Equal-weighted Portfolio Research Articles

The Impact of Proportional Transaction Costs on Systematically Generated Portfolios

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 19 August 2019Accepted: 02 June 2020Published online: 02 September 2020Keywordsdiversity-weighted portfolio, equally-weighted portfolio, functionally generated portfolio, portfolio analysis, Stochastic Portfolio Theory, transaction costAMS Subject Headings91G10Publication DataISSN (online): 1945-497XPublisher: Society for Industrial and Applied MathematicsCODEN: sjfmbj

Considering the use of an equal-weighted index as a benchmark for South African equity investors

We analyse and discuss the use of an equal-weighted index as an alternative to the market capitalisation weighted (cap-weighted) index as a benchmark for active equity portfolios in the South African equity market. Our findings indicate that equal-weighted portfolios are, in general, more efficient than capweighted portfolios and that random active portfolios tend to display significantly improved risk-return characteristics when using an equal-weighted index as a benchmark. We find our results are robust to transaction costs involved with rebalancing.Keywords: Benchmark; risk; capitalisation weight; equal weight; efficiency; rebalancing; costs; returns

The Impact of Currency Risk on Firm’s Value in Emerging Countries

This study is dedicated to estimating the impact of currency risk on the cost of equity in Brazil, Russia, India and South Africa. Our contribution to the literature is that we obtain further evidence on pricing of exchange rate risk in developing countries which for now is quite scarce. These motivates our research which is dedicated to BRICS capital markets with Chinese stock market excluded since it is heavily regulated. The aim of the research is to determine whether in emerging countries stock markets currency risk is a significant factor that influence cost of equity capital of a company.
 Changes in the value of exchange rate can impact cash flows of a firm and their riskiness, hence, the value of the company. In our research we will discuss the influence of exchange rate movements on the value of the firm through their impact on the cost of equity. Specifically, we investigate whether companies that report substantial currency gains or losses have to pay a higher required return on equity. Furthermore, in this study we take an attempt to estimate currency risk premia for exposure to appreciation and depreciation of currency separately and identify possible differences.
 For each country three models that extend Fama-French Three Factor Model by incorporating currency risk are estimated. We used equal-weighted portfolio approach to construction currency risk factors. They are estimated using information about the ratio of currency gains to sales or the magnitude of covariation between equity returns and exchange rate changes. In the second case appreciation and depreciation of domestic currency against US dollar is considered separately.
 Results indicate that in Russia firms which report substantial currency losses pay a positive risk premium, while in Brazil, India and South Africa companies with significantly positive or negative currency gains pay a lower required return on equity than firms with almost zero currency gains. Finally, we are trying to explain estimation results using sectoral breakdown of product exports in each country of data sample.

Capital Structure Arbitrage: Model Choice and Volatility Calibration

When identifying relative value opportunities across credit and equity markets, the arbitrageur faces two major problems, namely positions based on model misspecification and mismeasured inputs. Using credit default swap data, this paper addresses both concerns in a convergence-type trading strategy. In spite of differences in assumptions governing default and calibration, we find the exact structural model linking the markets second to timely key inputs. Studying an equally-weighted portfolio of all relative value positions, the excess returns are insignificant when based on a traditional volatility from historical equity returns. However, relying on an implied volatility from equity options results in a substantial gain in strategy execution and highly significant excess returns - even when small gaps are exploited. The gain is largest in the speculative grade segment, and cannot be explained from systematic market risk factors. However, although the strategy may seem attractive at an aggregate level, positions on individual obligors can be very risky.

Optimized Portfolio Using a Forward-Looking Expected Tail Loss

In this paper, I construct an optimal portfolio by minimizing the expected tail loss (ETL) derived from the forward-looking natural distribution of the Recovery Theorem (RT). The RT is one of the first successful attempts at deriving an unparameterized natural distribution of future asset returns. This distribution can be used as the criterion function in an expected tail loss (ETL) portfolio optimization problem. I find that the portfolio constructed using the RT outperforms both the equally-weighted portfolio and a portfolio constructed using historical ETL. The portfolio constructed using the RT has the smallest historical tail loss, smallest maximum drawdown, highest Sortino Ratio, and highest Sharpe Ratio.

