Bibliographic record
Abstract
This study examines the pattern of stock option time value decay and the implications of the time value decay pattern for option writing strategies. I also consider the returns to various options writing strategies. The central question is whether option writers can utilize a writing strategy that captures the time value of options as revenue to cover their risks and provides return on their investments. Using transaction data, I find that the time value of options that are near-the-money decays at a decreasing rate. The implications of this result are that a significant portion of the time value of near-the-money options decays in the early days of writing an option and the decay slows down as time to expiry approaches. This motivates us to compare over the same holding periods the writing returns of options with long times to expiry with the returns of options with short times to expiry. Overall, the results suggest that trading of options face significant transaction costs and it is mainly motivated by hedging or speculation as I did not find a systematic way to profit from option writing strategies. In addition, I examine the impact of market sentiment on the time value of options. The period of the study includes a sub-period when the general trend in the stock market was positive and another sub-period when the trend was negative. In particular, I study the price of puts relative to the price of calls during these two distinct market periods. I find that during bear markets both call and put options are more expensive than call and put options during bull markets. Yet, the ratio of put premiums to call premiums during rising markets is generally higher than the same ratio during bear markets. This observation suggests that speculators may be the dominant traders in options markets. Overall, I find that option writing strategies are not profitable. One of the reasons for this observation is transaction costs, which are significant in all the strategies that I examine. The bid-ask spread in the options market is large in comparison to the bid-ask spread in the underlying stock market.
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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 | 0.003 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".