A²PT · Strategy Learning Hub
Strategies, unpacked.
156 strategies. Learn the idea through a story, see the mechanics, then test the payoff against real observations.
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- 097Options & payoff structures
Option-based structured products
Structured notes combine bonds and options. Principal-protected participation notes often use a zero-coupon bond for a maturity floor and a call for upside participation. Reverse convertibles offer higher coupons in exchange for downside or share-delivery risk. Issuer credit, fees, liquidity, barriers and autocall features change the risk.
- 098Volatility
Cross-sectional volatility trading in options
Goyal and Saretto sort stocks by historical realised volatility relative to current implied volatility, buying straddles with the largest gaps and shorting those with the smallest. Straddle returns depend on direction, volatility risk premia, skew, costs and jumps; a variance gap alone is not full option P&L.
- 099Event-driven & special situations
Tailspotting：CEO travel alternative data
Private-aircraft tracking research links public flight records to potential deals, investor meetings or major visits. Flight data does not reveal meeting purpose and may reflect leases, managers or non-corporate travel. An anomaly is neither proof of a corporate event nor permission to trade on material non-public information. This simulator separates an anomaly score, assumed relevance and user-entered price response.
- 100Volatility
Variance risk premium market timing
The variance risk premium is commonly measured by option-implied variance relative to future realised variance. It reflects risk-bearing, hedging demand and volatility forecast error, not a simple directional signal; squared VIX is not directly tradable future realised variance. This simulator separates the squared-volatility gap, exposure multiple and assumed market return.
- 101Calendar & seasonality
Overnight vs intraday returns
Close-to-open overnight returns and open-to-close intraday returns can have different risk premia, information response and trading costs. Research compares these return legs and holding windows, but markets, samples and execution times matter. This tool keeps supplied historical decomposition separate from the next-period scenario.
- 102Factors & multi-factor investing
Crowdsourced opinion data
Employee reviews and consumer ratings offer views beyond conventional filings. Rating changes may relate to turnover, product quality or future revenue, but self-selection, platform rules, bots, sample size and company size create biases. The simulator uses a review-count threshold to switch an assumed long-short spread on or off; ratings are not treated as return forecasts.
- 103Factors & multi-factor investing
Cryptocurrency factors
Crypto-asset research tests cross-sectional market, size, momentum and investor-attention factors. Short token histories, delistings and survivorship bias, exchange quality, liquidity, token-supply changes and regime shifts can distort historical tests. This model combines user-entered standardised factor scores and applies assumed long/short basket returns.
- 104Machine learning
Machine-learning cross-sectional return prediction
Machine-learning models can fit nonlinear return relationships from firm characteristics and rank a long-short portfolio by predicted returns. Results depend on point-in-time data, out-of-sample testing, feature engineering, portfolio construction, benchmarks and costs; leakage and repeated tuning can create false performance. This scenario compares manually entered predicted and realised spreads with turnover costs.
- 105Cross-sectional momentum
Capital gains overhang／disposition effect
Grinblatt and Han link the disposition effect to a reference price: investors are more willing to realise gains than losses. A volume-weighted historical cost proxy can estimate market reference prices and capital-gains overhang. It is related to, but distinct from, past-return momentum; reference-price estimation, volume and market composition matter.
- 106Global macro
Presidential economic approval rating
Research builds a monthly index of presidential economic approval and estimates stock betas to its changes. A higher beta indicates closer return co-movement with the incumbent administration’s perceived policy alignment; the study finds higher future returns among low-beta stocks, not a simple partisan trade. Politics, industry exposure and estimation windows can change.
- 107Machine learning
News text sentiment
Tetlock and later research use news-column or corporate-text tone as proxies for sentiment and information response. General dictionaries can misclassify neutral financial terms as negative, motivating finance-specific Loughran–McDonald word lists. A model may use dictionaries, FinBERT or other NLP, but classification quality and text provenance need separate validation.
- 108Global macro
Investor sentiment timing
Baker and Wurgler combine several market proxies into a sentiment index and find stronger effects among hard-to-value, hard-to-arbitrage stocks. Stambaugh and co-authors find that short legs of several anomalies are more pronounced after high sentiment. Index construction, publication lags and regimes matter; sentiment alone is not a market-reversal instruction.