Pankaj Mani
Abstract
Quantitative investment strategies have witnessed significant adoption in India, supported by advances in data availability, computing power, and financial modeling. Far from being substitutes to fundamental analysis, quantitative methods function as complementary tools, strengthening portfolio construction, risk management, and decision-making. This article discusses the evolution and scope of quantitative strategies in India, their integration with artificial intelligence (AI) and machine learning (ML), the role of behavioural and causal approaches, and the importance of stress-adjusted and non-predictive methodologies. The discussion highlights the scientific mechanism behind economic factors ,randomness, convexity while critically evaluating conventional metrics such as “value” and “momentum.” Then performance analysis is also important area to look into ,for selecting robust strategies.These strategies should be the way forward for the Indian AIF Industry too..
1. Introduction
The Indian capital market has traditionally been dominated by discretionary and fundamental approaches. However, the increasing penetration of technology, coupled with expanding datasets, has facilitated the rise of quantitative strategies. “Quant” is not merely a set of mathematical and statistical techniques—it represents a scientific framework that integrates data, algorithms, and behavioural insights into investment management.
Contrary to the perception of competition between quantitative and traditional methods, the two are highly complementary. While fundamental analysis provides qualitative insights into business models, governance, and macroeconomic conditions, quantitative models offer data-driven validation, bias reduction, and scalability. Together, human judgment and computational models deliver superior outcomes.
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2. Scope of Quantitative Strategies
Quantitative investing extends beyond security selection. Its applications include:
• Portfolio Construction: Optimizing asset allocation through mathematical models.
• Risk Management: Applying advanced non-linear based measures and understand the deeper dynamics of correlation, beta ,challenges and higher order stress testing and extreme tail risk, model risks etc.
• Trade Execution: Entry and exit strategies designed to minimize slippage and market impact where algorithmically or manually.
• Testing methodologies of Strategies : The deeper understanding of methods, biasness, overfitting, challenges of Backtesting, forward-testing, and robustness checks to validate strategies. Testing Strategies Scientifically without Backtesting or with Backtesting.
In India, the following strategies are quite useful.
• Factor-based Models: Momentum, value, low volatility, quality, and size.
• Quanta mental Approaches: Integrating quantitative signals with fundamental research.
• Market-Neutral and Hedging Models: Long-short, statistical arbitrage, and F&O-based strategies
• Tail Risk/Extreme Risk Adjusted Strategies needs to be focussed on context of Black Swan events.
• Passive and Systematic Products: ETFs and index-tracking portfolios. New emerging would be Hybridized version of Passive and Active Strategies. Lot of Advanced Indices based on Factors and Other aspects can be created.
• Fund-of-Funds Allocation: Quantitative models applied to mutual funds, PMSs, and AIFs.
3. Integration of AI and Alternative Data
• The future of quantitative strategies lies in AI/ML-driven approaches too. Emerging paradigms include:
• Reinforcement Learning
Scientific AI/ML like Causal & Randomness Adjusted AI/ML
• Natural Language Processing (NLP): Mining sentiment from news, earnings calls, and social media.
• Agentic AI and LLMs: Adaptive decision systems capable of dynamic strategy adjustment.
• Alternative Data: Satellite imagery, transaction data, mobility trends,social media activities etc..
• ESG based Strategies
• Randomness, Uncertainty & Convexity based Strategies
These approaches extend the predictive and explanatory capacity of models beyond traditional financial and economic datasets.
4. Behavioural and Scientific Approaches
Human psychology remains central to market dynamics. Behavioural biases—overconfidence, loss aversion, herding—manifest in quantifiable ways. Quantitative behavioral strategies attempt to formalize these effects into investable models.
New branches of scientific investing emphasize causality are emerging rather than mere correlation. Traditional back tests, often biased and backward-looking, fail to capture the causal structures that drive market dynamics. Moreover, excessive reliance on deterministic factors can be misleading, given that markets embody randomness, uncertainty, and non-linear dynamics. It’s very easy to analyze and establish the deterministic factors or markets in backward direction but not so easier in forward direction of time. There always may come up unknown factors in future that any expert human mind , intuition, economic guesses and even predictive quant models may not capture.
