Welcome to IVCA’s Fund in Focus series, where we profile member funds, unpack their investment philosophy, and spotlight differentiated strategies shaping India’s alternate capital ecosystem. As Indian capital markets undergo a structural transition—marked by deepening domestic participation, improved market infrastructure, and growing investor sophistication—quantitative and systematic investing is emerging as a critical pillar of portfolio construction. In this edition, we speak with Himadri Roy, Head – Quantitative Fund Management at Ashika Investment Managers, on why the current moment is uniquely suited for systematic AIF strategies, how “India Alpha” is evolving, and what it takes to build future-ready asset management platforms.
Fund in Focus: Decoding India Alpha Through Data, Discipline & Systematic Precision
Himadri Roy – Head, Quantitative Fund Management – Ashika Investment Managers Pvt. Ltd.
Spokesperson: Himadri Roy, Head – Quantitative Fund Management, Ashika Investment Managers Pvt. Ltd.
1. India's public and private markets have matured dramatically over the last decade. What makes this the right moment for a data-driven, systematic AIF strategy such as SmartAlpha?
Three structural shifts are converging. First, India's data infrastructure has reached an inflection point. Mobile data costs collapsed 98% over 11 years while digital transaction volumes grew 3,200x. We're processing behavioral and transactional signals that simply didn't exist at scale five years ago.
Second, there's been a fundamental ownership shift. FPI shareholding peaked at 22% in FY14 and FY21, declining to 17% in Q1FY26. Meanwhile, domestic mutual funds grew from 3-4% through the 2000s to 11% in Q1FY26. This structural rebalancing creates predictable flow patterns and longer-duration behavioral signals that systematic strategies can capture.
Third, market microstructure has matured T+1 settlement, consistent liquidity, institutional depth but informational anomalies persist. Post-earnings drift, analyst revision lags etc create quantifiable anomalies. Further, the international trends of institutional investors, who primarily look at the strategy driven and passive modes of investment.
SmartAlpha is built for exactly this moment.
2. How does Ashika interpret “India Alpha” today?
India Alpha is the quantifiable edge at the intersection of information asymmetry, behavioral persistence, and structural market anomalies. We decompose it into three layers. First, cross-sectional return dispersion in Indian equities remains significantly higher than developed markets, creating exploitable patterns with the right signal extraction framework.
Second, information diffusion velocity varies dramatically. A fundamental shift in a Nifty 50 company gets priced quickly, whereas the same shift in a Nifty 500 mid-cap takes substantially longer. We have built proprietary metrics around earnings surprise magnitude, analyst revision velocity, and institutional flow timing to stay ahead.
Third, we are amid a structural ownership transition. FPI ownership compressed from 22% to 17% while domestic mutual funds grew from 3% to 11% an 800-bps permanent shift driven by SIP flows and household financialization. Domestic mutual funds have fundamentally different decision frameworks than FPIs, creating predictable rebalancing patterns our models explicitly track.
India Alpha today isn't about stock-picking. It's about building quantitative frameworks that harvest multiple low-correlation return streams simultaneously.
3. What are the long-term India-specific market behaviors or inefficiencies you believe are persistent enough to be systematically harvested at scale?
Four structural anomalies show statistically significant persistence in our analysis. First, earnings information is systematically under-reacted to. Earnings surprises above 2 standard deviations generate drift persisting many days post-announcement, with abnormal returns clustering in the 10-30 day window. Beyond quantitative surprises, we've deployed proprietary AI/ML-based NLP models analyzing management commentary during earnings concalls. We systematically track whether management "walks the talk" measuring consistency between guidance and delivery, tone shifts, and capital allocation rhetoric versus reality. This creates a quantifiable "management credibility score" that significantly enhances post-earnings drift prediction.
Second, cross-sectional momentum exhibits regime-dependent persistence, which we identify using hidden Markov models based on volatility clustering and participation breadth.
Third, fundamental quality is systematically mispriced. Top quartile companies in our proprietary quality score consistently outperform bottom quartile by 800-1000 bps annually.
Fourth, flow dynamics create predictable price discovery patterns. Large block transactions by influential investors, mutual fund holding changes, and FII accumulation patterns have measurable correlation with returns.
These aren't behavioral quirks—they're embedded in market structure, making them persistent enough for systematic capture at scale.
4. As more domestic institutions consider AIFs, how do you see India's risk-appetite, sophistication, and expectations evolving?
The conversation is fundamentally shifting from returns optimization to risk-adjusted returns optimization. Five years ago, institutional allocators focused on gross IRR. Today, they're asking about Sharpe ratios, maximum drawdown profiles, tail risk management, and factor exposure decomposition a maturation we have seen globally in the US during the 1990s and Europe in the 2000s.
SEBI's SIF framework signals regulatory recognition of this sophistication curve. There's now a structural arbitrage between mutual fund constraints and traditional PMS. Systematic AIFs occupy the middle ground institutional-grade quantitative infrastructure, transparent factor exposures, monthly rebalancing flexibility.
As domestic mutual funds grow from 11% ownership to higher levels, they're bringing longer time horizons and sophisticated risk management frameworks.
EPFO has 9.5% in equity ETFs essentially pure beta. Very few have systematic access to multi-factor, regime-aware strategies targeting 300-500 bps alpha with controlled volatility. That allocation gap will close, and systematic AIFs are positioned to capture it.
5. Quantitative investing is still at a nascent ~5% penetration in India. What misconceptions do Indian investors hold about quantitative strategies—and how do you demystify them?
