Validity Of Asset Pricing Model Research Articles

Diagnostics for asset pricing models

AbstractThe validity of asset pricing models implies white‐noise pricing errors (PEs). However, we find that the PEs of six well‐known factor models all exhibit a significant reversal pattern and are predictable by their lagged values up to 12 months. Moreover, the predictability of the PEs can produce substantial economic profits. Similar conclusions hold for recently developed machine learning models too. Additional analysis reveals that the significant PE profits cannot be explained by common behavioral biases. Our results imply that much remains to be done and there is a great need to develop new asset pricing models.

Interval-Based Hypothesis Testing and Its Applications to Economics and Finance

This paper presents a brief review of interval-based hypothesis testing, widely used in bio-statistics, medical science, and psychology, namely, tests for minimum-effect, equivalence, and non-inferiority. We present the methods in the contexts of a one-sample t-test and a test for linear restrictions in a regression. We present applications in testing for market efficiency, validity of asset-pricing models, and persistence of economic time series. We argue that, from the point of view of economics and finance, interval-based hypothesis testing provides more sensible inferential outcomes than those based on point-null hypothesis. We propose that interval-based tests be routinely employed in empirical research in business, as an alternative to point null hypothesis testing, especially in the new era of big data.

Reappraising Empirical Validity of Asset-Pricing Models with Consideration of Statistical Power

This paper re-evaluates the empirical validity of popular asset-pricing models, explicitly considering the statistical power of the employed test. We argue that past studies conduct the test with the power of 1 or extremely close to 1, leading to an acute imbalance between Type I and II error probabilities. As a result, at a conventional level such as 0.05, the test rejects the empirical validity of the asset-pricing models even when the null hypothesis is violated by an economically negligible margin. We re-evaluate the validity of the models by adjusting the level of significance; and by conducting the equal-probability test with moderate sample sizes. Based on these alternatives, we find evidence that the popular asset-pricing models have been adequately capturing the systematic variation of portfolio returns.

Relationship between downside risk and return: new evidence through a multiscaling approach

In the multiscaling approach, a time series is decomposed into different time horizons referred to as timescales. In this article, we investigate the risk–return relationship in a downside framework using timescales. Two measures of downside risk; downside beta and downside co-skewness are investigated. A sample of Australian industry portfolios does not reveal a positive linear relationship between downside beta and portfolio return. At a high timescale where dynamics over a longer horizon (32–64 days) is captured, a positive linear association between downside co-skewness and portfolio return is observed. Overall, our results suggest that when investigating the validity of asset pricing models whether in the downside framework or in the traditional mean-variance framework, it may be prudent to consider other horizons in addition to the usual daily and monthly frequencies.

Relationship between Downside Risk and Return: New Evidence through a Multiscaling Approach

In the multiscaling approach a time series is decomposed into different time horizons referred to as timescales. In this paper we investigate the risk-return relationship in a downside framework using timescales. Two measures of downside risk; downside beta and downside co-skewness are investigated. A sample of Australian industry portfolios does not reveal a positive linear relationship between downside beta and portfolio return. At a high timescale where dynamics over a longer horizon (32-64 days) is captured a positive linear association between downside co-skewness and portfolio return is observed. Overall, our results suggest that when investigating the validity of asset pricing models whether in the downside framework or in the traditional mean-variance framework it may be prudent to consider other horizons in addition to the usual daily and monthly frequencies.

Evaluating mutual fund performance: an application of minimum convex input requirement set approach

This paper has employed Chang's (Eur. J. Oper. Res. 115 (1999) 497–506) minimum convex input requirement set (MCIRS) approach to evaluate the performance of US mutual funds. Unlike the traditional methods, the MCIRS approach does not need to assume a particular functional form for the return generating process. The empirical results show that maximum capital gain and growth funds have done worse than growth and income funds, actively managers underperform a passive investment strategy, low risk funds outperform high risk funds, and no load funds outperform load funds. The paper also finds that funds with low beta and small assets have operated more efficiently. Scope and purpose Most of empirical studies of mutual fund performance use some asset pricing model (e.g., the capital asset pricing model) that has a specific functional form. The derived empirical results are controversial since the validity of asset pricing models is questionable. This paper has employed Chang's (1999) minimum convex input requirement set (MCIRS) approach to evaluate the performance of US mutual funds. The MCIRS approach can assign an efficiency score for each mutual fund. Unlike the traditional asset pricing methods, the MCIRS approach does not need to assume a particular functional form for the return generating process. The empirical results show that maximum capital gain and growth funds have done worse than growth and income funds, actively managers underperform a passive investment strategy (e.g., index funds), low risk funds outperform high risk funds, and no load funds outperform load funds. The paper also finds that funds with low beta (i.e., risk) and small assets have operated more efficiently.