OPTIMAL HIGH‐FREQUENCY TRADING IN A PRO RATA MICROSTRUCTURE WITH PREDICTIVE INFORMATION

We propose a framework to study optimal trading policies in a one‐tick pro rata limit order book, as typically arises in short‐term interest rate futures contracts. The high‐frequency trader chooses to post either market orders or limit orders, which are represented, respectively, by impulse controls and regular controls. We discuss the consequences of the two main features of this microstructure: first, the limit orders are only partially executed, and therefore she has no control on the executed quantity. Second, the high‐frequency trader faces the overtrading risk, which is the risk of large variations in her inventory. The consequences of this risk are investigated in the context of optimal liquidation. The optimal trading problem is studied by stochastic control and dynamic programming methods, and we provide the associated numerical resolution procedure and prove its convergence. We propose dimension reduction techniques in several cases of practical interest. We also detail a high‐frequency trading strategy in the case where a (predictive) directional information on the price is available. Each of the resulting strategies is illustrated by numerical tests.     

Do aggressive orders affect liquidity? An evidence from an emerging market

This paper investigates how aggressive orders affect spreads and trading activity measures on the stock market. Based on a sample of stocks listed on the Warsaw Stock Exchange this study finds that spreads and trading activity measures increase significantly when aggressive orders are executed, but quickly revert to initial levels. The reaction to these orders on the bid and ask side of the market is similar. The effect of aggressive orders differ depending on the size of the firms. Trading activity measures such as volumes or number of transactions increase stronger for bigger than for smaller stocks, while spreads increase more for smaller firms than for bigger ones. These findings enrich the understanding of liquidity dynamics especially on the emerging markets where liquidity is an important price formation factor.     

Post‐Earnings Announcement Drift: Bounds on Profitability for the Marginal Investor

The persistence of the post‐earnings announcement drift (PEAD) leads many to believe that trading barriers prevent investors from eliminating it. We examine two factors that have not been adequately addressed by the literature: the exact timing of earnings announcements and liquidity costs. Under a wide range of timing and cost assumptions, our results leave little doubt that over our sample period the PEAD was highly profitable after trading costs. An additional incremental investor could have earned hedged‐portfolio returns of at least 14% per year after trading costs. Over our sample period, investors did indeed leave money on the table.     

珠光体含量对铁素体-珠光体钢CO_2腐蚀的影响

为了研究组织对钢二氧化碳腐蚀的影响,采用不同珠光体含量的铁素体-珠光体钢进行了高压CO2腐蚀实验,并通过定量金像分析研究了珠光体含量同钢的腐蚀形貌和腐蚀速率之间的关系。实验结果表明,由于珠光体含量不同,不同的钢在相同的条件下产生局部腐蚀的温度范围相差很大。珠光体含量越高,局部腐蚀程度越低;钢中珠光体含量在82%以上时,不发生局部腐蚀。在CO2腐蚀过程中,铁素体-珠光体钢中的珠光体作为阴极、铁素体相作为阳极发生电偶腐蚀。珠光体含量越高,阳极腐蚀电偶电流密度越大。在55℃时,随着钢中珠光体含量的增加,腐蚀速率增加。     

The Effects of Foreign Trading of U.S. Treasuries

We examine the effects of foreign trading of U.S. Treasuries on the market's microstructure. Two intervals, the first characterized by heavy run‐ups in foreign ownership (1/1994–6/1997), and the second by multiple indicators of peaking of foreign ownership (7/1997–2000), are followed. Our findings reveal systematic effects associated with foreign trading. For instance, reductions in liquidity and trade sizes, and increases in informational asymmetry and dealer risk aversion, accompany falling demand for Treasury debt. Moreover, in this environment, foreign trading volume plays a larger explanatory role about the market's microstructure, than in an environment of rising demand. We also find dealer reactions to foreign transactions vary across the term‐structure.     

