Version 1
: Received: 23 September 2024 / Approved: 25 September 2024 / Online: 25 September 2024 (12:28:55 CEST)
How to cite:
Bogdan, P. Are Markets Naturally Random? Machine Learning Approach to Quantifying Randomness. Preprints2024, 2024092017. https://doi.org/10.20944/preprints202409.2017.v1
Bogdan, P. Are Markets Naturally Random? Machine Learning Approach to Quantifying Randomness. Preprints 2024, 2024092017. https://doi.org/10.20944/preprints202409.2017.v1
Bogdan, P. Are Markets Naturally Random? Machine Learning Approach to Quantifying Randomness. Preprints2024, 2024092017. https://doi.org/10.20944/preprints202409.2017.v1
APA Style
Bogdan, P. (2024). Are Markets Naturally Random? Machine Learning Approach to Quantifying Randomness. Preprints. https://doi.org/10.20944/preprints202409.2017.v1
Chicago/Turabian Style
Bogdan, P. 2024 "Are Markets Naturally Random? Machine Learning Approach to Quantifying Randomness" Preprints. https://doi.org/10.20944/preprints202409.2017.v1
Abstract
From some perspective, every natural phenomenon is just a combination of many elementary random events clustering and forming a pattern. Nonetheless, the nature of random events differs from one field of study to another. This research is an attempt to determine whether volatile stock price movement, considered random, accords to the randomness of real world phenomena. To accomplish the task, a deep learning model was built, trained on preprocessed data and evaluated to calculate it's accuracy. The acquired results were then compared, and it turned out that stock prices are very similar in randomness to natural phenomena as the model trained on large amounts of stock data was increasing the accuracy of predictions of all real world parameters.
Keywords
machine learning; data science; data analysis; stock exchange
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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.
