Search results

This research examines the use of econometric models to predict the total net asset value (NAV) of an asset allocation mutual fund. In particular, the mutual fund case used is the Vanguard Wellington Fund (VWELX). This fund maintains a balance between relatively conservative stocks and bonds. The period of the study on which the prediction of the total NAV is based is the 24-month period of 2010 and 2011 and the forecasting period is the first three months of 2012. Forecasting the total NAV of a massive conservative allocation fund, composed of an extremely large number of investments, requires a method that produces accurate results. Achieving this accuracy has no necessary relationship to the complexity of the methods typically employed in many financial forecasting studies.

This paper aims to analyze the portfolio characteristics and the performance measures of sustainability-themed mutual funds, compared to ethical mutual funds that implement different sustainable and responsible investment strategies.

The study refers to a European sample of 106 ethical funds and 51 sustainability-themed funds. The monthly performance of each fund is downloaded from Bloomberg for the period from January 1996 to December 2015. By applying a Fama and French (1993) three-factor model, the authors overcome the limits of a capital asset pricing model (CAPM) based-single index model, to compare the performance of the two categories of funds.

Sustainability-themed funds do not differ significantly from ethical funds in terms of portfolio attributes, except for market capitalization, age and net asset value. Regarding performance measures, the results shows that sustainability-themed funds have a lower underperformance than ethical funds (as measured by Jensen’s alpha), whereas the samples do not differ in terms of market risk (as measured by Beta coefficient). The idiosyncratic risk of sustainability-themed funds is positively influenced by the specific portfolio strategies. The sustainability-themed funds show a higher concentration in the industrial sector and a lower exposure to financial sector than ethical funds; in terms of geographical strategy, they are more global and international oriented; they mainly focus on small caps and value stocks.

The different sustainable and responsible investment strategies can be applied simultaneously and in a growing number of possible combinations. Mutual fund managers can consider thematic approach as an efficient opportunity for reconciling financial performance and economic sustainability. It is demonstrated that sustainability-themed funds adopt a portfolio strategy significantly different from ethical funds and from the environmental, social and governance benchmarks. Mutual fund managers implement a thematic specialization without any negative impact on the funds returns compared to ethical funds; actually, with a proper diversified portfolio, they are able to reduce idiosyncratic risk.

The analysis is extremely innovative, especially for the thematic sample. During the past 15 years, literature about sustainable and responsible investment has been focused especially on the differences in terms of risk and performance between socially responsible and conventional funds. This paper, starting from the methodology applied in these studies, wants to compare two different types of socially responsible strategies, with a specific focus on sustainability-themed mutual funds, given their exponential growth in the past few years.

Multi-criteria optimization by meta-goal programming of a portfolio of asset allocation mutual funds is the focus of this chapter. Asset allocation is generally defined as the allocation of an investor's portfolio across a number of different asset classes. The standard classical portfolio model uses the nonlinear model of quadratic programming to minimize risk and maximize return by mean absolute deviation. Instead of the variance measure of the risk of the rate of return, the mean absolute deviation is used as a measure of risk. In this chapter, three types of meta-goals are Type 1: a meta-goal relating to other percentage sum of unwanted deviations, Type 2: a meta-goal relating to the maximum percentage deviation, and Type 3: a meta-goal relating to the percentage of L∞ goals.

This paper examines the market timing ability of a sample of 62 Australian International equity funds using the returns‐based approach of Henriksson and Merton (1981) (H&M) and Treynor and Mazuy (1966) (T&M). Specifically, the primary focus is to investigate whether market timing ability bears any relationship to the stated fund allocation policy. Generally, the results indicate that fund managers do not successfully time the market. We also find that there is no relationship between the manager's stated level of activity on allocation and their market timing abilities as calculated using the H&M and T&M models. Managers are not successfully implementing their stated policies. These results are consistent with an irrelevance of perceived management style to fund policies and hence performance. Furthermore, it is indicative that fund managers are not successfully targeting particular classes of risk averse investors.

This research examines the use of a number of time series model structures of a moderate allocation mutual fund, PRWCX. PRWCX was rated as the top fund in its category during the past five years. The fund invests at least 50% of its total assets that the fund manager believes that have above average potential for capital growth. The remaining assets are generally invested in convertible securities, corporate and government debt bank loans, and foreign securities. Forecasting the total NAV of such a moderate allocation mutual fund, composed of an extremely large number of investments, requires a method that produces accurate results. These models are exponentially smoothing (single, double, and Winter’s Method), trend models (linear, quadratic, and exponential) are Box-Jenkins models.

The purpose of this paper is to present a discrete quantitative trading strategy to directly control a portfolio's maximum percentage of drawdown losses while trying to maximize the portfolio's long‐term growth rate.

