v1
Antonopoulou, H.; Theodorakopoulos, L.; Halkiopoulos, C.; Mamalougkou, V. On the Predictability of Greek Systemic Bank Stocks using Machine Learning Techniques. Preprints2022, 2022070462. https://doi.org/10.20944/preprints202207.0462.v1
APA Style
Antonopoulou, H., Theodorakopoulos, L., Halkiopoulos, C., & Mamalougkou, V. (2022). On the Predictability of Greek Systemic Bank Stocks using Machine Learning Techniques. Preprints. https://doi.org/10.20944/preprints202207.0462.v1
Chicago/Turabian Style
Antonopoulou, H., Constantinos Halkiopoulos and Vicky Mamalougkou. 2022 "On the Predictability of Greek Systemic Bank Stocks using Machine Learning Techniques" Preprints. https://doi.org/10.20944/preprints202207.0462.v1
Abstract
Background/Objectives: Accurate prediction of stock prices is an extremely challenging task because of factors such as political conditions, global economy, unexpected events, market anomalies, and relevant companies’ features. In this work, the random forest has been used to forecast the prices of the four major Greek systemic banks Methods/Analysis: We make use of a set of financial variables based on intraday data: (i) Open stock price, (ii) High stock price, (iii) Low stock price, and (iv) Close stock price of a particular Greek systemic bank. Results/Findings: The variables used here are crucial in predicting systemic banks' stock closing prices. These provide a better prediction of the next day's closing price of the bank series. Novelty /Improvement: To our knowledge, this is the first study that employs machine learning techniques in Greek systemic banks.
Keywords
Machine Learning; Random Forest; Google Trends; Predictability; Banks; Greece
Subject
Business, Economics and Management, Finance
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
