Streamlining Investors’ Perceptions and the Behaviour of Capital Market Returns Around the World
Bibliographic record
Abstract
This study adopted various econometric tools (Descriptive Statistics, Unit tests, Autocorrelation test, Pairwise Granger Causality test, Ordinary Least Square test, Normality/Random Walk test, Variance Ratio test and ARCH-GARCH models) to streamline the diverse investors' perceptions and behaviours of the capital market returns around the world.The study employed daily historical data from May 18, 2015 to June 6th, 2022, from prominent capital markets each from all the continents of the world: Nigeria, South Africa, USA, Germany, United Arab Emirate and China.Results of the analysis revealed that none of the market follows the random walk theory, hence investors cannot use the past data about the markets to predict their outcome.ARCH-GARCH models results showed that all the countries exhibited property of stock returns distribution known as volatility clustering or volatility pooling.The persistence parameter found that shocks to the conditional variance are persistent for all the capital markets under study.Asymmetric parameter results that all the countries except Nigeria corroborate the leverage effect theory; bad news create more volatility than good news of the same magnitude.Since all the markets under study do not follow random walk, demystifying the efficient market hypothesis, meaning that the behaviours of investors, heavily influenced by share prices deviated from the economic fundamentals or assumptions.This means that psychology of investors influence investment decision-making process and financial markets.Therefore, the researcher advises among others to place more emphasis on the theory of behavioural finance as a guide for decision concerning stock market investments.
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 | 0.001 | 0.006 |
| 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".