nep-mst New Economics Papers
on Market Microstructure
Issue of 2017‒02‒05
eleven papers chosen by
Thanos Verousis

  1. Dimensional Analysis and Market Microstructure Invariance By Albert S. Kyle; Anna Obizhaeva
  2. Smooth Trading with Overconfidence and Market Power By Albert S. Kyle; Anna Obizhaeva; Yajun Wang
  3. Intraday Trading Invariance in the E-mini S&P 500 Futures Market By Torben G. Andersen; Oleg Bondarenko; Albert S. Kyle; Anna Obizhaeva
  4. Microstructure Invariance in U.S. Stock Market Trades By Albert S. Kyle; Anna Obizhaeva; Tugkan Tuzun
  5. News Articles and Equity Trading By Albert S. Kyle; Anna Obizhaeva; Nitish Ranjan Sinha; Tugkan Tuzun
  6. Invariance of buy-sell switching points By Kyoung-hun Bae; Albert S. Kyle; Eun Jung Lee; Anna Obizhaeva
  7. Market Microstructure Invariance: A Dynamic Equilibrium Model By Albert S. Kyle; Anna Obizhaeva
  8. The Relevance of Broker Networks for Information Diffusion in the Stock Market By Marco Di Maggio; Francesco A. Franzoni; Amir Kermani; Carlo Sommavilla
  9. Quantifying Reflexivity in Financial Markets: Towards a Prediction of Flash Crashes By Vladimir Filimonov; Didier Sornette
  10. Large Bets and Stock Market Crashes By Albert S. Kyle; Anna Obizhaeva
  11. Beliefs Aggregation and Return Predictability By Albert S. Kyle; Anna Obizhaeva; Yajun Wang

  1. By: Albert S. Kyle (Robert H. Smith School of Business, University of Maryland); Anna Obizhaeva (New Economic School)
    Abstract: Market microstructure is the subfield of finance and econophysics1 which studies how prices result from the process of trading securities. Large trades move prices2 and incur trading costs. Here we combine dimensional analysis, leverage neutrality, and a principle of market microstructure invariance to derive scaling laws which express transaction costs functions, bid-ask spreads, bet sizes, number of bets, and other financial variables in terms of trading volume and volatility. For example, market liquidity is proportional to the cube root of the ratio of dollar volume to return variance. We illustrate the scaling by showing that bid-ask spreads in Russian stocks indeed scale with the cube root. In addition to being of interest to risk managers and traders, these scaling laws provide scientific benchmarks for evaluating controversial issues related to high frequency trading, market crashes, and liquidity measurement as well as guidelines for designing policies in the aftermath of financial crisis.
    Date: 2017–01
  2. By: Albert S. Kyle (Robert H. Smith School of Business, University of Maryland); Anna Obizhaeva (New Economic School); Yajun Wang (Robert H. Smith School of Business, University of Maryland)
    Abstract: We describe a symmetric continuous-time model of trading among relatively overconfident, oligopolistic informed traders with exponential utility. Traders agree to disagree about the precisions of their continuous flows of Gaussian private information. The price depends on a trader’s inventory (permanent price impact) and the derivative of a trader’s inventory (temporary price impact). More disagreement makes the market more liquid; without enough disagreement, there is no trade. Target inventories mean-revert at the same rate as private signals. Actual inventories smoothly adjust toward target inventories at an endogenous rate which increases with disagreement. Faster-than-equilibrium trading generates “flash crashes” by increasing temporary price impact. A “Keynesian beauty contest” dampens price fluctuations.
    Keywords: market microstructure, price impact, liquidity, transaction costs, double auctions, information aggregation, rational expectations, agreement-to-disagree, imperfect competition, Keynesian beauty contest, overconfidence, strategic trading, dynamic trading, flash crash
    JEL: D8 D43 D47 G02 G14
    Date: 2016–01
  3. By: Torben G. Andersen (Kellogg School of Management, Northwestern University); Oleg Bondarenko (Department of Finance (MC 168), University of Illinois at Chicago); Albert S. Kyle (Robert H. Smith School of Business, University of Maryland); Anna Obizhaeva (New Economic School)
    Abstract: The intraday trading patterns in the E-mini S&P 500 futures contract between January 2008 and November 2011 are consistent with the following invariance relationship: The return variation per transaction is log-linearly related to trade size, with a slope coefficient of -2. This association applies both across the pronounced intraday diurnal pattern and across days in the time series. The documented factor of proportionality deviates sharply from prior hypotheses relating volatility to transactions count or trading volume. Intraday trading invariance is motivated a priori by the intuition that market microstructure invariance, introduced by Kyle and Obizhaeva (2016c) to explain bets at low frequencies, also applies to transactions over high intraday frequencies.
