"Rules over instinct, rules over fundamental, maths over fundamental, right? That's what we believe, that's what we do." - Amar Shah [00:03:38]
"Fundamental investing, you try to buy, take a very long-term view... target a very big alpha out of it. Whereas in a systematic manner, we would have very short-term view... the alphas are smaller, but you take it large number of times." - Amar Shah [00:04:27]
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"I tell people that look, the product is the hero, there is absolutely no star manager that we have." - Amar Shah [00:14:29]
"75% of the holding has actually become institutional, research is available in many of the spaces... leading to diminishing alpha." - Amar Shah [00:19:18]
"In the investing world, one of the most inconsistent things that stays is performance. If you can get consistency on alpha, the probability of being parked in top quartile goes up significantly." - Amar Shah [00:31:00]
Speakers & Credentials
Amar Shah: Executive Director and Presenter at Qi-Cap, possessing 25 years of experience in the capital markets and asset management industry, transitioning from traditional fundamental research support to quantitative and systematic investing frameworks [00:00:07].
Amit: Co-founder of Qi-Cap, high-frequency trading (HFT) pioneer, and creator of Anathi Wealth [00:02:17].
Parul Sharma: Quant Fund Manager at Qi-Cap, previously with Goldman Sachs Asset Management (GSAM) where she managed $10B in emerging market quantitative funds and $1.5B in dedicated India quantitative strategies [00:18:37].
Hemant Parakh: Co-founder and Quant Fund Manager coming from an extensive high-frequency algorithmic trading (HFT) background [00:02:46, 00:18:22].
1. Executive Summary
Qi-Cap is a 5-year-old quantitative investment firm managing ₹450 crore ($50 million) in platform investments alongside a separate ₹3,000 crore derivative desk, powered by an in-house developed engineering stack and a 160-member team [00:01:18, 00:38:00].
The firm executes a strictly rules-based, systematic investment philosophy that prioritizes statistical modeling, mathematical factors, and data science over discretionary human instinct [00:03:38].
Institutional market saturation (75% institutional holding) and ubiquitous research have compressed stock-picking alpha, accelerating the shift toward high-breadth factor models [00:19:18].
Their flagship long-only product, Stratos, is a Category III open-ended AIF covering the top 1,000 listed Indian equities using 150 factors categorized across 10 macro themes [00:13:02, 00:23:38].
Stratos operates with a baseline beta constraint of 1 relative to the NSE 500 benchmark, seeking consistent risk-adjusted alpha rather than timing directional market swings [00:13:22, 00:25:16].
The platform ingests 2 billion daily data points across fundamental, price-volume market data, and alternative datasets including AI agent earnings transcript sentiment analysis [00:20:10, 00:27:08].
Risk parameters strictly govern execution, capping daily portfolio turnover at 2% and yielding an average position holding duration of 60 to 90 days [00:41:03, 00:41:19].
Traditional fundamental stock selection relies on discretionary analyst teams evaluating individual balance sheets, requiring long holding periods to capture large individual stock alphas [00:00:37, 00:04:27].
Modern quantitative strategies reverse this paradigm by automating stock evaluation through statistical models, executing smaller edge plays repeatedly across high-breadth universes [00:04:48].
Qi-Cap operates as a 5-year-old firm with ₹450 crore ($50 million) in platform investments, maintaining complete ownership of its proprietary tech infrastructure and intellectual property [00:01:18].
Out of 160 total firm members, 110 are tech engineers, coders, and data scientists dedicated to investment system architecture rather than traditional equity research [00:01:50].
The firm manages over 3 petabytes of financial data and controls approximately 2% of the total outstanding derivative positions on the New York Stock Exchange (NYSE) [00:01:41, 00:02:10].
Equity markets have experienced an alpha contraction as institutional ownership expanded to 75% of market capitalization, making public market research saturated and consensus-driven [00:19:18].
Domestic liquidity structural changes—such as steady ₹35,000–50,000 crore monthly mutual fund SIP inflows—have altered traditional price discovery mechanisms, neutralizing legacy valuation-anchored shorts during FII sell-offs [00:37:38].
Market liquidity down to the 500th listed stock on the NSE has expanded sufficiently to support high-breadth factor strategies that were execution-constrained 4 to 5 years ago [00:22:42].
Consistent fund outperformance depends on achieving marginal win rates (55%–60% statistical probability) executed dynamically across large statistical samples [00:11:17, 00:20:25].
