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The most popular method for calculating asset prices is the Capital Asset Pricing Model (CAPM). What is the appropriate amount of years to use in the estimation and which variation of the capital asset pricing beta provides the best results? This research looks at the out-of-sample forecasting capabilities of three popular CAPM ex-post constant beta models from 2005 to 2014. A total of 11 portfolios, five from developed and six from developing markets, are used to test the amount of input years that will reduce the mispricing in both types of markets. It is found that the best beta model to use varies between developed and developing markets. Additionally, in developing markets, a shortened span of historical years improves the pricing, contrary to popular studies that use 5 to 10 years of historical data. There are many different CAPM studies implementing various betas, using different data input lengths and run in various countries. This study empirically tests the best practices for those interested in successfully using the CAPM for their basic needs, finding that overall the simple ex-post constant beta is mispriced by 0.2 (developing) to 0.3 percent (developed). It is better to use short three-year estimation windows with the market beta in developing economies and longer nine-year estimation windows with the adjusted beta in developed economies.

The underlying principle of the Capital Asset Pricing Model (CAPM) is that there is a linear relationship between systematic risk, as measured by beta, and expected share returns. The CAPM attempts to describe this relationship by using beta to explain the differences between the expected returns on various shares and share portfolios. The CAPM has been the subject of considerable theoretical investigation and empirical research. The aim of this article is to establish the current knowledge of the usefulness of the CAPM, i.e. whether it provides a reasonable description of reality and whether it is a useful tool for investment decision‐making. The main conclusion drawn from the study is that the CAPM is useful and that it does describe and explain the risk/return relationship. However, other risk factors (i.e. other than beta) may also be useful for explaining share returns. Investors should therefore be cautious when using the model to evaluate investment performance.

For publicly traded firms, calculating the cost of capital is predicated typically on information from the financial markets. Small businesses do not have the necessary market‐based information. As an alternative to traditional proxy approaches, this paper argues for a multi‐criteria model to determine an appropriate equity risk premium, and thereby, a cost of capital.

The study proposes a multi‐criteria model – an analytical hierarchy process (AHP) – to determine the cost of capital for small businesses.

Since the three proxy methods are shown to have numerous shortcomings, the use of the AHP model is clearly a method to determine the equity risk premium and the cost of capital for small businesses.

The model requires small business managers to identify all information sources for the required input data.

The article offers practical help to lenders and small businesses wishing to invest in new capital projects.

This paper aims to describe a robust empirical approach to generating plausible historically based interest rate shocks, which can be applied to any market environment. These interest rate shocks can be readily linked to movements in other key risk factors, and used to measure market risk on institutions with large fixed-income portfolios.

Using yield curve factorization, we parameterize a time series of historical yield curves and measure interest rate shocks as the historical change in each of the model’s factors. We then demonstrate how to add these parameterized shocks to any market environment, while retaining positive rates and plausible credit spreads. Given a set of shocked interest rate curves, joint risk factor movements are calculated based upon historical, reduced form dependencies.

Our approach is based upon yield curve parameterization and requires a parsimonious yet flexible factorization model. In the process of selecting a model, we evaluate three variants of the Nelson–Siegel approach to yield curve approximation and find that, in the current low interest rate environment, a 5-factor parameterization developed by Björk and Christensen (1999) is best suited for accurately translating historical interest rate movements into plausible, current period shocks.

An accurate measure of market risk can help to inform institutions about the amount of capital needed to withstand a series of adverse market events. A plausible set of shocks is required to ensure market value, and cash flow projections are indicative of meaningful market sensitivities.

PurposeIn 1997, the Securities and Exchange Commission (SEC) issued Financial Reporting Release No. 48. FFR No. 48 requires that companies disclose both qualitative and quantitative market risk information for risks of loss arising from adverse changes in interest rates, foreign currency rates, commodity prices, and equity prices. This research focuses on the required disclosure of market risk related to equity prices which is commonly known as Beta in the capital asset pricing model (CAPM).Design/methodology/approachA sample of 323 companies listed in NYSE was selected to investigate the relationship between the market risk and the accounting measures of risk in order to determine the accounting variables that should be disclosed as a substitute of market risk, if there is no data, for the companies to fulfill the SEC requirements.FindingsBy identifying the accounting measures most closely associated with market Beta, the financial manager may be able to influence the Beta value by changing the company's structure as summarized in the successful accounting – determined risk measures. Such finding may also be used to estimate a company's Beta value in situations where historical stock market price data is limited or not available. An example of this later circumstance occurs in the case of initial public offer (IPO). Market price data may also be limited in acquisition cases, where the acquisition target is a subsidiary of a larger company.Originality/valueThe results of this study address these finance and accounting practice situations.

