A very basic run-through of the concepts around using quantitative strategies with fundamentals. Presented in a Quantinsti webinar on 21 Feb 2017 by Deepak Shenoy at Capitalmind.

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

13. Questions?
Mail: deepakshenoy@capitalmind.in
Twitter: @deepakshenoy
https://capitalmind.in