From genes to behavior: placing cognitive models in the context of biological pathways.

Connecting neural mechanisms of behavior to their underlying molecular and genetic substrates has important scientific and clinical implications. However, despite rapid growth in our knowledge of the functions and computational properties of neural circuitry underlying behavior in a number of important domains, there has been much less progress in extending this understanding to their molecular and genetic substrates, even in an age marked by exploding availability of genomic data. Here we describe recent advances in analytical strategies that aim to overcome two important challenges associated with studying the complex relationship between genes and behavior: (i) reducing distal behavioral phenotypes to a set of molecular, physiological, and neural processes that render them closer to the actions of genetic forces, and (ii) striking a balance between the competing demands of discovery and interpretability when dealing with genomic data containing up to millions of markers. Our proposed approach involves linking, on one hand, models of neural computations and circuits hypothesized to underlie behavior, and on the other hand, the set of the genes carrying out biochemical processes related to the functioning of these neural systems. In particular, we focus on the specific example of value-based decision-making, and discuss how such a combination allows researchers to leverage existing biological knowledge at both neural and genetic levels to advance our understanding of the neurogenetic mechanisms underlying behavior

Re-Examining the Profitability of Technical Analysis with White’s Reality Check

In this paper, we re-examine the profitability of technical analysis using the Reality Check of White (2000, Econometrica) that corrects the data snooping bias. Comparing to previous studies, we study a more complete “universe” of trading techniques, including not only simple trading rules but also investor’s strategies, and we test the profitability of these rules and strategies with four main indices from both relatively mature and young markets. It is found that profitable simple rules and investor’s strategies do exist with statistical significance for NASDAQ Composite and Russell 2000 but not for DJIA and S&P 500. Moreover, the best rules for NASDAQ Composite and Russell 2000 outperform the buy-and-hold strategy in most in- and out-of-sample periods, even when transaction costs are taken into account. We also find that investor’s strategies are able to improve on the profits of simple rules and may even generate significant profits from unprofitable simple rules.data snooping, investor’s strategies, stationary bootstrap, technical analysis, trading rules, White’s Reality Check.

A Model of Elections with Spatial and Distributive Preferences

This paper introduces a model where elections are games where voters have preferences over a public good (policy platforms) and a private good (transfers). The model produces the standard social choice results such as core convergence and policy separation. Furthermore, by introducing transfers, I am able to make more precise predictions about candidate locations and their dynamics than is possible under the standard spatial model. Another purpose of this paper lies in the creation of favored groups in elections. Ultimately, it is important to characterize political behavior while considering the different preferences that might exist in the constituents. By incorporating utility for private goods into standard utility assumptions, this model introduces these considerations into the standard spatial model, allowing us to have a richer and more nuanced look into elections