Investor Sentiment and Firm Financial Performance of Malaysian IPO Firms: Pre and Post Financial Crisis
Bibliographic record
Abstract
The main purpose of this study is to investigate an important issue in behavioural finance area that is the role of investor sentiment in determining firm performance, alongside with market timing and other fundamental firm factors in the context of Malaysian market. The impact of pre and post financial crisis during study period of 2004 to 2015 is incorporated in the analysis. The study uses a balanced panel data of 143 IPO firms in Malaysia during the study period. The sentiment index is developed using panel data cross section based on three IPOs proxies, which are IPO volume, market turnover and dividend premium. The findings indicate that market timing is found to have a strong influence towards firm performance with a positive and high level of significance relationship during pre and post financial crisis. Whereas investor sentiment does influence firm performance, particularly when timing is proxied by initial return before the financial crisis period. Other firm specific factors, growth show very strong positive relationship with firm performance. The remaining factors, namely tangibility of asset, profitability, size and industry show mix results either consistent or inconsistent with past literature irrespective of pre or post financial crisis. The finding offers a useful reference for firm managers’ to consistently time the market for the new and subsequent issue. While the investors have to pay attention on important information available in the market than become overly optimistic about future prospect of any investment as firm will continue to exploit timing strategy in their financing decision.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".