Bayesian emulation for optimization in multi-step portfolio decisions

We discuss the Bayesian emulation approach to computational solution of multi-step portfolio studies in financial time series. "Bayesian emulation for decisions" involves mapping the technical structure of a decision analysis problem to that of Bayesian inference in a purely synthetic "emulating" statistical model. This provides access to standard posterior analytic, simulation and optimization methods that yield indirect solutions of the decision problem. We develop this in time series portfolio analysis using classes of economically and psychologically relevant multi-step ahead portfolio utility functions. Studies with multivariate currency, commodity and stock index time series illustrate the approach and show some of the practical utility and benefits of the Bayesian emulation methodology.Comment: 24 pages, 7 figures, 2 table

Cost Functions and Model Combination for VaR-based Asset Allocation using Neural Networks

We introduce an asset-allocation framework based on the active control of the value-at- risk of the portfolio. Within this framework, we compare two paradigms for making the allocation using neural networks. The first one uses the network to make a forecast of asset behavior, in conjunction with a traditional mean-variance allocator for constructing the portfolio. The second paradigm uses the network to directly make the portfolio allocation decisions. We consider a method for performing soft input variable selection, and show its considerable utility. We use model combination (committee) methods to systematize the choice of hyperparemeters during training. We show that committees using both paradigms are significantly outperforming the benchmark market performance. Nous introduisons un cadre d'allocation d'actifs basé sur le contrôle actif de la valeur à risque d'un portefeuille. À l'intérieur de ce cadre, nous comparons deux paradigmes pour faire cette allocation à l'aide de réseaux de neurones. Le premier paradigme utilise le réseau de neurones pour faire une prédiction sur le comportement de l'actif, en conjonction avec un allocateur traditionnel de moyenne-variance pour la construction du portefeuille. Le deuxième paradigme utilise le réseau pour faire directement les décisions d'allocation du portefeuille. Nous considérons une méthode qui accomplit une sélection de variable douce sur les entrées, et nous montrons sa très grande utilité. Nous utilisons également des méthodes de combinaison de modèles (comité) pour choisir systématiquement les hyper-paramètres pendant l'entraînement. Finalement, nous montrons que les comités utilisant les deux paradigmes surpassent de façon significative les performances d'un banc d'essai du marché.Value-at-risk, asset allocation, financial performance criterion, model combination, recurrent multilayer neural networks, Valeur à risque, allocation d'actif, critère de performance financière, combinaison de modèles, réseau de neurones récurrents multi-couches

Term structure of risk under alternative econometric specifications

This paper characterizes the term structure of risk measures such as Value at Risk (VaR) and expected shortfall under different econometric approaches including multivariate regime switching, GARCH-in-mean models with student-t errors, two-component GARCH models and a non-parametric bootstrap. We show how to derive the risk measures for each of these models and document large variations in term structures across econometric specifications. An out-of-sample forecasting experiment applied to stock, bond and cash portfolios suggests that the best model is asset- and horizon specific but that the bootstrap and regime switching model are best overall for VaR levels of 5% and 1%, respectively.Time-series analysis ; Econometric models

The Factor-Portfolios Approach to Asset Management using Genetic Algorithms

We present an investment process that: (i) decomposes securities into risk factors; (ii) allows for the construction of portfolios of assets that would selectively expose the manager to desired risk factors; (iii) perform a risk allocation between these portfolios, allowing for tracking error restrictions in the optimization process and (iv) give the flexibility to manage dinamically the transfer coeffficient (TC). The contribution of this article is to present an investment process that allows the asset manager to limit risk exposure to macro-factors - including expectations on correlation dynamics - whilst allowing for selective exposure to risk factors using mimicking portfolios that emulate the behaviour of given specific. An Artificial Intelligence (AI) optimisation technique is used for risk-budget allocation to factor-portfolios.Active Management, Portfolio Optimization, Genetic Algorithms, Propensities. Classification JEL: G11; G14; G32.

PERFORMANCE MEASUREMENT AND EVALUATION

This chapter discusses methods and techniques for measuring and evaluating performance for the purpose of controlling the investment process. However, many of the methods discussed in this chapter are also used in communicating investment performance between the investment management company and it’s (potential) customers. Therefore, performance measurements also play an important role in the competition between investments management companies. Substantial evidence from the net sales of mutual funds shows that investors buy mutual funds with good past performance records although they fail to sell funds with bad past performance.Performance measurement; risk-adjusted performance

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001