Price Clustering in the U.S. Dollar/Taiwan Dollar Swap Market

Price clustering in financial markets is pervasive. Using transaction‐level data from the world's largest financial market, this study is the first to examine price clustering behavior in the foreign exchange swap market. In addition to existing hypotheses, we investigate new determinants of price clustering including the expected return, contract liquidity, and trader's identity. The results support both negotiation and price resolution hypotheses. We find a positive effect from the level of expected return on price clustering. Markets with greater liquidity experience reduced clustering. Transactions involving domestic banks have less clustering suggesting an information advantage over foreign banks.     

Order Flow and the Monetary Model of Exchange Rates: Evidence from a Novel Data Set

We propose an exchange rate model that is a hybrid of the conventional specification with monetary fundamentals and the Evans–Lyons microstructure approach. We estimate a model augmented with order flow variables, using a unique data set: almost 100 monthly observations on interdealer order flow on dollar/euro and dollar/yen. The augmented macroeconomic, or “hybrid,” model exhibits greater in‐sample stability and out of sample forecasting improvement vis‐à‐vis the basic macroeconomic and random walk specifications.     

换热器管板失效原因分析

某石化公司换热器管板运行仅一年,发现管板多处产生了严重的裂纹。通过对管板的化学成分和机械性能分析表明,发现材料符合标准;通过对管板的金相组织检查发现夹杂物超标,通过对材料的扫描电镜分析和腐蚀物分析,判定管板的失效应为在多种条件共同作用下应力腐蚀开裂。     

Money market microstructure and monetary policy: the Indian experience

Money market microstructure has recently started drawing attention in the empirical literature on financial markets of emerging market economies. In the Indian context, a GARCH(1, 1) model shows that policy instruments impact bid–ask spreads in the money market. Volatility of bid–ask spreads seems to be more persistent in the overnight market than in longer maturity segments. The results also suggest the dominance of policy interventions over the market microstructure across the term structure of the Indian money market. Unanticipated policy actions can delay mean reversion and, therefore, the return to stability.     

Real-time market microstructure analysis: online transaction cost analysis

Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of causes that lie behind poor trading performance. It also gives theoretical foundations to a generic framework for real-time trading analysis. The common acronym for investigating the causes of bad and good performance of trading is transaction cost analysis Rosenthal [Performance Metrics for Algorithmic Traders, 2009]). Automated algorithms take care of most of the traded flows on electronic markets (more than 70% in the US, 45% in Europe and 35% in Japan in 2012). Academic literature provides different ways to formalize these algorithms and show how optimal they can be from a mean-variance (like in Almgren and Chriss [J. Risk, 2000, 3(2), 5–39]), a stochastic control (e.g. Guéant et al. [Math. Financ. Econ., 2013, 7(4), 477–507]), an impulse control (see Bouchard et al. [SIAM J. Financ. Math., 2011, 2(1), 404–438]) or a statistical learning (as used in Laruelle et al. [Math. Financ. Econ., 2013, 7(3), 359–403]) viewpoint. This paper is agnostic about the way the algorithm has been built and provides a theoretical formalism to identify in real-time the market conditions that influenced its efficiency or inefficiency. For a given set of characteristics describing the market context, selected by a practitioner, we first show how a set of additional derived explanatory factors, called anomaly detectors, can be created for each market order (following for instance Cristianini and Shawe-Taylor [An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, 2000]). We then will present an online methodology to quantify how this extended set of factors, at any given time, predicts (i.e. have influence, in the sense of predictive power or information defined in Basseville and Nikiforov [Detection of Abrupt Changes: Theory and Application, 1993], Shannon [Bell Syst. Tech. J., 1948, 27, 379–423] and Alkoot and Kittler [Pattern Recogn. Lett., 1999, 20(11), 1361–1369]) which of the orders are underperforming while calculating the predictive power of this explanatory factor set. Armed with this information, which we call influence analysis, we intend to empower the order monitoring user to take appropriate action on any affected orders by re-calibrating the trading algorithms working the order through new parameters, pausing their execution or taking over more direct trading control. Also we intend that use of this method can be taken advantage of to automatically adjust their trading action in the post trade analysis of algorithms.