The loss control target is defined through a Rolling Economic Drawdown (REDD) with a constant look‐back time window. The authors specify risk aversion in the power‐law portfolio wealth utility function as the complement of maximum percentage loss limit and assume long‐term stable Sharpe ratios for asset class indexes while updating volatility estimation in dynamic asset allocation implementation.

Over a test period of the past 20 years (1992‐2011), a risk‐based out‐of‐sample dynamic asset allocation among three broad based indexes (equity, fixed income and commodities) and a risk free asset, is robust against variations in capital market expectation inputs, and out‐performs the in‐the‐sample calibrated model and traditional asset allocation significantly.

The current proposal can lead to a new mathematical framework for portfolio selection. Besides investors' liquidity and behavioural constraints, macroeconomic and market cycle, and the potential of central bank interventions following a market crash, could be additionally considered for a more rigorous dynamic asset allocation model.

Besides the benefit of a clear mandate to construct suitable client portfolios, the portfolio approach can be applied to design invest‐able securities, such as principal‐guaranteed investment products, target risk asset allocation ETFs, and target‐date mutual funds with a glide path, etc. The formulation can also be implemented as a managed futures hedge fund portfolio.

The paper introduces the Rolling Economic Drawdown (REDD) concept and specifies risk aversion as the floor of maximum percentage loss tolerance. Dynamic asset allocation is implemented through updating estimation of asset class volatilities.

Prior research has documented inconclusive and/or mixed empirical evidence on the timing performance of hybrid funds. Their performance inferences generally do not efficiently control for fixed-income exposure, conditioning information, and cross-correlations in fund returns. This study examines the stock and bond timing performances of hybrid funds while controlling and accounting for these important issues. It also discusses the inferential implications of using alternative bootstrap resampling approaches.

We examine the stock and bond timing performances of hybrid funds using (un)conditional multi-factor benchmark models with robust estimation inferences. We also rely on the block bootstrap method to account for cross-correlations in fund returns and to separate the effects of luck or sampling variation from manager skill.

We find that the timing performance of portfolios of funds is neutral and sensitive to controlling for fixed-income exposures and choice of the timing measurement model. The block-bootstrap analyses of funds in the tails of the distributions of stock timing performances suggest that sampling variation explains the underperformance of extreme left tail funds and confirms the good and bad luck in the bond timing management of tail funds. We report inference changes based on whether the Kosowski et al. or the Fama and French bootstrap approach is used.

This study provides extensive and robust evidence on the stock and bond timing performances of hybrid funds and their sensitivity based on (un)conditional linear multi-factor benchmark models. It examines the timing performances in the extreme tails funds using the block bootstrap method to efficiently identify (un)skilled fund managers. It also highlights the sensitivity of inferences to the choice of testing methodology.

Wrap fee programs are an increasingly popular product offered by broker‐dealers and investment managers to their clients. Wrap fee programs present unique issues under both the Investment Company Act of 1940 (“Investment Company Act”) and the Investment Advisers Act of 1940 (“Advisers Act”), the two primary bodies of law that govern the product and those who offer and manage it. The regulations and rules under those Acts applicable to wrap fee programs and related interpretive statements made by the SEC staff, however, are wide ranging and have not been provided in a single format. This article attempts to present a comprehensive discussion on the regulation of wrap fee programs, as well as the many compliance issues associated with these programs. The article is delivered in two parts. Part I, presented in this issue, addresses the regulation of wrap fee programs under the Investment Company Act. Part I also begins a review of unique issues arising under the Advisers Act, including registration requirements for wrap fee sponsors and other persons who manage or offer the product to their clients, as well as required contents for wrap fee brochures and related disclosure issues. Part II, which will be presented in the next issue, will discuss additional Advisers Act issues such as suitability, fees and advertising. It also will briefly review issues arising under the Securities Exchange Act of 1934 (“Exchange Act”) and the Employee Retirement Income Security Act of 1974 (“ERISA”).

Part I of this series appeared in the Summer 2002 issue of The Journal of Investment Compliance. It addressed the regulation of wrap fee programs under the Investment Company Act of 1940 (“Investment Company Act”) and the requirements of Rule 3a‐4 thereunder, which must be met so that a wrap fee program is not deemed to be an investment company. Part I also discussed certain issues arising under the Investment Advisers Act of 1940 (“Advisers Act”), including how program sponsors and any third‐party portfolio managers generally are viewed as investment advisers and are subject to the Advisers Act. Part II discusses additional Advisers Act issues such as suitability, fees, and advertising. It also briefly reviews issues arising under the Securities Exchange Act of 1934 (“Exchange Act”) and the Employee Retirement Income Security Act of 1974 (“ERISA”). The information provided in Part II assumes that readers have some basic familiarity with Part I.