nep-mst New Economics Papers
on Market Microstructure
Issue of 2019‒06‒24
six papers chosen by
Thanos Verousis

  1. Strategic Trading As a Response to Short Sellers By Marco Di Maggio; Francesco A. Franzoni; Massimo Massa; Roberto Tubaldi
  2. Competition among high-frequency traders, and market quality By Breckenfelder, Johannes
  3. Private information and client connections in government bond markets By Kondor, Peter; Pinter, Gabor
  4. Noise trading and informational efficiency By Zhang, Chris H.; Frijns, Bart
  5. On the Nature of Jump Risk Premia By Piotr Orłowski; Paul Schneider; Fabio Trojani
  6. Sentiment-Driven Stochastic Volatility Model: A High-Frequency Textual Tool for Economists By Jozef Barunik; Cathy Yi-Hsuan Chen; Jan Vecer

  1. By: Marco Di Maggio (Harvard Business School; National Bureau of Economic Research (NBER)); Francesco A. Franzoni (USI Lugano; Swiss Finance Institute; Centre for Economic Policy Research (CEPR)); Massimo Massa (INSEAD - Finance); Roberto Tubaldi (USI Lugano; Swiss Finance Institute)
    Abstract: We study empirically informed traders’ reaction to the presence of short sellers in the market. We find that investors with positive views on a stock strategically slow down their trades when short sellers are present in the same stock. Moreover, they purchase larger amounts to take advantage of the price decline induced by short sellers. Furthermore, they break up their buy trades across multiple brokers, suggesting that they wish to hide from the short sellers. This behavior may impact price discovery, as we find a sizeable reduction of positive information impounding for stocks more exposed to short selling during information sensitive periods. The evidence is confirmed exploiting exogenous variation in short interest provided by the Reg SHO Pilot Program. The findings have relevance for the regulatory debate on the market impact of short selling.
    Keywords: Short selling, Informed trading, Strategic traders, Institutional Investors, Market efficiency
    JEL: G30 M41
    Date: 2019–04
  2. By: Breckenfelder, Johannes
    Abstract: We study empirically how competition among high-frequency traders (HFTs) affects their trading behavior and market quality. Our analysis exploits a unique dataset, which allows us to compare environments with and without high-frequency competition, and contains an exogenous event - a tick size reform - which we use to disentangle the effects of the rising share of high-frequency trading in the market from the effects of high-frequency competition. We find that when HFTs compete, their speculative trading increases. As a result, market liquidity deteriorates and short-term volatility rises. Our findings hold for a variety of market quality and high-frequency trading behavior measures. JEL Classification: G12, G14, G15, G18, G23, D4, D61
    Keywords: competition, high-frequency trading, high-frequency trading strategies, tick size reform
    Date: 2019–06
  3. By: Kondor, Peter; Pinter, Gabor
    Abstract: In government bond markets the number of dealers with whom clients trade changes through time. Our paper shows that this time-variation in clients’ connections serves as a proxy for time-variation in private information. Using proprietary data covering close to all dealer-client transactions in the UK government bond market, we show that clients have systematically better performance when trading with more dealers, and this effect is stronger during macroeconomic announcements. Most of the effect comes from clients’ increased ability to predict future yield changes (anticipation component) rather than these clients facing tighter bid-ask spreads (transaction component). To explore the nature of this private information, we find that clients with increased dealer connections can better predict the fraction of the aggregate order flow that is intermediated by dealers they regularly trade with. Positive trading performance is concentrated in those periods when clients have more dealer connections than usual.
    Keywords: Government Bond Market; Private Information; Client-Dealer Connections
    JEL: G12 G14 G24
    Date: 2019–01–02
  4. By: Zhang, Chris H.; Frijns, Bart
    Abstract: We investigate how noise trading affects informational efficiency of financial markets. Using full order book data from the Australian Securities Exchange, we find that noise trading harms informational efficiency. However, this is driven mainly by higher levels of noise trading, indicating that not all noise trading has the same effects. Further, behind the aggregate effects lies rich heterogeneity in how noise trading affects informational efficiency cross-sectionally. Noise trading harms informational efficiency of large liquid stocks but can be beneficial in small illiquid stocks, indicating that markets interpret noise trading differently. Finally, our results suggest that current regulation such as European-wide financial transaction tax (FTT) could have unintended effects on market quality. Instead, carefully designing a tax policy considering both firm-level characteristics and different levels of noise trading is more likely to be an optimal regulatory response.
    Keywords: Market Microstructure,Noise trading,Informational efficiency,Regulation
    JEL: G14 G18
    Date: 2019
  5. By: Piotr Orłowski (HEC Montreal); Paul Schneider (University of Lugano - Institute of Finance; Swiss Finance Institute); Fabio Trojani (Swiss Finance Institute; University of Geneva)
    Abstract: We shed light on the nature of jump risk compensation by studying the profits from a trading strategy that bets on the high-frequency jump skew of S&P 500 returns. Earlier evidence suggests the jump risk premium is large and positive. We find it to be concentrated in periods when the index option market is closed, and investors cannot trade options. Whenever jump skew can be traded continuously, the premium vanishes. We conclude the jump skew premium in index options is not compensation for the risk of occasional, large returns, but for the investors’ inability to adjust their nonlinear risk exposure.
    Keywords: rare events, jump risk premium, options, high-frequency data
    JEL: G10 G12 C58
    Date: 2019–06
  6. By: Jozef Barunik; Cathy Yi-Hsuan Chen; Jan Vecer
    Abstract: We propose how to quantify high-frequency market sentiment using high-frequency news from NASDAQ news platform and support vector machine classifiers. News arrive at markets randomly and the resulting news sentiment behaves like a stochastic process. To characterize the joint evolution of sentiment, price, and volatility, we introduce a unified continuous-time sentiment-driven stochastic volatility model. We provide closed-form formulas for moments of the volatility and news sentiment processes and study the news impact. Further, we implement a simulation-based method to calibrate the parameters. Empirically, we document that news sentiment raises the threshold of volatility reversion, sustaining high market volatility.
    Date: 2019–05

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