The Specialness of Equally-Weighted Portfolios: The Rebalancing Fallacy

There is overwhelming evidence in equity markets that equally-weighted (𝐸𝑊) portfolios deliver higher average returns and higher Sharpe ratios than value-weighted (𝑉𝑊) portfolios. Rebalancing to fixed weights has typically been advanced as an argument to explain the overperformance of 𝐸𝑊 portfolios. The paper challenges the rebalancing to fixed-weights hypothesis and provides empirical evidence that the overperformance is entirely explained by portfolio volatility and that there is no additional contribution to returns from rebalancing. Conditionally, the long-short 𝐸𝑀𝑉 portfolios, (Equally Minus Value weighted), which are long 𝐸𝑊 portfolios and short 𝑉𝑊 portfolios are shown to overperform only to the extent that they are invested in high variance or in low market capitalization securities. Furthermore, when looking at their dynamics, the overperformance is observed only in a few states of the world. The paper identifies the relevant state variables and documents evidence of predictability using information contained in lagged returns and in lagged volatility. Both variables are found to predict the 𝐸𝑀𝑉 portfolio returns positively. The information is exploited to test simple dynamic strategies which allow the 𝐸𝑀𝑉 portfolio to be held long or short depending on the signals contained in the two conditioning variables. The Sharpe ratios of various long-short dynamic strategies significantly overperform the long-only strategy. When looking at risk-adjusted returns, factor models fail to explain the 𝐸𝑀𝑉 portfolio returns. However, 𝐸𝑀𝑉 portfolios display pricing performance by driving well known factors to statistical insignificance. More generally, the unconditional and the conditional results obtained for the 𝐸𝑀𝑉 portfolios are shown to depend on two cross-sectional correlations, the first between the securities’ active weights and their variances and the second between the securities’ active weights and their growth rates. The cross-sectional correlations are shown to be security and state dependent. The average return on the 𝐸𝑀𝑉portfolio can be positive or negative depending on which one of the two cross-sectional correlations dominates.

A learning curve of the market: Chasing alpha of socially responsible firms

This paper explores stock market reactions to corporate social performance. We construct a value-weighted portfolio based on the list of “100 Best CSR companies in the world” published on the Forbes’ website by Reputation Institute. This portfolio yields statistically significant annual abnormal returns of 1.81% and 1.26%, by controlling for Carhart four factors and Fama-French five factors, respectively (2.41% and 1.84%, respectively for an equal-weighted portfolio). Moreover, such abnormal returns decrease as time passes, especially after the inaugural publication of the CSR lists in 2013. Furthermore, we find that companies with better social performance are more likely to have positive earnings surprises, and that their returns are more sensitive to earnings surprises. The results have three implications: firstly, CSR reputation contributes positively to a firm's short-term superior equity performance; secondly, the CSR lists facilitate market correction of mispricing intangibles such as CSR reputation — abnormal returns decrease as the market gradually learns about the value of firms’ social performance; lastly, the paper contributes to the socially responsible investing (SRI) screens and provides guidance for investors who would like to do well financially by doing good socially.

Anomaly Investing: Out-of-Sample Performance and Intertemporal Considerations

I first show that the naive equal-weighted 1/N investing in the set of 34 stock market anomalies is a robust implementation for out-of-sample diversification. Two types of popular portfolio optimization methods, including Sharpe-Ratio-optimizing with weight constraints and Dimension-Reduction with machine learning techniques, do not achieve robustly higher out-of-sample performance. Further to explore the gains and risks in investing stock market anomalies, I take this equal-weighted anomaly portfolio to an intertemporal CAPM framework with stochastic volatility to understand the investment considerations of a specific anomaly investor. Based on my estimation, only the correlation-induced volatility news carries a significant risk premium, which highlights the economic importance of the comovement in anomaly asset prices.

And the Winner Is… A Comparison of Valuation Measures for Equity Country Allocation

The authors evaluate and compare the usefulness of various valuation ratios for equity country allocation. To this end, the performance of 73 national equity indexes is investigated for the period 1996 to 2017. The earnings before interest, tax, depreciation, and amortization (EBITDA)-to-enterprise value (EV) multiple is the best predictor of performance and outperforms other metrics. An equal-weighted portfolio that is long (short) in the tertile of countries with the highest (lowest) EBITDA-to-EV ratio produces a mean monthly return of 0.69% and a Sharpe ratio of 0.81, which is more than double the Sharpe ratios obtained from using traditional metrics such as the book-to-market ratio or dividend yield. Two major drawbacks of intercountry value strategies are identified: (1) payoffs are derived predominantly from emerging and frontier markets and (2) profitability has significantly declined in the last decade. <b>TOPICS:</b>Accounting and ratio analysis, mutual funds/passive investing/indexing, emerging

