PATH/INTERMEDIATE/behavioral-finance-market-efficiency/3 STOPS · 1.5 hr

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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01 / THE JOURNEY

The journey

3 STOPS · 1.5 hr · INTERMEDIATE

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.

Open lab →

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?

Open lab →

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?

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 —

02 / CASE STUDIES

Apply what you learned

2 SCENARIOS

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 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.

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

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

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Behavioral Finance & Market Efficiency

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

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?

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

Component

Variance Share

Annualized σ

Systematic

71%

17.4%

Idiosyncratic

29%

11.2%

Total

100%

20.6%