This misconception reflects our market maturity stage and recency bias. Recent quantitative fund launches underperformed because they used quant as a screening tool focused on momentum. Our internal analysis shows that since 2012, single factors show stellar performance followed by significant underperformance - consistency is missing. We have not seen true multi-factor quantitative schemes in India till date.
Three misconceptions persist. First, "quantitative is a black box." SmartAlpha uses multi-dimensional factor decomposition and ML-driven optimization, but every position is traceable to specific quantitative signals. The sophistication is in signal processing, not hiding logic.
Second, "quantitative strategies are fragile." SmartAlpha incorporates regime detection frameworks using volatility clustering models and correlation structure analysis to dynamically adjust exposures.
Third, momentum was used as a single factor not genuine multi-factor approaches.
We've tuned our models to Indian market ecology using India-specific alternative data: corporate action filings, bulk deal patterns, mutual fund flows that now drive price discovery. These aren't global factors copy-pasted they're indigenous signal sources.
6. How does the fund balance the purity of quantitative models with the practical realities of India's market structure—liquidity, event-driven volatility, and regulatory changes?
We operate on a principle: models generate signals, but implementation determines outcomes.
On liquidity, we've built multi-layer screening minimum daily volume thresholds, maximum bid-ask spread tolerances, market impact cost calculations, position size constraints relative to free float. We dynamically adjust these; in high volatility periods, liquidity filters tighten automatically. Monthly rebalancing keeps us within practical execution boundaries while capturing medium-term alpha.
On event-driven volatility, we don't predict specific events we manage exposure systematically. Our models incorporate volatility regime detection using GARCH-family processes, India VIX analogs, and market breadth metrics. Pre-budget periods, election cycles, and global risk-off episodes trigger pre-defined risk reduction protocols. This is quantified risk management responding to measurable regime shifts.
On event risk, there's human oversight. Under corporate actions like mergers/demergers, fraud, and regulatory changes, we manually update execution constraints. The quantitative framework remains intact we are not changing alpha generation logic, just implementation guardrails.
7. Having seen multiple cycles globally, what parallels or divergences do you observe in India's current market environment compared to other fast-growing economies?
India today tracks the developmental arc of South Korea (2000s) and Taiwan (1990s) with one critical divergence we are experiencing a fundamental ownership transition while building institutional infrastructure proactively.
The parallel is striking. FPI holdings peaked at 22% in FY14/FY21, declining to 17% in Q1FY26 a 500 bps compression. Simultaneously, domestic mutual fund ownership grew from 3-4% to 11%. This crossover pattern matches Korea around 2005 and Taiwan around 1998, signaling market maturation.
But here's what's different. Korea and Taiwan experienced this during crises Asian Financial Crisis, political tensions. India's transition is organic, driven by SIP flows (INR 2.42 lakh crore in 2024) and household financialization. DIIs at 17.6% now match FIIs at 17.2%.
This creates exploitable patterns. When FPIs dominated (2005-2020 at 18-22%), markets followed global risk appetite. Now, with domestic mutual funds as marginal buyers, we see longer holding periods and much lesser global correlation. Our frameworks model domestic institutional behavior with higher predictability.
The lesson: ownership transitions create maximum utilizable anomalies. India's at 5% quantitative penetration during this transition the opportunity is largest right now.
8. How should Indian regulators and the ecosystem rethink frameworks to better support systematic, model-based investing?
Creating regulatory sandboxes specifically for systematic strategies would be transformative. Allow qualified AIFs to experiment with broader derivatives usage, alternative data integration, and cross-asset factor exposure under close SEBI supervision but with more flexibility than current blanket restrictions.
Global quantitative funds use options extensively for tail risk hedging, volatility targeting, and return enhancement. A graduated licensing framework would allow sophisticated managers to demonstrate competence before gaining broader permissions.
Additionally, mandating standardized performance reporting across AIFs including Sharpe ratios, maximum drawdowns etc would improve transparency and investor education.
9. Your vision speaks to "revolutionizing asset management through data, technology, and precision." What does a future-ready asset manager look like in 2030—and how is Ashika preparing for that world?
I believe that by 2030, asset management bifurcates completely. You either have institutional-grade quantitative infrastructure or you're a distribution channel for someone else's intellectual property.
The future-ready manager has two non-negotiable capabilities. First, real-time alternative data ingestion and processing not just price and volume, but unstructured data from corporate filings, news sentiment, supply chain indicators, credit bureau signals, satellite imagery, transaction-level consumption patterns. We are building pipelines that process these heterogeneous sources, extract quantitative signals, and feed them into production models continuously.
Second, modular, scalable research infrastructure. Traditional asset managers build strategies in silos each fund is separate code, separate backtest, separate risk framework. We are building platform architecture where factor research, portfolio construction, risk management, and execution are modular components. Speed to market becomes a competitive advantage.
By 2030, Ashika Investment Managers will be a platform for quantitative strategies across market caps, geographies, and asset classes—built on shared technology infrastructure and transparent risk governance.
10.If you had to summarize India's next decade of investing in one sentence—from the lens of a quant-driven investor—what would it be?
India's next decade will be defined by the systematic utilisation of persistent market microstructure anomalies during a historic ownership transition from foreign to domestic capital—rewarding disciplined, multi-factor quantitative frameworks that can model predictable behavior patterns before these anomalies compress and markets achieve developed-market efficiency.