Financial Portfolio Research Articles

Risk‐return optimised energy asset allocation in transmission‐distribution system using tangency portfolio and Black–Litterman model

AbstractThe application of Markowitz and tangency portfolio and Black–Litterman models is extended by the authors to energy portfolio selection in transmission‐distribution environments with high penetration of renewable energy. As Transmission System Operator (TSO) and Distribution System Operator (DSO) contextually take mutualistic or conflicting positions in their portfolio selection process, their risk‐return interactions and behaviours depend on their subjective views on generation and operation. Here, the financial portfolio allocation tool Black–Litterman Model is adapted to incorporate subjective views of the operators to arrive at more intuitive portfolios. The best portfolios are searched within the acceptable risk‐return search space of each operator defined by their Markowitz efficient frontiers (EF), for Pareto‐optimising their profits. The tangency portfolio approach, which is generally used to determine the portfolio of risky and risk‐free assets in finance, is used here to determine the portfolio of renewable (energy‐risky) and fuel‐based sources (energy‐risk‐free). The proposed methodology is adopted in an HV–MV interconnected test system operated by one TSO and two DSOs, having wind, solar, coal, gas and nuclear generation technologies. It is observed that completely customisable portfolios can be constructed for TSO and DSO based on their inherent financial and energy risk‐return behaviours and posterior views.

Can Security Design Foster Household Risk‐Taking?

ABSTRACTThis paper shows that securities with nonlinear payoff designs can foster household risk‐taking. We demonstrate this effect by exploiting the introduction of capital guarantee products in Sweden between 2002 and 2007. Their fast and broad adoption is associated with an increase in expected financial portfolio returns. The effect is especially strong for households with low‐risk appetite ex ante. These empirical facts are consistent with a life‐cycle model in which households have pessimistic beliefs or preferences combining loss aversion and narrow framing. Our results illustrate how security design can mitigate behavioral biases to increase mean household portfolio returns.

New highly efficient high‐breakdown estimator of multivariate scatter and location for elliptical distributions

AbstractHigh‐breakdown‐point estimators of multivariate location and shape matrices, such as the ‐estimator with smoothed hard rejection and the Rocke ‐estimator, are generally designed to have high efficiency for Gaussian data. However, many phenomena are non‐Gaussian, and these estimators can therefore have poor efficiency. This article proposes a new tunable ‐estimator, termed the ‐estimator, for the general class of symmetric elliptical distributions, a class containing many common families such as the multivariate Gaussian, ‐, Cauchy, Laplace, hyperbolic, and normal inverse Gaussian distributions. Across this class, the ‐estimator is shown to generally provide higher maximum efficiency than other leading high‐breakdown estimators while maintaining the maximum breakdown point. Furthermore, the ‐estimator is demonstrated to be distributionally robust, and its robustness to outliers is demonstrated to be on par with these leading estimators while also being more stable with respect to initial conditions. From a practical viewpoint, these properties make the ‐estimator broadly applicable for practitioners. These advantages are demonstrated with an example application—the minimum‐variance optimal allocation of financial portfolio investments.

Why banks insure structured commodity trade finance risk: evidence from a worldwide survey

We identify major drivers of the demand for credit insurance, using a worldwide survey among banking executives in the structured commodity trade finance business. Our results show that a bank’s propensity to purchase insurance increases in its experience and expertise with the product, the impact of insurance coverage on its balance sheet, the risk of the underlying transaction, as well as the intensity of broker relationships. Other factors, such as the size of the commodity trade finance portfolio, the competitiveness of the insurance price, and the risks arising from commodity price volatility, seem to be of lesser relevance.

Integrating ESG risks into value-at-risk

This study addresses the regulatory request for sustainability-related risk integration into traditional financial risk measures. We propose a new risk metric that combines a traditional market risk measure expressed in terms of Value-at-Risk (VaR) and environmental, social, and governance (ESG) factors. The new metric, VaRESG, considers the orthogonality of the ESG criteria with fundamental variables, applying a perturbative approach and an entropy function of ESG factors. The pilot empirical application relies on a financial portfolio comprising approximately 3000 equities. The results show the predictive power of VaRESG to reduce unexpected losses (i.e., out-of-VaR). The results were confirmed especially under stress conditions, when the values of losses were higher (in terms of magnitude). To the best of our knowledge, this is the first attempt in the financial literature to effectively integrate ESG risks into the VaR measure to predict the expected losses of an equity portfolio. The measure of VaRESG can be useful to asset managers and institutional investors to reduce unexpected losses, and to supervisors interested in increasing the level of accuracy of VaR estimations.

