Transfer learning approach for financial applications

Artificial neural networks learn how to solve new problems through a computationally intense and time consuming process. One way to reduce the amount of time required is to inject preexisting knowledge into the network. To make use of past knowledge, we can take advantage of techniques that transfer the knowledge learned from one task, and reuse it on another (sometimes unrelated) task. In this paper we propose a novel selective breeding technique that extends the transfer learning with behavioural genetics approach proposed by Kohli, Magoulas and Thomas (2013), and evaluate its performance on financial data. Numerical evidence demonstrates the credibility of the new approach. We provide insights on the operation of transfer learning and highlight the benefits of using behavioural principles and selective breeding when tackling a set of diverse financial applications problems

Stochastic models of evidence accumulation in changing environments

Organisms and ecological groups accumulate evidence to make decisions. Classic experiments and theoretical studies have explored this process when the correct choice is fixed during each trial. However, we live in a constantly changing world. What effect does such impermanence have on classical results about decision making? To address this question we use sequential analysis to derive a tractable model of evidence accumulation when the correct option changes in time. Our analysis shows that ideal observers discount prior evidence at a rate determined by the volatility of the environment, and the dynamics of evidence accumulation is governed by the information gained over an average environmental epoch. A plausible neural implementation of an optimal observer in a changing environment shows that, in contrast to previous models, neural populations representing alternate choices are coupled through excitation. Our work builds a bridge between statistical decision making in volatile environments and stochastic nonlinear dynamics.Comment: 26 pages, 7 figure

Why do many animals move with a predominance of roughly forward directions?

Animal movements can influence their ecology and demographics. Animal movements are often characterized by path structures with directional persistence. The extent to which directional persistence improves forage success is investigated in this paper using theoretical simulations. It is shown that a movement strategy with directional persistence enables simulated animals to find more forage as compared to a random movement strategy. Situations where resources are chosen with certainty (optimally) are even more successful. Choosing resource with certainty cannot result in directional persistence. However, in cases where animals choose with certainty adjacent cells with resource but continue in their existing direction if none of these have resources then results include directional persistence. It is posited here that this combined strategy is the most effective because if optimal foraging works it is optimally efficient but where foraging is sub-optimal, for a variety of reasons, directional persistence will benefit foraging

The Neural Particle Filter

The robust estimation of dynamically changing features, such as the position of prey, is one of the hallmarks of perception. On an abstract, algorithmic level, nonlinear Bayesian filtering, i.e. the estimation of temporally changing signals based on the history of observations, provides a mathematical framework for dynamic perception in real time. Since the general, nonlinear filtering problem is analytically intractable, particle filters are considered among the most powerful approaches to approximating the solution numerically. Yet, these algorithms prevalently rely on importance weights, and thus it remains an unresolved question how the brain could implement such an inference strategy with a neuronal population. Here, we propose the Neural Particle Filter (NPF), a weight-less particle filter that can be interpreted as the neuronal dynamics of a recurrently connected neural network that receives feed-forward input from sensory neurons and represents the posterior probability distribution in terms of samples. Specifically, this algorithm bridges the gap between the computational task of online state estimation and an implementation that allows networks of neurons in the brain to perform nonlinear Bayesian filtering. The model captures not only the properties of temporal and multisensory integration according to Bayesian statistics, but also allows online learning with a maximum likelihood approach. With an example from multisensory integration, we demonstrate that the numerical performance of the model is adequate to account for both filtering and identification problems. Due to the weightless approach, our algorithm alleviates the 'curse of dimensionality' and thus outperforms conventional, weighted particle filters in higher dimensions for a limited number of particles

Impulsivity in rodents with a genetic predisposition for excessive alcohol consumption is associated with a lack of a prospective strategy

Increasing evidence supports the hypothesis that impulsive decision-making is a heritable risk factor for an alcohol use disorder (AUD). Clearly identifying a link between impulsivity and AUD risk, however, is complicated by the fact that both AUDs and impulsivity are heterogeneous constructs. Understanding the link between the two requires identifying the underlying cognitive factors that lead to impulsive choices. Rodent models have established that a family history of excessive drinking can lead to the expression of a transgenerational impulsive phenotype, suggesting heritable alterations in the decision-making process. In the present study, we explored the cognitive processes underlying impulsive choice in a validated, selectively bred rodent model of excessive drinking-the alcohol-preferring ("P") rat. Impulsivity was measured via delay discounting (DD), and P rats exhibited an impulsive phenotype as compared to their outbred foundation strain-Wistar rats. Steeper discounting in P rats was associated with a lack of a prospective behavioral strategy, which was observed in Wistar rats and was directly related to DD. To further explore the underlying cognitive factors mediating these observations, a drift diffusion model of DD was constructed. These simulations supported the hypothesis that prospective memory of the delayed reward guided choice decisions, slowed discounting, and optimized the fit of the model to the experimental data. Collectively, these data suggest that a deficit in forming or maintaining a prospective behavioral plan is a critical intermediary to delaying reward, and by extension, may underlie the inability to delay reward in those with increased AUD risk