AI-Driven Financial Modeling Techniques: Transforming Investment Strategies

Artificial Intelligence (AI) has revolutionized financial modeling and investment strategies by introducing sophisticated algorithms and advanced data processing capabilities. This article delves into a variety of AI-driven financial modeling techniques, such as machine learning, natural language processing, and deep learning, providing detailed examples of their applications. These techniques are shown to significantly enhance predictive accuracy, risk management, portfolio optimization, and trading strategies. Through case studies and empirical evidence, the article highlights the transformative impact of AI on financial modeling. Additionally, it addresses the challenges in implementing AI-driven models, such as data quality issues, model interpretability, and regulatory concerns, and identifies future research opportunities to further advance the field. The comprehensive analysis provided offers a clear understanding of how AI is reshaping the financial industry, the potential benefits it brings, and the hurdles that must be overcome to fully harness its capabilities.

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Institutions

Publication Details

Journal
Journal of Applied Business and Economics
Published
2024-08-19
DOI
https://doi.org/10.33423/jabe.v26i4.7181
Citations
5
Primary Topic
Stock Market Forecasting Methods
Type
article
Field-Weighted Citation Impact
2.45
Controls
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article

AI-Driven Financial Modeling Techniques: Transforming Investment Strategies

Jianglin Dennis Ding
5 citations
Journal of Applied Business and Economics
Stock Market Forecasting Methods
2.45
article

AI-Driven Financial Modeling Techniques: Transforming Investment Strategies

Jianglin Dennis Ding
article en
5 citations

Abstract

Artificial Intelligence (AI) has revolutionized financial modeling and investment strategies by introducing sophisticated algorithms and advanced data processing capabilities. This article delves into a variety of AI-driven financial modeling techniques, such as machine learning, natural language processing, and deep learning, providing detailed examples of their applications. These techniques are shown to significantly enhance predictive accuracy, risk management, portfolio optimization, and trading strategies. Through case studies and empirical evidence, the article highlights the transformative impact of AI on financial modeling. Additionally, it addresses the challenges in implementing AI-driven models, such as data quality issues, model interpretability, and regulatory concerns, and identifies future research opportunities to further advance the field. The comprehensive analysis provided offers a clear understanding of how AI is reshaping the financial industry, the potential benefits it brings, and the hurdles that must be overcome to fully harness its capabilities.

Journal of Applied Business and EconomicsVol. 26(4)
Roger Williams University (US), Ottawa University (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 10%
Stock Market Forecasting Methods
2.45
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