Capitalmind: Quant and Fundamentals
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The document discusses integrating quantitative strategies with fundamental analysis by utilizing machine-readable data from balance sheets and qualitative factors. It emphasizes screening stocks based on specific financial metrics, historical performance analysis, and the importance of backtesting, while acknowledging the challenges of unreliable data and the need for macroeconomic context. Additionally, it highlights various data sources and methods for obtaining insights into stock performance and industry dynamics.
Capitalmind: Quant and Fundamentals
- 1. Quantitative Strategies with Fundamentals Deepak Shenoy https://capitalmind.in
- 2. Quant and Fundamentals? • Fundamentals means things in the Balance Sheets, Profit and Loss, etc. • Typically qualitative, very company specific data points • Machine readable cross-industry data now available • Trading based on news already prevalent
- 3. The “Screener” approach forValue • Screen stocks for standardized input parameters. • Earnings Growth > 25% • P/E ratios < 15 (or < Earnings Growth) • Growing Positive Operating Cash Flow etc. • See video on how to get data for India (screener.in) • https://www.youtube.com/watch?v=JtH_PGzR66w
- 4. P/E Ratios: Concepts • Buy when P/E ratio low (Index or Stock) • Check historical returns when it happens • Can do with P/E to Growth rations (PEG) as well
- 5. Valuation versus Performance • Valuation fundamentals (“Fair price”) • Price to Earnings • Price to Book • Anything that provides a price context • Performance (“Good” companies) • Return on Equity • Return on Capital Employed • High asset turnover ratios
- 6. BackTesting • Data not available in any easy format (India) • Need to back test after adjusting for splits/bonuses for EPS as well • Stocks get eliminated due to mergers (survivor bias) for longer periods • Need macro indicators (recessionary period, growth period) • Subjective filters may still be required.
- 7. Slightly Higher Frequency Data • Regulatory data sources • Car Sales • Airline information • RBI and Bank regulators • Company Mandatory Disclosures • Banks • Insurers • “Scuttle butt” • Higher cost Primary Research
- 8. Piggybacking on Other Investors • Shareholding Pattern Changes • Companies release info every quarter • Large shareholders (>1%) • Institutional Holdings • Monthly data release (mutual funds, India) • Form 15-F type of release (US hedge funds) • Bulk or Block Deal Information • Insider trade info
- 9. Macro Factors • Inflation • Will boost earnings • Industry specific macro • Economic cycles • Price of raw materials (crude = tyres etc) • Fund flow • Institutional investors • Currency
- 10. Pain Points • Unstructured data (PDF etc) • XBRL, but not necessarily well structured • Data once in three months or so • Best data comes reasonably delayed • Worst: Data not reliable
- 11. Unreliable Data • Companies LIE about things • Or make them look good • Examples • Jump in Receivables, Jump in earnings • Sales to related companies • No consolidated numbers • Refusing to acknowledge losses (notes) • Lower provisions than required • Machines find it difficult to parse non-standard data
- 12. Rules toWorkWith • Use Companies that have a history of disclosing data properly • Qualitative Filters for small or unreliable stocks • Test over long periods of time (50-100 data points) • Macro concepts • Adjust earnings for inflation • Compare against industry numbers • Be aware of forward risks to industry