On-Line Portfolio Selection with Moving Average Reversion

On-line portfolio selection has attracted increasing interests in machine learning and AI communities recently. Empirical evidences show that stock's high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While the existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied in some real datasets, leading to poor performance when the assumption does not hold. To overcome the limitation, this article proposes a multiple-period mean reversion, or so-called Moving Average Reversion (MAR), and a new on-line portfolio selection strategy named "On-Line Moving Average Reversion" (OLMAR), which exploits MAR by applying powerful online learning techniques. From our empirical results, we found that OLMAR can overcome the drawback of existing mean reversion algorithms and achieve significantly better results, especially on the datasets where the existing mean reversion algorithms failed. In addition to superior trading performance, OLMAR also runs extremely fast, further supporting its practical applicability to a wide range of applications.Comment: ICML201

On-Line Portfolio Selection for a Currency Exchange Market

The purpose of this paper is to study on-line portfolio selection strategies for cur- rency exchange markets and our focus is on the markets with presence of decre- ments. To this end, we first analyze the main factors arising in the decrements. Then we develop a cross rate scheme which enables us to establish an on-line portfolio se- lection strategy for the currency exchange markets with presence of decrements. Fi- nally, we prove the universality of our on-line portfolio selections

Which portfolio is better? A discussion of several possible comparison criteria

During the last few years, there has been an interest in comparing simple or heuristic procedures for portfolio selection, such as the naive, equal weights, portfolio choice, against more "sophisticated" portfolio choices, and in explaining why, in some cases, the heuristic choice seems to outperform the sophisticated choice. We believe that some of these results may be due to the comparison criterion used. It is the purpose of this note to analyze some ways of comparing the performance of portfolios. We begin by analyzing each criterion proposed on the market line, in which there is only one random return. Several possible comparisons between optimal portfolios and the naive portfolio are possible and easy to establish. Afterwards, we study the case in which there is no risk free asset. In this way, we believe some basic theoretical questions regarding why some portfolios may seem to outperform others can be clarified

A three-stage experimental test of revealed preference

A powerful test of Varian's (1982) generalised axiom of revealed preference (GARP) with two goods requires the consumer's budget line to pass through two demand vectors revealed as chosen given other budget sets. In an experiment using this idea, each of 41 student subjects faced a series of 16 successive grouped portfolio selection problems. Each group of selection problems had up to three stages, where later budget sets depended on that subject'choices at earlier stages in the same group. Only 49% of subjects' choices were observed to satisfy GARP exactly, even by our relatively generous nonparametric test

Sector and regional factors in real estate returns

This paper presents a simple method to measure the effect of sector and regional factors in real estate returns, and thus provides a quantitative framework for analysing the relative impact of these two diversification categories to real estate portfolio selection. Using data on Retail, Office and Industrial properties spread across 326 real estate locations in the UK, over the period 1981 to 1995, the results show that the performance of real estate is largely sector-driven. A result in line with previous work. Which implies that the sector composition of the real estate fund should be the first level of analysis in constructing and managing the real estate portfolio. As a consequence real estate fund managers need to pay more attention to the sector allocation of their portfolios than the regional spread

A Three-Stage Experimental Test of Revealed Preference

A powerful test of Varian's (1982) generalised axiom of revealed preference (GARP) with two goods requires the consumer's budget line to pass through two demand vectors revealed as chosen given other budget sets. In an experiment using this idea, each of 41 student subjects faced a series of 16 successive grouped portfolio selection problems. Each group of selection problems had up to three stages, where later budget sets depended on that subject's choices at earlier stages in the same group. Only 49% of subjects' choices were observed to satisfy GARP exactly, even by our relatively generous nonparametric test.Rationality, revealed preference, uncertainty

Bayesian forecasting and scalable multivariate volatility analysis using simultaneous graphical dynamic models

The recently introduced class of simultaneous graphical dynamic linear models (SGDLMs) defines an ability to scale on-line Bayesian analysis and forecasting to higher-dimensional time series. This paper advances the methodology of SGDLMs, developing and embedding a novel, adaptive method of simultaneous predictor selection in forward filtering for on-line learning and forecasting. The advances include developments in Bayesian computation for scalability, and a case study in exploring the resulting potential for improved short-term forecasting of large-scale volatility matrices. A case study concerns financial forecasting and portfolio optimization with a 400-dimensional series of daily stock prices. Analysis shows that the SGDLM forecasts volatilities and co-volatilities well, making it ideally suited to contributing to quantitative investment strategies to improve portfolio returns. We also identify performance metrics linked to the sequential Bayesian filtering analysis that turn out to define a leading indicator of increased financial market stresses, comparable to but leading the standard St. Louis Fed Financial Stress Index (STLFSI) measure. Parallel computation using GPU implementations substantially advance the ability to fit and use these models.Comment: 28 pages, 9 figures, 7 table

Risk measures and their applications in asset management

Several approaches exist to model decision making under risk, where risk can be broadly defined as the effect of variability of random outcomes. One of the main approaches in the practice of decision making under risk uses mean-risk models; one such well-known is the classical Markowitz model, where variance is used as risk measure. Along this line, we consider a portfolio selection problem, where the asset returns have an elliptical distribution. We mainly focus on portfolio optimization models constructing portfolios with minimal risk, provided that a prescribed expected return level is attained. In particular, we model the risk by using Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). After reviewing the main properties of VaR and CVaR, we present short proofs to some of the well-known results. Finally, we describe a computationally efficient solution algorithm and present numerical results.conditional value-at-risk;elliptical distributions;mean-risk;portfolio optimization;value-at-risk