Momentum trading strategy and investment horizon: an experimental study
Bibliographic record
Abstract
Purpose Existing empirical studies that document momentum trading strategies do not provide any insight on how investors choose the time horizon that is used to compute the past stock returns. Indeed, since past returns over overlapping time periods are positively correlated, it is hard to identify the exact historical time period on which investors base their trading strategies and to investigate whether such a period is unique. The purpose of this paper is to investigate this and reach some conclusions. Design/methodology/approach In this paper the author uses experimental setting to analyze how investors choose which of the past returns to use as a basis for their trading strategies and whether this choice depends on their investment horizon. The advantage of this experimental setting over the existing empirical research is the ability to control for the investment horizon of the subjects and the ability to provide the subjects with a hand‐picked set of stocks with uncorrelated past returns over overlapping time periods. In the study subjects were asked to make short‐term investment decisions based on historical short‐term realized returns over two time intervals of different lengths. In each treatment the subjects were divided into two groups based on the lengths of their investment horizons, which were set to match the lengths of time intervals used to compute the historical returns. Findings It was found that subjects followed momentum trading strategies based on both historical returns provided to them and paid more attention to the historical returns over the shorter time period. In addition, some evidence was found that subjects with longer investment horizons rely less on momentum strategies. Originality/value A wide sample was used to create an original set of observations and conclusions.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".