New Economics Papers
on Financial Markets
Issue of 2011‒12‒13
seven papers chosen by

  1. Is Default Risk Priced in Equity Returns? By Nielsen, Caren Yinxia Guo
  2. Learning from experience in the stock market By Anton Nakov; Galo Nuno
  3. Can internet search queries help to predict stock market volatility? By Dimpfl, Thomas; Jank, Stephan
  4. Quantiles of the Realized Stock-Bond Correlation By Aslanidis, Nektarios; Christiansen, Charlotte
  5. Risk measures for autocorrelated hedge fund returns By Antonio Di Cesare; Philip A. Stork; Casper G. de Vries
  6. Predicting Financial Markets: Comparing Survey,News, Twitter and Search Engine Data By Huina Mao; Scott Counts; Johan Bollen
  7. Is the Chinese Stock Market Really Efficient By Yan, Isabel K.; Chong, Terence; Lam, Tau-Hing

  1. By: Nielsen, Caren Yinxia Guo (Department of Economics, Lund University)
    Abstract: Fama and French (1992, 1993, 1995 and 1996) declare that size and book-to-market equity (BM) have strong explanatory power for the cross-section of stock returns, and the risk captured by size and BM is the relative distress of small stocks and value stocks. Firstly, this study examines the pricing power of the default risk, measured by the market revealed credit default swap premiums for individual U.S. firms from 2004 to 2010, in average returns across stocks; secondly, it explores whether the size and BM effects are due to that they proxy for the default risk effect. The tests demonstrate that size effect dominates the joint effect of size and default risk, while both BM and default risk co-work for the joint effect of BM and default risk. Therefore, part of the size and BM effects can be interpreted as default risk effect. As expected, size is priced with a negative risk premium, and BM is priced with a positive risk premium. However, higher default risk is priced with higher expected stock returns only when BM is below a certain level and BM is not priced. Additionally, size indeed proxies for the sensitivity to the default risk factor. Furthermore, the Fama-French factors SMB (small-minus-big) and HML (high-minus-low), mimicking the risks related to size and BM, share some common information with the default risk factor in the asset pricing tests.
    Keywords: Asset Pricing; Equity Returns; Size Effect; Book-to-Market Effect; Default Risk Effect; Credit Default Swap Premium
    JEL: G12
    Date: 2011–11–04
  2. By: Anton Nakov (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt, Germany.); Galo Nuno (Banco de España, Alcala 48, 28014 Madrid, Spain.)
    Abstract: We study the dynamics of a Lucas-tree model with finitely lived agents who "learn from experience." Individuals update expectations by Bayesian learning based on observations from their own lifetimes. In this model, the stock price exhibits stochastic boom-and-bust fluctuations around the rational expectations equilibrium. This heterogeneous-agents economy can be approximated by a representative-agent model with constant-gain learning, where the gain parameter is related to the survival rate. JEL Classification: G12, D83, D84.
    Keywords: Learning from experience, OLG, assett pricing, bubbles, heterogeneous agents.
    Date: 2011–11
  3. By: Dimpfl, Thomas; Jank, Stephan
    Abstract: This paper studies the dynamics of stock market volatility and retail investor attention measured by internet search queries. We find a strong co-movement of stock market indices' realized volatility and the search queries for their names. Furthermore, Granger causality is bi-directional: high searches follow high volatility, and high volatility follows high searches. Using the latter feedback effect to predict volatility we find that search queries contain additional information about market volatility. They help to improve volatility forecasts in-sample and out-of-sample as well as for different forecasting horizons. Search queries are particularly useful to predict volatility in high-volatility phases. --
    Keywords: realized volatility,forecasting,investor behavior,noise trader,search engine data
    JEL: G10 G14 G17
    Date: 2011
  4. By: Aslanidis, Nektarios; Christiansen, Charlotte
