A rule-based AI framework for portfolio optimization using investor profiling and asset allocation models

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Authors: Soham Pingale, Sakshi Tupkar, Kesha Zala, Shyamal S. Virnodkar
Cited by: 0

Abstract

In this study, we introduce InvestiChat, a conversational framework that helps retail investors in India. The system creates an Investment Policy Statement (IPS) for each user and categorizes them as Conservative, Moderate, or Aggressive. We used real-time data from Yahoo Finance and Alpha Vantage, along with historical datasets, to build sample portfolios. Information of portfolio construction used Modern Portfolio Theory (MPT), and Monte Carlo simulations applied portfolio-per-user matching. Testing over an initial pilot study found evidence that the framework can represent a decrease in effort associated with portfolio selection. Barriers such as API rate limiting, and potential biases surfaced in the properties of classification processes.

Fields of Study & Subjects

Risk and Portfolio Optimization Financial Analysis and Corporate Governance Stock Market Forecasting Methods

Publication details

Journal / source
IET conference proceedings.
Volume
2025
Issue
43
Pages
266–271
Publication date
1 February 2026
Language
EN