Predicting Stock Return of UAE Listed Companies Using Financial Ratios
Bibliographic record
Abstract
Over the past few decades, numerous research across the globe has been conducted to examine the impact of firm performance on its stock return. The findings of these studies have been varied. In spite of the long standing research in this area, several attempt towards exploring this relationship has led to limited success owing largely to the existence of volatility across different stock markets. The variance in the volatility in these markets make it extremely difficult to obtain a uniform measure. A volatile stock market makes it difficult for the accounting and financial variables to accurately predict the stock returns (Feris & Erin, 2018). The primary aim of this paper is aimed to investigate whether financial ratios can be used as a predictor of stock returns in the context of United Arab Emirates (UAE). The sample of the study includes thirty companies from the Dubai Financial Market (DFM) and Abu Dhabi stock exchange (ADX). Data is collected for the period of 2017. This research comprises of five independent variables namely, Earning Per Share ratio (EPS), Price Earning ratio (PE), Return on Equity ratio (ROE), Dividend Yield ratio (DY) and Debt Equity ratio (DE) and stock return is taken as the dependent variable. The study examines which among the given ratios can better predict stock returns both in the short run and the long run. The analysis is based on the regression analysis and correlation matrix. The results of correlation test revealed less multicollinearity between the variables and the regression results showed that Dividend Yield and the Return on Equity are statistically significant to predict the stock returns. However, Earning Per Share, Price Earning and Debt Equity could not predict the stock returns and thus can be safely considered as statistically insignificant. The t-stats test and p-value analysis were key indicators for arriving at the conclusion. The study can significantly benefit investors who can examine closely the dividend yield and return on equity while selecting an optimal portfolio.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".