Equal-weighted Portfolio Research Articles

The Brand Premium

Abstract We highlight the limitations of using cumulative advertising expenses as an input measure of brand value. Using two output measures—Interbrand’s data and a novel text-based measure—we find that an equal-weighted portfolio of top brands yields an annual abnormal return of 3%. The excess returns are driven by companies that develop their brands internally. Intangible factors proposed in the literature have no explanatory power for the premium. Analysts underestimate the future earnings of top brands, leading to significant excess returns following earnings announcements. We find no abnormal returns associated with the input measure of brand value. (JEL G4, G12, G14, M41)

Measuring Portfolio Gains: The Case of Earnings Announcement Trading Signals

ABSTRACT This study offers solutions to help address practical issues researchers generally overlook when assessing gains from a trading signal. Specifically, the methodologies used in previous research generally ignore investor risk aversion when forming portfolios, do not update portfolios as signals arrive, exploit look-ahead biases, do not assess the incremental gains of a new signal, and do not consider market frictions. We examine trading signals based on post-earnings announcement drift (PEAD), the earnings announcement premium (EAP), and earnings announcement rescheduling (RES). Using our proposed approach, we find that portfolios that incorporate the individual signals produce higher Sharpe ratios than equal-weighted portfolios; however, the gains for each signal are concentrated to a few days around the announcement. The EAP and RES signals do not provide incremental portfolio gains over the PEAD signal. After considering market frictions, portfolio performance rapidly attenuates and becomes similar to the SPY ETF as the portfolio size increases. JEL Classifications: G12; G14; G17.

Gaining a Seat at the Table: Enhancing the Attractiveness of Online Lending for Institutional Investors

This paper investigates the suitability of online loans as an investment through the lens of a portfolio optimization framework. We propose general characteristics-based portfolio policy (GCPP), a framework which overcomes unique challenges associated with building a portfolio of online loans. GCPP directly models the portfolio weight of a loan as a flexible function of its characteristics and does not require direct estimation of the distributional properties of loans. Using an extensive data set spanning over one million loans from 2013 to 2020 from LendingClub, we show that GCPP portfolios can achieve an average annualized internal rate of return (IRR) of 8.86% to 13.08%, significantly outperforming an equal-weight portfolio of loans. To assess the attractiveness of online loans, we then compare the performance of the GCPP portfolio to traditional investment vehicles including stocks, bonds, and real estate. The results demonstrate that a portfolio of online loans earns competitive or higher rates of return compared to traditional asset classes with limited comovement. These results indicate that online loans are an attractive novel asset class for investors. Together, we demonstrate that GCPP is an approach that can help platforms better serve both borrowers and lenders en route to growing their business.

An empirical evaluation of the salience-based asset pricing model: Evidence from Australia

We empirically evaluate a salience-based asset pricing model in which the demand for stocks is affected by their most salient payoffs. We first reproduce the main results of Cosemans and Frehen (2021) for the U.S. market, that stocks with past upside (downside) salience returns earn lower (higher) subsequent returns, using portfolio sorts and firm-level cross-sectional regressions. We next examine the salience effect in the Australian market, where individual stock ownership is among the highest in the world. We also find that past salient returns have predictive power for the cross section of average returns in the Australian stock market, with this effect being particularly strong for equal-weighted portfolio returns.

Portfolio Optimization Based on U.S. Stock

In the financial domain, portfolio optimization is of immense significance due to its potential for risk diversification and return enhancement. Particularly in today's society where inflation is on the rise, a well-constructed portfolio can effectively assist investors in maintaining or increasing real assets. This study selected 10 U.S. stocks and analyzed their data for the past decade. Utilizing Monte Carlo simulation, the study identified the efficient frontier and constructed three types of portfolios: equal-weighted (1/N) portfolio, global minimum variance portfolio (hereinafter referred to as GMVP), and maximum Sharpe ratio portfolio. The findings show that Microsoft and Nvidia have the greatest investment weights in the portfolio with the maximum Sharpe ratio. In the GMVP, JNJ and KO have the highest investment weights. By contrasting the three portfolios' combined performance with the S&P 500 index, both the maximum Sharpe ratio and the 1/N portfolios outperformed the benchmark index, while the GMVP performed below the benchmark. The findings of this study may offer insights into portfolio management for investors looking to increase their real assets.

