Gianfranco Forte

A Comparison of Socially Responsible and Islamic Equity Investments

Defining social investing and its boundaries is a challenging task, since no general consensus exists about the ‘ideal’ characteristics that socially responsible investments (SRIs) should possess. Some faith-based investments, for instance, Islamic funds, are often associated with SRIs, even if there are some inconsistencies in the investment decisions. This paper explores whether Islamic investments can be included into the category of SRIs or whether they exhibit characteristics that would more fittingly classify them in a separate investment family. To answer this research question, we focus on equity investments from both a qualitative and a quantitative point of view. The first part of the study discusses and compares the screens generally used to build socially responsible (SR) and Islamic portfolios, while the quantitative section of our study analyses portfolios’ characteristics using relevant European indices as a proxy for SRI and Islamic funds. Covering the period from 2001 to 2011, we use style analysis to investigate the sector and country composition of SR and Islamic portfolios. In addition, through a cointegration analysis on FTSE indices, we show that the econometric profile of the FTSE Islamic series exhibits peculiar portfolio characteristics compared to conventional and SRI indices. Although the academic literature has extensively analysed SRIs and some authors have focused on Islamic investments, to the best of our knowledge, this is the first paper to investigate the qualitative and econometric differences between SRIs and Islamic investments.

A Portfolio Approach: Multiples’ Accuracy and Stock Selection

We investigate whether a stock selection strategy based on multiples’ accuracy can provide sustainable returns. More specifically, the aim of this part is twofold. On the one hand, we try to test whether a link exists between large valuation errors and future price performance; and on the other, to understand if the multiples previously introduced can be utilized as investment criteria to build profitable investment strategies.

Still crazy after all these years: the returns on carry trade

This paper proposes a novel approach to provide directional forecasts for carry trade strategies; this approach is based on Support VectorMachines (SVM), a learning algorithm which delivers extremely promising results. Building on recent findings of the literature on carry trade we condition the SVM on indicators of uncertainty and risk; we show that this provides a dramatic improvement of the performance of the strategy, in particular during periods of financial distress such as the recent financial crises. Disentangling between measures of risk we show that the best performances are obtained by conditioning the SVM on measures of liquidity risk rather than on market volatility.

Accuracy Performance of Relative Valuation

In this chapter we focus on multiples’ performance in the US stock market. The main aim is to examine the effectiveness of a broad selection of commonly used multiples and to understand if a specific multiple exists that is able to outperform the others. In doing so we are able to widen the comprehensive understanding on “market performances” of the tools practitioners are handling in their everyday work. In addition, a relevant feature of our work is that we are able to provide a detailed ranking across industries of the metrics performances, from which we can detect which sector or industry presents better or worse performance in terms of multiples accuracy in predicting the market price.

Relative Valuation: Issues and General Framework

A general overview of equity relative valuation, analyzing its assumptions, strengths and weaknesses, is essential. Equity valuation is a main application of finance and accounting theory. The theoretical emphasis usually focuses on discounted cash flow (DCF) and other equivalent models which are grouped as absolute valuation methods to equity evaluation. These models come up with some drawbacks when practitioners try to implement them. Market-based valuations based on multiples, on the other hand, present many advantages but we must be aware of their limitations too.

Fundamentalist Exchange Rate Trading Performances

The question on the predictive power of different exchange rate equilibrium models is one of the oldest and most intriguing in international finance and assets trading. On one side there is evidence - starting from Meese and Rogoff (1983) to Cheung, Chinn and Pascual (2005) - about the poor performance of fundamental models such as purchasing power parity (PPP) or covered interest parity (CIP). On the other side, practitioner asset managers often trade with a very simple rule - carry trade (CT) - assuming that currencies with high interest rates will appreciate against those with low interest rates, which goes exactly the opposite way than what predicted by CIP. Many studies, for example Gyntelberg and Remolona (2007) or Galati, Heath and McGuire P., Heath A., Galati G. (2007) revealed a good performance of CT models in recent years, but some authors underlined that returns exhibit a strong (left) asymmetry in their distribution - Gagnon and Chaboud (2007) - and are negative in periods of high volatility, such as market crises - Brunnermeier, Nagel and Pedersen (2009), Cairns, Ho and McCauley (2007). In the light of these new findings and relying on the evidence - by De Zwart, Markwat, Swinkels and van Dijk (2009), Jorda and Taylor (2009) - that fundamentals have an informative power we reconsider and compare the main fundamental relations on exchange rates by the trading performance obtained going long the under-valued currency and short the over-valued one. Our sample is made of G-10 currencies plus 8 currencies of minor/emerging countries with deliverable forward contracts. We tested the performance of simple trading rules based on mispricing signals from three different exchange rate fundamental relations: PPP, UIP in terms of real interest rates, GDP growth differentials through the so called Taylor Rule (TR). Models are estimated with monthly data and trading performances evaluated as annualised monthly return rates. Trading results are analysed in terms of return, volatility and Sharp ratio over the entire sample and portofolios made of selections of currencies. Henceforth the comparison between fundamentalist trading strategies takes into account differences across the sample, potentially linked to the different nature of the three equilibrium relations employed. Results are supportive for Purchasing Power Parity and for the Taylor Rule.

Forecasting Volatility in European Stock Markets with Non-Linear GARCH Models

This paper investigates the forecasting performance of three popular variants of the non-linear GARCH models, namely VS-GARCH, GJR-GARCH and Q-GARCH, with the symmetric GARCH(1,1) model as a benchmark. The application involves ten European stock price indexes. Forecasts produced by each non-linear GARCH model and each index are evaluated using a common set of classical criteria, as well as forecast combination techniques with constant and non-constant weights. With respect to the standard GARCH specification, the non-linear models generally lead to better forecasts in terms of both smaller forecast errors and lower biases. In-sample forecast combination regressions are better than those from single Mincer-Zarnowitz regressions. The out-of-sample performance of combining forecasts is less satisfactory, irrespective of the type of weights adopted.