A²PT · Strategy Learning Hub
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156 strategies. Learn the idea through a story, see the mechanics, then test the payoff against real observations.
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- 073Factors & multi-factor investing
Tax signals
Tax signals include taxable income relative to book income, tax-expense changes, effective tax rates and book-tax differences. Lev and Nissim study the predictive content of taxable/book income; Thomas and Zhang examine tax-expense momentum. Rates also reflect one-off items, international operations, deferred taxes and rules, so they are not a single quality label.
- 074Factors & multi-factor investing
R&D and intangible capital
R&D, advertising, brand and organisational capital are accounted for differently from physical assets; expensing may understate long-lived investment. Research finds information in R&D-intensive firms and R&D capital. Intensity can also mean higher uncertainty, and cross-industry comparability is limited. The simulator keeps intensity ratios separate from return assumptions.
- 075Factors & multi-factor investing
Net share issuance／external financing
Net-issuance signals compare equity issuance, repurchases and changes in shares outstanding. Research associates share expansion with weaker subsequent returns; explanations include managerial timing and investor extrapolation. Dilution, M&A, employee equity and splits also change share counts. Debt issuance is a distinct financing signal.
- 076Event-driven & special situations
Corporate event anomalies
Corporate-event studies compare price paths after IPOs, spin-offs, credit downgrades or exchange switches. Event directions differ: IPO underperformance and post-spin-off performance are distinct hypotheses; downgrades are confounded by credit news and distress. Event windows, benchmarks and survivorship treatment matter for long-run estimates.
- 077Cross-sectional momentum
Lead-lag／economic links
News about customers, suppliers, peers or conglomerate divisions may diffuse gradually across linked stocks. Research finds customer returns and earnings news can lead suppliers; price-delay measures estimate information absorption. Real economic links change, and a leader’s return is not a directly causal or replicable signal.
- 078Factors & multi-factor investing
Short interest and institutional ownership
Short interest, breadth of institutional ownership and institutional holdings may reflect negative information and short-sale constraints. Studies associate high short interest combined with low institutional ownership with weaker performance, but reporting lags, borrow costs, squeezes and delayed filings matter. The filter illustrates a rule and does not determine borrow availability.
- 079Event-driven & special situations
Dividend events
Dividend initiations and omissions convey board information about cash flows and capital needs, with possible post-announcement drift; research also studies expected dividend-month returns. Ex-dividend adjustments, announcement surprises, taxes, buyback alternatives and market cycles affect returns. These events should not be conflated.
- 080Factors & multi-factor investing
Industry concentration
Industry concentration is often measured by the Herfindahl–Hirschman Index (HHI), the sum of squared market shares. Hou and Robinson study a link between competition and equity returns; concentration may reduce innovation risk while also creating market power and entry barriers. Market definitions, product boundaries and conglomerate treatment change HHI.
- 081Mean reversion & reversal
Long-term reversal
Long-term reversal buys stocks with poor multi-year past returns and shorts prior winners. De Bondt and Thaler connect it to investor overreaction; later work points to long-run risk premia, intangible returns and small-stock effects. Formation windows, skipping recent months, delistings and costs all affect results.
- 082Calendar & seasonality
Return seasonality
Seasonality strategies compare a stock’s historical returns in the same calendar month with other months. Heston and Sadka document persistent calendar-month patterns; explanations include cash flows, investor behaviour and seasonal risk. Multiple testing, data mining, month selection and trading costs can inflate in-sample results.
- 083Value
Fundamental scorecards
Piotroski’s F-score sums nine binary signals across profitability, leverage/liquidity and operating efficiency into a 0–9 score, often used to distinguish fundamentals among high book-to-market stocks. The G-score applies a related scorecard to growth firms. This simulator takes 0–3 passes in each signal group, then separates out assumed long/short returns.
- 084Factors & multi-factor investing
Size effect
The size effect ranks firms by market capitalisation and studies whether small firms earn higher average returns than large firms; it also enters the Fama–French SMB factor. Historical effects may concentrate in microcaps, specific periods or January, and correlate with liquidity, age and analyst coverage. A size rank does not guarantee risk-adjusted excess returns.