Portfolio Construction Techniques Research Articles

Examining connections between the fourth industrial revolution and energy markets

We investigate the relationship between assets related to the fourth industrial revolution and alternatively dirty and clean energy markets. We examine portfolio diversification benefits and risk reduction gains based on three portfolio construction techniques. Results of static connectedness analysis indicate weak integration between the fourth industrial revolution assets and both clean and dirty energy markets with spillovers being mainly in the short term. Time-varying connectedness analysis shows that the connectedness is time-varying and responsive to external shocks Further, portfolio analysis shows that fourth industrial revolution assets are important for fixed-income portfolios that also comprise stocks and energy assets.

Interlinkages across US sectoral returns: time-varying interconnectedness and hedging effectiveness

This study examines the time-varying asymmetric interlinkages between nine US sectoral returns from January 2020 to January 2023. To this end, we used the time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach of Adekoya et al. (Resour Policy 77:102728, 2022a, Resour Policy 78:102877, 2022b) and analyzed the time-varying transmitting/receiving roles of sectors, considering the positive and negative impacts of the spillovers. We further estimate negative spillovers networks at two burst times (the declaration of the COVID-19 pandemic by the World Health Organization on 11 March 2020 and the start of Russian-Ukrainian war on 24 February 2022, respectively). Moreover, we performed a portfolio back-testing analysis to determine the time-varying portfolio allocations and hedging the effectiveness of different portfolio construction techniques. Our results reveal that (i) the sectoral return series are strongly interconnected, and negative spillovers dominate the study period; (ii) US sectoral returns are more sensitive to negative shocks, particularly during the burst times; (iii) the overall, positive, and negative connectedness indices reached their maximums on March 16, 2020; (iv) the industry sector is the largest transmitter/recipient of return shocks on average; and (v) the minimum correlation and connectedness portfolio approaches robustly capture asymmetries. Our findings provide suggestions for investors, portfolio managers, and policymakers regarding optimal portfolio strategies and risk supervision.

Dynamic Connectedness and Investment Strategies between Commodities and ESG Stocks: Evidence from India

The study investigates the connectedness between commodities and ESG stocks of India using the extended joint connectedness approach. The study found a time-varying relationship between commodities and ESG stocks. It also discovered that there is a low spillover between the two. However, the total connectedness increased during the Russia-Ukraine war but remained low. The study found that crude oil and natural gas act as net transmitters, while ESG stocks and gold act as net receivers. ESG stocks are negatively connected with gold and have a low degree of positive correlation with crude oil and natural gas. Therefore, portfolio diversification opportunities exist between commodities and ESG stocks. The study exhibits that investors may derive significant benefits by adjusting their portfolios based on the optimum weights provided by the portfolio construction technique. The study provides valuable insights for portfolio managers, investors, and policymakers.

Enhancing Portfolio Allocation by LSTM Model

Accurate forecasting of stock prices and the construction of optimized portfolios are essential for investors in the dynamic technology sector. This paper proposes a comprehensive approach that combines Long Short-Term Memory (LSTM) models with portfolio construction techniques specifically tailored to the stocks of Apple Inc. (AAPL), Meta Platforms Inc. (META), Amazon.com Inc. (AMZN), Microsoft Corporation (MSFT), and NVIDIA Corporation (NVDA) from January 1st ,2018 to May 31st, 2023. By leveraging the sequential nature of historical stock price data(80% of data), LSTM models capture complex patterns and dependencies, enabling more precise predictions of Adjusted Close (Adj Close) prices. Subsequently, the forecasted prices (20% of data)are utilized to construct optimized portfolios that maximize returns and minimize risks within the technology sector using Monte Carlo simulations, efficient frontier analysis, and key risk-return metrics. The overall result of the prediction data is similar to the actual data which implies that the integration of LSTM-based forecasting and portfolio construction provides a robust framework for informed investment decision-making and risk management. And the application of Monte Carlo simulations, efficient frontier analysis, and key risk-return metrics gave us two portfolio allocation options : Minimum Variance model (40% of AAPL,60% of MSFT) and Maximum Sharpe Ratio model ( 47% of META, 53% of NVDA). The evaluation of the two portfolios show that the strategy can significantly beat the SP500 index.

