Traditional Portfolio Research Articles

The prescriptive nature of market timing and predictive portfolios

Abstract We explore the ability of market timing, model-based forecasts that include incremental information over and above momentum, to create long-short portfolios that consistently provide superior returns to traditional benchmarks, and frame our analysis in the idea of “prediction-led prescription” for forward-looking decision making. Our methodology consists of linear and non-linear forecasts with model selection, that predict the direction of future returns and apply then into the construction of portfolios. Our results strongly recommend that predictive-based portfolio allocation can produce excess returns and higher Sharpe ratios, above and beyond the individual return series that are utilized to create the forecasts and traditional portfolio benchmarks. The use of “predictive methods in portfolio construction reinforces the idea of prediction-led prescription” as portfolios are prescriptions for investment management and have an exclusively forward-looking nature.

A Hierarchical Approach to a Tri-Objective Portfolio Optimization Problem Considering an ESG Index

Traditional portfolio construction primarily revolves around a bi-objective approach, focusing on minimizing portfolio variance while maximizing expected returns. However, this approach leaves out other objectives that could interest decision makers. In this work, we incorporate an extra objective, namely the environmental, social, and governance index (ESG), as a secondary objective. This addition empowers investors to customize their portfolios by defining explicit trade-off thresholds between expected returns and risk, considering the ESG index. To achieve this goal, we make use of external archiving techniques and evolutionary algorithms. In particular, we first find approximate solutions to the bi-objective problem; then, we look for efficient solutions for ESG. We tested our approach with data on the Dow Jones, S&P500, and Nasdaq100 from Yahoo Finance. The results show that the proposed methodology can identify portfolios with good returns and risks considering ESG.

Adaptive Risk Parity Strategies for Managing Portfolio Risk in Volatile Markets

This paper presents a novel model for Adaptive Risk Parity Portfolio Construction, designed to dynamically adjust portfolio weights based on changing market conditions and economic regimes. Traditional portfolio strategies, such as Modern Portfolio Theory (MPT), often rely on static assumptions that can lead to suboptimal performance in volatile markets. In contrast, the adaptive risk parity approach aims to equalize risk contributions across asset classes while responding to fluctuations in economic indicators, such as GDP growth and inflation rates. By employing techniques such as Markov switching models and machine learning for regime identification, the proposed model allows for real-time adjustments to asset allocations. The back-testing results demonstrate that the adaptive risk parity portfolio outperforms static risk parity and traditional mean-variance optimized portfolios in terms of risk-adjusted returns and drawdown management. This research contributes to the field of portfolio management by providing a framework that enhances resilience and adaptability in investment strategies.

Performance of Commodity Futures-Based Dynamic Portfolios

This paper analyzes the return performance of various commodity futures-based dynamic portfolios over the period from 31 January 1986 to 31 July 2023. By constructing 30 distinct portfolios categorized by style and performance, we assess their potential for enhancing the performance of traditional portfolios consisting of equity and bonds. We find that most commodity portfolios do not offer statistically significant returns, either in terms of average or risk-adjusted returns. Only the portfolios in the basis category and portfolios in the term structure category exhibit significantly positive risk-adjusted returns, indicating their potential value for portfolio enhancement. The performance of these portfolios is not different in pre-financialization and financialization periods.

On bank stock return spillovers in South Africa: Implications for portfolio hedging

The purpose of this study is to investigate the return spillovers among the five systemically important banks in South Africa. The study employs a time-varying parameter vector autoregression (TVP-VAR) framework utilising daily data between 2000 and 2024. A minimum return connectedness portfolio is constructed from the spillover indexes and compared with traditional portfolio optimisation methods in terms of risk reduction and reward-to-risk ratios. The research findings reveal that Capitec, the smallest bank in terms of assets, has weaker return connectedness with other banks while ABSA, FirstRand, Nedbank and Standard Bank are moderately connected. Moreover, the two largest banks in terms of assets, Standard and FirstRand, are more connected than any other pairwise connection in the network. During significant events such as national elections, the global financial crisis and the COVID-19 pandemic, system-wide connectedness increases.

