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Key Challenges for Quant Investing in India

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What Are Quant Funds and Why Are They Gaining Attention in India?

Quantitative investing – where, data, and systematic rules drive investment decisions – has become mainstream in developed markets like the US, UK, and Hong Kong. Some of the world’s largest hedge funds and asset managers today run portfolios entirely using quant models (see our report xyz on quant adoption in Usa and India). However, in India, quant investing is still in its early stages. While capital markets have grown rapidly and retail participation is rising, systematic investing faces several challenges – ranging from awareness, data quality to investor trust. This article explores the key structural and behavioral hurdles holding back quant adoption in India and how quant focused firms are working to overcome them.

Key Challenges for Quant Investing in India: Data, Trust & Market Hurdles

Limited Awareness and Financial Literacy

Despite the rapid growth of India’s financial sector, quant investing remains relatively unknown to retail and institutional investors.

Educational Gaps:

  1. Algorithm-driven investing is still a new concept in India, whereas traditional discretionary investing has been the norm.
  2. The investment culture heavily relies on personal relationships with brokers and fund managers, making it difficult for quant models to gain investor confidence.

Communication Barriers:

  1. Many financial advisors and wealth management firms do not actively promote quant strategies due to a lack of familiarity with algorithmic investing.
  2. “The article is an exploratory attempt to have an insight towards awareness of stock market investments and the financial literacy level of Indian investors.” (Sage Journals)

Impact:

Without a strong understanding of how quant models work, investors hesitate to allocate capital, slowing down the adoption of systematic investing.

Quant Investing Faces Data Quality Challenges in India

Quantitative strategies hinge on large, clean datasets for backtesting and model calibration. However, India’s market suffers from incomplete, inconsistent, and fragmented data—issues that systematic models cannot “override” as a human manager might. While a handful of firms (e.g. FidelFolio) now apply governance screens and clean-accounting checks at the data-ingest stage to filter out restatements or misreported metrics, most quant teams still lack access to a truly unified, high-integrity data pipeline.

Gaps in Historical Data and Reporting Delays:

  1. Historical series often contain gaps or post-publication restatements, undermining statistical robustness. (Daanik)
  2. Erroneous or delayed corporate filings introduce noise that discretionary managers can manually correct—but systematic models require trusted inputs to avoid spurious signals.

Fragmented Data Sources:

  1. No single repository for financial, alternative, or macro data: teams must stitch together multiple vendor feeds with differing formats and coverage.
  2. Cross-vendor inconsistencies demand extensive normalization, delaying research cycles and inflating operational overhead.

Impact:

Gaps and integrity issues erode confidence in back tests, slow strategy launches, and deter both internal stakeholders and external investors. By contrast, firms that bake in governance-screening and clean-accounting frameworks at the outset—like FidelFolio—can accelerate deployment and bolster model credibility.

Combining Automation with Oversight - The Quant + Man Model

Trust Issues and Regulatory Constraints' to - Investor Trust & SEBI Challenges in Quant Investing

Investor trust is crucial for any investment strategy, and in India, several factors contribute to skepticism towards quant investing.

Historical Reliance on Discretionary Investing:

  1. Indian investors prefer human-managed funds, where fund managers’ reputations build trust.
  2. Many believe algorithmic investing lacks the ability to adapt to unexpected market conditions.

Myth: Past Data Is Unreliable for Quant Models

  1. A common misconception in India is that past market data is too volatile or inconsistent to be useful.
  2. Myth Busted: Quant investing doesn’t rely blindly on history. Machines are specifically trained to find repeatable patterns in historical data, making them highly capable of generating predictive insights. Properly regularized models like Gradient Boosted Trees and XGBoost have outperformed human analysts in forecasting exercises, showing that past data is not just usable—it’s essential.

SEBI Regulations and Data Security Concerns:

  1. SEBI (Securities and Exchange Board of India) imposes strict regulations on algorithmic trading, making it difficult for firms to freely implement new models.
  2. Concerns around data security and privacy further impact investor sentiment.
  • “One of the biggest challenges of quant investing, specifically around back-testing, is the risk of overfitting, which leads to mistrust in model performance.” (AlphaGrep Investment Management)

Impact:

Without transparent disclosures, robust out-of-sample validation, and proven historical success, quant funds may struggle to earn investor trust, limiting both internal buy-in and external capital inflows.

