Robustness and Interpretability of Machine Learning Models in Financial Forecasting
DOI:
https://doi.org/10.47672/ejt.2005Keywords:
Boosting, Bagging, Forecasting, Neural Networks, Deep Learning, Penalized Regressions, Regularization, Nonlinear Models, Sieve Approximation, And Statistical Learning Theory.Abstract
Purpose: The practice of financial forecasting is an essential component of contemporary financial management and the process of making decisions on investments. As a result of the complexity and volatility of financial markets, traditional financial forecasting techniques often fail to adequately capture the situation.
Methodology/ Findings: One of the most effective methods for improving the precision and effectiveness of financial forecasting is the use of machine learning techniques. Through an examination of its potential, limitations, and future possibilities. They take linear and nonlinear possibilities into account in their study. Our focus is on linear methods, namely penalized regressions, and ensemble of models. The study considers a range of nonlinear techniques, including tree-based methods like boosted trees and random forests, and deep and shallow neural networks in both feed-forward and recurrent forms. They also think about ensemble and hybrid models, which combine features from several kinds of alternatives.
Implications to Theory, Practice and Policy: A brief overview of the tests used to measure outstanding predictive ability is provided. In the last part of this article, they focus on the possible applications of machine learning in the fields of economics and finance, and we provide an example that makes use of high-frequency financial data (Benti, Chaka, and Semie, 2023).
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Copyright (c) 2024 Samir Vinayak Bayani, Ikram Ahamed Mohamed & Selvakumar Venkatasubbu
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