Tail risk adjusted clean energy portfolios in P2P transactive markets using Rachev ratio

AbstractRenewable energy sources (RES) and electric vehicles (EVs) pose ‘energy‐risk’ in peer energy commitments due to their temporal and spatial uncertainties. Thus, optimistic commitments in the peer‐to‐peer transactive energy market (P2P TEM) are improbable. This paper proposes a two‐stage master–slave portfolio optimization approach for combining energy‐risk of RES and EVs, and welfare‐risk of peers, in building clean energy portfolios. The master portfolio (MP) refers to the shares of renewable and EVs in P2P market settlement, whereas the slave portfolio (SP) gives the wind‐solar mix within renewables. Here, Rachev Ratio (RR), an index used in financial portfolio selection for tail‐risk management, is adapted and combined with Markowitz Efficient Frontier (EF) to find the optimal slave portfolio. Both the extreme tails are optimized, encouraging energy outputs far above forecast and discouraging those far below forecast. The master portfolio is obtained by maximizing the sum of the average welfare of the peers at the best (right) and worst (left) tails of the welfare distribution curve using Stochastic Weight Trade‐off Particle Swarm Optimization (SWT‐PSO). The proposed portfolio selection approach is better in terms of increased expected energy output, improved utilization of RES and EVs, and better collective peer welfare.

A Heuristic Approach to Forecasting and Selection of a Portfolio with Extra High Dimensions

The performance of a financial portfolio depends on the output of two tasks: first, a forecasting process, where quantities of interest for the investors, such as the rate of return and risk for each stock, are predicted into the future, and second, an optimization process, where those individual stocks are formed into the portfolio optimizing the combined risk and reward features. However, in very large dimensions, when the number of stocks is high, those two quantitative problems often become intractable because of a loss in precision. This paper introduces a forecasting and portfolio formation strategy in multiple periods based on the splitting of the multivariate forecasting model into multiple bivariate forecasting models and updating investment weights at each period based on the predicted target quantities for the returns and the covariances. The methodology proposed is suitable for a very large portfolio of assets. The experimental results are based on a sample of one thousand stocks from the Chinese stock market. For such a large sample, the forecast and optimization process is executed speedily. The investment strategies are benchmarked with the equally weighted portfolio. In the long run, they offer a better investment performance in terms of a higher rate of return or lower risk, compared with this portfolio, demonstrating the applicability and economic value of the proposed methodology in practice.

Should we focus more on the offline retail when constructing financial portfolios?

Modern portfolio theory has been researched and applied widely. This paper shows an application of the modern portfolio theory to choose optimal weights for a composite stock s portfolio. It could help optimize investors portfolio by increasing returns and decreasing volatility. Considering returns and volatility at the same time, Sharpe ration is a good indicator. To research portfolio optimization, this article uses Monte Carlo methods to acquire the weights of securities in portfolio that has the highest Sharpe ratio. Current studies about portfolio theory do not take safe securities into consideration. Therefore, this paper first concludes risk-free asset with other risky assets in portfolio. The portfolio in this study includes five stocks (Walmart, Amazon, Dexcom, Warner Bros., T. Rowe) in different sectors and risk-free asset (one-year U.S. Treasury Securities). The result shows that the weight of offline retail increases as the proportion of risk-free asset rises. This has implication that risk-averse investor might prefer offline retail stocks compared with other stocks in portfolio.

What Information Variables Predict Bitcoin Returns? A Dimension-Reduction Approach

This article investigates the determinants of Bitcoin returns. The authors consider a comprehensive set of information variables under five categories: macroeconomics, blockchain technology, other assets, stress level, and investor sentiment. Their approach toward this large dataset is built upon dimension-reduction models such as Backward Elimination, least absolute shrinkage and selection operator (LASSO), principal component regression (PCR), and three-pass regression filter (3PRF). The empirical results show that blockchain technology, stress level, and investor sentiment have positive, negative, and positive predicting power on Bitcoin returns, respectively. Macroeconomic variables exhibit insignificant impacts on Bitcoin returns. Other asset variables show little predicting power until 2019, but some become a significant predictor during the COVID-19 pandemic. Overall, the authors caution against using Bitcoin as a risk-hedging device in financial portfolios. They also find that, consistent with other financial assets such as equities, Bitcoin shows increased predictability with a longer return horizon. Due to their empirical results, they also advocate the use of 3PRF; relative to other dimension-reduction methods under consideration, they observe superior performance of 3PRF in predicting both the level and the direction of future Bitcoin returns across all return horizons.