Dynamic interlinkages between cryptocurrencies, NFTs, and DeFis and optimal portfolio investment strategies

PurposeThis study aims to scrutinize time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens and DeFi assets between 1 July 2018 and 19 February 2023 and determine optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.Design/methodology/approachThis work examines time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens, and DeFi assets between 1 July 2018 and 19 February 2023. To this end, the time-varying parameter-vector autoregression (TVP-VAR)-based connectedness methodology of Antonakakis et al. (2020) This approach is an extended version of the Diebold–Yilmaz (DY) method (Diebold and Yılmaz, 2014) and has advantages over the original DY. First, unlike the DY, it is free of the selection of a particular window size. Second, it has robustness for the outliers. Furthermore, following Broadstock et al. (2022), the author estimates time-varying optimal portfolio weights and hedging effectiveness under different portfolio construction scenarios.FindingsThis study's results indicate the following results: (1) The overall connectedness indices prominently capture well-known financial/geopolitical distress incidents; (2) the leading cryptocurrencies (ETH, BTC and BNB) are the largest transmitter of return shocks, while LINK and BTC are the largest transmitters/recipients of volatility shocks; (3) cryptocurrencies, NFTs and DeFi form distinct cluster groups in terms of return and volatility connectedness; (4) the connectedness networks estimated around the 2022 cryptocurrency crash and the FTX's filing for the bankruptcy are characterized by the strongest return and volatility interlinkages; (5) optimal portfolio strategies computed by different portfolio construction techniques display similar motifs and have sustained growth paths except for some short-lived drop backs.Research limitations/implicationsThis study's findings imply several policy suggestions for investors, stakeholders and policymakers. First, the study's time-based dynamic interlinkages can help market participants in their optimal portfolio decisions. In particular, the persistent net receiving roles of the DeFi assets and the NFTs throughout the episode, especially around the financial/geopolitical turmoil, underpin their safe haven potentials (Umar et al., 2022a, b). Finally, since the total connectedness indices (TCIs) are prone to significantly increase around financial/geopolitical burst times, these tools can be valuable for policy makers to monitor risk.Originality/valueThe contribution of knowledge is at least threefold. First, the author focuses on the dynamic time interlinkages among major cryptocurrencies, NFTs and DeFi assets in July 2018 and February 2023 considering the prominent recent financial/geopolitical incidents. Second, the author estimates network topologies of dynamic connectedness around financial/geopolitical bursts and compared them in terms of interlinkages. Finally, the author calculates the time-varying optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.

Portfolio constructions in cryptocurrency market: A CVaR-based deep reinforcement learning approach

Cryptocurrency markets have much larger tail risk than traditional financial markets, and constructing portfolios with such large tail risk assets would be challenging. Therefore, cryptocurrency funds demand new superior risk management models and Conditional Value at Risk (CVaR) is a prevailing risk measure for constructing portfolios in stock markets with large tail risk. Consequently, our paper contributes to the literature by developing a new cryptocurrency portfolio model framework based on the CVaR risk measure and a deep reinforcement learning optimization framework. We use the data from cryptocurrency market starting 2015 to 2021, unfolding that CVaR measure with deep learning outperforms the traditional portfolio construction technique. Compared with traditional economic parameter-based portfolio models, our model free based approach can capture the nonlinear compounding effect of multiple risk shocks by deep reinforcement learning on the risk distribution with economic structural breakdown. It can guide investments in financial markets with high tail risks.

Cost of trading factor strategies

Recent academic literature claims that style premia are not robust to trading costs. Several papers written by practitioners counter that the assumptions made by academics on how style portfolios rebalance are too conservative. In this paper we analyse the cost of trading factor strategies using a market impact model estimated from actual transactions data, realistic and multiple portfolio construction techniques and up-to-date data. Our results suggest that, for realistic portfolio sizes, the typical costs of rebalancing a single-factor strategy are unlikely to erode the factor premium.
 In addition, by building portfolios across different factors and with alternative portfolio construction techniques, we are able to assess the degree of similarity amongst strategies over time, which can be viewed as a measure of crowding. The properties of our new crowding indicator are illustrated through an extensive empirical investigation. We then assess its relation to volatility, liquidity and market impact and find that increases in our crowding measure tend to be followed by increases in the volatility of a strategy’s returns.

