Sources of Over-performance in Equity Markets: Mean Reversion, Common Trends and Herding

In the field of optimisation models for passive investments, we propose a general portfolio construction model based on principal component analysis. The portfolio is designed to replicate the first principal component of a group of stocks, instead of a traditional benchmark, thus capturing only the common trend in the stock returns. The main advantage of this approach is that the reduction of the noise present in stock returns facilitates the replication task considerably and the optimal portfolio structure is very stable. We analyse the portfolio performance over different time horizons and in different international equity markets. The strategy over-performs both equally weighted and price weighted benchmarks, even after transaction costs. A market premium, a value premium associated with mean reversion in stock returns, and a volatility premium which give the strategy characteristics of a benchmark enhancer, all explain the over-performance, but have time-varying contributions to it. A behavioural explanation for the mean reversion mechanism leads to the conclusion that the portfolio performance is influenced by the extent of investors herding towards the common trend in stock returns.common trends, mean revrsion, herding, principal component analysis, abnormal returns, value strategies, behavioural finance

A review of portfolio planning: Models and systems

In this chapter, we first provide an overview of a number of portfolio planning models which have been proposed and investigated over the last forty years. We revisit the mean-variance (M-V) model of Markowitz and the construction of the risk-return efficient frontier. A piecewise linear approximation of the problem through a reformulation involving diagonalisation of the quadratic form into a variable separable function is also considered. A few other models, such as, the Mean Absolute Deviation (MAD), the Weighted Goal Programming (WGP) and the Minimax (MM) model which use alternative metrics for risk are also introduced, compared and contrasted. Recently asymmetric measures of risk have gained in importance; we consider a generic representation and a number of alternative symmetric and asymmetric measures of risk which find use in the evaluation of portfolios. There are a number of modelling and computational considerations which have been introduced into practical portfolio planning problems. These include: (a) buy-in thresholds for assets, (b) restriction on the number of assets (cardinality constraints), (c) transaction roundlot restrictions. Practical portfolio models may also include (d) dedication of cashflow streams, and, (e) immunization which involves duration matching and convexity constraints. The modelling issues in respect of these features are discussed. Many of these features lead to discrete restrictions involving zero-one and general integer variables which make the resulting model a quadratic mixed-integer programming model (QMIP). The QMIP is a NP-hard problem; the algorithms and solution methods for this class of problems are also discussed. The issues of preparing the analytic data (financial datamarts) for this family of portfolio planning problems are examined. We finally present computational results which provide some indication of the state-of-the-art in the solution of portfolio optimisation problems

How to measure Corporate Social Responsibility

Compliance with Corporate Social Responsibility (CSR) standards may require capacity that varies from one aspect to the other and companies in different industries may encounter different difficulties. Since CSR is a multidimensional concept, latent variable models may be usefully employed to provide a unidimensional measure of the ability of a firm to fulfil CSR standards. A methodology based on Item Response Theory has been implemented on the KLD sustainability dataset. Results show that companies in the industries Oil & Gas, Industrials, Basic Materials and Telecommunications have a higher difficulty to meet the CSR standards. Criteria based on Environment, Community relations and Product quality have a large capacity to select the firms with the best CSR performance, while Governance does not exhibit similar behavior. A stock selection based on the ranking of the firms according to our CSR measure outperforms, in terms of risk-adjusted returns, stock selection based on other criteria.Socially Responsible Investment, CSR ability, latent variable model, item response theory

How To Pick The Best Regression Equation: A Review And Comparison Of Model Selection Algorithms

This paper reviews and compares twenty-one different model selection algorithms (MSAs) representing a diversity of approaches, including (i) information criteria such as AIC and SIC; (ii) selection of a “portfolio” or best subset of models; (iii) general-to-specific algorithms, (iv) forward-stepwise regression approaches; (v) Bayesian Model Averaging; and (vi) inclusion of all variables. We use coefficient unconditional mean-squared error (UMSE) as the basis for our measure of MSA performance. Our main goal is to identify the factors that determine MSA performance. Towards this end, we conduct Monte Carlo experiments across a variety of data environments. Our experiments show that MSAs differ substantially with respect to their performance on relevant and irrelevant variables. We relate this to their associated penalty functions, and a bias-variance tradeoff in coefficient estimates. It follows that no MSA will dominate under all conditions. However, when we restrict our analysis to conditions where automatic variable selection is likely to be of greatest value, we find that two general-to-specific MSAs, Autometrics, do as well or better than all others in over 90% of the experiments.Model selection algorithms; Information Criteria; General-to-Specific modeling; Bayesian Model Averaging; Portfolio Models; AIC; SIC; AICc; SICc; Monte Carlo Analysis; Autometrics

A blocking and regularization approach to high dimensional realized covariance estimation

We introduce a regularization and blocking estimator for well-conditioned high-dimensional daily covariances using high-frequency data. Using the Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a) kernel estimator, we estimate the covariance matrix block-wise and regularize it. A data-driven grouping of assets of similar trading frequency ensures the reduction of data loss due to refresh time sampling. In an extensive simulation study mimicking the empirical features of the S&P 1500 universe we show that the ’RnB’ estimator yields efficiency gains and outperforms competing kernel estimators for varying liquidity settings, noise-to-signal ratios, and dimensions. An empirical application of forecasting daily covariances of the S&P 500 index confirms the simulation results