Analysis of Implicit Strategy Optimization and Economic Returns of Machine Learning Techniques in Financial Asset Management

Abstract This paper formulates and adjusts investment portfolios based on machine learning techniques, adopts the B-L model after combining the GJR-GARCH-M model and the RBF model, combines the subjective view of the investor with the market equilibrium rate of return, and realizes the optimization of the implicit strategy in the management of the financial assets in the process, and finally analyzes the economic returns through the B-L model. The return of the asset portfolio under the improved B-L model is 0.37% higher than that of the market capitalization-weighted asset portfolio. The Improved B-L model’s asset allocation improves economic returns. In terms of cumulative return, the mean cumulative return of the improved B-L model is 50.7%, which is higher than the economic return of the regular B-L model and the equal-weight portfolio strategy.

Does commodity exposure benefit traditional portfolios? Evidence from India

Commodities and commodity futures are expected to benefit stock and bond portfolio diversification because traditional asset types like equities and bonds have low correlations with commodities. During periods when stocks and bonds may underperform, commodities may provide a hedge against inflation and other economic uncertainties. This study investigates the diversification benefits of adding commodities to a traditional portfolio of stock and bonds from the perspective of an Indian investor. It employs several commonly used asset allocation strategies such as mean-variance, equal risk contribution, most diversified portfolio, and equal weight portfolio on different commodity derivative groups. The performance of various portfolios indicates that not all commodity groups provide substantial diversification benefits to a traditional portfolio. Agricultural commodities enhance performance (with an Omega ratio of 1.654), whereas metal and energy-related commodities do not diversify the traditional portfolio significantly (Omega ratio of 1.087 and 0.945, respectively). Gold and different equity sectors also provide some diversification benefits. This study also supports the hypothesis that the behavior of different commodity groups is quite different. AcknowledgmentThe infrastructural support provided by the FORE School of Management, New Delhi, in completing this paper is gratefully acknowledged.

Portfolio Optimization Analysis -- Based on Six Stocks from Different Industries in the U.S. Stock Market

As the stock market becomes more and more unstable, investors all over the world pay more attention to risk management. Creating a portfolio is an effective way to make the trade-off between risk and return rate. This paper selects six stocks from the U.S. stock market in different industries, including the real estate industry, high technology, financial services company, pharmaceutical industry, automotive manufacturing industry and gas industry. This paper simulates 1,000,000 different investment portfolios by using the Monte Carlo simulation, then drawing the efficient frontier of the portfolio and figuring out the maximum Sharpe ratio and minimum volatility portfolio. As the results showed, McKesson Corporation accounted for the max proportion in both the maximum Sharpe ratio portfolio and the minimum volatility portfolio, which are 45.7149% and 60.8960% respectively. J.P. Morgan takes up 0.1232%, which is the min proportion in the maximum Sharpe ratio portfolio. However, Amazon accounted for the min proportion in the minimum volatility portfolio, which is 0.4828%. In the last, this paper compared the cumulative return of the three portfolios with the S&P 500 Index, only the return rate of the equal-weight portfolio and minimum volatility portfolio is better than the benchmark. Hence, this result may provide suggestions for picking certain kinds of portfolio in this special period.

Performance of Risk Parity Strategy in Developed Markets

This study examines the performance and effectiveness of the Risk Parity (RP) strategy in strategic asset allocation specifically utilizing the inverse volatility (IV) approach. The study utilizes data from 13 developed countries, encompassing stocks and government data, to analyze the performance of RP portfolio. A comparative analysis is conducted between the RP portfolio, traditional 60/40 and equal-weighted portfolios across significant market crises, including the dot-com crisis, financial crisis, sovereign debt crisis, and COVID-19 pandemic. This study reveals that inverse volatility approach or a portfolio with a higher weight towards safer assets compared to riskier assets delivers superior risk-adjusted returns. It presents evidence that challenges the notion of the market or 60/40 portfolio as an efficient portfolio and suggests that an alternative portfolio such as RP may offer superior efficiency in terms of risk and return. Moreover, the study examines the role of leveraging in RP portfolios, highlighting how investors can align risk levels with benchmark portfolios while achieving higher risk-adjusted returns. The findings highlight the substantial impact of leveraging on RP portfolio performance, demonstrating their resilience and superiority even in challenging market conditions.

