--- title: "Alternative Data in Indian Markets | RupeeCase Learn" description: "What alternative data actually exists for Indian equity markets, what's accessible, what's institutional-only, and how to evaluate alt data edge." source_url: "https://www.rupeecase.com/learn/path-5/module-5-4-alternative-data-india" --- Skip to main content [Learn](/learn)›[Path 5: Advanced Quant Methods](/learn/path-5)›Module 5.4 # Alternative Data in Indian Markets What alternative data actually exists for Indian equities, what's genuinely accessible, what requires institutional budgets, and the one category of free alt data that serious systematic investors should already be using. TK Tanmay Kurtkoti Founder & CEO, RupeeCase ⏱ 14 min read ⟳ Updated 14 Jun 2026 ◆ Expert "Alternative data" means any data source beyond standard financial statements and price/volume data. In US markets, the alt data ecosystem is mature | satellite imagery of parking lots, credit card transactions, web scraping, job postings, and dozens of other signals are commercially available. In India, the ecosystem is earlier stage and more constrained by data availability and regulation. This module maps the alt data landscape for Indian equities honestly | what exists, what's accessible at different cost levels, and how to evaluate whether any specific alt data source has edge worth pursuing. 14 BSE corporate action feeds I follow daily 0 Cost of NSE bhavcopy data 40L Monthly GST returns processed 7 Days SEBI disclosure lag promoter pledging Most of the alt data edge in Indian equity sits in free government sources nobody reads. BSE disclosures, GST e-way bills, MoSPI micro surveys. The data is public, the attention is scarce. 1 Source ingest BSE corp action XML, GST portal, MoSPI CMIE 2 Normalise Map PAN or ISIN, strip filler text 3 Feature extract Derive signals: pledging delta, e-way bill growth 4 Backtest Walk forward on 5Y, look for IC > 0.04 5 Paper trade 90 days Only then size any real capital My alt data pipeline. Step 4 kills 90% of ideas because the information coefficient is weaker than the screen on an aggregator. If IC is above 0.04 after real transaction cost, I move to paper trade. Indian alt data sources by effort * BSE corp actions 36% * SEBI disclosures 24% * GST e-way bill 18% * MoSPI CMIE 14% * Satellite imagery 8% Ideas that survive backtest * Kill zone 54% * Weak but additive 32% * Standalone alpha 14% Out of every 100 alt data ideas I have tested, only 14 held up standalone after cost. Another 32 were additive to existing factor models. The rest were survivorship bias or lookahead leaks. Information coefficient of Indian alt data signals (out of sample) Promoter pledging delta 0.082 GST e-way bill YoY 0.061 Insider buy sell ratio 0.052 Twitter sentiment index 0.018 Satellite parking pilot 0.004 Boring data wins. Promoter pledging delta is the single strongest alt signal I have found in Indian equity and it is literally downloadable from BSE for free. From my notebook In 2020 a vendor sold us satellite parking lot data for Indian retail. 12 lakh rupees a year, lovely dashboards, beautiful pitch deck. I insisted on a one year backtest before signing. The IC came in at 0.004, statistically zero. That same year I built a free scraper for BSE promoter pledging weekly. IC 0.082, a genuine signal. We never signed the satellite vendor. Rule I keep returning to: the alt data edge in India is almost never in the expensive stuff, it is in the unread public filings. If you are willing to read the BSE corporate action page at 11:45 pm when it is updated, you are already in the top 5 percent of Indian investors. ## The Indian alt data landscape Corporate filings & disclosures BSE/NSE mandatory disclosures: board meeting outcomes, pledging changes, related party transactions, management remuneration, capex announcements, order wins. This is structured, machine-readable, and directly predictive. Promoter pledging is particularly useful | high pledging is a distress signal that precedes many Indian corporate failures (Eveready, DHFL, Cox & Kings). FREE Earnings call text NSE-listed companies are required to publish earnings call transcripts. These can be processed for management tone (confident vs hedging language), guidance consistency (does management guidance match actual delivery over time), and early warning signals for deteriorating business outlook. Moderate implementation effort; transcripts are in PDF format and require extraction. FREE Web search sentiment Google Trends for stock-related searches. Social media (Twitter/X, Reddit) for retail sentiment. Shows some short-term predictive power in US markets. In India: retail social media for stocks is dominated by noise and manipulation | penny stock pumps, WhatsApp forward chains. Institutional-grade filtered data is more useful but still noisy. Use cautiously and with very short holding periods only. LIMITED Satellite imagery Counting cars in retail/factory parking lots, monitoring construction activity, measuring agricultural crop area. Commercially available from Planet Labs, Maxar, and Indian providers. Meaningful edge for consumer retail and