Option straddle trading: Financial performance and economic significance of direct profit forecast and conventional strategies
Bibliographic record
Abstract
The present study focuses on the trading of at-the-money straddles using options on foreign currency futures, namely British Pound, Canadian Dollar, and Japanese Yen. The financial performance and economic significance of a direct profit forecast trading strategy are examined. This strategy uses a linear projection to directly forecast the profit (net of transaction costs) of engaging in a straddle. The straddle is purchased when the forecast is positive and sold when negative. This differs from the conventional option trading strategy of basing trading decisions on a two-step procedure of first generating a volatility forecast and then inputting the volatility forecast into an appropriate option pricing model to price the straddle. The direct profit forecast trading strategy removes volatility forecasting and option pricing models from the straddle trading decision process altogether. This method has only one source of model risk, compared to the conventional two step method which has two sources of model risk. It is possible that the direct forecast trading strategy with only one source of model risk may outperform the conventional method of trading straddles. The experimental investigation confirms this notion and the out-of-sample results indicate that, for each of the currencies analysed, the direct forecasting strategy is more profitable than the conventional two-step method. Furthermore, the results are robust with respect to different transaction cost assumptions. Finally, tests of economic significance indicate consistent market timing value for the direct forecast method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".