A health-conformant reading of the GDPR's right not to be subject to automated decision-making.

As the use of Artificial Intelligence (AI) technologies in healthcare is expanding, patients in the European Union (EU) are increasingly subjected to automated medical decision-making. This development poses challenges to the protection of patients' rights. A specific patients' right not to be subject to automated medical decision-making is not considered part of the traditional portfolio of patients' rights. The EU AI Act also does not contain such a right. The General Data Protection Regulation (GDPR) does, however, provide for the right 'not to be subject to a decision based solely on automated processing' in Article 22. At the same time, this provision has been severely critiqued in legal scholarship because of its lack of practical effectiveness. However, in December 2023, the Court of Justice of the EU first provided an interpretation of this right in C-634/21 (SCHUFA)-although in the context of credit scoring. Against this background, this article provides a critical analysis of the application of Article 22 GDPR to the medical context. The objective is to evaluate whether Article 22 GDPR may provide patients with the right to refuse automated medical decision-making. It proposes a health-conformant reading to strengthen patients' rights in the EU.

Robust, extended goal programming with uncertainty sets: an application to a multi-objective portfolio selection problem leveraging DEA

AbstractThis study presents a two-phase approach of Data Envelopment Analysis (DEA) and Goal Programming (GP) for portfolio selection, representing a pioneering attempt at combining these techniques within the context of portfolio selection. The approach expands on the conventional risk and return framework by incorporating additional financial factors and addressing data uncertainty, which allows for a thorough examination of portfolio outcomes while accommodating investor preferences and conservatism levels. The initial phase employs a super-efficiency DEA model to streamline asset selection by identifying suitable investment candidates based on efficiency scores, setting the stage for subsequent portfolio optimization. The second phase leverages the Extended GP (EGP) framework, which facilitates the comprehensive incorporation of investor preferences to determine the optimal weights of the efficient assets previously identified within the portfolio. Each goal is tailored to reflect specific financial factors spanning both technical and fundamental aspects. To tackle data uncertainty, robust optimization is applied. The research contributes to the robust GP (RGP) literature by analyzing new RGP variants, overcoming limitations of traditional and other uncertain GP models by incorporating uncertainty sets. Robust counterparts of the EGP models are accordingly developed using polyhedral and combined interval and polyhedral uncertainty sets, providing a flexible representation of uncertainty in financial markets. Empirical results, based on real data from the Tehran Stock Exchange comprising 779 assets, demonstrate the superiority of the proposed approach over traditional portfolio selection methods across various uncertainty settings. Additionally, a comprehensive sensitivity analysis investigates the impact of uncertainty levels on the robust EGP models. The proposed framework offers guidance to investors and fund managers through a pragmatic approach, enabling informed and robust portfolio decisions by considering efficiency, uncertainty, and extended financial factors.

Do socially responsible indices outperform conventional indices? Evidence from before and after the onset of Covid‐19

AbstractAccording to traditional portfolio theories constraints, restrictions, and screens applied in portfolio selection reduces the diversification opportunities which can impact financial performance. Is this true in the case of socially responsible investment (SRI)? To answer this, present study analyzes the performance of Socially Responsible (SR) indices in comparison to conventional indices in an emerging economy. The uniqueness of the study is that it analyses the performance of Shariah, ESG, and thematic indices in a single study. Further, the study measures the impact of Covid‐19 on them. Comparative performance evaluation was conducted by using absolute return analysis and risk‐adjusted measures namely, Sharpe ratio, Treynor ratio, tracking error, information ratio, capital asset pricing model (CAPM), Fama–French three‐factor, and Carhart's four‐factor models. The Structural break was identified, hence analysis was conducted for the total period (January 2017–March 2023) and two sub‐periods, that is, pre and post‐Covid‐19 period. No significant difference was found between the returns of SR indices and conventional indices as against the benchmark index on the basis of absolute return analysis. Sharpe ratio and Treynor ratio both were having negative values for all the SR and conventional indices. Tracking error for all the SR and Conventional indices were very low. The CAPM and both multi‐factor models univocally pointed toward the underperformance of all the SR (except S&P BSE 100 ESG index which had equal performance) and both conventional indices against the benchmark index. Noteworthy point is that only Shariah indices gave the highest returns during post‐Covid period. This research will help in deepening the SRI in the capital market. Companies should increase their ESG scores and make efforts to be listed on the SR indices. Policymakers should announce some kind of rebates, or recognition for star‐performing companies in the field of sustainability to encourage other companies to adopt SR practices in their business operations. The novelty of the current study is that it adds to the socially responsible literature by analyzing the performance of Shariah, ESG, and Thematic indices and conventional indices in a single study in the fastest‐growing economy of India and analyses the impact of Covid‐19 on this performance.

