Parameters that Provide Higher Explanation Estimating Betas in the Portuguese Stock Market1
Bibliographic Data
| ID | 15068181 |
|---|---|
| Authors | Angel Barajas (0000-0002-0198-4457, Universidade de Vigo), Sónia Carvalho (0000-0003-1925-5081, Polytechnic Institute of Viana do Castelo) |
| Year | 2013 |
| Volume | 26 |
| Issue | 2 |
| Pages | 117-128 |
| Publication date | 2013-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Economic Research-Ekonomska Istraživanja (JOURNAL) |
| Journal identifiers | ISSN: 1331-677X • E-ISSN: 1848-9664 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/1331677x.2013.11517610 |
| OpenAlex | W3123767330 |
| Language | EN |
| References cited | 2 |
During decades, tests have been developed to verify whether the beta is the best tool to explain the returns of securities on the stock market. Moreover, the value of the beta and its coefficient of determination (R-squared) vary with different parameters used for estimating the beta. In this paper, we investigate for the parameters that provide a higher explanation when we estimate the beta on the Portuguese stock market. We use all nine economic groups listed on the Euronext Lisbon and, for each of those groups, determine which company has the highest market capitalization and highest turnover at the same time, measured in millions of Euros and thousands of Euros, respectively. The linear regression and correlation coefficient between each of the companies can be calculated by studying two national indexes (PSI20 and PSI General) to determine if they get better results with respect to a given period, frequency data or index.We conclude that the explanatory power of R-squared in the Portuguese stock market is very low, independent of the parameters used. When analyzing the year 2008 using linear regression, it remains unclear whether it is preferable to use daily returns or weekly returns, since half of the surveyed companies report the highest yield using either method. The explanatory power of R-squared is higher when using extended time periods and monthly returns, and the results become more accurate when doing the regressions with the PSI20 Index
Econometrics · Economics · Euros · Explanatory power · Financial economics · Geography · Humanities · Linear regression · Market capitalization · Regression analysis · Statistics · Stock market · Stock market index · Computer Science · Corporate Finance and Governance · Financial Markets and Investment Strategies · Financial Reporting and Valuation Research · Mathematics
| Highly cited | No |
|---|