Beta Risk in the Cross-Section of Equities

2019 ◽  
Vol 33 (9) ◽  
pp. 4318-4366
Author(s):  
Ali Boloorforoosh ◽  
Peter Christoffersen ◽  
Mathieu Fournier ◽  
Christian Gouriéroux
Keyword(s):  
Expected Returns ◽  
Capital Asset Pricing ◽  
Stochastic Discount Factor ◽  
Security Market ◽  
Expected Return ◽  
Asset Pricing Model ◽  
Oxford University ◽  
Beta Risk ◽  
High Beta ◽  
Published Paper

Abstract We develop a conditional capital asset pricing model in continuous time that allows for stochastic beta exposure. When beta comoves with market variance and the stochastic discount factor (SDF), beta risk is priced, and the expected return on a stock deviates from the security market line. The model predicts that low-beta stocks earn high returns, because their beta positively comoves with market variance and the SDF. The opposite is true for high-beta stocks. Estimating the model on equity and option data, we find that beta risk explains expected returns on low- and high-beta stocks, resolving the “betting against beta” anomaly. Authors have furnished code and an Internet Appendix, which are available on the Oxford University Press Web site next to the link to the final published paper online.

Factor Timing

2020 ◽  
Vol 33 (5) ◽  
pp. 1980-2018 ◽  
Author(s):  
Valentin Haddad ◽  
Serhiy Kozak ◽  
Shrihari Santosh
Keyword(s):  
Stock Returns ◽  
Substantial Improvement ◽  
Expected Returns ◽  
Stochastic Discount Factor ◽  
Portfolio Performance ◽  
Oxford University ◽  
Static Factor ◽  
Cyclical Behavior ◽  
New Challenges ◽  
Published Paper

Abstract The optimal factor timing portfolio is equivalent to the stochastic discount factor. We propose and implement a method to characterize both empirically. Our approach imposes restrictions on the dynamics of expected returns, leading to an economically plausible SDF. Market-neutral equity factors are strongly and robustly predictable. Exploiting this predictability leads to substantial improvement in portfolio performance relative to static factor investing. The variance of the corresponding SDF is larger, is more variable over time, and exhibits different cyclical behavior than estimates ignoring this fact. These results pose new challenges for theories that aim to match the cross-section of stock returns. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

scholarly journals Cross country evaluation of equity return concept: An empirical study about the relationship between actual and expected returns

2021 ◽  
Vol 18 (4) ◽  
pp. 30-41
Author(s):  
Sunny Oswal ◽  
Kushagra Goel
Keyword(s):  
Stock Returns ◽  
Equity Returns ◽  
Expected Returns ◽  
Capital Asset Pricing ◽  
Expected Return ◽  
Asset Pricing Model ◽  
Significant Difference ◽  
Cross Country ◽  
Actual Return ◽  
The Relationship

This paper studies the concept of equity returns and sees whether there is a significant difference between the expected return which is calculated through the capital asset pricing model (CAPM) and the actual return given by the stock. For this study, 10 stocks with maximum market capitalization are taken focusing on 12 countries for our research subdivided into developed and developing countries. The period of study is 10 calendar years from 2010 to 2019. The hypothesis being whether the actual stock returns are significantly different from the expected stock return, for the same paired t-test has been deployed on 120 stocks to check the significance. Further evaluation has been done to check whether the expected return is undervalued or overvalued in reference to the actual return. To check whether there is a significant difference between the actual and expected return across the companies, panel regression was used, and then the same was done to check whether there is a significant difference between countries and also whether there is a significant difference on the basis whether the countries are developed or developing. The authors have existing research confined to particular geographies that discuss VAR models

scholarly journals On the Validity of the Capital Asset Pricing Model (CAPM)

2020 ◽  
Vol 12 (4) ◽  
pp. 1
Author(s):  
M. J. Alhabeeb
Keyword(s):  
Asset Pricing ◽  
Market Risk ◽  
Capital Asset Pricing Model ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Capital Asset ◽  
Technical Tool ◽  
Expected Return ◽  
Asset Pricing Model

This study exposes the meaning and role of the Capital Asset Pricing Model (CAPM) and lays out the key elements that make it work. It shows the model’s theoretical strength and examines its applicability and validity as a technical tool to measure the expected return to the investment in stock, along with assessing the market risk associated with that investment.

scholarly journals Investment decision by using Capital Asset Method Pricing Model (CAPM) (Case studies on five automotive companies listed in stock exchange)

2021 ◽  
Vol 1 (2) ◽  
pp. 165-175
Author(s):  
Ahmad Musodik ◽  
Arrum Sari ◽  
Ida Nur Fitriani
Keyword(s):  
Asset Pricing ◽  
Automotive Industry ◽  
Stock Exchange ◽  
Investment Decision ◽  
Capital Asset Pricing Model ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Capital Asset ◽  
Expected Return ◽  
Asset Pricing Model

Investment is a tool for investors to get more profit than what has been invested. Investors must be able to predict the possibilities that occur when investing. Capital Asset Pricing Model is a tool to predict the development of investment in a particular company used to calculate and determine the Expected Return in minimizing risk investments. The authors conducted research using a sample of 5 companies in the automotive industry, namely PT Astra International Tbk, PT Indokordsa Tbk, PT Indomobil Sukses Internasional Tbk, PT Astra Otoparts Tbk, and PT Gajah Tunggal Tbk. This study uses a descriptive quantitative approach with Microsoft Excel 2016 analysis tools. This study aims to determine Portfolio Analysis with the Capital Asset Pricing Model (CAPM) approach which is used as the basis for making stock investment decisions in automotive industry sector companies listed on the Indonesia Stock Exchange. Use from the results of the analysis of the results by comparing the value of E(Ri) has a directly proportional relationship, meaning that the higher the value of, then the stock return (E(Ri)) will be high as well. Of the 5 companies, there are 2 companies that are in the Undervalued category and 3 companies that are in the overvalued category. This means that investors who will invest in companies engaged in the automotive industry can decide to buy shares of the companies PT Indomobil Sukses Internasional Tbk and PT Gajah Tunggal Tbk, because they are classified as undervalued. Meanwhile, investors who want to invest in shares are not advised to buy company shares that are in the overvalued category, but are advised to sell them to investors who already have shares in the company.