The Stratos AIF Portfolio Architecture & Multi-Factor Engine
Stratos is structured as an open-ended Category III AIF targeting the top 1,000 listed Indian stocks, maintaining a diversified holding portfolio of 100+ stocks [00:13:02, 00:20:17].
The strategy evaluates 2 billion data points daily using 150 unique quantitative factors mapped across 10 overarching structural themes [00:20:10, 00:23:38].
The model blends fundamental factors (profitability, earnings quality, value) with price-volume market data (momentum, volatility) and dynamic sentiment metrics [00:23:33].
Factor exposures are dynamically reweighted without factor bias; during low-profitability macro regimes, profitability factor allocations scale down while industry linkage and sentiment factor weights expand [00:24:28].
During its initial live year (launched August 25, 2025), Stratos delivered a post-management fee alpha of ~9% over its underlying benchmark across volatile market conditions [00:16:41, 00:24:41].
Alternative Data Pipelines: NLP Scoring & Supply Chain Telemetry
Custom AI agents parse earnings conference call transcripts, calculating sentiment tone shifts, executive optimism variance, and guidance departures relative to historic management patterns [00:26:36, 00:27:16].
Macro agents continuously monitor trade, policy, and tariff announcements; for instance, assigning Tata Motors a negative sentiment score of 7 due to supply chain JLR exposure risks [00:27:43, 00:28:08].
Systemic supply chain linkage models map inter-company dependencies across all listed entities (e.g., mapping Coal India to NTPC to state utility boards), identifying operational disruptions 2 to 3 quarters before materializing in financial statements [00:28:31, 00:29:14].
Portfolio Guardrails, Execution Limits, and Risk Controls
The portfolio operates under strict structural constraints, maintaining a market beta of 1 relative to the NSE 500 benchmark while generating pure active share alpha [00:25:16, 00:26:02].
In systematic "Risk-On" regimes, the engine shifts risk parameters, automatically biasing stock selection toward defensive, large-cap equities [00:25:32].
Under extreme market drawdowns, cash allocations remain systematically capped at 3%–5% under normal regimes, scaling up to a historical ceiling of 12% only when risk-adjusted alpha scores across all universe stocks deteriorate [00:39:39, 00:40:24].
Execution parameters strictly mandate a daily portfolio churn cap of 2%, balancing factor responsiveness against transaction costs and slippage [00:41:03].
Churn rates below 2% lead to factor decay and alpha degradation, while turnover exceeding 2% adds transaction noise without incremental return generation [00:41:28].
Average position holding periods range between 60 to 90 days, resulting in short-term capital gains tax execution within the Category III AIF structure [00:41:09, 00:41:19].
The Reference Vault
4. Data & Figures
Data Point
Value
Context
Timestamp
Industry Experience
25 years
Amar Shah's total tenure in financial capital markets
Traditional asset management relies on discretionary fund managers using subjective judgment, intuition, and concentrated stock bets to generate alpha. In contrast, quantitative systematic frameworks replace subjective decision-making with strict, algorithmic rules executed across broad stock universes. Rather than seeking outsized returns on a small set of high-conviction ideas, systematic models compound micro-edges across thousands of setups [00:03:38, 00:04:48].
The Shopper Digital Footprint Analog
Modern market analysis operates similarly to modern e-commerce platforms. While traditional brick-and-mortar grocers rely on personal relationships to estimate customer preferences, e-commerce platforms analyze cross-sectional data—such as credit card tiers, zip codes, and browsing patterns—to predict buying behaviors with high probability. Quantitative asset management mirrors this transition: instead of analyzing companies in isolation, models process cross-asset price signals, behavioral footprints, and alternative data streams to predict market risk and return dynamics [00:06:06, 00:07:16].
Multi-Factor Dynamic Rebalancing Engine
Rather than committing to a single style factor—such as pure momentum, value, or quality—multi-factor quantitative models operate dynamic theme allocations. Different investment factors perform better depending on the macroeconomic regime. By evaluating factor interactions in real time, the engine systematically reallocates capital toward factors gaining market traction while scaling down underperforming factors, eliminating single-style drag [00:23:38, 00:24:28].
Unstructured NLP Tone & Sentiment Scoring
Quantitative modeling extends beyond structured numerical data into unstructured text using Natural Language Processing (NLP). AI agents process management earnings call transcripts, evaluating executive tone, guidance modifications, and word choices relative to historical calls. The system converts qualitative management commentary into normalized numerical risk scores, establishing an objective framework to detect shifts in corporate fundamentals [00:26:36, 00:27:16].
