Macroeconomic influences on beta

doi:10.1016/0148-6195(89)90016-7

Journal of Economics and Business

Volume 41, Issue 2, May 1989, Pages 185-193

https://doi.org/10.1016/0148-6195(89)90016-7 Get rights and content

Abstract

In this article we examine the influence of macroeconomic variables on the traditional measure of systematic risk by allowing beta to vary with a set of economic characteristics in the context of the single index market model. The results reveal the sensitivity of beta estimates to a number of macroeconomic variables. Furthermore, we demonstrate that the prediction of future betas using a variable beta model, which incorporates significant economic variables, is more accurate than utilizing historical betas.

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Systematic risk behavior in cyclical industries: The case of shipping
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Using an extended version of the single-index market model that allows beta to change over different economic regimes by including a dummy variable, they find that systematic risk in single stocks experiences statistically significant shifts between expansion and recession periods. Using similar methodology, but allowing beta to interact with macroeconomic conditions, Abell and Krueger (1989) find that industries are affected by different macroeconomic factors. In another strand of literature, and closely related to our study, Andersen et al. (2005) model time-varying systematic risk in a state-space framework assuming that macroeconomic factors directly impact the stochastic process underlying beta.
This study explores macroeconomic and industry-level effects on corporate systematic risk (or beta) for the international shipping industry. We document the extent to which stock market betas fluctuate over time in this asset-intensive and cyclical industry. Moreover, we analyze the fundamental determinants of systematic risk. We find evidence for high levels of systematic risk in shipping stocks, which match the fundamental risk characteristics of the industry (such as high financial and operating leverage). Shipping firms exhibit distinct industry-specific beta dynamics compared to firms from benchmark sectors or the average firm in the S&P 500 index. Changes in both economic conditions and industry-specific risk factors explain large proportions of beta variation in the cross-section of firms and over time.
Do asset backed securities ratings matter on average?
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While the rating agencies have agreed to some of the criticism and have been working with the regulators to provide better quality and more credible rating, our results indicate that the rating changes on the asset backed securities are overshadowed by other key factors that have contributed to the crisis. In fact our results are in line with the literature highlighting the validity of macroeconomic variables in security analysis, see for example Chen et al. (1986), Abell and Krueger (1989), Gangemi et al. (2000). This study is primarily motivated by the market's lack of understanding towards the complexities of asset backed securities and the associated criticism that the rating agencies have faced during the global financial crisis.
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Conditional market beta for REITs: A comparison of modeling techniques
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There has accumulated strong evidence in the literature that market beta (β) is time varying. This paper contributes to the literature by studying how to best model the time varying beta for REITs. We include several commonly used methods and evaluate their performances in terms of in-sample beta estimates and out-of-sample beta forecasts. We apply these methods to U.S. equity REITs. Our results overwhelmingly suggest that the state space model is the best performer. Such a conclusion is supported by different evaluation criteria and robust to different sample splitting. Our findings have direct financial implications. The forecasted betas (preferably through the state space model) can be used in many applications such as estimating the cost of capital for the purpose of capital budgeting involving REITs, identifying equity REIT mispricing, evaluating the performance of managed REIT portfolios, etc.
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Previous research has found that company specific factors like debt ratios, profitability ratios, and firm size can impact systematic risk (Rosenberg et al., 1973; Rosenberg and Marathe, 1975; Rosenberg and Guy, 1976; Thompson, 1978). Abell and Krueger (1989), investigating the impact of macroeconomic factors on systematic risk, find that interest rates, budget deficits, trade deficits, inflation, and oil prices to significantly influence beta. In general, larger firms are more likely to have more extensive resources, capabilities and experience in deploying their resources, realize economies of scale and have higher productivity (Caves and Barton, 1990) or profitability (Bradburd and Ross, 1989).
The renewable energy sector is one of the fastest growing components of the energy industry and along with this increased demand for renewable energy there has been an increase in investing and financing activities. The tradeoff between risk and return in the renewable energy sector is, however, precarious. Renewable energy companies are often among the riskiest types of companies to invest in and for this reason it is necessary to have a good understanding of the risk factors. This paper uses a variable beta model to investigate the determinants of renewable energy company risk. The empirical results show that company sales growth has a negative impact on company risk while oil price increases have a positive impact on company risk. When oil price returns are positive and moderate, increases in sales growth can offset the impact of oil price returns and this leads to lower systematic risk.
The changing sensitivity of realized portfolio betas to U.S. output growth: An analysis based on real-time data
2011, Journal of Economics and Business
Citation Excerpt :
A key challenge is to trace out factors that drive time variation in portfolio betas. Results reported in earlier literature suggest that macroeconomic factors in general and output growth in particular may play an important role in this respect (Abell & Krueger, 1989; Chan & Chen, 1988; Chen, 1991; Chen, Roll, & Ross, 1986; Vassalou, 2003; to name just a few).1 Output growth may be thought of to represent changing economic conditions that drive cash flows and payouts of firms.
Our results shed light on the sensitivity of the betas of portfolios formed on market capitalization (“size”) and book-to-market value (“value”) to output growth in the United States. We estimate a state-space model to analyze the sensitivity of portfolio betas to output growth. We measure output growth using real-time and revised data. Output growth has a significant effect on portfolio betas when size and value are high. Such portfolio betas exhibit countercyclical dynamics. They are more sensitive, in absolute terms, to output growth when the latter is measured using real-time data. Their sensitivity to output growth has grown over time. Portfolio betas with respect to output growth have become smaller over time, in contrast, when size is large but value is low.

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¹: We would like to thank two anonymous referees for their helpful comments on an earlier draft of this paper. Djohan Halima provided able research assistance. The views expressed are those of the authors.

View full text

Macroeconomic influences on beta

Abstract

Journal of Economics and Business

The association between market determined and accounting determined risk measures

The Accounting Review

The arbitrage price theory and macroeconomic factor measures

The Financial Review

Modern Portfolio Theory and Investment Analysis

The study of money and common stock prices

Journal of Finance

Discussion of Hamburger and Kochin

Money and Stock Prices: The Channels of Influence

Investment Analysis and Portfolio Management