Strategy #0000000114 · Calendar and seasonal effects
Calendar-anomaly holding windows
Choose an anomaly type and enter signal days, total days, window return, cash return and costs.
Turn-of-month, pre-holiday, weekday and January effects describe average calendar-time return differences. Early findings can reflect market structure, sample choices and multiple testing, and may decay after publication. This tool does not connect a trading calendar; it compares entered signal days, total days, period return and cash return.
No real dates, holidays or historical anomalies are identified; calendar effects can decay.
Three ways in
Understand the story. Then inspect the risk.
Start with a familiar situation, check the payoff, then test how the numbers change.
The idea · in two minutes
What are time exposure and scenario returns when holding only during a calendar window?
Turn-of-month, pre-holiday, weekday and January effects describe average calendar-time return differences. Early findings can reflect market structure, sample choices and multiple testing, and may decay after publication. This tool does not connect a trading calendar; it compares entered signal days, total days, period return and cash return.
Choose an anomaly type and enter signal days, total days, window return, cash return and costs.
No real dates, holidays or historical anomalies are identified; calendar effects can decay.
- Input assumptionsChoose an anomaly type and enter signal days, total days, window return, cash return and costs.
- Model outputNo real dates, holidays or historical anomalies are identified; calendar effects can decay.
Illustrative research examples only. They are not live signals or a recommendation.
The mechanics · payoff and exposure
R=\frac{d_s}{d}R_s+(1-\frac{d_s}{d})R_{cash}-C
d_s is the signal-window day count and d the total; all returns are period scenario inputs.
Greeks: the directional intuition
- 模型
- Measures sensitivity to implied volatility.
- 樣本
- Measures sensitivity to implied volatility.
- 執行
- Measures sensitivity to implied volatility.
R=\frac{d_s}{d}R_s+(1-\frac{d_s}{d})R_{cash}-C
Practical context · published observations
Calendar-anomaly holding windows: read the record with its caveats.
The chart uses the published figures or explicitly labelled study inputs below. A historical index or model replay is not the same as an investor’s realised account return.
No real dates, holidays or historical anomalies are identified; calendar effects can decay. Source ↗ · Methodology ↗
Change assumptions and inspect the scenario
Inputs illustrate the strategy mechanics; they are not live quotes, forecasts or investment advice.
Variants · same family, different risk levers
Model definitions, data splits and implementation costs affect results.
Compare methodology and execution assumptions.
| Related strategy | Relationship | What changes | New cost or risk |
|---|---|---|---|
| Base scenario | Manual inputs | Turn-of-month, pre-holiday, weekday and January effects describe average calendar-time return differences. Early findings can reflect market structure, sample choices and multiple testing, and may decay after publication. This tool does not connect a trading calendar; it compares entered signal days, total days, period return and cash return. | No real dates, holidays or historical anomalies are identified; calendar effects can decay. |
| Conservative version | Lower risk | Raise cost assumptions and cap exposure or search space. | May reduce model adaptability. |
| Validation version | Out-of-sample test | Freeze rules before evaluating untouched data. | Needs more data and governance. |
No real dates, holidays or historical anomalies are identified; calendar effects can decay.
Read beyond the summary
References
Primary sources and research used to describe the strategy and its limits.