Yes,The APT Is Testable

The Arbitrage Pricing Theory (APT) has been proposed as an alternative to the mean-variance Capital Asset Pricing Model (CAPM). This paper considers the testability of the APT and points out the irrelevance for testing of the approximation error. We refute Shanken's objections, including his assertion that Roll's critique of the CAPM is applicable to the APT. We also explain the testability of the APT on subsets, and we explore the relationship between the APT and the CAPM.     

Alternative Models for Estimating the Cost of Equity Capital for Property/Casualty Insurers

This paper estimates the cost of equity capital for Property/Casualty insurers by applying three alternative asset pricing models: the Capital Asset Pricing Model (CAPM), the Arbitrage Pricing Theory (APT), and a unified CAPM/APT model (Wei (1988). The in-sample forecast ability of the models is evaluated by applying the mean squared error method, the Theil U2 (1966) statistic, and the Granger and Newbold (1978) conditional efficiency evaluation. Based on forecast evaluation procedures, the APT and Wei's unified CAPM/APT models perform better than the CAPM in estimating the cost of equity capital for the PC insurers and a combined forecast may outperform the individual forecasts.     

Some Empirical Tests of the Theory of Arbitrage Pricing

We estimate the parameters of Ross's Arbitrage Pricing Theory (APT). Using daily return data during the 1963–78 period, we compare the evidence on the APT and the Capital Asset Pricing Model (CAPM) as implemented by market indices and find that the APT performs well. The theory is further supported in that estimated expected returns depend on estimated factor loadings, and variables such as own variance and firm size do not contribute additional explanatory power to that of the factor loadings.     

An Empirical Test of the Arbitrage Pricing Theory on Australian Stock Returns 1974-85

This paper examines empirically, issues concerning the Arbitrage Pricing Theory (APT). Firstly, in the spirit of Chamberlain and Rothschild [1983], the existence of an approximate factor structure is explored. Secondly, following Beggs [1986] and employing a principal components approach, a test of arbitrage pricing and the importance of the error of approximation, is conducted. Finally, using a non nested framework, the APT and CAPM are tested against each other. The results show mixed support for the APT having up to 3 priced factors.     

Possible explanations of no-synergy mergers and small firm effect by the Generalized Capital Asset Pricing Model

The Sharpe-Lintner Capital Asset Pricing Model (CAPM) and the General Capital Asset Pricing Model (GCAPM) suggested by Levy (1978), Merton (1987), and Markowitz (1989) are compared and analyzed. Under the GCAPM we obtain the following main results: 1) the value additivity principle breaks down, which explains mergers and acquisitions; 2) beyond a certain limit, the profit from additional merger is negative; and 3) in a GCAPM equilibrium, small firms earn an abnormal profit in comparison to what is predicted by the CAPM. These results, which are indeed observed in the market, are fully consistent with the GCAPM, but are in contradiction to the CAPM.     

The Arbitrage Pricing Theory: Is it Testable?

This paper challenges the view that the Arbitrage Pricing Theory (APT) is inherently more susceptible to empirical verification than the Capital Asset Pricing Model (CAPM). The usual formulation of the testable implications of the APT is shown to be inadequate, as it precludes the very expected return differentials which the theory attempts to explain. A recent competitive-equilibrium extension of the APT may be testable in principle. In order to implement such a test, however, observation of the return on the true market portfolio appears to be necessary.     

Multifactor Asset Pricing Models

Since the early 1960s, the mean-variance Capital Asset Pricing Model (CAPM) has been a dominant paradigm in modern finance. Recently, the accumulation of anomalous evidence, and a realisation that empirical tests of the model are tautologically related to the efficiency of the market index, have pushed that paradigm to a point of crisis. This paper reviews alternative asset pricing models which coexisted with the CAPM and may provide plausible substitutes. The major distinguishing feature of these models is that they predict multiple risk factors and, with the exception of the Arbitrage Pricing Theory (APT), are extensions of the CAPM.     

Arbitrage Pricing Theory and Utility Stock Returns

This paper presents some new evidence that Arbitrage Pricing Theory may lead to different and better estimates of expected return than the Capital Asset Pricing Model, particularly in the case of utility stock returns. Results for monthly portfolio returns for 1971–1979 lead to the conclusion that regulators should not adopt the single-factor risk approach of the CAPM as the principal measure of risk, but give greater weight to APT, whose multiple factors provide a better indication of asset risk and a better estimate of expected return.     

THE ARBITRAGE PRICING THEORY AND COST-OF-CAPITAL ESTIMATION: THE CASE OF ELECTRIC UTILITIES

Capital asset pricing model (CAPM) and alternative arbitrage pricing theory (APT) methodologies are used to estimate the cost of capital for a sample of electric utilities. The statistical factors APT method is found to produce significantly different estimates depending on the number of factors specified and the set of firms factor analyzed. The use of macroeconomic factors is explored, and it is shown that this methodology has advantages over the statistical factors APT and the market model.     

The role of data limitations,seasonality and frequency in asset pricing models

We demonstrate that the estimates of the Capital Asset Pricing Model (CAPM) parameters significantly differ across samples, which are based on different days of the week (representing different seasons). Our evidence suggests that the “noise” in the data is not an issue. We also show that parameter differences for mixed samples, which contain information on different seasons, are too small to distort statistical analysis. This is so because parameter estimates converge to some values (but not necessarily to the true values) for high frequencies or as the sample size becomes large. Our evidence suggests a possibility that the CAPM may hold empirically if seasonality in the parameters of the population model are taken into consideration.