nep-fmk New Economics Papers
on Financial Markets
Issue of 2014‒08‒28
three papers chosen by

  1. Maximum Entropy Production Principle for Stock Returns By Paweł Fiedor
  2. Granger Causality Stock Market Networks: Temporal Proximity and Preferential Attachment By Tom\'a\v{s} V\'yrost; \v{S}tefan Ly\'ocsa; Eduard Baum\"ohl
  3. QE Auctions of Treasury Bonds By Song, Zhaogang; Zhu, Haoxiang

  1. By: Paweł Fiedor
    Abstract: In our previous studies we have investigated the structural complexity of time series describing stock returns on New York's and Warsaw's stock exchanges, by employing two estimators of Shannon's entropy rate based on Lempel-Ziv and Context Tree Weighting algorithms, which were originally used for data compression. Such structural complexity of the time series describing logarithmic stock returns can be used as a measure of the inherent (model-free) predictability of the underlying price formation processes, testing the Efficient-Market Hypothesis in practice. We have also correlated the estimated predictability with the profitability of standard trading algorithms, and found that these do not use the structure inherent in the stock returns to any significant degree. To find a way to use the structural complexity of the stock returns for the purpose of predictions we propose the Maximum Entropy Production Principle as applied to stock returns, and test it on the two mentioned markets, inquiring into whether it is possible to enhance prediction of stock returns based on the structural complexity of these and the mentioned principle.
    Date: 2014–08
  2. By: Tom\'a\v{s} V\'yrost; \v{S}tefan Ly\'ocsa; Eduard Baum\"ohl
    Abstract: The structure of return spillovers is examined by constructing Granger causality networks using daily closing prices of 20 developed markets from 2nd January 2006 to 31st December 2013. The data is properly aligned to take into account non-synchronous trading effects. The study of the resulting networks of over 94 sub-samples revealed three significant findings. First, after the recent financial crisis the impact of the US stock market has declined. Second, spatial probit models confirmed the role of the temporal proximity between market closing times for return spillovers, i.e. the time distance between national stock markets matters. Third, preferential attachment between stock markets exists, i.e. spillover from market j to market i is more likely if A) market j influences other markets other than i, or when B) market i is influenced by other markets other than j.
    Date: 2014–08
  3. By: Song, Zhaogang (Board of Governors of the Federal Reserve System (U.S.)); Zhu, Haoxiang (MIT Sloan School of Management)
    Abstract: The Federal Reserve (Fed) uses a unique auction mechanism to purchase U.S. Treasury securities in implementing its quantitative easing (QE) policy. In this paper, we study the outcomes of QE auctions and participating dealers' bidding behaviors from November 2010 to September 2011, during which the Fed purchased $780 billion Treasury securities. Our data include the transaction prices and quantities of each traded bond in each auction, as well as dealers' identities. We find that: (1) In QE auctions the Fed tends to exclude bonds that are liquid and on special, but among included bonds, purchase volumes gravitate toward more liquid bonds; (2) The auction costs are low on average: the Fed pays around 0.7 cents per $100 par value above the secondary market ask price on auction dates; (3) The heterogeneity of Fed's costs across bonds relates to their liquidity and specialness, suggesting that dealers respond to both valuation and information uncertainties; (4) Dealers exhibit strong heterogeneity in their participation, trading volumes, and profits in QE auctions; (5) Auction bidding variables forecast bond returns only one day after the auction, suggesting that dealers have price-relevant information but the information decays quickly.
    Keywords: Auction; quantitative easing; Federal Reserve; treasury bond; specialness
    JEL: G12 G13
    Date: 2014–06–16

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