commodity companies | measuring footfall at Dmart, construction progress at real estate developers. High cost (₹20L+ per year for serious coverage); institutional players only. INSTITUTIONAL Credit card / payment data Aggregated UPI and credit card transaction data for consumer spend tracking | early signals for retail companies' quarterly revenue before official results. Available from a few data vendors but expensive and restricted to institutional buyers. SEBI's data localisation requirements add compliance complexity. Excellent predictive value for consumer-facing companies; impractical for most investors. INSTITUTIONAL Job postings LinkedIn, Naukri.com, and Indeed postings for companies | a leading indicator of business expansion or contraction. Companies that are aggressively hiring 6 months before reporting revenue acceleration can be identified early. Partially accessible via scraping (check ToS); structured feeds are commercial. Useful for IT/services sector where headcount directly drives revenue. Moderate edge; moderate implementation cost. PARTIAL ## The underused free signal: promoter pledging Of all alt data available for Indian markets, **promoter share pledging** is the most underused relative to its predictive power. Promoters pledging their shares to take loans signals financial stress | they need liquidity but can't or won't sell equity. High pledging (above 30 to 40% of promoter holding) consistently precedes corporate stress events in Indian markets. * DHFL: promoters pledged heavily before the NBFC crisis hit. Pledging data was public months before default. * Eveready Industries: promoter pledging rose sharply in 2018 to 2019 before the company ran into severe financial difficulties. * Suzlon Energy: multiple rounds of high pledging correlated with periods of extreme financial stress and debt restructuring. Pledging data is available for free on BSE/NSE. Filtering strategies to exclude stocks with promoter pledging above 25% improves quality and reduces tail risk substantially. The best alt data for most systematic investors in India isn't exotic | it's the structured disclosure data that BSE and NSE already publish and most people ignore. Pledging changes, board composition changes, and bulk/block deal data are all free, machine-readable, and predictive. ## How to evaluate whether alt data has genuine edge Any alt data source needs to pass four tests before you invest time building it into a strategy: * **Is there economic intuition?** Why should this signal predict returns? If you can't construct a plausible mechanism, the backtest result is probably noise. * **Is the data truly available at signal time?** Many alt data signals suffer from look-ahead bias | in hindsight, the data was available; in practice, there was a lag. Always check exact publication dates. * **Does the edge survive costs?** Alt data signals are often short-horizon | they decay quickly. Short-horizon signals require frequent trading, which generates costs that eat the edge. * **Will the edge persist?** Once a signal is known, it gets arbitraged away as more capital chases it. Alt data signals with low barriers to adoption decay faster than fundamental factor premia. Alt data and RupeeCase RupeeCase incorporates **promoter pledging as a quality filter** | strategies can optionally exclude stocks where promoter pledging exceeds a user-defined threshold. BSE/NSE disclosure filings are parsed to flag recent board-level changes. Both are free data sources that add genuine risk management value without requiring institutional infrastructure. Available at [invest.rupeecase.com](https://invest.rupeecase.com). ## Three free Indian alt-data signals worth tracking Most retail investors think alt data means satellite feeds and credit-card panels. The reality is that the highest-edge alt signals in India are free, public, and ignored because they require some assembly. Three worth pulling. **1. SHP filings (Shareholding Pattern, quarterly).** Every listed company files an SHP within 21 days of quarter end. SEBI prescribes the format. The structured fields disclose promoter holding, FII holding, DII holding and individual large shareholder positions over 1 percent. The signal: rising FII or DII stake on a quality name, with promoter holding stable, is a positive concurrent indicator. Falling promoter holding paired with rising pledged percentage is the single strongest distress signal you can find in public Indian data. Both BSE and NSE publish SHP files in machine-readable format. Build a parser once and you have a quarterly screen running for free. **2. Bulk and block deal data (daily).** NSE publishes bulk-deal data (any single trade above 0.5 percent of equity) and block-deal data (negotiated trades on the special block window) every trading day. The alt-data play: track which buyers are accumulating which names, and at which prices. A buyer who repeats across multiple bulk deals in adjacent quarters is signalling structural conviction. The data is free, the URL is public, the parsing is one CSV per day. **3. Insider trading filings (event-driven).