    Keywords: market microstructure, invariance, bets, high-frequency trading, liquidity, volatility, volume, business time, time series, intraday patterns
    Date: 2016–03
  4. By: Albert S. Kyle (Robert H. Smith School of Business, University of Maryland); Anna Obizhaeva (New Economic School); Tugkan Tuzun (Board of Governors of the Federal Reserve System)
    Abstract: This paper studies invariance relationships in tick-by-tick transaction data in the U.S. stock market. Over the 1993-2001 period, the estimated monthly regression coefficients of the log of trade arrival rate on the log of trading activity have an almost constant value of 0:666, strikingly close to the value of 2=3 predicted by the invariance hypothesis. Over the 2001-14 period, the estimated coefficients rise, and their average value is equal to 0:79, suggesting that the reduction in tick size in 2001 and the subsequent increase in algorithmic trading resulted in a more intense order shredding in more liquid stocks. The distributions of trade sizes, adjusted for differences in trading activity, resemble a log-normal before 2001; there is clearly visible truncation at the round-lot boundary and clustering of trades at even levels. These distributions change dramatically over the 2001-14 period with their means shifting downward. The invariance hypothesis explains about 88 percent of the cross-sectional variation in trade arrival rates and average trade sizes; additional explanatory variables include the invariance-implied measure of effective price volatility.
    Keywords: market microstructure, transactions data, market frictions, trade size, tick size, order shredding, clustering, TAQ data
    JEL: G10 G23
    Date: 2016–04
  5. By: Albert S. Kyle (Robert H. Smith School of Business, University of Maryland); Anna Obizhaeva (New Economic School); Nitish Ranjan Sinha (Board of Governors of the Federal Reserve System); Tugkan Tuzun (Board of Governors of the Federal Reserve System)
    Abstract: Using a database of news articles from Thomson Reuters for 2003-2008, we investigate how the arrival rate of news articles mentioning an individual stock varies with the level of trading activity in that stock. Defining trading activity W as the product of dollar volume and volatility, we estimate that the arrival rate of news articles is proportional to W0.68. Market microstructure invariance predicts that the stock trading process unfolds in "business time" which passes at a rate proportional to W2=3. Since the estimated exponent of 0.68 is close to 2=3, we conclude that information in news articles ows into the market in the same units of business time that microstructure invariance predicts to govern the trading process for stocks. The arrival of news articles is well approximated by a negative binomial process with the over-dispersion parameter equal to 2:11.
    Date: 2017–01
  6. By: Kyoung-hun Bae (Ulsan National Institute of Science and Technology); Albert S. Kyle (Robert H. Smith School of Business, University of Maryland); Eun Jung Lee (Hanyang University); Anna Obizhaeva (New Economic School)
    Abstract: Define the number of buy-sell “switching points” as the number of times that individual traders change the direction of their trading. Based on the hypothesis that switching points take place in business time, market microstructure invariance predicts that the aggregate number of switching points is proportional to the 2=3 power of the product of dollar volume and volatility. Using trading data from the Korea Exchange (KRX) from 2008 to 2010, we estimate the exponent to be 0.675 with standard error of 0.005. Invariance explains about 93% of the variation in the logarithm of the number of switching points each month across stocks. Most of the variation represents changes in the number of accounts trading the stock and not the number of switching points per account.
    Keywords: Finance, market microstructure, invariance, trading volume, volatility, liquidity, price impact, market depth
    JEL: G00 G12 G14
    Date: 2016–10
  7. By: Albert S. Kyle (Robert H. Smith School of Business, University of Maryland); Anna Obizhaeva (New Economic School)
    Abstract: We derive invariance relationships for a dynamic infinite-horizon model of market microstructure with risk-neutral informed trading, noise trading, market making, and endogenous production of information. Equilibrium prices follow a martingale with endogenously derived stochastic volatility. The invariance relationships for bet sizes and transaction costs are obtained under the assumption that the effort required to generate one discrete bet does not vary across securities and time. The invariance relationships for pricing accuracy and market resiliency require the additional assumption that private information has the same signal-to-noise ratio across markets. Since bets are based on the arrival of discrete chunks of information, the structural model describes how the invariance relationships reflect differences in the granularity of information flows across markets. The model links proportionality coefficients in invariance relationships to fundamental parameters.