On the Properties of Equally-Weighted Risk Contributions Portfolios

Minimum variance and equally-weighted portfolios have recently prompted great interest both from academic researchers and market practitioners, as their construction does not rely on expected average returns and is therefore assumed to be robust. In this paper, we consider a related approach, where the risk contribution from each portfolio components is made equal, which maximizes diversification of risk (at least on an ex-ante basis). Roughly speaking, the resulting portfolio is similar to a minimum variance portfolio subject to a diversification constraint on the weights of its components. We derive the theoretical properties of such a portfolio and show that its volatility is located between those of minimum variance and equally-weighted portfolios. Empirical applications confirm that ranking. All in all, equally-weighted risk contributions portfolios appear to be an attractive alternative to minimum variance and equally-weighted portfolios and might be considered a good trade-off between those two approaches in terms of absolute level of risk, risk budgeting and diversification.

Public Trust and Equity Returns

We make use of the Edelman Trust Barometer to test for the relationship between public trust and international equity returns. The data allows for a two-dimensional split as to differentiate between the levels of trust expressed by (1) the general public and (2) informed publics. We examine the relationship between public trust indices and subsequent equity returns and find that portfolios of international stock indices, weighted and ranked according to the general level of trust, outperform classical GDP and equal-weighted portfolios. Furthermore, we differentiate between trust associated to three global institutions: business, government and media, as well as on a regional level according to developed and emerging markets. We document varying degrees of importance with respect to trust levels associated to considered institutions and across regions. Overall, we interpret public trust as a source of alpha rather than a priced risk factor.

Algorithmic Portfolio Tilting to Harvest Higher Moment Gains

Many financial portfolios are optimized without taking the higher moments into account. We recommend tilting these portfolios in a direction that increases their estimated mean and third central moment and decreases their variance and fourth central moment. The advantages of tilting come at the cost of deviation from the initial optimality criterion. In this paper, we show the usefulness of portfolio tilting applied to the equally-weighted, equal-risk-contribution and maximum diversification portfolios in a UCITS-compliant asset allocation setting.

Factor-Based Allocation: Is There a Superior Strategy?

Factor-based allocation embraces the idea of factors, as opposed to asset classes, as the ultimate building blocks of an investment portfolio. Our study contributes to the literature by addressing the question whether there is a superior way of combining factors in a portfolio. We provide a comprehensive comparison of factor-based allocation strategies within a multiple testing framework. Factor-based allocation is profitable, even when applying the necessary multiple testing corrections. Investment portfolios can be efficiently diversified using factor-based allocation since we find robust economic performance over various market states. Ultimately, we provide guidance for investors on the best method to choose when constructing factor-based portfolios. A naive equal-weighted factor portfolio, albeit simple and costless, cannot be outperformed by more sophisticated allocation strategies.

Dynamic causality between the U.S. stock market, the Chinese stock market and the global gold market: implications for individual investors’ diversification strategies

ABSTRACTThis paper proposes a generalization of the prior VAR and EGARCH model to explore the linkage between returns and volatility transmissions in the U.S. stock market, the Chinese stock market, and the global gold market from 10 July 1996 to 20 July 2018. We found that past returns of the U.S. stock market can predict the current returns of the other two markets, and that significant reciprocal volatility transmission existed within and across all three markets. We further implemented average out-of-sample (OOS) forecasting to show that a risk-adjusted portfolio, such as mean-variance with sample estimator, does not outperform an equal-weighted portfolio. This provides insights for individual investors and helps to explain the ongoing disagreement in the portfolio literature concerning the effectiveness of risk-adjusted portfolios and equal-weighted portfolios when the number of assets is small.

Return Predictability: Evidence from the US–China Supply Chain

In this article, by investigating return predictability across a transnational supply chain, the authors present new evidence supporting the hypothesis that value-relevant information diffuses gradually in financial markets because of limited attention from investors. They find that the corresponding trading strategy delivers superior abnormal return. Using a sample of supply chains between Chinese customers and US suppliers from 2009 to 2015 and corresponding financial and return data, the authors show that Chinese customer returns can predict US supplier future return at the firm level. A long–short portfolio strategy based on these findings yields significant abnormal monthly returns of 2.179% (equal-weighted portfolio) in the Fama–French five-factor model. The authors also employ Fama–MacBeth regression analysis and propensity score matching–matched sample analysis, and the conclusions continue to hold.

