Giffen goods and market making

This paper shows that information effects per se are not responsible forthe Giffen goods anomaly affecting competitive traders demands in multi-asset, noisy rational expectations equilibrium models. The role thatinformation plays in traders strategies also matters. In a market withrisk averse, uninformed traders, informed agents havea dual motive for trading: speculation and market making. Whilespeculation entails using prices to assess the effect of private signalerror terms, market making requires employing them to disentangle noisetraders effects in traders aggregate orders. In a correlated environment,this complicates a trader s signal-extraction problem and maygenerate upward-sloping demand curves. Assuming either (i) that competitive,risk neutral market makers price the assets, or that (ii) the risktolerance coefficient of uninformed traders grows without bound, removesthe market making component from informed traders demands, rendering themwell behaved in prices.

Shared feature extraction for nearest neighbor face recognition

In this paper, we propose a new supervised linearfeature extraction technique for multiclass classification problemsthat is specially suited to the nearest neighbor classifier (NN).The problem of finding the optimal linear projection matrix isdefined as a classification problem and the Adaboost algorithmis used to compute it in an iterative way. This strategy allowsthe introduction of a multitask learning (MTL) criterion in themethod and results in a solution that makes no assumptions aboutthe data distribution and that is specially appropriated to solvethe small sample size problem. The performance of the methodis illustrated by an application to the face recognition problem.The experiments show that the representation obtained followingthe multitask approach improves the classic feature extractionalgorithms when using the NN classifier, especially when we havea few examples from each class