What does this research mean for the field?

A market-regime-aware portfolio analytics framework based on a Gaussian Hidden Markov Model improves risk-adjusted returns, reduces volatility, and minimizes drawdowns compared to static buy-and-hold strategies for retail investors. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop and validate an unsupervised machine learning framework that identifies market regimes and dynamically modulates equity exposure to improve risk-adjusted outcomes for retail investors.

August 23, 2026Open Access

Market Regime Aware Portfolio Analytics for Retail Investors Using AI Techniques

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Authors

BBBhagyesh Bagul

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Overview

Quantitative modeling demonstrates improved risk-adjusted returns and reduced drawdown in equity trading, indicating dynamic AI regime awareness protects retail investors from major market crashes.

Key Points

To develop and validate an unsupervised machine learning framework that identifies market regimes and dynamically modulates equity exposure to improve risk-adjusted outcomes for retail investors.
Fitted a four-state unsupervised Gaussian Hidden Markov Model (HMM) on daily log returns and squared returns of the NIFTY 50 index from January 2014 to June 2024 to identify Bull, Bear, High-Volatility, and Crash regimes.
Formulated regime-conditional trading signals from transition matrices and tested performance using in-sample data, periodic walk-forward out-of-sample retraining, and a hybrid trough-recovery re-entry rule.
In-sample testing yielded higher cumulative returns (4.17× vs. 3.35×), an improved Sharpe ratio (1.09 vs. 0.80), and lower maximum drawdown (−35.4% vs. −40.0%) compared to buy-and-hold.
Out-of-sample walk-forward validation reduced annualised volatility (10.9% vs. 17.3%), halved maximum drawdown (−20.1% vs. −40.0%), and achieved a superior Sharpe ratio (1.02 vs. 0.79; 1.08 vs. 0.82 with the hybrid re-entry trigger).

Cite This Study

Bhagyesh Bagul (2026) studied this question.

synapsesocial.com/papers/6a8aae0f7677a34114446cbe https://doi.org/10.5281/zenodo.22050578

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August 23, 2026Open Access

Market Regime Aware Portfolio Analytics for Retail Investors Using AI Techniques

View Full Paper

Ask AI

Bookmark

Authors

BBBhagyesh Bagul

Discussion

Member takes

Overview

Quantitative modeling demonstrates improved risk-adjusted returns and reduced drawdown in equity trading, indicating dynamic AI regime awareness protects retail investors from major market crashes.

Key Points

To develop and validate an unsupervised machine learning framework that identifies market regimes and dynamically modulates equity exposure to improve risk-adjusted outcomes for retail investors.
Fitted a four-state unsupervised Gaussian Hidden Markov Model (HMM) on daily log returns and squared returns of the NIFTY 50 index from January 2014 to June 2024 to identify Bull, Bear, High-Volatility, and Crash regimes.
Formulated regime-conditional trading signals from transition matrices and tested performance using in-sample data, periodic walk-forward out-of-sample retraining, and a hybrid trough-recovery re-entry rule.
In-sample testing yielded higher cumulative returns (4.17× vs. 3.35×), an improved Sharpe ratio (1.09 vs. 0.80), and lower maximum drawdown (−35.4% vs. −40.0%) compared to buy-and-hold.
Out-of-sample walk-forward validation reduced annualised volatility (10.9% vs. 17.3%), halved maximum drawdown (−20.1% vs. −40.0%), and achieved a superior Sharpe ratio (1.02 vs. 0.79; 1.08 vs. 0.82 with the hybrid re-entry trigger).

Cite This Study

Bhagyesh Bagul (2026) studied this question.

synapsesocial.com/papers/6a8aae0f7677a34114446cbe https://doi.org/10.5281/zenodo.22050578

View Full Paper

Ask AI

Bookmark