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RESEARCH PAPERS | CORPORATE VALUATION


Mutual Fund Valuations

PRIVATE COMPANY VALUATIONS BY MUTUAL FUNDS
Vikas Agarwal, Brad M. Barber, Si Cheng, Allaudeen Hameed, and Ayako Yasuda
2022
Mutual fund families set and report values of their private startup holdings, which affect the fund net asset value (NAV) at which investors buy/sell fund shares. We test three hypotheses related to the valuation practice: (i) information cost/access, (ii) litigation risk, and (iii) strategic NAV management. Consistent with (i), families with larger PE holdings and/or stronger information access update valuations more frequently in the absence of public information releases, their updates co-move less with other families, and their fund returns jump less at follow-on financings. We find no support for hypotheses (ii) or (iii). We also find that high-PE-exposure funds are subject to greater financial fragility. >more


Risk Factors

THE SOCIAL MEDIA RISK PREMIUM
Amin Hosseini, Gergana Jostova, Alexander Philipov, and Robert Savickas
2020
We show that social media risk is priced in the cross-section of stocks and bonds. New social media stock and bond factors, based on firms' presence on Twitter, earn annual premiums of 7.2% and 3.3%, respectively. Their contributions to explaining the cross-section are significant when tested both with classical and recent machine-learning asset pricing methodologies. The social media risk premium is traced to both retail and institutional investors and is higher in low sentiment. Unlike other risk factors, the social media factor origins are clearly identified: prior to the age of social media their premiums did not exist. >more


Equity Risk Premium

EQUITY RISK PREMIUMS (ERP): DETERMINANTS, ESTIMATION, AND IMPLICATIONS – THE 2022 EDITION
Aswath Damodaran
2022
The equity risk premium is the price of risk in equity markets, and it is not just a key input in estimating costs of equity and capital in both corporate finance and valuation, but it is also a key metric in assessing the overall market. Given its importance, it is surprising how haphazard the estimation of equity risk premiums remains in practice. We begin this paper by looking at the economic determinants of equity risk premiums, including investor risk aversion, information uncertainty and perceptions of macroeconomic risk. In the standard approach to estimating the equity risk premium, historical returns are used, with the difference in annual returns on stocks versus bonds, over a long period, comprising the expected risk premium. We note the limitations of this approach, even in markets like the United States, which have long periods of historical data available, and its complete failure in emerging markets, where the historical data tends to be limited and volatile. We look at two other approaches to estimating equity risk premiums – the survey approach, where investors and managers are asked to assess the risk premium and the implied approach, where a forward-looking estimate of the premium is estimated using either current equity prices or risk premiums in non-equity markets. In the next section, we look at the relationship between the equity risk premium and risk premiums in the bond market (default spreads) and in real estate (cap rates) and how that relationship can be mined to generate expected equity risk premiums. We close the paper by examining why different approaches yield different values for the equity risk premium, and how to choose the “right” number to use in analysis. >more


Portfolio Theory

POLITICAL BETA
Raymond J. Fisman, April M. Knill, Sergey Mityakov, and Margarita Portnykh
2021
Using a portfolio theory framework, we introduce the concept of political beta to model firm-level export diversification in response to global political risk. Our model predicts that firms are less responsive to changes in political relations with lower beta countries – those that contribute less to the firm’s total political risk. We document patterns consistent with our model using disaggregated Russian firm-by-destination-country data during 2001-2011: Trade is positively correlated with political relations, though the effect is far weaker for trading partners whose political relations with Russia are relatively uncorrelated with those of other partners in a firm’s export portfolio. >more


