
on Market Microstructure 
By:  Cespa, Giovanni; Foucault, Thierry 
Abstract:  We consider a multiperiod rational expectations model in which riskaverse investors differ in their information on past transaction prices (the ticker). Some investors (insiders) observe prices in realtime whereas other investors (outsiders) observe prices with a delay. As prices are informative about the asset payoff, insiders get a strictly larger expected utility than outsiders. Yet, information acquisition by one investor exerts a negative externality on other investors. Thus, investors’ average welfare is maximal when access to price information is rationed. We show that a market for price information can implement the fraction of insiders that maximizes investors’ average welfare. This market features a high price to curb excessive acquisition of ticker information. We also show that informational efficiency is greater when the dissemination of ticker information is broader and more timely. 
Keywords:  Hirshleifer effect; Market data sales; Price discovery; Transparency 
JEL:  G10 G14 
Date:  2008–04 
URL:  http://d.repec.org/n?u=RePEc:cpr:ceprdp:6794&r=mst 
By:  Alejandro Reveiz Herault 
Abstract:  The focus of this study is to build, from the ‘bottomup’, a market with artificially intelligent adaptive agents based on the institutional arrangement of the Colombian Foreign Exchange Market (19941999) in order to determine simple agents’ design, rules and interactions that are sufficient to create interesting behaviours at the macroscopic level – emerging patterns that replicate the properties of the time series from the case study. Tools from artificial intelligence research, such as genetic algorithms and fuzzy logic, are the basis of the agents’ mental models, which in turn are used for forecasting, quoting and learning purposes in a double auction market. Sets of fuzzy logic rules yield adequate, approximately continuous risk and utility preferences without the need to fix their mathematical form exante. Statistical properties of financial time series are generated by the artificial market, as well as some additional nonlinearity linked to the existence of a crawling band. Moreover, the behaviour of the simulated exchange rate is consistent with currency band theory. Agent’s learning favours forecasting rules based on regulatory signals against rules based on fundamental information. Also, intraday volatility is strongly linked to the rate of arrival and size of real sector trades. Intraday volatility is also a function of the frequency of learning and search specialisation. It is found that when a moderately low frequency of learning is used, volatility increases. 
Date:  2008–04–17 
URL:  http://d.repec.org/n?u=RePEc:col:000094:004616&r=mst 
By:  Alejandro Reveiz Herault 
Abstract:  The focus of this study is to build, from the ‘bottomup’, a market with artificially intelligent adaptive agents based on the institutional arrangement of the Colombian Foreign Exchange Market (19941999) in order to determine simple agents’ design, rules and interactions that are sufficient to create interesting behaviours at the macroscopic level  emerging patterns that replicate the properties of the time series from the case study. Tools from artificial intelligence research, such as genetic algorithms and fuzzy logic, are the basis of the agents’ mental models, which in turn are used for forecasting, quoting and learning purposes in a double auction market. Sets of fuzzy logic rules yield adequate, approximately continuous risk and utility preferences without the need to fix their mathematical form exante. Statistical properties of financial time series are generated by the artificial market, as well as some additional nonlinearity linked to the existence of a crawling band. Moreover, the behaviour of the simulated exchange rate is consistent with currency band theory. Agent’s learning favours forecasting rules based on regulatory signals against rules based on fundamental information. Also, intraday volatility is strongly linked to the rate of arrival and size of real sector trades. Intraday volatility is also a function of the frequency of learning and search specialisation. It is found that when a moderately low frequency of learning is used, volatility increases. 
Keywords:  Adaptive agents, artificial markets, constrained generating procedures, fuzzy logic and genetic algorithms. Classification JEL: G1; G12; G39. 
URL:  http://d.repec.org/n?u=RePEc:bdr:borrec:510&r=mst 
By:  Prof D.S.G. Pollock 
Abstract:  This paper shows how a frequencyselective filter that is applicable to short trended data sequences can be implemented via a frequencydomain approach. A filtered sequence can be obtained by multiplying the Fourier ordinates of the data by the ordinates of the frequency response of the filter and by applying the inverse Fourier transform to carry the product back into the time domain. Using this technique, it is possible, within the constraints of a finite sample, to design an ideal frequencyselective filter that will preserve all elements within a specified range of frequencies and that will remove all elements outside it. Approximations to ideal filters that are implemented in the time domain are commonly based on truncated versions of the infinite sequences of coefficients derived from the Fourier transforms of rectangular frequency response functions. An alternative to truncating an infinite sequence of coefficients is to wrap it around a circle of a circumference equal in length to the data sequence and to add the overlying coefficients. The coefficients of the wrapped filter can also be obtained by applying a discrete Fourier transform to a set of ordinates sampled from the frequency response function. Applying the coefficients to the data via circular convolution produces results that are identical to those obtained by a multiplication in the frequency domain, which constitutes a more efficient approach. 
Keywords:  Linear filtering; Frequencydomain analysis 
JEL:  C22 
Date:  2008–04 
URL:  http://d.repec.org/n?u=RePEc:lec:leecon:08/13&r=mst 