Kurtosis-based vs volatility-based asset allocation strategies: Do they share the same properties? A first empirical investigation

Using a sample of international equity markets over the period 2001–2020, this paper aims to empirically investigate the implications in terms of asset allocation and the key properties of kurtosis-based strategies compared to the more traditional volatility-based strategies for financial portfolios construction. Furthermore, the contribution demonstrates that the portfolio recommended by the novel Kurtosis-based Risk Parity strategy introduced by Braga et al. (2023) admits to being interpreted as an intermediate portfolio between the Minimum-Kurtosis portfolio and the Equally Weighted portfolio in terms of the fourth root of the portfolio fourth moment.

Matrix Factor Analysis: From Least Squares to Iterative Projection

In this article, we study large-dimensional matrix factor models and estimate the factor loading matrices and factor score matrix by minimizing square loss function. Interestingly, the resultant estimators coincide with the Projected Estimators (PE) in Yu et al. which was proposed from the perspective of simultaneous reduction of the dimensionality and the magnitudes of the idiosyncratic error matrix. In other word, we provide a least-square interpretation of the PE for the matrix factor model, which parallels to the least-square interpretation of the PCA for the vector factor model. We derive the convergence rates of the theoretical minimizers under sub-Gaussian tails. Considering the robustness to the heavy tails of the idiosyncratic errors, we extend the least squares to minimizing the Huber loss function, which leads to a weighted iterative projection approach to compute and learn the parameters. We also derive the convergence rates of the theoretical minimizers of the Huber loss function under bounded fourth or even th moment of the idiosyncratic errors. We conduct extensive numerical studies to investigate the empirical performance of the proposed Huber estimators relative to the state-of-the-art ones. The Huber estimators perform robustly and much better than existing ones when the data are heavy-tailed, and as a result can be used as a safe replacement in practice. An application to a Fama-French financial portfolio dataset demonstrates the empirical advantage of the Huber estimator.

Industry Clusters and the Geography of Portfolio Choice

AbstractUsing detailed data on U.S. households’ locations, employment, and financial portfolios, we document that individuals employed in locally clustered industries are more likely to invest in risky assets. This pattern is strongest among individuals with high labor income, employed in skilled occupations, and with strong cognitive skills. Our overall evidence suggests the relation between industry clusters and investment decisions is best explained by clusters enhancing human capital among local industry workers, in turn amplifying their effective risk tolerance. Our findings highlight the important role of local labor market composition in generating household portfolio patterns within and across geographies.

Solving Estimating Equations With Copulas

Thanks to their ability to capture complex dependence structures, copulas are frequently used to glue random variables into a joint model with arbitrary marginal distributions. More recently, they have been applied to solve statistical learning problems such as regression or classification. Framing such approaches as solutions of estimating equations, we generalize them in a unified framework. We can then obtain simultaneous, coherent inferences across multiple regression-like problems. We derive consistency, asymptotic normality, and validity of the bootstrap for corresponding estimators. The conditions allow for both continuous and discrete data as well as parametric, nonparametric, and semiparametric estimators of the copula and marginal distributions. The versatility of this methodology is illustrated by several theoretical examples, a simulation study, and an application to financial portfolio allocation. Supplementary materials for this article are available online.

