Bibliographic record
Abstract
Factors affecting stock prices have been studied by many scholars on different stock markets. However, the number of empirical studies applying technical analysis indicators to measure investor sentiment is quite limited. To explore this interesting topic, this study uses the Money Flow Index (MFI) indicator to measure an investor's sentiment by various thresholds and to test its effect on the excess return on Vietnam stock market. Data series including market, interest rate, finance and transaction data of 138 companies listed on the Ho Chi Minh City Stock Exchange from 2015 to June 2020 are used in the equations Regression. The study's findings show that, after controlling for market factors, individual characteristics and liquidity of each company, investor sentiment as measured by the MFI indicator still has a significant impact on the return of stocks at all thresholds. In addition, when the MFI value area is near the starting and ending point of the scale (less than 20, greater than 80), the regression coefficients of these two thresholds and control variables both increase compared to the remaining models, return and significant effect to the excess return of the securities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".