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.

بنهاية هذا المسار ستتمكن من…

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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الرحلة

المحطة 1، 30 دقيقة، Performance Measurement

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Market Efficiency Tester

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

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

افتح المختبر ←

مثال تطبيقي

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

المحطة 2، 30 دقيقة، 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?

افتح المختبر ←

مثال تطبيقي

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%

المحطة 3، 30 دقيقة، 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?

افتح المختبر ←

مثال تطبيقي

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

Sharpe

0.78

مقابل 0.66

Treynor

0.052

مقابل 0.060

Jensen α

−0.6%

مقابل -

Info Ratio

0.18

مقابل -

طبّق ما تعلمته

دراسة حالة

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.

جرّب Market Efficiency Tester ←جرّب Sharpe / Treynor / Jensen ←

دراسة حالة

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.

جرّب Risk Decomposition ←جرّب Sharpe / Treynor / Jensen ←

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.

بنهاية هذا المسار ستتمكن من…

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?

0 من 3 labs تم استكشافها

محفوظ على هذا الجهاز

ابدأ المسار ←

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%