Supply Chain Interconnection Telemetry
Equities do not trade in isolation; they exist within linked operational ecosystems. By mapping multi-tier supply chain dependencies (such as raw material suppliers, power generators, and state distributors), quantitative systems track operational disruptions across industry networks. This network analysis allows models to capture fundamental inflection points months before they register in lagging quarterly reporting [00:28:31, 00:29:14].
Optimal Churn Execution Frontier
In systematic long-only strategies, portfolio turnover directly impacts net alpha. Truncating turnover below 2% daily causes factor decay, preventing the portfolio from adapting to shifting market dynamics. Conversely, exceeding 2% turnover introduces execution drag and transaction costs without generating additive return. Maintaining turnover at this optimal execution boundary maximizes factor responsiveness while controlling transaction friction [00:41:03, 00:41:28].
6. Anecdotes
The Local Grocer vs. E-Commerce Predictive Recommendation
Why the speaker told it: To illustrate how capital allocation has shifted from qualitative, relationship-based human instinct to scale data analytics [00:06:06].
Context: Shah contrasted his childhood experience at a local neighborhood grocer—where the shopkeeper personally knew customer preferences—with modern e-commerce algorithms. Platforms recommend complementary purchases using anonymized behavioral data, postal codes, and payment methods without ever knowing the buyer personally [00:06:26, 00:07:16].
Predicting Conference Room Investor Actions via Behavioral Trails
Why the speaker told it: To demonstrate how combining fundamental, market, and social alternative data enables high-probability behavioral predictions [00:08:05].
Context: Shah posed a thought experiment to the 250 summit attendees: if a system tracked their background, career cash flows, risk tolerance, social networks, and drawdown reaction patterns, an algorithm could accurately predict whether an individual would invest, wait, or divest following the presentation [00:08:28, 00:10:41].
Anomaly Detection in Private Sector Banking Group
Why the speaker told it: To highlight how automated risk-scoring models detect balance sheet red flags before public market disclosures [00:29:21].
Context: In 2025, Qi-Cap’s quantitative model flagged an unexpected surge in the risk score of a leading private sector bank stock. The system detected anomalous insider share dumping across parent group entities. Shortly thereafter, public accounting discrepancies emerged, driving a 27% stock drop that the quantitative system had preemptively avoided [00:29:37, 00:30:18].
Industrial Chain Ripple Effects: Coal India to Utility Boards
Why the speaker told it: To explain how multi-node network tracking provides real-time fundamental insights [00:28:31].
Context: Shah showed a dynamic network graph connecting Coal India to power producers like NTPC down to regional utilities like Punjab State Power. Tracing operational and payment disruptions across these supply chain linkages gives systematic engines forward-looking signals quarters before they reflect in earnings reports [00:28:48, 00:29:14].
7. References & Recommendations
Companies & Institutions
Qi-Cap: Quantitative asset management firm specializing in factor-based systematic strategies and derivative execution [00:01:18].
Anathi Wealth: Wealth management entity previously created by co-founder Amit [00:02:25].
Goldman Sachs Asset Management (GSAM): Investment bank where key fund manager Parul Sharma formerly managed $10B in emerging market quant strategies [00:18:37].
Renaissance Technologies: Pioneer US quantitative hedge fund referenced during audience Q&A regarding long-term quant model robustness [00:32:01].
Tata Motors & JLR: Automobile manufacturer cited in an AI agent case study evaluating trade and tariff exposure risks [00:28:08].
Coal India & NTPC: Listed state entities referenced in supply chain dependency mapping [00:28:48].
Punjab State Power Corporation: Regional power utility used as a downstream node example in network models [00:28:54].
Products & Asset Classes
Stratos: Category III open-ended AIF long-only systematic equity fund targeting top 1,000 Indian listed equities [00:12:16].
NSE 500 Index: Benchmark index utilized for tracking beta, active share, and risk guardrails [00:17:40, 00:26:02].
New York Stock Exchange (NYSE) Derivatives: Global derivatives exchange where Qi-Cap executes platform volume [00:01:41].
Historical Macro Events & Market Regimes
FY 2025–2026 Market Volatility: Period marked by changing expiry rules, tariff disputes, geopolitical conflict, and sharp equity drawdowns [00:17:16].
March Market Correction: Period when the NSE 500 index declined 11% in a single month [00:17:40].
2021 COVID Market Regime: Market phase where value factors underperformed, leading systematic engines to temporarily scale down value factor weights [00:24:05].
Sep 11, 2026
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