Portfolio Optimization Based on Return Prediction and Semi Absolute Deviation (SAD)

A portfolio is a collection of investment financial assets managed by financial institutions or individuals. In investment activities, investors expect minimal loss risk and optimal stock portfolio weight to get maximum profit. Investors can monitor changes in stock index values to compare portfolio performance. This research has discussed how to build a portfolio based on stock datasets with the LQ45 index using return predictions from the artificial neural network (ANN) method with semi-absolute deviation (SAD). Furthermore, the portfolio is optimized by looking for weights that match it. After that, a comparison of portfolio performance was carried out using the Sharpe ratio (SR) method between the semi-absolute deviation (SAD) portfolio and the portfolio resulting from the formation of the equal weight (EW) portfolio. Portfolio performance with ANN prediction and SAD is better than equal-weight portfolios in terms of mean return, standard deviation, and sharpe ratio for portfolios with few stocks, namely 2 and 3 stocks. In addition, a portfolio with a higher number of stocks can make the portfolio value from the ANN close prediction algorithm process and the selection of weights based on SAD is better than portfolios with equal weight for each list of stocks in the portfolio.

Prediction-based mean–variance portfolios with risk budgeting based on neural networks

The integration of return prediction and portfolio models has been widely investigated and proven effective. The derived prediction-based portfolios can be classified into five main categories. This paper comprehensively compares the out-of-sample performance of these categories based on the traditional mean–variance (MV) model for the first time, where different neural networks are used for return prediction. The MV model and equal-weighted portfolio are used as comparisons. Due to the high volatilities of these prediction-based portfolios, the risk budgeting method is applied to advance these portfolios, where stocks with higher predicted volatilities own higher proportions of portfolio risk contribution. The equal risk contribution is used as the benchmark. Further, the out-of-performance of these advanced portfolios is investigated after deducting transaction fees since their high turnovers can seriously affect the obtained profits in practical investments. Empirical tests are implemented with component stocks of the Shanghai Stock Exchange (SSE) 50 index, where daily data range from 2011 to 2021. Experimental results indicate that the prediction-based MV portfolio built with forecasted errors (MV-FE) is significantly superior to the others under different risk aversion levels, followed closely by the prediction-based MV portfolio built with forecasted returns (MV-FR). In particular, medium risk aversion is more suitable for these portfolios. Moreover, the risk budgeting method considerably reduces the volatilities of these portfolios and improves their risk-adjusted returns. The risk budgeting method is more efficient than the equal risk contribution method. After subtracting their transaction fees of turnovers, the MV-FR portfolio with risk budgeting, i.e., the MV-FR-RB portfolio, outperforms the others and still obtains satisfying performance. Also, there is little difference between the MV-FR and MV-FE portfolios with risk budgeting.

Asset Allocation through Grey Wolf Optimization: A Case of KSE-30 Index

This article is an effort to aid investors by drawing attention to select investment items. Grey Wolf Optimization (GWO), TOPSIS with Eigenvector, Market Capitalization, and the Equal Weighted Technique are the four main methods discussed in this paper. This study uses the KSE-30 Index as its sample size; however, because to a lack of data, only 26 businesses are chosen for analysis using 10 criteria. All four methods are implemented and weights are determined based on these criteria. These weights are then utilized in conjunction with MATLAB's in-built tools to construct a portfolio. Based on its ability to generate the greatest possible portfolio, GWO appears to be a powerful resource for affluent investors. Equal-weighted portfolios performed the worst, followed by the Eigenvector-TOPSIS technique, then Market Capitalization.

