nep-fmk New Economics Papers
on Financial Markets
Issue of 2016‒10‒09
ten papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. The Cross-section of Expected Returns on Penny Stocks: Are Low-hanging Fruits Not-so Sweet? By Ananjan Bhattacharyya; Abhijeet Chandra
  2. Volatility Spillovers Across User-Generated Content and Stock Market Performance By van Dieijen, M.; Borah, A.; Tellis, G.J.; Franses, Ph.H.B.F.
  3. Sharpe portfolio using a cross-efficiency evaluation By Juan F. Monge; Mercedes Landete; Jos\'e L. Ruiz
  4. Nominal Exchange Rate Stationarity and Long-Term Bond Returns By Lustig, Hanno; Stathopoulos, Andreas; Verdelhan, Adrien
  5. Volatility Inference and Return Dependencies in Stochastic Volatility Models By Oliver Pfante; Nils Bertschinger
  6. Stock Illiquidity, option prices, and option returns By Kanne, Stefan; Korn, Olaf; Uhrig-Homburg, Marliese
  7. Markets are Smart! Structural Reforms and Country Risk By Christopher Findlay; Silvia Sorescu; Camilo Umana Dajud
  8. What Makes US Government Bonds Safe Assets? By He, Zhiguo; Krishnamurthy, Arvind; Milbradt, Konstantin
  9. Raising capital when the going gets tough: U.S. bank equity issuance from 2001 to 2014 By Sengupta, Rajdeep; Black, Lamont K.; Floros , Ioannis
  10. The unemployment-stock market relationship in South Africa: Evidence from symmetric and asymmetric cointegration models By Tapa, Nosipho; Tom, Zandile; Lekoma, Molebogeng; Phiri, Andrew

  1. By: Ananjan Bhattacharyya; Abhijeet Chandra
    Abstract: In this paper, we study the determinants of expected returns on the listed penny stocks from two perspectives. Traditionally financial economics literature has been devoted to study the macro and micro determinants of expected returns on stocks (Subrahmanyam, 2010). Very few research has been carried out on penny stocks (Liu, Rhee, & Zhang, 2011; Nofsinger & Verma, 2014). Our study is an effort to contribute more empirical evidence on penny stocks in the emerging market context. We see the return dynamics of penny stocks from corporate governance perspective. Issues such as shareholding patters are considered to be of much significance when it comes to understand the price movements. Using cross-sectional data on 167 penny stocks listed in the National Stock Exchange of India, we show that (i) Returns of portfolio of lower market-cap penny stocks are significantly different(higher) than that of higher market-cap penny stocks. (ii)Returns of portfolio lower P/E stocks are significantly different (higher) than that of higher P/E stocks. Similarly, returns of portfolio of lower P/B stocks are significantly different (higher) than that of higher P/B stocks, and returns of portfolio of lower priced penny stocks are significantly different(higher) than that of higher priced penny stocks. (iii) Trading volume differences due to alphabetism are insignificant. (iv)Differences in returns of portfolios based on beta and shareholding patterns are insignificant.
    Date: 2016–10
  2. By: van Dieijen, M.; Borah, A.; Tellis, G.J.; Franses, Ph.H.B.F.
    Abstract: Volatility is an important metric of financial performance, indicating uncertainty or risk. So, predicting and managing volatility is of interest to both company managers and investors. This study investigates whether volatility in user-generated content (UGC) can spill over to volatility in stock returns and vice versa. Sources for user-generated content include tweets, blog posts, and Google searches. The authors test the presence of these spillover effects by a multivariate GARCH model. Further, the authors use multivariate regressions to reveal which type of company-related events increase volatility in user-generated content. Results for two studies in different markets show significant volatility spillovers between the growth rates of user-generated content and stock returns. Further, specific marketing events drive the volatility in user-generated content. In particular, new product launches significantly increase the volatility in the growth rates of user-generated content, which in turn can spill over to volatility in stock returns. Moreover, the spillover effects differ in sign depending on the valence of the user- generated content in Twitter. The authors discuss the managerial implications.
    Keywords: user-generated content, stock market performance, volatility, multivariate GARCH model, spillover effects, natural language processing
    Date: 2016–09–30
  3. By: Juan F. Monge; Mercedes Landete; Jos\'e L. Ruiz
    Abstract: The Sharpe ratio is a way to compare the excess returns (over the risk free asset) of portfolios for each unit of volatility that is generated by a portfolio. In this paper we introduce a robust Sharpe ratio portfolio under the assumption that the risk free asset is unknown. We propose a robust portfolio that maximizes the Sharpe ratio when the risk free asset is unknown, but is within a given interval. To compute the best Sharpe ratio portfolio all the Sharpe ratios for any risk free asset are considered and compared by using the so-called cross-efficiency evaluation. An explicit expression of the Cross-Eficiency Sharpe ratio portfolio is presented when short selling is allowed.