This paper aims to examine the contemporary association between accounting information and a number of measures of systematic (beta) risk that incorporate dynamic market features. The goal is to determine the fundamental accounting drivers of beta and to assess whether their explanatory variable power has changed or declined over time.

Beta estimates are calculated using adjustments for thin‐trading, central tendency, leverage, and time variance. Accounting risk variables are derived from theoretical foundations and prior empirical research, and classified as operating, financial or growth.

Results show a strong association between accounting variables (operating and growth) and systematic risk that is consistent over time, but with some industry and size differences and possible country effects. Accounting variables are able to capture dynamic risk shifts and generally are able to outperform naïve M‐GARCH and industry betas in predicting next year's systematic risk.

Internal management and external decision making enable the development of more efficient ex‐post risk measures, isolating actual risk determinants rather than just determining the level of risk, overcoming the problem that conventional ex‐post measures cannot be used for non‐listed entities, initial public offering firms, or those that do not have sufficient trading history, reduces the noise found in traditional risk estimates that rely on historical security returns, and the development of trading and valuation strategies.

This is the first paper that assesses the association between a range of dynamic risk measures and accounting variables and tests whether this long‐run association has changed over time.

Estimates of systematic risk or beta are an important determinant of the cost of capital. The standard technique used to compile beta estimates is an ordinary least squares regression of stock returns on market returns using four to five years of monthly data. This convention assumes that a longer time series of data will not adequately capture risks associated with existing assets. This paper seeks to address this issue.

Each year from 1980 to 2004, equity betas are estimated for 1,717 Australian firms over periods of four to 45 years, and form equal value portfolios of high, medium and low beta stocks. The paper compares expected returns – derived from the capital asset pricing model (CAPM) and subsequent realised market returns – and actual returns over subsequent annual and four‐year periods.

The paper shows that the ability of beta estimates to predict future stock returns systematically increases with the length of the estimation window and when the Vasicek bias correction is applied. However, estimation error is insignificantly different from that associated with a naïve assumption that beta equals one for all stocks.

The implication is that using all available returns data in beta estimation, along with the Vasicek bias correction, reduces the imprecision of expected returns estimates derived from the CAPM. A limitation of the method is the use of conditional realised returns as a proxy for expected returns, given that it is not possible directly to observe expected returns incorporated into share prices.

The paper contributes to the understanding of corporate finance practitioners and academics, who routinely use beta estimates derived from ordinary least squares regression.

As the Association of Southeast Asian Nations (ASEAN) becomes an emerging market, US investors will want to know how their favorite method of calculating asset pricing fits into this new undeveloped market. Also, as the ASEAN becomes more internationalized, managers within will look for ways in which the capital asset pricing model (CAPM) can be applied for their needs. This research looks at the capabilities of the CAPM using ex-post time varying and compares it with the traditional constant beta model. The data include five US sectors and five ASEAN countries, for 10 total portfolios. Find that using a simple nonparametric method that allows for time variation is not statistically different from the traditional constant beta model for portfolios. This research provides additional support for the constant beta.

This paper aims to examine the pricing effects of risks conditional on market situations.

The model used to test for the conditional pricing effects of risks is a modified version of Pettengill et al.'s cross‐sectional regression model, based on Hong Kong equity data.

The paper postulates a five‐factor asset pricing model, which hypothesizes that five risk factors are relevant in the pricing of equity stocks, namely beta, size, book‐to‐market equity, market leverage, and share price, but conditional on market situations, i.e. whether the market is up or down.

The findings enrich our understanding of capital market behaviour, and should prove helpful to investors and corporate managers in both their domestic and international financial decisions.

This study yields important results on a Chinese market, which lend support to the conditional risk pricing hypotheses originally developed in the US, implying that conditional risk pricing is applicable not only in the US market but also in other markets around the globe.