Influence of Earnings per Share on Idiosyncratic Volatility of Stock Returns among Listed Firms in Kenya
Bibliographic record
Abstract
Idiosyncratic volatility has not always been considered in pricing of financial assets; this is as a result of capital asset pricing model’s proposition that idiosyncratic volatility is diversifies away and that investors hold portions of diversified portfolio. In reality however, this is not always the case. Research studies have revealed that investors do not always hold diversified portfolios and idiosyncratic risk is therefore priced in order to compensate their inability to hold the market portfolio, therefore the main objective of the study was to establish the effect of financial statement information on idiosyncratic volatility of stocks return among listed firms in Kenya. Idiosyncratic volatility was the dependent variable while independent variable was Earning Per Share (EPS). The study used correlational and descriptive research design, it also used census technique which targeted all 39 listed companies that existed and their shares were actively traded at the Nairobi Securities Exchange (NSE) from the year 1998 to 2017. STATA was used to generate Descriptive and inferential statistics. The study employed a dynamic panel data regression model, the analysis of variance (ANOVA) was used to reveal the overall model significance, the calculated F-statistic was compared with the tabulated F-statistic and a critical p-value of 0.05 was used to determine whether the overall model is significant. The study results found that there was a positive and significant relationship between EPS and Idiosyncratic Volatility of stock returns among listed firms in Kenya (r= 0.001, p=0.027), and therefore the null hypotheses were rejected. Based on the findings, the study concluded that, EPS has a significant relationship with Idiosyncratic Volatility of stock returns among listed firms in Kenya. The study recommended that market investors analyst should review their investment strategies taking into consideration EPS in pricing firm specific risk. These indicators will further guide in expanding the interpretation of the financial dynamics in the listed firms at the NSE and other related firms. Keywords : Earnings per Share, Idiosyncratic Volatility, Stock Returns, NSE & Kenya. DOI : 10.7176/RJFA/10-22-03 Publication date: November 30 th 2019
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".