The Effects of Investor Behavior on Market Predictability
Bibliographic record
Abstract
This study aimed to test the effects of investor behavior on the predictability of the market by testing CAPM estimation errors in negative growth period and growth period of the market. This study also aims to draw attention to the existence of some factors that may affect the CAPM estimation errors. Negative growth period is the period in which irrational behavior is likely to occur. Growth period is the period when irrational behaviors are less common. CAPM estimation errors calculated by jensen’s alpha, sharpe ratio, sortino ratio and treynor ratio were compared by T-Test and Mann-Whitney U Test during negative growth periods and growth periods.USA-S&P 500, Germany-DAX 100, England-FTSE 100, France-CAC All Tradable, Canada-S&P TSX, Japan-Nikkei 225 developed countries and their indices and India S&P BSE 200, China-SSE Composite, South Africa &-FTSE JSE African All Share, Turkey-BIST 100 developing countries and their indices included in the study. Between January 31,2005 and December 31,2018 monthly closing prices of the indices, monthly closing prices of stocks listed in consumer staples sectors and consumer discretionary sectors were used.As a result of the study, it has been observed that CAPM estimation errors calculated by jensen’s alpha and treynor ratio in consumer staples and consumer discretionary sectors in developed and developing countries do not differ during negative growth periods and growth periods of the market. It cannot be said that CAPM is more reliable or unreliable in negative growth periods compared to growth periods. It has been determined that CAPM estimation errors calculated by sharpe ratio and sortino ratio differ during negative growth periods and growth periods. It can be said that CAPM is less reliable in negative growth periods compared to growth periods.
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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 | 0.006 | 0.038 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".