The Conditional CAPM Does Not Explain Asset-Pricing Anomalies

Globalization, Corporate Finance, and the Cost of Capital

The Stock Market's Reaction to Unemployment News: Why Bad News Is Usually Good for Stocks

We investigate the effect of relatively loose monetary policy on bank risk through a large panel including quarterly information from listed banks operating in the European Union and the United States. We find evidence suggesting that relatively low levels of interest rates over an extended period of time contributed to an increase in bank risk. This result holds for a wide range of measures of risk, as well as macroeconomic and institutional controls including the intensity of supervision, securitization activity, and bank competition. The results suggest that monetary policy is not neutral from a financial stability perspective.

/pdf/does-monetary-policy-affect-bank-risk-2afxudgtz2.pdf

Does Monetary Policy Affect Bank Risk

Factors determining European bank risk

This paper provides evidence on the risk factors that are priced in bank equities. Alternative empirical models with precedent in the nonfinancial asset pricing literature are tested, including the single-factor CAPM, three-factor Fama–French model, and ICAPM. Our empirical results indicate that an unconditional two-factor ICAPM model that includes the stock market excess return and shocks to the slope of the yield curve is useful in explaining the cross-section of bank stock returns. However, we find no evidence that firm specific factors such as size and book-to-market ratios are priced in bank stock returns. These results have a number of important implications for the estimation of the banks’ cost of capital as well as regulatory initiatives to utilize market discipline to evaluate bank risk under Basel II.

/pdf/common-risk-factors-in-bank-stocks-2zqtmlxj54.pdf

Common risk factors in bank stocks.

Why do merger premiums vary across industries and over time

We examine the empirical implications of learning under ambiguity for the cross-section of stock returns. We introduce a theoretically-motivated ambiguity measure and find that ambiguity is priced in the cross-section of average stock returns. Ambiguity is not subsumed by state variables known to predict stock returns, nor by value, size, and momentum factors. In R-squared comparative tests, a model that takes ambiguity into account performs better than empirical implementations of the Bayesian learning model, the intertemporal CAPM, and the four-factor model of Fama and French (1993) and Carhart (1997). (JEL G12) Conditional asset pricing models based on rational expectations perform poorly empirically (Lewellen and Nagel 2006; Kan, Robotti, and Shanken 2013). While the rational expectations hypothesis assumes investors know the probability law governing asset retu rns, authors such as Keynes (1921), Knight (1921), Shackle (1949), and Roy (1952) have emphasized that investors form expectations based on vague information that cannot be quantified precisely. Related evidence from experimental studies (Ellsberg 1961) has confirmed that individuals are averse not only to uncertainty regarding the outcome of events with known probabilities (risk), but also uncertainty regarding the outcome of events with unknown probabilities (Knightian We are especially grateful to the editor, Wayne Ferson, and an anonymous referee fortheir extensive comments that substantially improved the article. We also appreciate the valuable comments of Damian Damianov, Andrei Nikoforov, Jaime Casassus, and participants at the 2011 regional meetings of the Econometric Society in Santiago, Chile, the 2010 FMA meetings in New York City, and seminar at UTPA, Texas. Send correspondence

/pdf/safety-first-learning-under-ambiguity-and-the-cross-section-3mlmvwdj9v.pdf

Safety First, Learning Under Ambiguity, and the Cross-Section of Stock Returns

https://ir.library.oregonstate.edu/xmlui/bitstream/handle/1957/46902/ChiraIngaBusinessBankExposureMarket.pdf?sequence=1

Bank exposure to market fear

Using a two-state Markov regime-switching intertemporal capital asset pricing model, we find that the exposure to systematic risk of bank stocks varies with size and the state of the economy. Across large banks, those with higher net interest margins and lower capital ratios are the most exposed to unexpected shifts in the term structure of interest rates. Small banks with higher net interest rate margins and that rely less on noninterest income are the most exposed to unexpected shifts in the stance of monetary policy. We apply the asset pricing model out of sample to assess its ability to detect troubled banks during a financial crisis.

http://www.netviale.com/wp-content/uploads/2013/08/JFR-044-13-August-20.pdf

Ariel M. Viale

Papers

Common risk factors in bank stocks.

Why do merger premiums vary across industries and over time

Safety First, Learning Under Ambiguity, and the Cross-Section of Stock Returns

Bank exposure to market fear

Learning banks' exposure to systematic risk: evidence from the financial crisis of 2008