Active Stock-Picking Still Dominates Investor Preference

Indian investors continue to favor actively managed funds, driven by historical performance and investment culture.

Cultural Investment Practices:

  1. Active stock-picking is deeply ingrained in India’s investment mindset.
  2. Investors trust human managers who can make quick, discretionary decisions, rather than rule-based models.

Perceived Limitations of Quant Tools in Indian Context:

  1. Many believe quant models rely too heavily on past data and may not adapt quickly to emerging markets like India.
  2. SEBI’s limitations on derivatives trading reduce the tools available for quant strategies to hedge risks.
"Low interest, strict regulations, and the success of active stock-picking are limiting the growth of quant funds in India." (FT.com)

While fully machine-driven models offer consistency, they often lack the “human face” investors still seek. A “Quant + Machine + Man” approach—as adopted by firms like FidelFolio—can help strike a balance between automation and oversight, building trust while retaining scale.

Impact:

Even if quant funds outperform, changing investor behavior requires educational campaigns and marketing efforts to build confidence in algorithmic investing.

How Market Volatility and Liquidity Limit Quant Investing in India

India’s high volatility and liquidity constraints pose challenges for quant strategies.

High Volatility:

  1. India’s market fluctuations increase risk exposure for quant funds, especially those dependent on historical patterns.

Liquidity Constraints:

  1. Some quant funds require longer periods to liquidate portfolios, raising concerns over exit strategies.
  2. Stress test results from Quant Small Cap Fund (May 2024) showed a significant increase in liquidation time, impacting investor confidence.
  3. “Quant funds face increased liquidation periods, with 50% portfolio liquidations taking up to 28 days.” (Upstox)

Impact:

For quant funds to thrive in India, they must develop robust risk management frameworks to handle liquidity concerns and market shocks.

Quant vs Traditional Investment Strategies

Competition from Traditional Investment Approaches

Quant investing competes with traditional stock-picking strategies, which have historically performed well in India.

Challenges:

  1. Traditional funds have a strong track record, making it hard for quant funds to prove their superiority
  2. Many investors still believe human judgment outperforms machine-based models in uncertain or volatile conditions
  3. “Quant strategies have the potential to deliver superior returns, but investor skepticism remains a major barrier to widespread adoption.” (Economic Times)

The lack of a “human face” often adds to this resistance. While quant strategies bring precision, combining them with experienced oversight—through a Quant + Machine + Man approach ( by Fidelfolio)—helps alleviate investor hesitation.

Impact:

To compete effectively, quant funds must not only deliver long-term performance but also address psychological and cultural resistance by enhancing transparency and incorporating human oversight where it matters.

Shorter Track Record of Quant Funds

Quantitative (quant) investing in India remains in its nascent stages compared to traditional asset management approaches. This emerging sector faces several challenges:​

Challenges:

  1. Limited Historical Performance Data: Quant funds in India have not yet established long-term performance records across multiple market cycles, leading to concerns about their resilience and scalability.
  2. Investor Skepticism: The lack of an extensive track record may cause potential investors to question the consistency and reliability of quant strategies. ​The Economic Times

The Road Ahead for Quant Investing in India

Building trust in this investment management domain necessitates time, transparency, and demonstrable success within the unique conditions of the Indian market. 

Despite these challenges, things are changing for quant long-term investing in India. Just as quant investing once started slow in the US and Hong Kong before becoming dominant, India too is seeing early signs of a shift. Several quant strategies have begun outperforming discretionary benchmarks across market cycles. Platforms like Smallcase have made rule-based portfolios accessible to retail investors. At FidelFolio, we’ve seen firsthand how awareness and trust grow rapidly when investors experience transparent systematic process, and consistent performance.

We believe the next decade belongs to systematic investing in India. And with the right mix of investor education, robust research, and responsible execution, quant strategies will not just catch up—they will lead.