An Integrative Review of Portfolio Management

This paper delves into the evolution of portfolio theories, tracing their development from the seminal work of Markowitz's traditional portfolio theory to the contemporary landscape of modern portfolio management. Emphasizing the relevance for real-world investors, the study scrutinizes the advantages, applications, and limitations of these investment theories. The exploration commences with a comprehensive analysis of return, risk, and various factors integral to portfolio theory. Leveraging qualitative analysis and real-world examples, the paper elucidates the mathematical model underpinning the theory, furnishing investors with a structured investment strategy. The focal points of the paper are the VaR (Value at Risk) model and the beta coefficient. Through an in-depth examination of their mathematical foundations and theoretical underpinnings, the research elucidates the symbiotic relationship between their economic significance and mathematical intricacies. Furthermore, the paper expounds on their collective contribution to the overarching portfolio theory, shedding light on their role as guiding principles for risk-averse investors. By weaving together theoretical frameworks and practical implications, this paper seeks to provide a nuanced understanding of portfolio theories, equipping investors with valuable insights for informed decision-making in the dynamic landscape of financial markets.

Performance Evaluation of State-Owned Company Stocks in Indonesia

This study delves into the performance of Indonesian state-owned enterprises (BUMN) stocks from February 2021 to October 2023, attracting investor attention due to their strategic position in the national economy. However, the stock performance during this period exhibited suboptimal movements, failing to surpass the returns of the Indonesia Stock Exchange Composite Index (IHSG). To address this, a prudent investment strategy is essential, encompassing portfolio construction and management. In portfolio construction, the study employs proven smart beta strategies, known for delivering superior returns compared to traditional portfolios. Portfolio management involves testing two strategies: a passive approach utilizing the buy-and-hold technique and an active approach employing portfolio recomposition. This quantitative descriptive study utilizes historical closing price data, forming portfolios based on selected beta groups. Weighting is adjusted, with higher beta stocks receiving a larger allocation in the portfolio. The research compares two beta groups (high and low) and two investment strategies (active and passive) using the Sharpe, Treynor, and Jensen indices. The results indicate that the high-beta group and active strategy outperform, aligning with the investment adage "high risk, high return." The high-beta group yields higher returns due to its increased risk profile, consistent with the notion of high-risk, high-reward investments. The active strategy provides opportunities to discard underperforming stocks and enhance the weighting of well-performing ones during recomposition. These findings are valuable for investors seeking to capitalize on BUMN stocks at lower prices. The study aids in minimizing negative returns (losses) by identifying the most suitable strategy for BUMN portfolios. Investors can leverage this research to make informed decisions and navigate the challenges associated with investing in state-owned enterprises.

АЛЛОКАЦИЯ В ТЕМАТИЧЕСКИЕ ИНВЕСТИЦИИ: НОВЫЙ ПОДХОД К ФОРМИРОВАНИЮ ПОРТФЕЛЯ

The article discusses the concept of thematic investment in asset allocation as an additional investment dimension that goes beyond asset classes, regions, sectors and styles, and suggests a structure for the distribution of thematic investments at the strategic level of asset allocation. The approach uses a structure based on reliable portfolio optimization, which takes into account the expected excess return from the impact of the topic and from exposure to traditional risk factors, and provides an example illustrating how thematic investments fit into traditional portfolios with multiple assets.

