Comparing Traditional Financial Models vs. AI-Based Financial Models: Recommendations for Retail Investors
Author- Shubhangi Gupta
Research scholar , Dr DY Patil School of Management
Co-author-Dr.Ganesh sambhaji Lande
Research guide Dr DY Patil School of Management
Abstract :Retail investors rely on financial models to make sound investment decisions, with traditional models such as Markowitz Mean-Variance Optimization, Capital Asset Pricing Model (CAPM), and Discounted Cash Flow (DCF) analysis serving as foundational tools for portfolio management and valuation. However, as artificial intelligence (AI) and machine learning improve, AI-driven financial models emerge as an alternative, providing data-driven, adaptive, and predictive capabilities that challenge the static and assumption-driven character of traditional models. This research paper compares traditional financial models to AI-based financial models in the context of retail investor decision making. The study compares the effectiveness, accuracy, flexibility, and risk-adjusted returns of both methodologies under different market scenarios. This article compares the effectiveness of traditional financial models, such as the Markowitz Mean-Variance Model and the Capital Asset Pricing Model, to AI-based financial models in supporting retail investors. The study looks at their efficiency, accuracy, and risk-adjusted returns. The study examines historical performance, real-time applications, and investor preferences to determine whether AI-driven models outperform traditional investment approaches.
Traditional models are based on historical data and theoretical frameworks, which makes them ideal for stable markets but less effective in capturing non-linear correlations and real-time market movements. In contrast, AI-driven models use machine learning algorithms, big data analytics, and alternative data sources (such as social media sentiment, macroeconomic indicators, and news analysis) to deliver more personalized, real-time investment recommendations. This study uses quantitative back testing and empirical analysis to compare the risk-return profiles, efficiency, and practical applicability of AI-based models to traditional financial models.
Keywords: Financial Modelling, AI-Based Investment, Traditional Financial Models, Retail Investors, Machine Learning, Risk-Adjusted Returns
