A Generalized Description Length Approach for Sparse and Robust Index Tracking

We develop a new minimum description length criterion for index tracking, which deals with two main issues affecting portfolio weights: estimation errors and model misspecification. The criterion minimizes the uncertainty related to data distribution and model parameters by means of a generalized q-entropy measure, and performs model selection and estimation in a single step, by assuming a prior distribution on portfolio weights. The new approach results in sparse and robust portfolios in presence of outliers and high correlation, by penalizing observations and parameters that highly diverge from the assumed data model and prior distribution. The Monte Carlo simulations and the empirical study on financial data confirm the properties and the advantages of the proposed approach compared to state-of-art methods

Robust One Period Option Modelling

AMS classifications: 90C15; 90C20; 90C90; 49M29;return on investment;option pricing models;optimization;portfolio investment

Risk reduction and diversification in UK commercial property portfolios

The issue of diversification in direct real estate investment portfolios has been one of the most widely studied topics in academic and practitioner literature. Most work, however, has been done using mean returns and risks for broad market segments as inputs to asset allocation models, or in a few cases using data from small sets of individual properties. This paper reports results from a comprehensive testing of asset allocation modelling drawing on records of 10,000+ UK properties tracked by Investment Property Databank. It provides for the first time robust estimates of the diversification gains attainable given return, risk and cross-correlations across individual properties actually available to fund managers. The discussion of results covers implications for the number of assets and amount of money needed to construct “balanced” portfolios by direct investment, or via indirect specialist vehicles.Publisher PD

A Comparison of Cointegration & Tracking Error Models for Mutual Funds & Hedge Funds

We present a detailed study of portfolio optimisation based on cointegration, a statistical tool that here exploits a long-run equilibrium relationship between stock prices and an index price. We compare the theoretical and empirical properties of cointegration optimal equity portfolios with those of portfolios optimised on the tracking error variance. From an eleven year out of sample performance analysis we find that for simple index tracking the additional feature of cointegration between the tracking portfolio and the index has no clear advantages or disadvantages relative to the tracking error variance (TEV) minimization model. However ensuring a cointegration relationship does pay off when the tracking task becomes more difficult. Cointegration optimal portfolios clearly dominate the TEV equivalents for all of the statistical arbitrage strategies based on enhanced indexation, in all market circumstancescointegration, tracking error, index tracking, statistical arbitrage

Mean-variance investment strategy applied in emerging financial markets: evidence from the Colombian stock market

Copyright 2015, Mykolas Romeris University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).In any investment, an analysis of the expected return and the assumed risk constitutes a fundamental step. Investing in financial assets is no exception. Since the portfolio selection theory was proposed by Markowitz in 1952, this methodology has become the benchmark in portfolio management. However, it is not always possible to apply it, especially when investing in emerging financial markets, which are characterised by a scant variety of available stocks and very lowliquidity. In this paper, using the Colombian case, we will examine the challenges found by investors who want to create a portfolio using only stocks listed on a scarcely developed stock market.García García, F.; Gonzalez Bueno, JA.; Oliver Muncharaz, J. (2015). Mean-variance investment strategy applied in emerging financial markets: evidence from the Colombian stock market. Intellectual Economics. 9(1):22-29. doi:10.1016/j.intele.2015.09.003S222991Barak, S., & Modarres, M. (2015). Developing an approach to evaluate stocks by forecasting effective features with data mining methods. Expert Systems with Applications, 42(3), 1325-1339. doi:10.1016/j.eswa.2014.09.026Becker, F., Gürtler, M., & Hibbeln, M. (2013). Markowitz versus Michaud: portfolio optimization strategies reconsidered. The European Journal of Finance, 21(4), 269-291. doi:10.1080/1351847x.2013.830138Belghitar, Y., Clark, E., & Deshmukh, N. (2014). Does it pay to be ethical? Evidence from the FTSE4Good. Journal of Banking & Finance, 47, 54-62. doi:10.1016/j.jbankfin.2014.06.027Chen, C., & Kwon, R. H. (2012). Robust portfolio selection for index tracking. Computers & Operations Research, 39(4), 829-837. doi:10.1016/j.cor.2010.08.019Edirisinghe, N. C. P. (2013). Index-tracking optimal portfolio selection. Quantitative Finance Letters, 1(1), 16-20. doi:10.1080/21649502.2013.803789García, F., Guijarro, F., & Moya, I. (2011). The curvature of the tracking frontier: A new criterion for the partial index tracking problem. Mathematical and Computer Modelling, 54(7-8), 1781-1784. doi:10.1016/j.mcm.2011.02.015García, F., Guijarro, F., & Moya, I. (2013). A MULTIOBJECTIVE MODEL FOR PASSIVE PORTFOLIO MANAGEMENT: AN APPLICATION ON THE S&P 100 INDEX. Journal of Business Economics and Management, 14(4), 758-775. doi:10.3846/16111699.2012.668859Hsu, C.-M. (2014). A hybrid SVR-PSO portfolio optimization procedure for multi-period stock investments. Computational Intelligence and Industrial Engineering. doi:10.2495/ciie140231Jablonskienė, D. (2013). Influence of Pension Funds and Life Insurance on Old-Age Pension. Intellectual Economics, 7(3), 375-388. doi:10.13165/ie-13-7-3-08Jacobs, H., Müller, S., & Weber, M. (2014). How should individual investors diversify? An empirical evaluation of alternative asset allocation policies. Journal of Financial Markets, 19, 62-85. doi:10.1016/j.finmar.2013.07.004Loukeris, N., & Eleftheriadis, I. (2015). Support Vector Machines Networks to Hybrid Neuro-Genetic SVMs in Portfolio Selection. Intelligent Information Management, 07(03), 123-129. doi:10.4236/iim.2015.73011Nazemi, A., Abbasi, B., & Omidi, F. (2014). Solving portfolio selection models with uncertain returns using an artificial neural network scheme. Applied Intelligence, 42(4), 609-621. doi:10.1007/s10489-014-0616-zXia, H., Min, X., & Deng, S. (2015). Effectiveness of earnings forecasts in efficient global portfolio construction. International Journal of Forecasting, 31(2), 568-574. doi:10.1016/j.ijforecast.2014.10.00

Direct Data-Driven Portfolio Optimization with Guaranteed Shortfall Probability

This paper proposes a novel methodology for optimal allocation of a portfolio of risky financial assets. Most existing methods that aim at compromising between portfolio performance (e.g., expected return) and its risk (e.g., volatility or shortfall probability) need some statistical model of the asset returns. This means that: ({\em i}) one needs to make rather strong assumptions on the market for eliciting a return distribution, and ({\em ii}) the parameters of this distribution need be somehow estimated, which is quite a critical aspect, since optimal portfolios will then depend on the way parameters are estimated. Here we propose instead a direct, data-driven, route to portfolio optimization that avoids both of the mentioned issues: the optimal portfolios are computed directly from historical data, by solving a sequence of convex optimization problems (typically, linear programs). Much more importantly, the resulting portfolios are theoretically backed by a guarantee that their expected shortfall is no larger than an a-priori assigned level. This result is here obtained assuming efficiency of the market, under no hypotheses on the shape of the joint distribution of the asset returns, which can remain unknown and need not be estimate