Underpricing Process of IPOs in Tunis Stock Exchange: An Agent-Based Modelling Approach
Bibliographic record
Abstract
The fundamental problematic treated in our study was an attempt to explain an anomaly in the issuance of new stocks in IPOs process. The objective of this research is to analyze the effect of certain variables on the level of undervaluation by presenting certain econometric models issued from Agent-based modelling approach. Certain variables can be predictive of the phenomenon of undervaluation such as: the Stock equity distributed to institutional investors, liquidity in the secondary market measured by the price range and the type of investor who can be insiders or outsiders, in addition to these variables we have introduced some control variables which in turn help explain the level of underpricing and which are the age of the company, its size and dimension, the volume of trade and the volatility. Empirically and based on a sample of 16 companies, we were able to respond to our problematic. In fact, according to the hypotheses tests, the prices of the newly introduced stocks on the stock exchange are mostly undervalued which were aligned with our study. Thereby, the methodology adopted based to Dynamic linear models (DLM) that allows offering a very generic framework to analyse time series data. The results of this research were, in part, consistent with work done in developed countries (especially in USA and Europe). Indeed, the undervaluation is in a positive relationship with certain explanatory variables such as the Institutional ownership (INST), Insiders ownership (INSID), Price range (FOUR), etc. On the other hand, we were able to identify significant negative relationships between the initial undervaluation and the basic variable Outsiders ownership (OUTSID), the size of companies listed on the Tunis Stock exchange (BVMT) and the volume of issued stocks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".