Multiple-factor optimistic value based model and parameter estimation for uncertain portfolio optimization

In the traditional portfolio models, the return rates of risky assets are usually defined as random variables or fuzzy variables. However, when some events such as financial crises or wars occur that can cause instability in financial markets, the results of these assumptions are not always satisfactory. Therefore, some scholars try to use uncertain variables to express the return rates. In this paper, a two-factor uncertain portfolio problem under optimistic value criteria is studied, where we use the optimistic value instead of expected value to describe the investment return and study the impact of economic circumstance factor and corporation’s specificity factor on the return rates. Firstly, a two-factor optimistic value-variance-entropy model with background risk is constructed, in which the total investment return consists of the risky asset return and the background asset return. Then, considering the investors with two kinds of risk preferences, a three-step method is applied to convert the bi-objective optimization model. Moreover, a moment estimation method is used to process the expert-estimated data on the return rates. Finally, a numerical simulation is presented for showing the applicability of our models and solution method.

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.

Deep reinforcement learning for portfolio management

Portfolio management facilitates trading off risks against returns for multiple financial assets. Reinforcement Learning (RL) is one of the most promising algorithms for portfolio management. However, these state-of-the-art RL algorithms only complete the task of portfolio management, i.e., acquire the different asset features of portfolio, without considering the global context information from portfolio, which leads to non-optimal portfolio representations; Moreover, the corresponding optimizations are implemented using only the loss function in the viewpoint of RL, without considering the relationships between the local asset information and global context embeddings, which leads to non-optimal portfolio policies. To deal with these issues, this paper proposes a Task-Context Mutual Actor–Critic (TC-MAC) algorithm for portfolio management. Specifically, TC-MAC algorithm is developed based on: (1) representation learning introduces a proposed Task-Context (TC) learning algorithm, which not only encodes the task (i.e., acquire different asset features) of portfolio, but also encodes the global dynamic context of portfolio, thus which helps to learn optimal portfolio embeddings; (2) policy learning introduces a proposed Mutual Actor–Critic (MAC) framework, which can measure the relationships between local embedding of each asset and global context embeddings by maximizing mutual information, the corresponding Mutual-Information loss function combines with RL loss function (i.e., Actor–Critic loss) to collectively optimize the whole algorithm, thus which helps to learn optimal portfolio policies. Experimental results on real-world datasets demonstrate the superior performance of TC-MAC algorithm over the well-known traditional portfolio methods and these state-of-the-art RL algorithms, at the same time, show its advantageous transferability.

LEGAL DIVERSIFICATION AS A STRATEGY TO REDUCE INVESTMENT RATIOS

Everyone who invests will want to get optimal profits with a growing capital value, whether using long-term investments or short-term investment products. Therefore, a strategy is needed in making investments. One of them is by investment diversification. Investment diversification is a widely accepted strategy for reducing investment risk. In analysing the portfolio, continuous analysis is needed in order to obtain relevant information, so that the target of portfolio formation through diversification provides optimal results. At the time when the traditional portfolio was recognised, the simple diversification of investments was the commonly used strategy, however, due to its inability to recognise the correlation between returns on different investments, simple diversification was replaced with efficient diversification. This study is aimed at conducting a comparative analysis between the simple and efficient diversification of investments, and to determine the combination of expected return and LQ-45 stock risk in order to select investments in Indonesia Stock Exchange through the establishment of an optimum portfolio.

Collective Dynamics, Diversification and Optimal Portfolio Construction for Cryptocurrencies.

