Labs/Paths/Behavioral Finance & Market Efficiency
Curated learning path
Intermediate
1.5 hr · 3 labs

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: 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

3 stops · 1.5 hr of focused work · Intermediate

1
Stop 1 · 30 min · Performance Measurement
Market Efficiency Tester
Autocorrelation, runs, and variance-ratio tests for the EMH.
Three weak-form EMH tests on the data of your choice.
Open lab →
Worked example
Sample EMH test results on SPY (5y daily returns).
TestStatisticp-valueVerdict
Autocorrelation lag-1ρ = 0.012—Tiny — efficient
Ljung-Box (10 lags)Q = 14.30.16Fail to reject
Runs testz = 1.40.16Random sequence
Variance ratio (q=2)VR = 0.980.62≈ random walk
Verdict: weak-form efficient over this window.
2
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?
Open lab →
Worked example
Risk attribution for a sample fund — fairly market-driven.
ComponentVariance ShareAnnualized σ
Systematic71%17.4%
Idiosyncratic29%11.2%
Total100%20.6%
High systematic share → most 'alpha' is hidden β.
3
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?
Open lab →
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

Real-world scenarios that pull together the path. Each links back to the Labs you just used.

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.

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 shrunk 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.

Free for any classroom or self-study use. Each Lab works standalone too — this path is one suggested ordering of the foundations.