In unfrictionless markets, one measure of asset pricing is its height of friction. This study develops a three-factor model by loosening the assumptions about stocks without friction, without risk, and perfectly liquid. Friction is used as an indicator of transaction costs to be included in the model as a variable that will reduce individual profits. This approach is used to estimate return, beta and other variable for firms listed on the Indonesian Stock Exchange (IDX). To test the efficacy of friction-adjusted three-factor model, we use intraday data from July 2016 to October 2018. The sample includes all listed firms; intraday data chosen purposively from regular market are sorted by capitalization, which represents each tick size from the biggest to smallest. We run 3,065,835 intraday data of asking price, bid price, and trading price to get proportional quoted half-spread and proportional effective half-spread. We find evidence of adjusted friction on the three-factor model. High/low trading friction will cause a significant/insignificant return difference before and after adjustment. The difference in average beta that reflects market risk is able to explain the existence of trading friction, while the difference between SMB and HML in all observation periods cannot explain returns and the existence of trading friction.

The objective of this study is to examine whether the four-factor model explains variation in the expected return of stocks on the Stock Exchange of Thailand. The study used individual monthly data for all stock with continuous trading on the Stock Exchange of Thailand. The study used sample data of 429 listed stocks to construct 8 portfolios bases on the industries. In this study, subject to market factors such as size, the book-to-market ratio, the market beta, and stock liquidity are taken into account. The Empirical analysis reveals that not all of the variables included in the four-factor asset pricing model are statistically significant to do affect the formation of the rate of return on stocks calculated on a monthly basis. The result shows that market beta, stock liquidity, and the book-to-market ratio has a significant increase in the rate of return on shares listed on the Consumer Products. It is therefore apparent that at least in respect of monthly analysis, the predictions of bass models in the field of modern finance theory systematic risk measured by the beta coefficient did play a significantly important role in the formation of the rate of return on the Stock Exchange of Thailand.

This research examined the alternatives of Jensen’s alpha (α) estimation models in the Capital Asset Pricing Model, discussed by Treynor (1961), Sharpe (1964), and Lintner (1965), using the robust maximum likelihood type m-estimator (MM estimator) and Bayes estimator with conjugate prior. According to finance literature and practices, alpha has often been estimated using ordinary least square (OLS) regression method and monthly return data set. A sample of 50 securities is randomly selected from the list of the S&P 500 index. Their daily and monthly returns were collected over a period of the last five years. This research showed that the robust MM estimator performed well better than the OLS and Bayes estimators in terms of efficiency. The Bayes estimator did not perform better than the OLS estimator as expected. Interestingly, we also found that daily return data set would give more accurate alpha estimation than monthly return data set in all three MM, OLS, and Bayes estimators. We also proposed an alternative market efficiency test with the hypothesis testing Ho: α = 0 and was able to prove the S&P 500 index is efficient, but not perfect. More important, those findings above are checked with and validated by Jackknife resampling results.