article · Advances in Economics Management and Political Sciences · 2024
Multi-factor Models in Finance
Cited by 1Open access
Abstract
This literature review explores the significance of multi-factor models in finance, emphasizing their dynamic nature and pivotal role in understanding global market patterns. The multi-factor framework, adept at capturing the complexities of asset pricing, incorporates various variables, enabling a comprehensive analysis of relationships impacting market conditions. The Fama-French Three-Factor Model, an influential extension of traditional models, introduces size and value components, enriching the understanding of asset valuations. Assumptions underpinning multi-factor models, such as market efficiency and rational investor behavior, form essential foundations. Despite their advantages, these models face challenges, including the risk of overfitting and the complexity of incorporating numerous variables. The study underscores the need to recognize these limitations while appreciating the models' contributions to portfolio management and risk assessment, ultimately contributing to the ongoing evolution of financial analysis.
Cite this paper
Liang, G. (2024). Multi-factor Models in Finance. Advances in Economics Management and Political Sciences, 76(1), 149–153. https://doi.org/10.54254/2754-1169/76/20241814
Liang, Guyi. “Multi-factor Models in Finance.” Advances in Economics Management and Political Sciences, vol. 76, no. 1, 2024, pp. 149–53, https://doi.org/10.54254/2754-1169/76/20241814.
Liang, Guyi. 2024. “Multi-factor Models in Finance.” Advances in Economics Management and Political Sciences 76 (1): 149–53. https://doi.org/10.54254/2754-1169/76/20241814.
Liang, G. (2024) ‘Multi-factor Models in Finance’, Advances in Economics Management and Political Sciences, 76(1), pp. 149–153. Available at: https://doi.org/10.54254/2754-1169/76/20241814.
@article{liang2024multi,
title = {{Multi-factor Models in Finance}},
author = {Liang, Guyi},
journal = {Advances in Economics Management and Political Sciences},
volume = {76},
number = {1},
pages = {149--153},
year = {2024},
doi = {10.54254/2754-1169/76/20241814}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.
Related papers
Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models
Kaiyue Wen, Jiaye Teng, Jingzhao Zhang · arXiv (Cornell University) · 2022
Cited by 5Open access
Lazy Overfitting Control
Armand E. Prieditis, Stephanie Sapp · Lecture notes in computer science · 2013
Cited by 9
Machine Learning Students Overfit to Overfitting
Matías Valdenegro-Toro, Matthia Sabatelli · arXiv (Cornell University) · 2022
Cited by 12Open access
Overfitting: Causes and Solutions (Seminar Slides)
Marcos López de Prado · SSRN Electronic Journal · 2020
Cited by 4Open access
Underpinning : a practical guide
Roger A. Bullivant, Helen Bradbury · Blackwell Science Ltd eBooks · 1996
Cited by 5
An Overview of Overfitting and its Solutions
Ying Xue · Journal of Physics Conference Series · 2019
Cited by 2,677Open access
Conventional piles in underpinning
K. W. Cole · Springer eBooks · 1993
Cited by 2