** SEBI requires designated persons (promoters, directors, KMPs) to disclose any trade above INR 10 lakh within 2 trading days. The disclosures are filed via NSE and BSE in the Insider Trading window. The signal: clusters of insider buying across multiple insiders within a short window have historically preceded outperformance over the following 6 to 12 months. Insider selling is noisier (could be tax planning or estate liquidity), but cluster buying is a strong positive signal in academic studies of Indian markets. ## What does NOT work as alt data in India yet Two kinds of alt data that work in the US and crush in India because the underlying ecosystem is different. Credit-card transaction panels. The US has decade-deep card transaction databases that feed retail and consumer-facing equity research. India runs heavily on UPI, which is bank-to-bank, and on cash. Aggregated UPI data exists at NPCI level but is not granular at merchant-name level for outsiders. Card data alone misses most of the spending. Wait until UPI-merchant aggregation matures before betting capital here. Satellite imagery on commercial real estate or factory output. These work in the US because companies tend to operate in single locations with verifiable footprints. In India, manufacturing is scattered, contract-based, and often shared with peers. The signal-to-noise of an Indian satellite feed for a typical mid-cap is poor. Satellite works best for ports, airports, large mining operations and a handful of integrated manufacturing facilities; it does not generalise across the broader market. ## Glossary Key terms from this module Alternative dataAny data source beyond standard financial statements and price/volume history | satellite imagery, credit card data, web sentiment, corporate filings, job postings. Promoter pledgingThe fraction of promoter-held shares pledged as collateral for loans. High pledging is a financial stress signal | BSE/NSE publish this data quarterly for all listed companies. Look-ahead biasUsing data in a backtest that would not have been available at the actual signal date. A common error with alt data that makes backtests look better than live performance. Signal decayThe loss of predictive power of a signal as more participants discover and act on it. Alt data signals typically decay faster than fundamental factor signals. TK A note from the author Why this matters Alternative data is where India's next generation of alpha will come from. Satellite imagery of port traffic, GST filing patterns, UPI transaction volumes | these signals are uniquely Indian and still largely unexploited. I built this module to show you where to look before the rest of the market catches on. TK Tanmay Kurtkoti Founder & CEO, RupeeCase · 17 years systematic trading · QC Alpha RC **Want to put this into practice?** RupeeCase is the systematic investing terminal built around everything you're learning here, factor scores, strategy backtests, portfolio construction for Indian markets. [Explore the terminal →](https://invest.rupeecase.com) #### Sources & further reading * → [BSE India — Promoter Pledging Data](https://www.bseindia.com/markets/MarketInfo/PledgedShares.aspx) * → [NSE India — Pledged Share Details](https://www.nseindia.com/companies-listing/corporate-filings-pledged-details) * → [SEBI — Takeover Code (pledging disclosure rules)](https://www.sebi.gov.in/legal/regulations/nov-2011/sebi-substantial-acquisition-of-shares-and-takeovers-regulations-2011_11296.html) * → Kolanovic, M. & Krishnamachari, R. (2017). Big Data and AI Strategies. JP Morgan Global Markets Research. ### Quick check, Module 5.4 0 correct · 0 answered ✓ Module complete! Next module unlocked. 🎉 Module 5.4 complete 3 correct. Continue to Module 5.5 🎓 Spread the knowledge This course is free. Help someone else learn about Alternative Data in Indian Markets, share it with one person who needs this. 📋 Suggested LinkedIn post, copy & paste Just completed Module 5.4 of Tanmay Kurtkoti's free investing course on RupeeCase. Learning about Alternative Data in Indian Markets. Completely free at rupeecase.com/learn Copy text Share on X Post on LinkedIn Copy link Research Lab Qualifier Path 5, Module 4 of 5 done, complete all 5 + path test to unlock [Explore terminal →](https://invest.rupeecase.com) ✅ 5.1 Statistics → ✅ 5.2 Time Series → ✅ 5.3 ML for Alpha → 📍 5.4 Alt Data → 5.5 Out-of-Sample [← Previous](module-5-3-machine-learning-for-alpha.html) Previous, Module 5.3 Machine Learning For Alpha Calculator ### Bulk Deal Significance Classifier NSE publishes bulk deals (single trade above 0.5% of equity) every trading day. Reading them as a signal needs context on size relative to ADV and free float. Bulk deal value (INR cr)Stock 30-day ADV (INR cr)Free float market cap (INR cr)SideBuySell Quick check, Module 5.4 ## 3 questions. Get 2 right to mark this module complete. 0 of 3 answered ✓ Module complete. Keep going. Up next, Module 5.5 Building Strategies That Survive Out-of-Sample The final module in Path 5 | how to design, test, and validate systematic strategies that actually hold up when you deploy them live. [Continue →](module-5-5-out-of-sample-strategies.html)