    Keywords: market microstructure, invariance, liquidity, bid-ask spread, market impact, transaction costs, market efficiency, efficient markets hypothesis, pricing accuracy, resiliency, order size
    Date: 2016–02
  8. By: Marco Di Maggio (Harvard Business School and National Bureau of Economic Research (NBER)); Francesco A. Franzoni (University of Lugano and Swiss Finance Institute); Amir Kermani (University of California and National Bureau of Economic Research (NBER)); Carlo Sommavilla (University of Lugano and Swiss Finance Institute)
    Abstract: This paper shows that the network of relationships between brokers and institutional investors shapes the information diffusion in the stock market. We exploit trade-level data to show that trades channeled through central brokers earn significantly positive abnormal returns. This result is not due to differences in the investors that trade through central brokers or to stocks characteristics, as we control for this heterogeneity; nor is it the result of better trading execution. We find that a key driver of these excess returns is the information that central brokers gather by executing informed trades, which is then leaked to their best clients. We show that after large informed trades, a significantly higher volume of other investors execute similar trades through the same central broker, allowing them to capture higher returns in the first few days after the initial trade. The best clients of the broker executing the informed trade, and the asset managers affiliated with the broker, are among the first to benefit from the information about order flow. This evidence also suggests that an important source of alpha for fund managers is the access to better connections rather than superior skill.
    Keywords: broker networks, institutional investors, asset prices, information
    JEL: G12 G14 G24
  9. By: Vladimir Filimonov (ETH Zürich); Didier Sornette (Swiss Finance Institute and ETH Zürich)
    Abstract: We introduce a new measure of activity of financial markets that provides a direct access to their level of endogeneity. This measure quantifies how much of price changes are due to endogenous feedback processes, as opposed to exogenous news. For this, we calibrate the self-excited conditional Poisson Hawkes model, which combines in a natural and parsimonious way exogenous influences with self-excited dynamics, to the E-mini S&P 500 futures contracts traded in the Chicago Mercantile Exchange from 1998 to 2010. We find that the level of endogeneity has increased significantly from 1998 to 2010, with only 70% in 1998 to less than 30% since 2007 of the price changes resulting from some revealed exogenous information. Analogous to nuclear plant safety concerned with avoiding “criticality†, our measure provides a direct quantification of the distance of the financial market to a critical state defined precisely as the limit of diverging trading activity in absence of any external driving.
    Keywords: complex systems, econophysics, exogenous- versus endogenous, high-frequency trading, criticality, trading activity, volume
    JEL: C32 C53 G01 G14 G17
  10. By: Albert S. Kyle (Robert H. Smith School of Business, University of Maryland); Anna Obizhaeva (New Economic School)
    Abstract: For five stock market crashes, we compare price declines with predictions from market microstructure invariance. During the 1987 crash and the sales by Soci?et?e G?en?erale in 2008, prices fell by magnitudes similar to predictions from invariance. Larger-than-predicted temporary price declines during two flash crashes suggest rapid selling exacerbates transitory price impact. Smaller-than-predicted price declines for the 1929 crash suggest slower selling stabilized prices and less integration made markets more resilient. Quantities sold in the three largest crashes suggest fatter tails or larger variance than the log-normal distribution estimated from portfolio transitions data.
    Keywords: finance, market microstructure, invariance, crashes, liquidity, price impact, market depth, systemic risk
    JEL: G01 G28 N22
    Date: 2016–01
  11. By: Albert S. Kyle (Robert H. Smith School of Business, University of Maryland); Anna Obizhaeva (New Economic School); Yajun Wang (Robert H. Smith School of Business, University of Maryland)
    Abstract: We study return predictability using a dynamic model of speculative trading among relatively overconfident competitive traders who agree to disagree about the precision of their private information. The return process depends on both parameter values used by traders and empirically correct parameter values. Although traders apply Bayes Law consistently, equilibrium returns are predictable based on current and past dividends and prices. We derive specific conditions under which excess returns exhibit realistic patterns of short-run momentum and long-run mean-reversion. We clarify the concepts of rational expectations and market efficiency in a setting with differences in beliefs.
    Keywords: asset pricing, predictability, market microstructure, market efficiency, momentum, mean-reversion, anomalies, agreement to disagree
    JEL: B41 D8 G02 G12 G14
    Date: 2016–08

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