nep-fmk New Economics Papers
on Financial Markets
Issue of 2016‒11‒20
five papers chosen by

  1. Nascent markets: Understanding the success and failure of new stock markets By Albuquerque de Sousa, Jose; Beck, Thorsten; van Bergeijk, Peter; Van Dijk, Mathijs A
  2. Social Media, News Media and the Stock Market By Peiran Jiao; Andre Veiga; Ansgar Walther
  3. Immediate price impact of a stock and its warrant: Power-law or logarithmic model? By Hai-Chuan Xu; Zhi-Qiang Jiang; Wei-Xing Zhou
  4. Informed trading in Hybrid Bond Markets By Valseth, Siri
  5. Extreme dependence between crude oil and stock markets in Asia-Pacific regions: Evidence from quantile regression By Zhu, Huiming; Huang, Hui; Peng, Cheng; Yang, Yan

  1. By: Albuquerque de Sousa, Jose; Beck, Thorsten; van Bergeijk, Peter; Van Dijk, Mathijs A
    Abstract: We study the success and failure of 59 newly established ("nascent") stock markets since 1975 in their first 40 years of activity. Nascent markets differ markedly in their success, as measured by number of listings, market capitalisation, and trading activity. Long-term success is in part determined by early success: a high initial number of listings and trading activity are necessary, though not sufficient, conditions for long-term success. Banking sector development at the time of establishment and development of national savings over the life of the stock market are the other two most reliable predictors of success. We find little evidence that structural factors such as country size or legal and political institutions matter. Rather, our results point to an important role of banks, demand factors, and initial success in fostering long-term stock market development.
    Keywords: stock exchanges; financial development
    JEL: G10 G18 O16
    Date: 2016–11
  2. By: Peiran Jiao; Andre Veiga; Ansgar Walther
    Abstract: Abstract: We contrast the impact of traditional news media and social media coverage on stock market volatility and trading volume. We develop a theoretical model of asset pricing and information processing, which allows for both rational traders and a variety of commonly studied behavioral biases. The model yields several novel and testable predictions about the impact of news and social media on asset prices. We then test the model’s theoretical predictions using a unique dataset which measures coverage of individual stocks in social and news media using a broad spectrum of print and online sources. Stocks with high social media coverage in one month experience high idiosyncratic volatility of returns and trading volume in the following month. Conversely, stocks with high news media coverage experience low volatility and low trading volume in the following month. These effects are statistically and economically significant and robust to controlling for stock and time fixed effects, as well as time-varying stock characteristics. The empirical evidence on news media is consistent with a market in which some traders are overconfident when interpreting new information. The evidence on social media is consistent with Tetlock (2011)’s “stale news” hypothesis (investors treat repeated information on social networks as though it were new) and with a model where investors’ perceptions are subject to random sentiment shocks.
    Keywords: Social media, news media, behavioral finance, volatility, trading volume
    JEL: G02 G12 G14
    Date: 2016–10–12
  3. By: Hai-Chuan Xu (ECUST); Zhi-Qiang Jiang (ECUST); Wei-Xing Zhou (ECUST)
    Abstract: Based on the order flow data of a stock and its warrant, the immediate price impacts of market orders are estimated by two competitive models, the power-law model (PL model) and the logarithmic model (LG model). We find that the PL model is overwhelmingly superior to the LG model, regarding the robustness of the estimated parameters and the accuracy of out-of-sample forecasting. We also find that the price impacts of ask and bid orders are consistent with each other for filled trades, since significant positive correlations are observed between the model parameters of both types of orders. Our findings may provide valuable insights for optimal trade execution.
    Date: 2016–11
  4. By: Valseth, Siri (UiS)
    Abstract: This paper investigates whether establishing an electronic limit order book (LOB) in a current over-the-counter (OTC) bond market will move the price discovery process onto the new venue and if so, whether informed traders supply or demand liquidity. A detailed data set from the hybrid Norwegian government bond market shows that informed dealers prefer market orders in the LOB. The results further show that uninformed dealers tend to provide liquidity to informed dealers. Informed dealers’ preference for speed can reflect that limit orders and OTC trading are exposed to waiting costs which can be substantial in many bond markets. These findings suggest that recently proposed pre-trade transparency requirements will contribute to a more effcient price discovery process in current OTC bond markets and that an incentive scheme for liquidity suppliers could enhance it further.
    Keywords: Bonds; informed dealers; order flow
    JEL: G12 G14 G17
    Date: 2016–11–09
  5. By: Zhu, Huiming; Huang, Hui; Peng, Cheng; Yang, Yan
    Abstract: This paper investigates the extreme dependence between the Asia-Pacific stock markets and the international crude oil market by applying the quantile regression theory and using daily data from January 4th, 2000 to July 4th, 2016. The authors obtain a more detailed result on the degree and structure of the dependence, and furthermore, the results present an asymmetric and heterogeneous dependence. Moreover, the dependence increases dramatically after a structural break point, meaning a crisis. Additionally, the authors observe a more significant dependence at the lower tails than the upper tails. They demonstrate the positive relationship at low quantiles, which is evidence of positive dependence in recessions or bearish markets.
    Keywords: Extreme dependence,Crude oil,Asia-Pacific stock market,Quantile regression,Structural breaks
    JEL: E44 Q43
    Date: 2016

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