Seifi, N.; Shavarani, H.S. Predicting Idiosyncratic Volatility from Stock Market Trade Records: A Machine Learning Approach. Preprints2023, 2023070288. https://doi.org/10.20944/preprints202307.0288.v1
APA Style
Seifi, N., & Shavarani, H.S. (2023). Predicting Idiosyncratic Volatility from Stock Market Trade Records: A Machine Learning Approach. Preprints. https://doi.org/10.20944/preprints202307.0288.v1
Chicago/Turabian Style
Seifi, N. and Hassan S. Shavarani. 2023 "Predicting Idiosyncratic Volatility from Stock Market Trade Records: A Machine Learning Approach" Preprints. https://doi.org/10.20944/preprints202307.0288.v1
Abstract
Financial markets require a great deal of decision making from the investors and market makers. One metric that can help ease the process of decision making is investment risk which can be measured in two parts; systematic risk and idiosyncratic risk. Clear understanding of the volatilities in each risk component can be a powerful signal in recognizing the right assets to maximize the investment returns. In this paper, we focus on the idiosyncratic volatility values and pre-calculate the idiosyncratic volatility values for 31,198 members of NYSE, Amex and Nasdaq markets for the trades occurring between January 1963 and December 2019. Utilizing a subset of dataset, limited to Nasdaq100 index, we consider the application of machine learning techniques in predicting the idiosyncratic volatility values using the raw trade data to explore a data extension option for the future market trade records that have not yet occurred. We offer a deep learning based regression model and compare it with traditional tree-based methods on a small subset of our per-calculated idiosyncratic volatility dataset. Our analytical results show that the performance of the deep learning techniques is much more robust in comparison to that of the traditional tree-based baselines.
Keywords
Idiosyncratic Volatility Estimation/Prediction; Machine Learning; Deep learning Based Regression; Tree-Based Regression; Artificial Intelligence
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.