    Abstract: Abstract: We scrutinize the realized stock-bond correlation based upon high frequency returns. We use quantile regressions to pin down the systematic variation of the extreme tails over their economic determinants. The correlation dependence behaves differently when the correlation is large negative and large positive. The important explanatory variables at the extreme low quantile are the short rate, the yield spread, and the volatility index. At the extreme high quantile the bond market liquidity is also important. The empirical fi…ndings are only partially robust to using less precise measures of the stock-bond correlation. The results are not caused by the recent …financial crisis. Keywords: Extreme returns; Financial crisis; Realized stock-bond correlation; Quantile regressions; VIX. JEL Classifi…cations: C22; G01; G11; G12
    Keywords: Cartera de valors -- Gestió, Actius financers, 336 - Finances. Banca. Moneda. Borsa,
    Date: 2011
  5. By: Antonio Di Cesare (Bank of Italy); Philip A. Stork (VU University Amsterdam); Casper G. de Vries (Erasmus University Rotterdam)
    Abstract: Standard risk metrics tend to underestimate the true risks of hedge funds because of serial correlation in the reported returns. Getmansky, Lo, and Makarov(2004) derive mean, variance, Sharpe ratio, and beta formulae adjusted for serial correlation. Following their lead, we derive adjusted downside and global measures of individual and systemic risks. We distinguish between normally and fat tailed distributed returns and show that adjustment is particularly relevant for downside risk measures in the case of fat tails. A hedge fund case study reveals that the unadjusted risk measures considerably underestimate the true extent of individual and systemic risks.
    Keywords: hedge funds, serial correlation, systemic risk, VaR, Pareto distribution
    JEL: G12 G23 G28
    Date: 2011–11
  6. By: Huina Mao; Scott Counts; Johan Bollen
    Abstract: Financial market prediction on the basis of online sentiment tracking has drawn a lot of attention recently. However, most results in this emerging domain rely on a unique, particular combination of data sets and sentiment tracking tools. This makes it difficult to disambiguate measurement and instrument effects from factors that are actually involved in the apparent relation between online sentiment and market values. In this paper, we survey a range of online data sets (Twitter feeds, news headlines, and volumes of Google search queries) and sentiment tracking methods (Twitter Investor Sentiment, Negative News Sentiment and Tweet & Google Search volumes of financial terms), and compare their value for financial prediction of market indices such as the Dow Jones Industrial Average, trading volumes, and market volatility (VIX), as well as gold prices. We also compare the predictive power of traditional investor sentiment survey data, i.e. Investor Intelligence and Daily Sentiment Index, against those of the mentioned set of online sentiment indicators. Our results show that traditional surveys of Investor Intelligence are lagging indicators of the financial markets. However, weekly Google Insight Search volumes on financial search queries do have predictive value. An indicator of Twitter Investor Sentiment and the frequency of occurrence of financial terms on Twitter in the previous 1-2 days are also found to be very statistically significant predictors of daily market log return. Survey sentiment indicators are however found not to be statistically significant predictors of financial market values, once we control for all other mood indicators as well as the VIX.
    Date: 2011–12
  7. By: Yan, Isabel K.; Chong, Terence; Lam, Tau-Hing
    Abstract: Groenewold et al (2004a) documented that the Chinese stock market is inefficient. In this paper, we revisit the efficiency problem of the Chinese stock market using time-series model based trading rules. Our paper distinguishes itself from previous studies in several aspects. First, while previous studies concentrate on the viability of linear forecasting techniques, we evaluate the profitability of the forecasts of the self-exciting threshold autoregressive model (SETAR), and compare it with the conventional linear AR and MA trading rules. Second, the finding of market inefficiency in earlier studies mainly rest on the statistical significance of the autocorrelation or regression coefficients. In contrast, this paper directly examines the profitability of various trading rules. Third, our sample covers an extensive period of 1991-2010. Sub-sample analysis shows that positive returns mainly concentrate in the pre-SOE reform period, suggesting that China’s stock market has become more efficient after the reform.
    Keywords: Efficient Market Hypothesis; SETAR Model; Bootstrapping; SOE reform
    JEL: G12 C22 G10
    Date: 2011–08

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