Keynes and the Interwar Commodity Option Market
Bibliographic record
Abstract
In the first quarter of the twentieth century, options began to be widely employed in the main financial centres in Europe and the USA for trading in spot and futures markets. From 1921 onward, Keynes embarked upon investment in these derivatives mainly—but not exclusively—in the commodity markets, showing a true fascination for this method of speculation. This type of financial investment he pursued mainly in the 1920s, with only a few operations undertaken during the 1930s. The option markets in which Keynes traded were metals—in particular copper, lead, spelter and, especially, tin. Besides metals, Keynes dealt in options also in other commodity markets, such as rubber and linseed oil, and sparingly in ordinary stocks and government securities. In this paper we offer a reconstruction of Keynes’s speculative activity in commodity options, drawing on the archival material kept in the Keynes Papers held at King’s College, Cambridge. This reconstruction is, to the best of our knowledge, entirely new to the literature and aims to provide an analysis of this particular aspect of Keynes’s investment behaviour, investigating his capacity to predict market trends and offering a preliminary assessment of his performance.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".