5. Randomness, Convexity, Tail-Risk and Market Science
The study of randomness provides a rigorous foundation for risk-aware investing. Randomness-based strategies, often linked to convexity principles, are particularly effective in tail-risk management and Black Swan. This is quite important as many well -known funds including large, small have got exposed to tail/extreme risks while chasing higher rand higher return. This is equally true for investors as well. So, Such hidden and silent risks are quite important and most importantly psychological inertial risk that doesn’t let someone come out of one’s sedimented biased thought process.
To mention categorically as I have been quoting earlier “Randomness may Not be Randomly Random but Deterministically Random” underlines the structured uncertainty embedded in financial markets.
The historical roots of quantitative finance—Brownian motion, modelled by French mathematician Louis Bachelier for modelling financial prices and later used by Einstein five years later—highlight how financial markets mirror stochastic processes first observed in biology and then came to physics later through finance.. Modern models, from stochastic calculus to option pricing frameworks, extend this legacy.
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6. Critical Evaluation of Conventional Economic and Intuition based Factors and the way forward for Scientific Factors.
Factors such as “value” and “momentum” are widely referenced but often oversimplified. For example, defining value merely as low price-to-book ratio ignores deeper and higher order dynamics of inter relationship between ratios. Contrary to popular thought process, Cheapness doesn’t always necessarily mean Value if one looks at the scientific Mechanism of market prices movement. And moreover, what is cheapness needs deeper understanding. Similarly, momentum is dynamic, interacting with other factors like value in a very interconnected ways . There exists Higher Order analysis of detecting Value and Scientific Dynamics between Value and Momentum etc. and how they play mutually. The point is there exists some scientific dynamics and mechanism how these different factors play dynamically and inter-related ways.
Recent studies, including some at ADIA (Abu Dhabi Investment Authority) Lab, claim that many factor-based models are insufficiently rigorous, operating effectively only in historical analysis while failing forward-looking tests. This underscores the need for higher scientific standards in quantitative investing not just guesswork and intuition based economic commentary as often done.
So, one can explore the scientific dynamics behind Value, Momentum etc. and other factors beyond these driven by scientific mechanism of markets. That would be quite useful for AIF managers to remain scientific and advanced and competitive .
7. Stress-Adjusted Returns and Non-Predictive Approaches
Global evidence indicates that financial stress contributes to trillions of dollars in economic losses. Prediction-centric investment approaches not only face mathematical limitations but also exacerbate stress among investors and managers. Non-predictive, convexity-based, and randomness-aware strategies offer an alternative, producing robust returns that are sustainable under both favorable and adverse conditions.
By moving beyond the human tendency to seek certainty and over-predict, these strategies align more closely with the scientific realities of uncertainty and stochasticity in markets.
And it’s not that even for predictive and normal conventional strategies, one should look forward to generate return in a behavioral adjusted ways. Infact there exists scientific dynamics of human and investor behaviour that needs to be incorporated while developing strategies and also from educating the investors’ perspective.
Many of the above things are happening globally which Indian managers should look up to
Performance analysis : This is a major area where Quant analysis helps too for all type of funds including traditional and fundamental or technical. Many of the performance analysis are often exposed to biasness that needs to be made robust as it helps managers and investors both understand technically and take decision about selection of strategies in prudent ways. This is quite statistical . There need practical research and education in these areas too.
8. Education & Research : Infact lot of technical education and research things are important for the industry and everyone to practically understand and get benefitted that we keep on doing. We have also future plans to do all these things in more detailed ways to help industry people in multiple practical ways and equip them with global standard things happening in other economies.
9. Conclusion
Quantitative strategies in India are evolving from niche applications to mainstream adoption. The integration of AI, alternative data, behavioral science, and causal inference marks a paradigm shift toward more scientific and globally competitive practices. While traditional factors remain relevant, their limitations highlight the need for greater rigor and adaptability.
Ultimately, the path forward lies in blending human expertise with scientific, data-driven methods—acknowledging randomness, embracing convexity, and focusing on stress-adjusted rather than purely predictive returns. In this framework, quantitative strategies emerge not as competitors to traditional approaches, but as their necessary complement in navigating the complexities of modern markets. There is emphasizing need for educ and research to understand these things in detail and remain in line with global development.
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