Why Do Equally Weighted Portfolios Beat Value-Weighted Ones?

Equal-weighted (EW) portfolios have outperformed their value-weighted (VW) counterparts over multiple decades in various investment universes. This article investigates the long-term evidence for the EW–VW return spread in a broad US equity universe across multiple factor models. Unsurprisingly, EW investing comes with a highly significant positive size factor exposure. Given its acyclic rebalancing character, EW investing is also found to benefit from short-term reversal effects while suffering from negative momentum exposure. The authors also document a pronounced seasonality effect in EW investing that would see outsized returns in January. They revisit these findings in the more investible universe of S&P 500 stocks and discuss how to best harvest the embedded factor premiums.

Empirical Research on Momentum and Reversal Effects in Software Industry

This paper uses monthly data from Jan 2002 to Sep 2022 from fifty top Software companies of 2022 rated by the Software Report to examine the existence of momentum and reversal effects both in short term and long term in Software industry. The relationship between investment sentiment and anomalies is further explained and the robustness of FF-3F Model is tested. Based on two ways of forming portfolios, equal-weighted portfolio and value-weighted portfolio, the result shows that the software industry has stronger momentum and reversal effects in the medium term and short term. The Mkt-RF factor in CAPM and FF-3F model explain the momentum and reversal effects better for software industry in the short run, and HML factor explains better in the long term. Value weighted portfolio is better on explaining these effects. It is better not to utilize long term investment strategy and investors are preferring value stocks in Software industry.

The empirical performance of option implied volatility surface-driven optimal portfolios

We apply a two-step strategy to forecast the dynamics of the volatility surface implicit in option prices to all American-style options written on the stocks that have entered the Dow Jones Industrial Average Index between 2004 and 2016. We explore whether the implied volatilities extracted through the two-step approach help improve the out-of-sample performance of minimum-variance portfolios. We find that, by using option-implied volatilities in estimating the covariance matrix, the ex-post volatility of the minimum-variance portfolio is lower when compared with the equal-weighted portfolio and a minimum-variance portfolio simply derived from the historical, sample covariance matrix estimator. Moreover, over most of our 13-year sample, the realized Sharpe, Sortino and information ratios increase when the sample covariance matrix estimator is replaced with its implied counterpart. However, the benefits of using option-implied information are countered by an increase in portfolio turnover that may imply higher (implicit) transaction costs. We also apply shrinkage methods to both the sample covariance estimator and the implied covariance estimator and note that they often lead to significant improvements in portfolio performance.

Application of Maximum Sharpe Ratio and Minimum Variance Portfolio Optimization for Industries

Since the start of 2022, expectations regarding the continuous raising of interest rates by the Fed to curb the 40-year-high inflation has been negatively impacting the US stock market, leading to volatility spikes. Thus, portfolio optimization plays an important role to risk-averse investors. This paper investigated an optimal portfolio strategy that aims to maximize the Sharpe ratio to outperform a common benchmark, the S&P500. Eleven US stocks were selected, one from each of the eleven market sectors, based on market capitalization. Then, mean-variance portfolio optimization was conducted to obtain an optimal distribution of stocks weighing in Maximum Sharpe Ratio Portfolio (MSRP) and Global Minimum Variance Portfolio (GMVP), and backtesting was adopted to construct portfolio performance for MSRP, GMVP and Equal-Weight Portfolio (1/N) strategies by using historical data from the first eight months of 2022. The Sharpe ratio was calculated to compare the performance between the three strategies---MSRP, GMVP and 1/N---and the S&P500. Empirical results, supported by a robustness test, suggested that MSRP strategy outperformed S&P500 with the highest Sharpe ratio, which indicated that MSRP achieved a higher return per unit of risk regardless of market volatility presented in different time intervals. Therefore, this research provides insights into portfolio investment decision-making for risk-averse investors.

Multivariate models of commodity futures markets: a dynamic copula approach.

