New Economics Papers
on Market Microstructure
Issue of 2012‒04‒10
five papers chosen by
Thanos Verousis

  1. Price jump detection in limit order book By Ban Zheng; Eric Moulines; Frédéric Abergel
  2. An analysis of OTC interest rate derivatives transactions: implications for public reporting By Michael Fleming; John Jackson; Ada Li; Asani Sarkar; Patricia Zobel
  3. Liquidity Hoarding By Douglas Gale; Tanju Yorulmazer
  4. Direct and Indirect Effects of Index ETFs on Spot-Futures Pricing and Liquidity : Evidence from the CAC 40 Index. By Gresse, Carole; Deville, Laurent; De Séverac, Béatrice
  5. BREAKING INTO THE BLACKBOX: Trend Following, Stop Losses, and the Frequency of Trading: the case of the S&P500 By Andrew Clare; James Seaton; Stephen Thomas; Peter N Smith

  1. By: Ban Zheng (LTCI - Laboratoire traitement et communication de l'information - CNRS : UMR5141 - Institut Télécom - Télécom ParisTech, FiQuant - Chaire de finance quantitative - Ecole Centrale Paris); Eric Moulines (LTCI - Laboratoire traitement et communication de l'information - CNRS : UMR5141 - Institut Télécom - Télécom ParisTech); Frédéric Abergel (FiQuant - Chaire de finance quantitative - Ecole Centrale Paris, MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris)
    Abstract: A limit order book provides information on available limit order prices and their volumes. Based on these quantities, we give an empirical result on the relationship between the bid-ask liquidity balance and trade sign and we show that liquidity balance on best bid/best ask is quite informative for predicting the future market order's direction. Moreover, we de ne price jump as a sell (buy) market order arrival which is executed at a price which is smaller (larger) than the best bid (best ask) price at the moment just after the precedent market order arrival. Features are then extracted related to limit order volumes, limit order price gaps, market order information and limit order event information. Logistic regression is applied to predict the price jump from the limit order book's feature. LASSO logistic regression is introduced to help us make variable selection from which we are capable to highlight the importance of di erent features in predicting the future price jump. In order to get rid of the intraday data seasonality, the analysis is based on two separated datasets: morning dataset and afternoon dataset. Based on an analysis on forty largest French stocks of CAC40, we nd that trade sign and market order size as well as the liquidity on the best bid (best ask) are consistently informative for predicting the incoming price jump.
    Keywords: limit order book, price jumps, predictibility, LASSO,
    Date: 2012–03–14
  2. By: Michael Fleming; John Jackson; Ada Li; Asani Sarkar; Patricia Zobel
    Abstract: This paper examines the over-the-counter (OTC) interest rate derivatives (IRD) market in order to inform the design of post-trade price reporting. Our analysis uses a novel transaction-level data set to examine trading activity, the composition of market participants, levels of product standardization, and market-making behavior. We find that trading activity in the IRD market is dispersed across a broad array of product types, currency denominations, and maturities, leading to more than 10,500 observed unique product combinations. While a select group of standard instruments trade with relative frequency and may provide timely and pertinent price information for market participants, many other IRD instruments trade infrequently and with diverse contract terms, limiting the impact on price formation from the reporting of those transactions. Nonetheless, we find evidence of dealers hedging rapidly after large interest rate swap trades, suggesting that, for this product, a price-reporting regime could be designed in a manner that does not disrupt market-making activity.
    Keywords: Derivative securities ; Transparency ; Over-the-counter markets ; Interest rates ; Swaps (Finance) ; Hedging (Finance)
    Date: 2012
  3. By: Douglas Gale; Tanju Yorulmazer
    Abstract: Banks hold liquid and illiquid assets. An illiquid bank that receives a liquidity shock sells assets to liquid banks in exchange for cash. We characterize the constrained efficient allocation as the solution to a planners problem and show that the market equilibrium is constrained inefficient, with too little liquidity and inefficient hoarding. Our model features a precautionary as well as a speculative motive for hoarding liquidity, but the inefficiency of liquidity provision can be traced to the incompleteness of markets (due to private information) and the increased price volatility that results from trading assets for cash.
    Date: 2011–06
  4. By: Gresse, Carole; Deville, Laurent; De Séverac, Béatrice
    Abstract: This paper investigates how the introduction of an index security directly or indirectly impacts the underlying-index spot-futures pricing. Using intraday data for financial instruments related to the CAC 40 index, we do not find that the spot-futures price efficiency improvement observed after ETF introduction is explained either by the direct effect of ETF shares being used in arbitrage trades or by the indirect effect of ETF trading improving the liquidity of index stocks in the short run. Some of our findings suggest that the efficiency improvement could rather result from a structural change in the way index traders distribute across index markets, with the ETF market absorbing the liquidity demand from some hedgers or passive index traders.
    Keywords: Efficiency; Futures; ETF; Exchange-Traded Fund; Liquidity; Arbitrage;
    JEL: G14 G13 G12
    Date: 2012
  5. By: Andrew Clare; James Seaton; Stephen Thomas; Peter N Smith
    Abstract: In this paper we compare a variety of technical trading rules in the context of investing in the S&P500 index. These rules are increasingly popular both among retail investors and CTAs and similar investment funds. We find that a range of fairly simple rules, including the popular 200-day moving average trading rule, dominate the long only, passive investment in the index. In particular, using the latter rule we find that popular stop loss rules do not add value and that monthly end of month investment decision rules are superior to those which trade more frequently: this adds to the growing view that trading can damage your wealth. Finally we compare the MA rule with a variety of simple fundamental metrics and find the latter far inferior to the technical rules over the last 60 years of investing.
    Keywords: trend following, S&P500, stop losses, trading frequency, fundamental investment metrics.
    Date: 2012–04

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