| Summary: | This research argued for estimating the Capital Asset Pricing Model (CAPM) using daily and medium-horizon data over monthly and short horizon-data. Using a Gibbs sample, the Bayesian framework via both parametric and non-parametric Bayes estimators, confidence interval approach, and six data sets (two daily, two weekly, and two monthly data) from a sample of 150 randomly selected S&P 500 stocks from 2007 – 2019, the empirical results showed that the CAPM using daily data yielded a statistically significant higher model fit and smaller Beta standard deviation, model error, and Alpha compared with monthly data. The CAPM using medium-horizon data yielded a statistically significant higher model fit, smaller Beta standard deviation and Alpha, and much less zeroed Betas compared with short-horizon data. These findings show 1) daily data is more reliable and efficient, has higher forecasting power, and fits better with the assumption of market efficiency compared with monthly data. 2) Medium-horizon data is more reliable and efficient, has more explanatory power, and fits better with the assumption of market efficiency compared with monthly data. Therefore, these findings challenge the common practices of using monthly (quarterly/annually) and short-horizon data among the practitioners and researchers in asset pricing work.
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