Robust Machine Learning Models for Predictive Analytics in Finance

Authors

  • Robin Allen
  • Drew Anderson
  • Kim Scott

Keywords:

Machine Learning, Predictive Analytics, Financial Models, Risk Management, Investment Strategies

Abstract

This study presents the development of robust machine learning models tailored for predictive analytics in the financial sector. By enhancing data processing capabilities and model accuracy, these models offer valuable insights for risk management and investment strategies. The research includes validation through historical financial data, demonstrating model reliability and effectiveness.

Author Biographies

Robin Allen

PhD
University of Chicago
5801 S Ellis Ave, Chicago, IL 60637, USA

Drew Anderson

PhD
University of Zurich
Rämistrasse 71, 8006 Zürich, Switzerland

Kim Scott

PhD
Australian National University
Canberra ACT 0200, Australia

References

Рагимов, Э. Р. О. (2011). Метрология элементов безопасности программных комплексов, реализующих систему защиты информации корпоративных сетей. Вопросы защиты информации, (2), 36-41.

Рагимов, Э. Р. О. (2010). Механизм верификации безопасности программных средств, функционирующих в системе защиты информации корпоративных сетей. Вопросы защиты информации, (4), 37-40.

Published

2024-12-24

Issue

Section

Articles