    Date: 2016–10
  4. By: Lustig, Hanno (Stanford University); Stathopoulos, Andreas (University of WA); Verdelhan, Adrien (MIT)
    Abstract: We derive a novel test for nominal exchange rate stationarity that exploits the forward-looking information in long maturity bond prices. When nominal exchange rates are stationary, no arbitrage implies that the return on the foreign long bond expressed in dollars is identical to the return on the U.S. bond. In the data, we do not find significant differences in long-term government bond risk premia in dollars across G10 countries, contrary to the large differences in risk premia at short maturities documented in the FX carry trade literature. Moreover, in most of the cases examined, we cannot reject that realized foreign and domestic long-term bond returns in dollars are the same, as if nominal exchange rates were stationary in levels, contrary to the academic consensus.
    Date: 2016–01
  5. By: Oliver Pfante; Nils Bertschinger
    Abstract: Stochastic volatility models describe stock returns $r_t$ as driven by an unobserved process capturing the random dynamics of volatility $v_t$. The present paper quantifies how much information about volatility $v_t$ and future stock returns can be inferred from past returns in stochastic volatility models in terms of Shannon's mutual information.
    Date: 2016–10
  6. By: Kanne, Stefan; Korn, Olaf; Uhrig-Homburg, Marliese
    Abstract: We provide evidence of a strong effect of the underlying stock's illiquidity on option prices by showing that the average absolute difference between historical and implied volatility increases with stock illiquidity. This pattern translates into significant excess returns of option trading strategies that are not explained by common risk factors. Simulation results show, however, that our results can be explained by the hedging costs of market makers who are net long in options on some underlyings and net short in options on other underlyings. Our empirical findings are robust with respect to the chosen illiquidity measure, the measure of option expensiveness, and the return period.
    Keywords: illiquidity,equity options,option returns,option strategies
    JEL: G12 G13
    Date: 2016
  7. By: Christopher Findlay; Silvia Sorescu; Camilo Umana Dajud
    Abstract: The level of public debt and other macroeconomic fundamentals are the main variables used in economic literature to explain the evolution of sovereign debt risk premiums. We show that the evolution of sovereign credit default swaps (CDS) is explained not only by the evolution of these fundamentals, but also by the structural capacity of countries to grow. Introducing a set of structural capacity variables along debt-to-GDP ratio in estimations explains a much higher share of the variation in the CDS data. Moreover, we show that all optimal models to predict the behavior of risk premiums (defined by the residual sum of squares and common information criteria) include several variables describing the growth potential of countries. Many of the optimal models include only structural capacity variables. The results suggest that markets take into account the future benefits of structural reforms when evaluating the risk of investing in sovereign debt.
    Keywords: Structural Reform;Risk Premiums;Sovereign Debt
    JEL: F34 G12 G15 H63
    Date: 2016–09
  8. By: He, Zhiguo (University of Chicago); Krishnamurthy, Arvind (Stanford University); Milbradt, Konstantin (Northwestern University)
    Abstract: U.S. government bonds are considered to be the world's safe store of value, especially during periods of economic turmoil such as the events of 2008. But what makes U.S. government bonds "safe assets?" We highlight coordination among investors, and build a model in which two countries with heterogeneous sizes issue bonds that may be chosen as safe asset. Our model illustrates the benefit of a large absolute debt size as safe asset investors have "nowhere else to go" in equilibrium, and the large country's bonds are chosen as the safe asset. Moreover, the effect becomes stronger in crisis periods.
    Date: 2016–01
  9. By: Sengupta, Rajdeep (Federal Reserve Bank of Kansas City); Black, Lamont K.; Floros , Ioannis
    Abstract: The authors studied bank equity issuance during 2001–14 by publicly traded U.S. banks through seasoned equity offerings (SEOs), private investment in public equity (PIPEs), and the Troubled Asset Relief Program (TARP). Results show that private investors were an active and important source of bank recapitalization in the United States as issuance through SEOs and PIPEs peaked in the recent crisis.
    Keywords: Bank equity; Financial crisis; Troubled Asset Relief Program (TARP); Equity issuance; Commercial banks
    JEL: G21 G28 G32
    Date: 2016–06–01
  10. By: Tapa, Nosipho; Tom, Zandile; Lekoma, Molebogeng; Phiri, Andrew
    Abstract: In this study, we examine linear and nonlinear cointegration and causal relations between unemployment and stock market returns in South Africa using quarterly data collected between 1994:Q1 and 2016:Q1. Our empirical results reveal significant cointegration effects between the time series in both linear and nonlinear models, even though both frameworks ultimately reject the notion of any causal relations between the variables. Collectively, our study rejects the notion of unemployment being a good predictor for stock market returns and neither do developments in the stock market have any effect on the unemployment rate. Such evidence advocates for weak-form efficiency in the JSE equity prices whereby unemployment data cannot help investors to predict the movement of future share prices and further suggests that policymakers cannot rely on stock market development as an avenue towards lowering the prevailingly high levels of unemployment as set in current macroeconomic policy objectives.
    Keywords: Stock market returns; Unemployment; Cointegration; Causality effects; MTAR model; South Africa.
    JEL: C13 C22 C52 E24 E44
    Date: 2016–09–27

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