Satisfying exclusions in portfolio construction: The ESG case

In this paper, we show that quantitative portfolio construction techniques can deal with ‘reasonable’ constraints and deliver most of the expected (ie before constraints) value-added. To do so, we use the case of a hypothetical environmental, social and corporate governance (ESG) investor who chooses to not invest in companies typically excluded for ESG reasons (eg weapons manufacturing, nuclear energy generation, tobacco or high carbon intensity) back in the late 1980s. While satisfying ESG objectives, we capture the market’s exposure using several portfolio constructions such as pro-rata cap-weighting and minimum tracking. We also capture other ‘styles’ that could mimic exposures some asset owners want to be exposed to, such as value or momentum. Our results are helpful to deliver an appropriate, risk-controlled investment experience to asset owners who must integrate externally defined constraints in their long-only equity investments.

Evaluating Hierarchical Equal Risk Contribution Portfolios in the Chinese Stock Market

This paper investigates the usefulness of the Hierarchical Equal Risk Contribution algorithm to exploit correlation structure in China’s equity market over 2001-2020. By running a horse race of different combinations of metrics and linkages, we demonstrate that the winner strategy always beats traditional portfolio construction techniques. Better-performing risk-based hierarchy strategies vary with stock-sorting methods by size, mean return, volatility, and Sharpe ratio. However, our treatment results in extremely imbalanced asset allocation, implying that we capture information other than the standard Chinese industrial classification.

About Factor Investing: Analysis of This Industry, Financial Literature, Strategies, and Key Trends

Many equity investors looking for higher returns at a lower cost have turned to factor investing. After identifying other risk factors in addition to the market component, alternative portfolio construction techniques were created to compensate for the inefficiencies of market-cap strategies. By analyzing the current literature and examining how well factor strategies performed, this research article seeks to provide a formal foundation for this important theme in asset management.

Uncertain risk parity

Risk parity is portfolio construction technique that, using risk alone, scales each part of a portfolio — e.g., stocks, bonds, currencies, commodities — so that its contribution to net portfolio risk matches its budgeted risk. Because risks are measured using a point-estimate of covariance, the method is subject to problems of estimation error. This paper performs risk parity with covariance modeled as uncertain in order to achieve a weighting robust to changes in regime and hidden risks from misperceived hedging. The uncertain risk contributions, calculated en route, have value well beyond risk parity. Reporting a portfolio’s “uncertain risk decomposition” reveals the range around numbers and, more important, the risks that arise from inexact knowledge, e.g., market’s going from invisible to the biggest latent risk.

Diversification and the Distribution of Portfolio Variance, Part 3: Polynomial optimisation for asset allocation

Diversification is a fundamental topic for all investors but there remains little agreement on how to measure it. Often it is defined ambiguously through risk-based portfolio construction techniques. Recently it has been suggested to connect maximising diversification with minimising risk instability, via kurtosis, which presents practical optimisation challenges. In particular, minimising kurtosis is a non-convex problem that is typically solved using deterministic Branch-and-Bound methods, that do not scale well, or stochastic methods that provide limited guarantees on finding minima. We thus apply a deterministic hierarchical polynomial optimization framework that allows realistic asset allocation problems to be readily solved and also provides a numerical certificate of optimality.

Diversification benefits of cat bonds: An in‐depth examination

AbstractWe investigate whether the inclusion of Cat Bonds in portfolios composed of traditional assets and common factors is beneficial to investors. Various mean‐variance spanning tests performed for the period of 2002 to 2017 show that under different market conditions, the addition of Cat Bonds gives rise to previously unattainable portfolios. Using the Engle (2002) Dynamic Conditional Correlation (DCC) model, we find that including Cat bonds increases significantly the time‐varying Sharpe ratio and the Choueifaty and Coignard (2008) maximum diversification ratio. Cat Bonds provide needed diversification during critical times particularly during episodes of crisis and of high volatility. Under the second‐order stochastic dominance efficiency (SDE) tests, the null hypothesis that portfolios without Cat Bonds are efficient cannot be rejected. Out‐of‐sample analyses indicate that the performance of portfolios with Cat Bonds included varies depending on the performance measures employed, the portfolio construction techniques used and the assets or factors considered.