Minsky vs. Machine: New Foundations for Quant-Macro Investing

Systematic macro investors’ use of the regime-switching models that have been developed in academia over the last several decades is infrequent at best and, when used, generally tangential to their core investment process. The roots of this less-than-enthusiastic uptake can be found in two familiar sources: models that possess an overly complex formal structure and poor predictive ability. As a remedy to the current state of affairs, the authors present a new foundation for regime-based investing, one based on spectral clustering, a graph theoretic approach to classifying data. Drawing inspiration from the work of Hyman Minsky and John Geanakoplos, the authors present a macro framework that uses measures of growth, inflation, and leverage to define regimes and drive portfolio decisions. To the latter end, the authors show how the framework can be used to build portfolios using information about regimes as defined, to outperform a no-information equal-weight portfolio both out-of-sample and in bootstrapped and cross-validated simulations. The authors thus show that spectral clustering can provide both an elegant mathematical description of the leverage cycle and a robust foundation for quant-macro investing.

An Empirical Investigation of the Performance of Japanese Mutual Funds: Skill or Luck?

This paper assesses the performance of 355 actively managed Japanese Equity Mutual Funds between April 2011 and April 2016. The equal weight portfolio and Jensen’s alpha measures of active management provide strong evidence that Japanese Mutual Funds fail to outperform the benchmark four-factor capital asset pricing model. When it comes to market timing, the Treynor and Mazuy measure shows that 33 funds have significant positive market timing ability which is largely offset by 31 funds with significant negative timing ability. To ensure the statistical inference is robust to the non-normality found in 33 funds we employ Fama and French’s cross-sectional bootstrap. The results show that a large proportion of funds fail to outperform a hypothetical world with no skill. On the persistence of skill we find that there is stronger persistence for poor performing funds than for strong performing funds.

Testing out-of-sample portfolio performance

This paper studies the quality of portfolio performance tests based on out-of-sample returns. By disentangling the components of the out-of-sample performance, we show that the observed differences are driven largely by the differences in estimation risk. Our Monte Carlo study reveals that the puzzling empirical findings of inferior performances of theoretically superior strategies result mainly from the low power of these tests. Thus, our results provide an explanation as to why the null hypothesis of equal performance of the simple equally-weighted portfolio compared to many theoretically-superior alternative strategies cannot be rejected in many out-of-sample horse races. Our findings turn out to be robust with respect to different designs and the implementation strategies of the tests.For the applied researcher, we provide some guidance as to how to cope with the problem of low power. In particular, we make use of a novel pretest-based portfolio strategy to show how the information regarding performance tests can be used optimally.

Optimized Fundamental Portfolios

We integrate fundamental analysis with mean-variance portfolio optimization to form fully optimized fundamental portfolios. We find that fully optimized fundamental portfolios produce large out-of-sample factor alphas with high Sharpe ratios. They substantially outperform equal-weighted and value-weighted portfolios of stocks in the extreme decile of expected returns, an approach commonly used in fundamental analysis research. They also outperform the factor-based and parametric portfolio policy approaches used in the prior portfolio optimization literature. The relative performance gains from mean-variance optimized fundamental portfolios are persistent through time, robust to eliminating small capitalization firms from the investment set, and robust to incorporating estimated transactions costs. Our results suggest that future fundamental analysis research could implement this portfolio optimization approach to provide greater investment insights.

Public Mood–Driven Asset Allocation: the Importance of Financial Sentiment in Portfolio Management

The study of the impact of investor sentiment on stock returns has gained increasing momentum in the past few years. It has been widely accepted that public mood is correlated with financial markets. However, only a few studies discussed how the public mood would affect one of the fundamental problems of computational finance: portfolio management. In this study, we use public financial sentiment and historical prices collected from the New York Stock Exchange (NYSE) to train multiple machine learning models for automatic wealth allocation across a set of assets. Unlike previous studies which set as target variable the asset prices in the portfolio, the variable to predict here is represented by the best asset allocation strategy ex post. Experiments performed on five portfolios show that long short-term memory networks are superior to multi-layer perceptron and random forests producing, in the period under analysis, an average increase in the revenue across the portfolios ranging between 5% (without financial mood) and 19% (with financial mood) compared to the equal-weighted portfolio. Results show that our all-in-one and end-to-end approach for automatic portfolio selection outperforms the equal-weighted portfolio. Moreover, when using long short-term memory networks, the employment of sentiment data in addition to lagged data leads to greater returns for all the five portfolios under evaluation. Finally, we find that among the employed machine learning algorithms, long short-term memory networks are better suited for learning the impact of public mood on financial time series.