Impact of High-Frequency Trading on the Stock Returns of Large and Small Companies in the Tehran Stock Exchange
Bibliographic record
Abstract
The main objective of this study is to evaluate the effect of high-frequency trading on stock returns of the Exchange market in Tehran Stock Exchange. The research methodology in this study is in terms of the purpose, functional and in terms of the method of data collection, descriptive and in terms of the type, solidarity. Statistical society of this research is all companies in the Tehran Stock Exchange which in the past two years had been active in the stock market. In this study, companies are divided into two categories: large and small companies. Large companies that their assets logarithm is greater than the average total sample and small companies that their assets logarithm is less than the average total sample. To collect information has been used from the financial statements of accepted companies in Tehran Stock Exchange. MATLAB software has been used for data analysis. Used tests in this study are include (DF) Dickey-Fuller test, (ADF) Generalized Dickey-Fuller test, Phillips-Perron test, and time series methods. The results of this study show that the dynamics of stock returns of the Tehran Stock Exchange are non-linear functions and high Frequency trading of the large companies affect the turnover of small companies. As a result, volume of the high-frequency trading and the returns of small and large companies are different from each other.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".