Neural Embeddings of Financial Time-Series Data

The dominant approaches for financial portfolio construction rely on estimating sample covariance and correlations matrixes, which serve as an input into a number of classical portfolio construction techniques. These classical approaches are not forward looking, are constrained by the ability to estimate covariance and correlation matrixes, and are inflexible to incorporating additional information. The authors propose a new approach of using learned representations from deep learning networks to augment such classical techniques. This approach can incorporate learned estimates of future performance and can be customized to create tailored representations best suited to meeting varying financial objectives. This article showcases one example of such an embedding, compares and contrasts it with classical approaches to portfolio construction, and discusses additional possibilities for applying representation learning in quantitative finance. TOPICS:Big data/machine learning, performance measurement, portfolio construction, simulations, statistical methods Key Findings • The authors propose a general methodology for creating tailored embeddings of financial time-series data. • The authors demonstrate one approach for creating such embeddings, analyze the induced similarity measure, and compare and contrast it with the traditional approach of using covariance and correlation as a similarity measure for securities. • The authors discuss other applications and advantages of this approach, including accounting for probabilistic estimates of future behavior and flexibility in incorporating varying business objectives.

A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing

The financial crisis of 2008 generated interest in more transparent, rules-based strategies for portfolio construction, with smart beta strategies emerging as a trend among institutional investors. Whilst they perform well in the long run, these strategies often suffer from severe short-term drawdown (peak-to-trough decline) with fluctuating performance across cycles. To manage short term risk (cyclicality and underperformance), we build a dynamic asset allocation system using Hidden Markov Models (HMMs). We use a variety of portfolio construction techniques to test our smart beta strategies and the resulting portfolios show an improvement in risk-adjusted returns, especially on more return-oriented portfolios (up to 50% of return in excess of market adjusted by relative risk annually). In addition, we propose a novel smart beta allocation system based on the Feature Saliency HMM (FSHMM) algorithm that performs feature selection simultaneously with the training of the HMM, to improve regime identification. We evaluate our systematic trading system with real life assets using MSCI indices; further, the results (up to 60% of return in excess of market adjusted by relative risk annually) show model performance improvement with respect to portfolios built using full feature HMMs.

Adaptive Optimal Risk Budgeting

In this article, the authors suggest Bayesian-style adaptive enhancement to a popular equal risk contribution (ERC) portfolio construction technique they call adaptive optimal risk budgeting (AORB). The enhancement has the potential to bring portfolios closer to mean–variance efficiency when Sharpe ratios and correlations of assets vary while retaining some of the ERC’s robustness to estimation errors. The authors test AORB’s viability by putting it in competition with ERC itself and with a version of the Bayesian shrinkage mean–variance technique in a carefully simulated setting. They find that the new method appears to deliver measurable advantages over its competition in a broad range of realistic settings. Multiple possible applications to portfolios of risk premia strategies and a multi-asset universe more generally are discussed by the authors. TOPICS:Portfolio construction, analysis of individual factors/risk premia Key Findings • We suggested a computationally simple yet powerful portfolio construction approach that is formulated in terms of risk contributions but is designed to prescribe an approximately mean-variance efficient solution even when Sharpe ratios and correlations between assets vary in magnitude and over time. We called the new method Adaptive Optimal Risk Budgeting (AORB). • When tested against popular Equal Risk Contributions and “classical” Bayesian shrinkage methods, AORB was competitive in all cases and emerged as a clear winner when Sharpe ratios and correlations between assets were substantially differentiated. • AORB can also be trivially expanded to incorporate tail risk considerations.

