Behavioral Finance & Market Efficiency

When Markets Aren't Random, and What to Do About It

Test the Efficient Market Hypothesis directly. Run autocorrelation, runs, and variance-ratio tests on real data. Decompose risk through a behavioral lens to see which anomalies are real signal and which are just survivorship.

Quants and portfolio managers curious about EMH violations and edge measurement.

By the end you will…

Test weak-form efficiency on any ticker using three rigorous statistical tests.

Quantify autocorrelation, runs, and variance-ratio deviations.

Decompose realized returns into systematic and behavioral components.

Evaluate fund performance through a behavioral lens: is alpha skill or β masquerading?

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The journey

Stop 1, 30 min, Performance Measurement

● You are here

Market Efficiency Tester

Autocorrelation, runs, and variance-ratio tests for the EMH.

Three weak-form EMH tests on the data of your choice.

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Worked example

Sample EMH test results on SPY (5y daily returns).

Test	Statistic	p-value	Verdict
Autocorrelation lag-1	ρ = 0.012	-	Tiny: efficient
Ljung-Box (10 lags)	Q = 14.3	0.16	Fail to reject
Runs test	z = 1.4	0.16	Random sequence
Variance ratio (q=2)	VR = 0.98	0.62	≈ random walk

Stop 2, 30 min, Risk & Return

Risk Decomposition

Split a stock's risk into systematic (β-driven) vs idiosyncratic.

How much variance is explained by the market vs idiosyncratic noise?

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Worked example

Risk attribution for a sample fund: fairly market-driven.

Component	Variance Share	Annualized σ
Systematic	71%	17.4%
Idiosyncratic	29%	11.2%
Total	100%	20.6%

Stop 3, 30 min, Performance Measurement

Sharpe / Treynor / Jensen

Compute every major risk-adjusted return ratio side-by-side.

Sharpe vs Treynor vs Jensen: which one tells you the most?

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Worked example

Famous 'alpha' fund: does it survive a behavioral audit?

Sharpe

0.78

vs 0.66

Treynor

0.052

vs 0.060

Jensen α

−0.6%

vs -

Info Ratio

0.18

vs -

Apply what you learned

Case Study

Is Bitcoin's weekend autocorrelation a tradable signal?

Crypto markets trade 24/7, but volume drops 40-60% on weekends. Pre-2020 papers documented mild positive autocorrelation in BTC weekend returns (ρ ≈ 0.05-0.10). Run the Efficiency Tester on BTC-USD with daily data and see what the 5-year picture looks like now. With venues like Polymarket and Kalshi, weekend prediction-market liquidity has tightened the gap. Result: lag-1 ρ has decayed to ~0.02, no longer statistically significant after transaction costs. Lesson: documented anomalies often disappear once they're known and capital chases them.

Try the Market Efficiency Tester →Try the Sharpe / Treynor / Jensen →

Case Study

The 'low-vol anomaly': real signal or post-hoc rationalization?

Multiple papers show low-volatility stocks outperform high-vol stocks risk-adjusted (a violation of CAPM). Run risk decomposition on USMV (low-vol ETF) vs SPHB (high-beta ETF): USMV's β is ~0.7, SPHB's is ~1.4. Backtest 10 years: USMV returned 9.5% with σ=11%, SPHB returned 12% with σ=22%. Sharpe ratios: 0.50 vs 0.36 — the low-vol anomaly held over this window. But the gap shrank in the 2021-2023 'meme' regime. Lesson: anomalies persist when behavioral biases (lottery preference, leverage aversion) are durable; they fade when they're mispriced into ETFs.

Try the Risk Decomposition →Try the Sharpe / Treynor / Jensen →