scholarly journals An analysis of the performance of private equity: Agency cost approach

2009 ◽  
Vol 6 (3) ◽  
pp. 424-428
Author(s):  
C.R. Krishnaswamy
Keyword(s):  
Agency Costs ◽  
Private Equity ◽  
Agency Cost ◽  
Capital Asset Pricing ◽  
Capital Asset ◽  
Asset Pricing Model ◽  
Equity Funds ◽  
Private Equity Funds ◽  
High Beta ◽  
Model Approach

In this paper, we explore the effects of agency costs on the performance of private equity. We discuss why private equity firms generally have much lower agency costs. We show using Capital Asset Pricing Model approach that private equity funds would be better off by investing in firms with low beta than high beta firms.

Patent Thickets, Stock Returns, and Conditional CAPM

2022 ◽  
Author(s):  
Po-Hsuan Hsu ◽  
Hsiao-Hui Lee ◽  
Tong Zhou
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Capital Asset Pricing ◽  
Stochastic Discount Factor ◽  
Aggregate Productivity ◽  
Capital Asset ◽  
Conditional Capm ◽  
Asset Pricing Model ◽  
Patent Rights ◽  
Patent Thickets

Patent thickets, a phenomenon of fragmented ownership of overlapping patent rights, hamper firms’ commercialization of patents and thus deliver asset pricing implications. We show that firms with deeper patent thickets are involved in more patent litigations, launch fewer new products, and become less profitable in the future. These firms are also associated with lower subsequent stock returns, which can be explained by a conditional Capital Asset Pricing Model (CAPM) based on a general equilibrium model that features heterogeneous market betas conditional on time-varying aggregate productivity. This explanation is supported by further evidence from factor regressions and stochastic discount factor tests. This paper was accepted by Karl Diether, finance.

Corporate Innovation and Returns

2019 ◽  
Author(s):  
Jan Bena ◽  
Lorenzo Garlappi
Keyword(s):  
Strong Support ◽  
Race Model ◽  
Expected Returns ◽  
Expected Return ◽  
Firm Level ◽  
Public Firms ◽  
Oxford University ◽  
Small Set ◽  
Editorial Decision ◽  
Winner Takes All

Abstract Among U.S. public firms, technological innovation is concentrated on a small set of large players, with innovation “leaders” having considerably lower systematic risk than “laggards.” To understand this fact, we build a winner-takes-all patent race model and show that a firm’s expected return decreases in its innovation output and increases in that of its rivals. Using a comprehensive firm-level panel of information on patenting activity by fields of technology in 1950–2010, we find strong support for the model’s predictions. Our results highlight that strategic interactions among firms competing in innovation are an important determinant of risk and expected returns. (JEL G12, G31) Received August 6, 2018; editorial decision October 19, 2019 by Andrew Ellul. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

scholarly journals Testing Beta-Pricing Models Using Large Cross-Sections

2019 ◽  
Vol 33 (6) ◽  
pp. 2796-2842 ◽  
Author(s):  
Valentina Raponi ◽  
Cesare Robotti ◽  
Paolo Zaffaroni
Keyword(s):  
Cross Sections ◽  
Risk Premia ◽  
Expected Returns ◽  
Firm Characteristics ◽  
Pricing Models ◽  
Cross Sectional ◽  
Misspecified Models ◽  
Oxford University ◽  
Fama French Factors ◽  
Published Paper

Abstract We propose a methodology for estimating and testing beta-pricing models when a large number of assets is available for investment but the number of time-series observations is fixed. We first consider the case of correctly specified models with constant risk premia, and then extend our framework to deal with time-varying risk premia, potentially misspecified models, firm characteristics, and unbalanced panels. We show that our large cross-sectional framework poses a serious challenge to common empirical findings regarding the validity of beta-pricing models. In the context of pricing models with Fama-French factors, firm characteristics are found to explain a much larger proportion of variation in estimated expected returns than betas. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Equity Is Cheap for Large Financial Institutions

2020 ◽  
Vol 33 (9) ◽  
pp. 4231-4271 ◽  
Author(s):  
Priyank Gandhi ◽  
Hanno Lustig ◽  
Alberto Plazzi
Keyword(s):  
Asset Pricing ◽  
Financial Crises ◽  
Capital Regulation ◽  
Pricing Model ◽  
Asset Pricing Model ◽  
Oxford University ◽  
Rare Disasters ◽  
Country Characteristics ◽  
Nonfinancial Firms ◽  
Published Paper

Abstract Across a wide panel of countries, the top-10% of financial stocks on average account for over 20% of a country’s market capitalization but earn on average significantly lower returns than do nonfinancial firms of the same size and risk exposures. In a bailout-augmented, rare disasters asset pricing model, the spread in risk-adjusted returns between large and small institutions depends on country characteristics that determine the likelihood of bailouts. Consistent with this model, we find larger spreads in countries with large and interconnected financial sectors, weaker capital regulation and corporate governance, and fiscally stronger governments. Valuation gaps increase in anticipation of financial crises. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

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