Portfolio selection and unsystematic risk optimisation using swarm intelligence

The financial crisis of 2008, got investment managers to study alternate methods of portfolio construction by focusing on optimisation of risk distribution. In this paper, we introduce a new method of risk diversification of an equity portfolio, based on the individual securities risk contribution. Markowitz’s portfolio optimisation methods were widely believed to diversify the risks until 2010, when it caught the financial market practitioners by surprise. But the scale of misjudgement raised doubts and opened the drawbacks of existing methods in portfolio construction and risk management. In this study, we propose a new technique for portfolio construction and unsystematic risk optimisation, using a well known meta heuristic from evolutionary computation, known as Grey Wolf Optimizer (GWO). We build a portfolio construction model using machine learning methods, to minimise risk contributors from our universe of securities, and then target to increase the asset diversification by maximising the maximum-diversification ratio. We apply boosting techniques to construct portfolio using ensemble of two well known techniques, known as maximum diversification ratio and risk budgeting of portfolio. We have benchmarked our findings by examining the returns of major global indices, and then illustrate our results by comparing with an alternate well known portfolio construction technique, the risk parity portfolio. For comparison, we have considered the length of investment horizon, returns and risks, as factors to present our findings by using the proposed portfolio optimisation and diversification method. Our technique offers practical insights and scope for further investigation into alternate methods of portfolio diversification and risk management using machine learning algorithms.

Speculative pressure

AbstractThe paper investigates the information content of speculative pressure across futures classes. Long‐short portfolios of futures contracts sorted by speculative pressure capture a significant premium in commodity, currency, and equity markets but not in fixed income markets. Exposure to commodity, currency, and equity index futures’ speculative pressure is priced in the broad cross‐section after controlling for momentum, carry, global liquidity, and volatility risks. The findings are confirmed by robustness tests using alternative speculative pressure signals, portfolio construction techniques, and subperiods interalia. We argue that there is an efficient hedgers‐speculators risk transfer in commodity, currency, and equity index futures markets.

Sources of Excess Return and Implications for Active Fixed-Income Portfolio Construction

The continued development of indexes, factor frameworks, and attribution tools in fixed-income markets provides a deeper understanding of manager performance and optimal portfolio construction techniques. In this article, the authors use quarterly holdings data for a broad sample of fixed-income mutual funds to attribute active returns to (1) the returns to static factor exposures, such as a structural tilt to credit spreads; (2) time-varying factor exposures, such as varying duration over the cycle; and (3) individual bond security selection. They find that, although bond funds in aggregate demonstrated positive alpha, a nontrivial amount of their performance was driven by exposure to static factors as opposed to dynamic timing or security selection. The authors illustrate how active portfolio managers can employ index products to more efficiently express factor views and help capture more excess return through reduced costs and frictions. TOPICS:Portfolio theory, portfolio construction, fixed-income portfolio management Key Findings • A holdings-based factor model provides more accurate views of the sources of alpha than time-series regressions when managers vary their exposures to factors such as credit spread and duration dynamically. • Active bond mutual funds had positive alpha relative to their stated benchmarks; a nontrivial amount of their performance was driven by exposure to static factors such as duration and credit spread. • Active portfolio managers can employ index products to more efficiently express factor views and help capture more excess return.

The impact of sectorial and geographical segmentation on risk-based asset allocation techniques

In the last decades, risk-based portfolio construction techniques have enjoyed a widespread diffusion in the financial community. This study aims at evaluating how these portfolio construction techniques produce different results depending on whether the segmentation of the stock market investment universe is based on sectorial or geographical criteria. An empirical analysis, applied on the global equity market, is carried out by making use of the typical and most advanced statistical and financial evaluation measures. Geographical segmentation is carried out in relation to the listing market, while sectorial segmentation is made in relation to the productive sectors to which individual companies belong. Our comparative analysis provides substantially coherent results, demonstrating a significant preference for the sectorial criterion compared to the geographic one. In conclusion, this result can be attributed to the subdivision of the investment universe into sectorial indices characterized by greater internal coherence and better external differentiation, in addition to the lower concentration of sectorial segmentation compared to the geographical one.