Comparison of the Effectiveness of Option Price Forecasting: Black-Scholes vs. Simple and Hybrid Neural Networks
Bibliographic record
Abstract
The purpose of this study is to forecast option prices with simple backpropagation neural networks and to compare the results between conventional Black-Scholes model, the Black-Scholes model with pure implied volatility and neural network models over a seven-year period. This longitudinal study used 64,280 OEX 100 index call option prices trading on the Chicago Board Options Exchange from January 1986 to June 1993. In addition to simple models, two hybrid models were constructed. Using optimal models in each sub-period, the following results are demonstrated: 1. neural networks outperform the conventional Black-Scholes model when using historical volatility as an input; 2. the Black-Scholes model has better predictability when implied volatility is used; and 3. the hybrid neural network model with implied volatility often outperforms the implied volatility version of the Black-Scholes model.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".