Since its conception, the cryptocurrency market has been frequently described as an immature market, characterized by significant swings in volatility and occasionally described as lacking rhyme or reason. There has been great speculation as to what role it plays in a diversified portfolio. For instance, is cryptocurrency exposure an inflationary hedge or a speculative investment that follows broad market sentiment with amplified beta? We have recently explored similar questions with a clear focus on the equity market. There, our research revealed several noteworthy dynamics such as an increase in the market's collective strength and uniformity during crises, greater diversification benefits across equity sectors (rather than within them), and the existence of a "best value" portfolio of equities. In essence, we can now contrast any potential signatures of maturity we identify in the cryptocurrency market and contrast these with the substantially larger, older and better-established equity market. This paper aims to investigate whether the cryptocurrency market has recently exhibited similar mathematical properties as the equity market. Instead of relying on traditional portfolio theory, which is grounded in the financial dynamics of equity securities, we adjust our experimental focus to capture the presumed behavioral purchasing patterns of retail cryptocurrency investors. Our focus is on collective dynamics and portfolio diversification in the cryptocurrency market, and examining whether previously established results in the equity market hold in the cryptocurrency market and to what extent. The results reveal nuanced signatures of maturity related to the equity market, including the fact that correlations collectively spike around exchange collapses, and identify an ideal portfolio size and spread across different groups of cryptocurrencies.

When Unconscious Desires Motivate Fund Selection: Active Share and Position Count May Do More to Relieve Angst Than Identify Skill

Identifying equity funds that are likely to outperform remains a daunting task for capital allocators. Traditional portfolio analyses provide useful insights into how a fund is being managed, but offer little clarity on the potential for ongoing success. Active share and position count were introduced as metrics that identify funds that will generate excess returns via skilled management. But this article argues that these metrics provide no information regarding future fund performance or manager skill. We further propose that active share and position count do more to relieve unconscious anxiety provoked during the allocation process than to enhance rigorous judgment.

Performance comparison of multifractal techniques and artificial neural networks in the construction of investment portfolios

This work aims to compare the performance of the traditional portfolios of the S&P500, Markowitz, and Sharpe with the multifractal trend fluctuation portfolios (MF-DFA) and portfolios of artificial neural networks with Student's asymmetric probability classification (ANN-t). In this study, we use daily data for S&P500 stocks between January 18, 2018, and July 12, 2022, where we backtest return and risk metrics such as annual volatility, Value at Risk, Sharpe Ratio, Sortino Ratio, Beta, and Jensen´s Alpha. For both return and risk, we obtain the results confirming that the ANN-t technique might indicate better investment entries, which contradicts the Efficient Market Hypothesis (EMH).

Does reinforcement learning outperform deep learning and traditional portfolio optimization models in frontier and developed financial markets?

Advancements in machine learning have opened up a wide range of new possibilities for using advanced computer algorithms, such as reinforcement learning in portfolio risk management. However, very little evidence has been provided on the superior performance of reinforcement learning models over traditional optimization models following the mean-variance framework in different financial market settings. This study uses two experiments with data from the Vietnamese and U.S. securities markets to justify whether advanced machine learning models could outperform traditional portfolios' cumulative returns while optimizing the Sharpe ratio. The results suggest that reinforcement learning consistently outperforms the established methods and benchmarks in both experiments, even when using a very similar degree of diversification in portfolio construction and the same input data. This study confirms the ability of reinforcement learning to provide dynamic responses to market conditions and redefine the risk-return standard in the financial system.

The beneficial role of green bonds as a new strategic asset class: Dynamic dependencies, allocation and diversification before and during the pandemic era

The paper proposes a full comprehensive analysis of green bond diversification benefits, their co-movement with multiple market indices, and the corresponding implications for portfolio allocation. Based on a time frame of seven years, divided into four sub-periods, the co-movements of green-bond indices, i.e. Solactive Green Bond Index and Bloomberg Barclays MSCI Green Bond Index, and the stock/bond market have been described, shedding light on the connections with sectors most affected by the Covid-19 pandemic. The Solactive Green Bond Index is found to provide the greater diversification benefit of the two green-bond indices, on average during the seven years and also during the pandemic. Allocation strategies and risk performances have also been analyzed to assess the impact of green-bond indices on otherwise traditional portfolios; their diversification power is discussed by use of traditional measures and an additional behavioral approach, drawing attention to its evolution in time and its consistency in terms of diminished risks and increased returns. Portfolios constructed with the inclusion of green bonds prove preferable in terms of risk, in all periods and for all strategies, while the superiority of returns depends on the allocation strategy.