Peer Group Analysis

PEER SELECTION AND VALUATION IN MERGERS AND ACQUISITIONS
Gregory W. Eaton, Feng Guo, Tingting Liu, and Micah S. Officer
2021
Using unique data, this paper examines investment banks’ choice of peers in comparable companies analysis in mergers and acquisitions. We find strong evidence that product market space is amongst the most important factors in peer selection, but we provide evidence indicating that Standard Industrial Classification (SIC) codes, particularly three- and four-digit codes, do a poor job of categorizing related firms in this setting. Banks strategically select large, high growth peers with high valuation multiples, factors that are also positively related to premiums. Our evidence is consistent with target-firm advisors selecting peers with high valuation multiples to negotiate higher takeover prices. >more
 


Pricing Bubbles

TESTING FOR ASSET PRICE BUBBLES USING OPTIONS DATA
Nicola Fusari, Robert Jarrow, and Sujan Lamichhane
2021
We present a new approach to identifying asset price bubbles based on options data. We estimate asset bubbles by exploiting the differential pricing between put and call options. We apply our methodology to two stock market indexes, the S&P 500 and the Nasdaq-100, and two technology stocks, Amazon and Facebook, over the 2014-2018 sample period. We find that, while indexes do not exhibit significant bubbles, Amazon and Facebook show frequent and significant bubbles. The estimated bubbles tend to be associated with high trading volume and earning announcement days. Since our approach can be implemented in real time, it is useful to both policy-makers and investors. As an illustration we consider two case studies: the Nasdaq dot-com bubble (between 1999 to 2002) and GameStop (between December 2020 and January 2021). In both cases we identify significant and persistent bubbles. >more


Globalization

IS FINANCIAL GLOBALIZATION IN REVERSE AFTER THE 2008 GLOBAL FINANCIAL CRISIS? EVIDENCE FROM CORPORATE VALUATIONS
Craig Doidge, George Andrew Karolyi, and René M. Stulz
2020
For the last two decades, non-US firms have lower valuations than similar US firms. We study the evolution of this valuation gap to assess whether financial markets are less integrated after the 2008 global financial crisis (GFC). The valuation gap for firms from developed markets increases by 31% after the GFC – a reversal in financial globalization – while the gap for firms from emerging markets (excluding China) stays stable. There is no evidence of greater segmentation for non-US firms cross-listed on major US exchanges and the typical valuation premium of such firms relative to domestic counterparts stays unchanged. However, the number of such firms shrinks sharply, so that the importance of US cross-listings as a mechanism for market integration diminishes. >more


CAPM

SURVEY: MARKET RISK PREMIUM AND RISK-FREE RATE USED FOR 88 COUNTRIES IN 2021
Pablo Fernandez, Sofia Bañuls, and Pablo Fernandez Acin
2021
This paper contains the statistics of a survey about the Risk-Free Rate (RF) and the Market Risk Premium (MRP) used in 2021 for 88 countries. We got answers for 92 countries, but we only report the results for 88 countries with more than 6 answers. Many respondents use for European countries a RF higher than the yield of the 10-year Government bonds. The coefficient of variation (standard deviation / average) of RF is higher than the coefficient of variation of MRP for the Euro countries. The paper also contains the links to previous years surveys, from 2008 to 2020. >more


Required Return to Equity

NORMALIZED RISK-FREE RATE: FICTION OR SCIENCE FICTION?
Pablo Fernandez
2020
As interest rates on Government Bonds have decreased, some analysts and consultants in Europe and in the US are using what they call “Normalized Risk-Free rate”. We show several inconsistencies and errors in the use of “Normalized Risk-Free rate”. Section 5 is a short case that may be used in class. It contains 26 interesting comments. >more


Return Prediction

EMPIRICAL ASSET PRICING VIA MACHINE LEARNING
Shihao Gu, Bryan T. Kelly, and Dacheng Xiu
2019
We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best performing methods (trees and neural networks) and trace their predictive gains to allowance of nonlinear predictor interactions that are missed by other methods. All methods agree on the same set of dominant predictive signals which includes variations on momentum, liquidity, and volatility. Improved risk premium measurement through machine learning simplifies the investigation into economic mechanisms of asset pricing and highlights the value of machine learning in financial innovation. >more


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