Efficient market hypothesis says prices fully reflect all available information | meaning returns are essentially unpredictable. Forty years of empirical research says: mostly true, but with well-documented exceptions. Those exceptions are what factor investing is built on.
This module maps the five key statistical properties of equity return time series that systematic strategies exploit, hedge against, or must account for. Each one has extensive academic documentation and observable behavior in Indian markets.
The five properties
Large moves tend to be followed by large moves | in either direction. Calm periods cluster together; turbulent periods cluster together. This is the basis of GARCH models and the observation that "market stress arrives in waves." In Indian markets, the pattern is clear: the 2008 crash, 2011 correction, 2018 small-cap selloff, and 2020 COVID crash all featured days of extreme moves clustered together, not scattered randomly.
Implication: Risk management should be dynamic | position sizes should reduce during high-volatility regimes, not remain fixed.
Over horizons of 1 to 4 weeks, individual stocks that have fallen significantly tend to bounce, and vice versa. This is the basis of short-term reversal strategies. The mechanism is microstructure-driven: market makers and liquidity providers absorb large directional flows, then prices revert as the temporary imbalance unwinds.
Implication: This is why momentum strategies skip the most recent month (the 12-1 construction). The last month's return predicts reversal, not continuation.
Over 3 to 12 month horizons, winners continue to outperform and losers continue to underperform. This is the momentum factor | documented by Jegadeesh & Titman (1993) in US markets, subsequently replicated in Indian markets. The mechanism is behavioral: investors underreact to fundamental information, so strong earnings or business improvement takes months to be fully priced in.
Implication: The 12-1 momentum signal (12-month return, skipping last month) is the canonical implementation. NSE Momentum Index tracks this systematically.
Over 3 to 5 year horizons, prior losers outperform prior winners | the opposite of momentum. De Bondt & Thaler (1985) documented this in the US. The mechanism is overreaction: investors become overly pessimistic about long-term losers and overly optimistic about long-term winners, creating mean reversion over multi-year horizons. This is partly why value investing works: buying long-term underperformers captures the reversal premium.
Implication: Momentum and contrarian strategies can coexist | they operate at different time horizons.
Certain calendar periods show systematically different returns | the "January effect" (small caps outperform in January) in the US, budget-season volatility in India, FII rebalancing around quarter-end. Some of these are robust; others are data-mined artifacts. Indian-specific: pre-budget volatility (January to February), dividend stripping effects in August to September, and year-end FII position squaring in March.
Implication: Calendar effects are real but small. They're worth being aware of for execution timing but too unstable to build primary strategies around.
GARCH: modelling volatility clustering
GARCH (Generalised Autoregressive Conditional Heteroskedasticity) models are the standard tool for modelling time-varying volatility. The core idea: today's volatility depends on both yesterday's volatility and yesterday's return shock.
For practical systematic investing, GARCH models are most useful for dynamic position sizing | reducing exposure when estimated volatility is high, increasing it when volatility is low. This is not market timing (you're not predicting returns), just volatility-adjusted sizing.
India-specific note: NSE data from 2003 gives roughly 20 years of usable daily price history | about 5,000 trading days. This is sufficient for robust GARCH estimation, but means you have at most 3 to 4 complete market cycles (bull + bear) in the sample. Structural regime changes (2010 liquidity growth, 2018 SEBI re-categorisation) should be accounted for.
Mean reversion vs trend following: the regime question
Strategies built on mean reversion (contrarian, value) and strategies built on trend following (momentum) both have empirical support. They don't always work simultaneously | there are periods where one dominates.
- Mean reversion dominates in choppy, range-bound markets (e.g., 2011 to 2013 Nifty consolidation). Momentum strategies generate many false signals, getting whipsawed repeatedly.
- Trend following dominates in directional markets with strong fundamentals (e.g., 2014 to 2017 bull run, 2020 to 2021 recovery). Contrarian strategies buy "cheap" stocks that keep getting cheaper.
- The solution is diversification across signal types | not market timing between regimes. Regime identification in real-time is extremely difficult; regime diversification is implementable.
The RupeeCase terminal shows rolling 252-day volatility for each strategy and the underlying index | allowing you to see when you're in a high-volatility regime. The backtester uses 20-day rolling volatility for inverse volatility weighting. Strategy return autocorrelation is computed and displayed to help identify when a strategy is behaving differently than its historical pattern. Available at invest.rupeecase.com.
Glossary
Sources & further reading
- → Engle, R.F. (1982). Autoregressive Conditional Heteroscedasticity. Econometrica. (Original ARCH paper)
- → Jegadeesh, N. & Titman, S. (1993). Returns to Buying Winners and Selling Losers. Journal of Finance.
- → De Bondt, W. & Thaler, R. (1985). Does the Stock Market Overreact? Journal of Finance.
- → NSE Nifty Momentum Index methodology
Quick check, Module 5.2
Rolling Correlation Regime Detector
Indian equity correlation jumps in stress. A jump from baseline to current is a regime signal.