Precise Portfolio Optimization Based on Novel Modern Portfolio Theory Using Time Series Model Compared with LASSO Regression

K. Sravan, P. Sriramya

2023

Abstract

This study aims to augment the accuracy of stock market prediction by amalgamating Time Series Model algorithms with LASSO Regression. Historical financial data for various assets is amassed to generate optimal portfolios employing MPT 2.0 and LASSO Regression. Performance metrics such as the Sharpe ratio and portfolio variance are harnessed to appraise these portfolios. The aim is to juxtapose the predictive precision of the two methodologies and ascertain which one affords more precise portfolio optimisation results. Materials and Methods: The prediction process involves Time Series Model (N=10) coupled with LASSO Regression (N=10). Determining sample size utilises Gpower, with pretest power set at an alpha value of 0.8 and a beta value of 0.2. The accumulated financial data is employed to construct optimal portfolios through MPT 2.0 and LASSO Regression. Evaluation criteria encompass the Sharpe ratio for risk-adjusted performance and portfolio variance for risk assessment. Result: The Time Series Model showcases a lofty accuracy rate of 90.1252%, whereas the LASSO Regression method attains an accuracy of 80.1423%. The significance of accuracy and loss is underscored by the p-value being less than 0.05 (p=0.000), signifying the marked significance of the Time Series Model in contrast to LASSO Regression. Conclusion: Within the realm of portfolio optimisation, the Time Series Model approach manifests a marginally elevated predictive rate when compared to the LASSO Regression method. This infers that the Time Series Model algorithm endows advanced predictive capabilities for stock market performance.

Paper Citation

in Harvard Style

Sravan K. and Sriramya P. (2023). Precise Portfolio Optimization Based on Novel Modern Portfolio Theory Using Time Series Model Compared with LASSO Regression. In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 133-140. DOI: 10.5220/0012602600003739

in Bibtex Style

@conference{ai4iot23,
author={K. Sravan and P. Sriramya},
title={Precise Portfolio Optimization Based on Novel Modern Portfolio Theory Using Time Series Model Compared with LASSO Regression},
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={133-140},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012602600003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Precise Portfolio Optimization Based on Novel Modern Portfolio Theory Using Time Series Model Compared with LASSO Regression
SN - 978-989-758-661-3
AU - Sravan K.
AU - Sriramya P.
PY - 2023
SP - 133
EP - 140
DO - 10.5220/0012602600003739
PB - SciTePress