A fully consistent,minimal model for non-linear market impact

We propose a minimal theory of non-linear price impact based on the fact that the (latent) order book is locally linear, as suggested by reaction–diffusion models and general arguments. Our framework allows one to compute the average price trajectory in the presence of a meta-order that consistently generalizes previously proposed propagator models. We account for the universally observed square-root impact law, and predict non-trivial trajectories when trading is interrupted or reversed. We prove that our framework is free of price manipulation and that prices can be made diffusive (albeit with a generic short-term mean-reverting contribution). Our model suggests that prices can be decomposed into a transient ‘mechanical’ impact component and a permanent ‘informational’ component.     

Market depth,domestic investors and price monotonicity violations

We re-examine the monotonicity violations of option price dynamics considering the roles of market depth and domestic investors. Violations caused by option price movements in conflict with underlying price movements tend to occur less frequently as the market depth increases, especially in the case of out-of-the-money options. In contrast, violations caused by option prices that remain sticky despite underlying price changes occur more frequently as the market depth increases. Both of these relationships are amplified by domestic investors.     

牙轮钻头金属密封环表面激光熔覆合金涂层的性能

金属密封环的密封稳定性直接影响到牙轮钻头的使用寿命,利用激光熔覆技术在牙轮钻头金属密封环材料20CrNiMo合金钢表面熔覆合金涂层,达到提高其表面耐磨性能的目的,从而提高牙轮钻头的寿命。分析了激光熔覆层的微观组织,测试了激光熔覆层的微观显微硬度及摩擦磨损性能。结果表明:高度弥散的金属间化合物、纳米晶与有较高韧性的非晶相夹杂的复合组织提高了涂层的力学性能,试样表面硬度、抗磨损性能大幅提高。     

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.     

A GUIDED TOUR OF THE MARKET MICROSTRUCTURE APPROACH TO EXCHANGE RATE DETERMINATION

Abstract.  We propose a critical review of recent developments in exchange rate economics which have offered a novel approach to exchange rate determination. This new strand of research, the market microstructure approach to exchange rates, is motivated by some very stark empirical evidence, relating exchange rate dynamics to the imbalance in the sequence of purchases and sales of foreign currencies in the markets for foreign exchange. Through our review we outline the results this new strand of research has achieved alongside its open questions and future challenges.     

Liquidity and firm characteristics: Evidence from mergers and acquisitions

We explore factors affecting liquidity by examining the relation between liquidity changes and changes in firm characteristics around mergers and acquisitions. We find that spreads decline as the number of analysts, number of shareholders, number of market makers, firm size, and volume increase or as volatility decreases. Increased volume and firm size, and decreased volatility, are associated with increased depth. We find no evidence diversifying and non-diversifying mergers affect liquidity differently. We note that mergers and acquisitions are associated with reductions, on average, in spreads but that the reductions are fully explained by the accompanying changes in firm characteristics.     

Modeling a Multivariate Transaction Process

In this paper the dynamics of a joint transaction process areinvestigated. The transaction process is characterized by fourmarks: price changes, transaction volumes, bid–ask spreadsand intertrade durations. Based on a copula approach, a modelfor their joint density is proposed, which avoids forcing apriori assumptions on the instantaneous causality relationshipsbetween the four variables as necessary in decomposition models,where the joint density is decomposed into its conditional andunconditional densities. The price change process is treatedas a discrete process and specified with an integer count hurdlemodel and the transaction volumes, bid–ask spreads, andtrade durations processes are modeled along the lines of fractionallyintegrated autoregressive conditional models, which are suitedvery well to capture the high persistency, empirically observedin these processes. The model is applied to three stocks tradedat the New York Stock Exchange (NYSE) in May, 2001 and we investigateseveral market microstructure hypotheses in the empirical partof this paper.     

Liquidity and capital structure

We examine the relation between equity market liquidity and capital structure. We find that firms with more liquid equity have lower leverage and prefer equity financing when raising capital. For example, after sorting firms into size quintiles and then into liquidity quintiles, the average debt-to-asset ratio of the most liquid quintiles is about 38% while the average for the least liquid quintiles is 55%. Similar results are observed in panel analyses with clustered errors and using instrumental variables. Our results are consistent with equity market liquidity lowering the cost of equity and, therefore, inducing a greater reliance on equity financing.     

An empirical model for durations in stocks

This paper considers an extension of the univariate autoregressive conditional duration model to which durations from a second stock are added. The model is empirically used to study duration dependence in four traded stocks, Nordea, Föreningssparbanken, Handelsbanken and SEB A on the Stockholm Stock Exchange. The stocks are all active in the banking sector. It is found that including durations from a second stock may add explanatory power to the univariate model. We also find that spread changes have significant effect for all series.