Review of Financial Economics

Publisher:: Wiley
Publication date:: 2021-02-01

ISBN:: 1058-3300

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Latest documents

Non‐operating earnings and firm risk*
We find that non‐operating earnings reduce total earnings volatility, stock price volatility, idiosyncratic risk, and crash risk. The risk‐reducing effects of non‐operating earnings are higher than those of operating earnings for risk measures based on stock market data. Non‐operating earnings serve to mitigate risks among firms with operating losses, high financial leverage, high growth uncertainty, and low‐ability managers.
The 2020 Pandemic: Economic repercussions and policy responses
This paper analyses the economic and financial repercussions of the 2020 COVID‐19 pandemic. It argues that the pandemic has inflicted serious injuries to the labor force but has not damaged the physical capital stock. Therefore, the resolution policies of this crisis ought to be carefully tailored to supporting structural adjustments to the labor market. The analysis asserts that the impact of the pandemic crisis is exacerbated by the identification gap between the unobserved and the officially reported cases of COVID‐19. The gap increases financial risks, including market‐, credit‐, default‐, and foreign exchange risks.
From phase transitions to Modern Monetary Theory: A framework for analyzing the pandemic of 2020
Analyzing the economic impact of the COVID‐19 pandemic of 2020 requires an appreciation that price signals were no longer primary determinants of supply and demand. Economic agents were acting out of health fears, government‐mandated shutdown rules, and dealing with financial distress. The economy had entered a state that was far from equilibrium. Orthodox tools, such as comparative equilibrium analysis, can tell one about state “A” and state “B,” but provide no guidance as to how to analyze the phase transition. We turn to the physics of phase transitions to help us understand what essentially was a network collapse. The analysis is extended to examine whether the initial policy responses were more likely to cushion the blow or to accelerate the eventual economic recovery, which is extended into an examination of Modern Monetary Theory. Finally, we study the behavioral changes induced by the pandemic that are likely to be long‐lasting and impact the pace of the recovery. And we note a variety of data anomalies that are sure to vex empirical researchers as they study the pandemic of 2020.
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A macroeconomic hedge portfolio and the cross section of stock returns
We use a stock's returns on days when important macroeconomic news is released to form a hedge portfolio, which is long (short) in stocks which have a sensitive (insensitive) reaction to the surprise component of the macroeconomic news. This macroeconomic hedge portfolio (MHP) earns a risk premium of about 5% p.a. over time and a similar premium when used as a risk factor in an asset pricing model. This premium can be interpreted as a cost of an insurance against unexpected changes in an investor's marginal utility. We show that risk premiums associated with the MHP are estimated with a higher precision than traditional macroeconomic tracking portfolios. Furthermore, when the MHP is present in a common factor model, risk factors like high minus low lose much of their ability to explain the cross section of stock returns.
Investor sentiment‐styled index in index futures market
In this paper, we define and analyze the sentiment‐styled index for the CSI 300 index futures in the Chinese futures market. Our sentiment‐styled index for the CSI 300 index futures from April 16, 2010 to April 30, 2019 is constructed by the first and second principal component analyses, rather than only by the first principal component analysis used in the Baker and Wurgler (Journal of Finance 61(4): 1645–1680, 2006) method. The sentiment‐styled index explains 78.38% of the sample variance. The vector error correction model is adapted to study the dynamics of cointegration of the sentiment‐styled index and the logarithmic futures price. We use the GARCH‐DCC model to illustrate the spillover effect between the sentiment‐styled index and the Chinese futures market. We show that this investor sentiment‐styled index does have the price discovery from the Granger causality and common factor weights and the hedging function from the Baba–Engle–Kraft–Kroner model empirically; furthermore, we use the curvature term of the sentiment‐styled index to determine the multiple unit roots. More empirical results for the sentiment‐styled index of the Chinese stock market, the sentiment‐styled index of the CSI 300 index futures, and the return of the CSI 300 index futures market are studied in this paper.
Business strategy, stock price informativeness, and analyst coverage efficiency
We examine how business strategy affects stock price informativeness which in turn influences analyst coverage efficiency. Using stock price synchronicity and the probability of informed trading as proxies for stock price informativeness, we show that stock prices of prospectors are less informative than those of defenders. Next, we explore two channels through which business strategy influences analyst coverage efficiency. We first test and find support for an information transfer channel, i.e., the higher stock price synchronicity of prospectors facilitates more information transfer by analysts, resulting in higher analyst coverage efficiency of prospectors than defenders. Next, we test and find support for an informed trading channel, i.e., the higher probability of informed trading on stocks of defenders intensifies competition between informed traders and analysts. Such competition adversely affects analyst coverage efficiency, leading to lower analyst coverage efficiency of defenders than prospectors. Our findings are robust to an array of robustness checks including 2SLS/IV tests, differences‐in‐difference tests, and high‐tech industry sensitivity analyses.
Empirical analysis of the cross‐interdependence between crude oil and agricultural commodity markets
This paper aims to investigate the cross‐interdependence between crude oil and agricultural commodity prices. We apply a test of persistence in order to verify whether crude oil prices' effect on the agricultural commodity markets is immediate or delayed. Using the daily data covering the period 2003–2017, results show that the delayed effect of crude oil prices on the agricultural commodity prices is lower than the immediate effect. Furthermore, the dependence is strongly persistent and more affected by the food crisis than the oil crisis. Additionally, a contagion effect is detected during the food crisis for almost agricultural commodity markets, while during the oil crisis, it is verified only for the soybean and wheat markets. The study is designed to determine a reliable framework for returns and volatility forecasting in commodity markets based on the oil market changes.
Sentiment and its asymmetric effect on housing returns
We use Google search frequency to construct sentiment indices (positive and negative) for the housing market. We find that future housing prices are negatively related to our measure of negative sentiment but not significantly related to that of positive sentiment. These relationships are robust to controls for macroeconomic variables, stock market return, and Housing Market Index, a survey‐based housing sentiment index. Furthermore, we find that an increase in negative sentiment results in a significant negative response in housing prices, while a decrease evokes little response. Thus, the housing market exhibits asymmetric responses to negative and positive sentiment and to increases versus decreases in negative sentiment. We attribute these asymmetric responses to the “negativity effect.” Finally, we find that home prices are more sensitive to sentiment during recessionary periods.
What drives the short‐term fluctuations of banks' exposure to interest rate risk?
We investigate whether banks actively manage their exposure to interest rate risk in the short run. Using bank‐level data of German banks for the period 2011Q4–2017Q2, we find evidence that banks actively manage their interest rate risk exposure in their banking books. Specifically, they adjust their exposure to the earning opportunities presented by this risk, take account of their regulatory situation, and manage this exposure using interest swaps. We also find that the fixed‐interest period of housing loans has an impact on the banks' overall exposure to interest rate risk. This last finding, in combination with the empirical evidence that customer preferences predominantly determine the fixed‐interest period of these loans, is not in line with active interest rate risk management.