The effect of differentiated margin on futures market investors' behavior and structure
Bibliographic record
Abstract
Purpose The purpose of this paper is to empirically analyze the role of differentiated margin system in leading investors' investing behavior and then optimize investor structure in futures markets. Design/methodology/approach Using economic experimental research method, this paper designs and conducts a futures market experiment according to experimental research's basic norms, thus acquiring needed and credible empirical data. Findings By analyzing the experimental data, it is found that compared with situations in futures markets that implement uniform margin system, investors' (especially speculators') futures open position and the ratio of their open position and futures turnover are both significantly higher, in futures markets that implement differentiated margin system. On the other hand, differentiated margin system has no effects on hedgers' futures turnover, but significantly reduces speculators' futures turnover. Research limitations/implications The findings suggest that compared with uniform margin system, differentiated margin system is beneficial to effectively restrict both speculators' and hedgers' speculating behavior and lead hedgers' market participation. Practical implications In order to resolve the problem of unreasonable investor structure in China's futures market, i.e. lack of hedgers and over‐speculating, China's futures market's regulators should reform the margin system and adopt differentiated margin system to lead investors' rational behavior and optimize investor structure. Originality/value This paper empirically analyzes and verifies, for the first time, the roles of differentiated margin system in affecting investors' investing behavior. The futures market experiment designed and used in this study is a pioneering and exploratory experiment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".