We apply flexible multivariate dynamic models to capture the dependence structure of various US commodity futures across different sectors between 2004 and 2022; particular attention is paid to the 2008 financial crisis and the COVID-19 pandemic. Our copula-based models allow for time-varying nonlinear and asymmetric dependence by integrating elliptical and skewed copulas with dynamic conditional correlation (DCC) and block dynamic equicorrelation (Block DECO). Flexible copula models that allow for multivariate asymmetry and tail dependence are found to provide the best performance in characterizing co-movements of commodity returns. We also find that the connectedness between commodities has dramatically increased during the financial distress and the COVID-19 pandemic. The impacts of the financial crisis appear to be more persistent than those of the pandemic. We apply our models to some risk management tasks in the commodity markets. Our results suggest that optimal portfolio weights based on dynamic copulas have persistently outperformed the equal-weighted portfolio, demonstrating the practicality and usefulness of our proposed models.

A parallel non-convex approximation framework for risk parity portfolio design

In this paper, we propose a parallel non-convex approximation framework (NCAQ) for optimization problems whose objective is to minimize a convex function plus the sum of non-convex functions. Based on the structure of the objective function, our framework transforms the non-convex constraints to the logarithmic barrier function and approximates the non-convex problem by a parallel quadratic approximation scheme, which will allow the original problem to be solved by accelerated inexact gradient descent in the parallel environment. Moreover, we give a detailed convergence analysis for the proposed framework. The numerical experiments show that our framework outperforms the state-of-art approaches in terms of accuracy and computation time on the high dimension non-convex Rosenbrock test functions and the risk parity problems. In particular, we implement the proposed framework on CUDA, showing a more than 25 times speed-up ratio and removing the computational bottleneck for non-convex risk-parity portfolio design. Finally, we construct the high dimension risk parity portfolio which can consistently outperform the equal weight portfolio in the application of Chinese stock markets.

The stable tail dependence and influence among the European stock markets: a score-driven dynamic copula approach

This paper studies the time-varying tail dependence among the European stock markets and assesses the influence of individual financial markets. By utilizing two generalized autoregressive score (GAS) copulas, we compute the tail dependence for 11 European stock indexes and 1 American stock index over the last 16 years. Notably, it is found that the dependencies among European stock indexes are generally stable, even during the crisis periods. Then an influence measure is proposed for each individual market based on the tail dependence. Interestingly, the influence ranking is also stable for both the whole period and two big drawdown days in crisis periods. To be specific, AEX (Netherlands), FCHI (France), and GDAXI (Germany) always show the most significant influences, while SPX (USA) is always the least influential one. Finally, an equal-weighted portfolio is built to measure the systemic risk in the European stock markets. It is found that both patterns of risk (VaR) and expected shortfall (ES) are different from the time-varying tail dependence. There exist obvious inverted spikes on 16 October 2008 and 16 March 2020. The results indicate that the extreme portfolio risk does not come from the increase of dependence among European stock markets, but from the individual jumps.

Is an equally weighted global investment portfolio the outperformer?

The paper builds, in the first part, a benchmark index based on the optimal mix of indices for the global asset classes of equity, fixed-income securities, real estate, commodities, and currencies including cryptocurrencies so as to maximize the ex-post Sharpe ratio. The objective of the first part is to help investors across the globe compare portfolio performance with a uniform benchmark. In the second part, a comparison of portfolio performances is based on five methods of portfolio construction viz; 1) historical returns and variance matrix used along with Markowitz model to discover optimal weights for portfolio components, 2) modification to this approach by using autoregressive integrated moving average (ARIMA) based predicted returns in place of historical returns, 3) global minimum volatility (GMV) portfolio, 4) global market weight portfolio and 5) equal weight portfolio. The objective in the second part is to explore an easy-to-use and at the same time conceptually sound method to build portfolios for any investor worldwide even if such an investor does not have access to or does not wish to rely upon the views and opinions of investment experts. The ex-post performance of portfolios based on these five methods is compared with the ex-post performance of 207 global active and passive funds. This comparison suggests that an equal-weighted portfolio with periodical rebalancing gives the best Sharpe ratio for a global investor.