Issue |
SHS Web Conf.
Volume 169, 2023
4th International Symposium on Frontiers of Economics and Management Science (FEMS 2023)
|
|
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Article Number | 01062 | |
Number of page(s) | 7 | |
DOI | https://doi.org/10.1051/shsconf/202316901062 | |
Published online | 29 May 2023 |
Enhancing predictive accuracy of asset returns by experimenting with ML techniques
1 Vishwakarma Institute of Technology, Jamshedpur, 831014, India
2 Sri Sairam Institute of Technology, Chennai, 600044, India
The unparalleled success of machine learning is indisputable. It has transformed the world with unimaginable solutions to insistent problems. The remarkable accuracy that machine learning manifests for making estimations is an object of fascination for plenty of researchers all over the world. The financial industry has also benefited from the growth of this electrifying field to predict asset returns, creditworthiness of a customer, and portfolio management, among others. In this research, we spotlight how this accuracy is contingent upon the analysis of various aspects of the data. We also experiment with simple techniques to make predictions and our findings suggest how these methods overshadow neural nets. The results indicate that the penalized linear models deliver the best performance. Random forest models had not been effective though. Machine learning models fitted with respect to median quantile loss were similarly observed to typically offer improvements across all machine learning models across all loss metrics. While little is known about the future of asset return that involves various risk and uncertainty, the recent enhancements in a machine learning field can contribute to deep domain training. Machine learning is increasingly gaining popularity nowadays in sectors including engineering, charity work, etc. Recently, even behavioral economics has started to leverage machine-learning expertise.
Key words: machine learning / financial industry / predictive analytics / asset pricing
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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