Semi-Metric Portfolio Optimization: A New Algorithm Reducing Simultaneous Asset Shocks

This paper proposes a new method for financial portfolio optimization based on reducing simultaneous asset shocks across a collection of assets. This may be understood as an alternative approach to risk reduction in a portfolio based on a new mathematical quantity. First, we apply recently introduced semi-metrics between finite sets to determine the distance between time series’ structural breaks. Then, we build on the classical portfolio optimization theory of Markowitz and use this distance between asset structural breaks for our penalty function, rather than portfolio variance. Our experiments are promising: on synthetic data, we show that our proposed method does indeed diversify among time series with highly similar structural breaks and enjoys advantages over existing metrics between sets. On real data, experiments illustrate that our proposed optimization method performs well relative to nine other commonly used options, producing the second-highest returns, the lowest volatility, and second-lowest drawdown. The main implication for this method in portfolio management is reducing simultaneous asset shocks and potentially sharp associated drawdowns during periods of highly similar structural breaks, such as a market crisis. Our method adds to a considerable literature of portfolio optimization techniques in econometrics and could complement these via portfolio averaging.

How to Optimize a Financial Portfolio for a Firefighter

How to increase the amount of pension is a major concern for many retirees. In this paper, the question of whether retirees need to buy out their pensions is examined. Two portfolios are set up and their Sharpe rates are compared under two assumptions. The purpose of the study is to effectively assist retirees in their asset allocation. In this paper, diversified assets from four different sectors were selected to construct the portfolios. Three methods, the mean-variance model, CAPM model, and FF3F model, are used in this paper for portfolio construction and comparison. Meanwhile, the performance of the portfolios was analyzed using the weighting analysis method. The results show that "Disney" has the largest share when it chooses to buy out its pension and reinvest it using the CAPM model, while "LVMH" has the largest share in all other cases. This study may be useful for retirees who are faced with the decision of whether to buy out their pensions.

The Split-Screen Approach for Project Appraisal (Part I: The Theory)

This paper illustrates an innovative approach to financial modeling of engineering decision-making and industrial projects. The approach is a minimal one, grounded as it is on three notions, two laws, and one matrix that combines them, called Split-Screen Matrix (SSM). This split-screen approach consists in linking the accounting and financial input data and systematizes them into the SSM, whose columns report the pro forma book values of capital (balance sheets), the corresponding income components (income statements), and the associated cash flows (cash-flow statements) while the rows show the project’s dynamical evolution. The SSMs are then linked via a continuous split-screen strip. To appraise the project, we use a pair of SSMs, namely, the project matrix and the benchmark Matrix (with the related strips), the latter containing the alternative amount invested and the associated foregone profit of a financial portfolio replicating the project’s cash flows. Using differences between the corresponding elements of the two strips, the economic profitability of the project can be easily measured, in both absolute terms (e.g., net present value, market value added, residual income) and relative terms (e.g., average return on assets, cash-flow return on capital). The accounting-and-finance engineering system (AFES) obtained with the split-screen approach is particularly helpful when using spreadsheet modeling because it does not require (knowledge and) use of financial spreadsheet functions. The application of this approach on spreadsheet modeling is essentially based on the continuous split-screen strip, here described, and is illustrated in a following paper (Baschieri and Magni 2023, “The Split-Screen Approach for Project Apraisal (Part II: Spreadsheet Modeling)”).

Household finance: a systematic literature review and directions for future research

PurposeThe objective of this systematic literature review (SLR) is to explore the current state of research in the field of household finance (HF). This study aims to summarize the existing research to highlight the importance of household finance in a nation’s economy. By exploring all conceptual and applied implications of HF, this study projects directions for future research to develop a comprehensive understanding of the subject.Design/methodology/approachThis SLR is based on 112 articles published in peer-reviewed journals between 2006 and 2020 (Table 3). The methodology comprises five steps, namely, formulation of research questions, identification of studies, their selection and evaluation, analyses and syntheses and presentation of results.FindingsThe findings of this study show that studies on HF are gradually increasing worldwide with the USA registering the highest number of published research on the topic during the period under scrutiny. Notwithstanding the increasing attention and research on HF, empirical research in emerging economies is lagging. Additionally, this study finds that HF structure presents a perfect setting to understand how households compose their financial portfolio, make financial decisions and what factors influence their decisions.Research limitations/implicationsThis study is an SLR – an accurate and accepted method of reviewing available literature on a selected subject. However, the selection of inclusion and exclusion criteria depends on the researchers’ rationale which might lead to research bias. This should be considered an inherent limitation of SLR.Practical implicationsBy synthesizing the contents of extant literature, this study presents important insights into HF. This study underlines the most discussed topics in the domain and identifies potential investigation areas. This study gives the knowledge of leading articles, authors and journals and informs scholars and academicians about the areas that need further investigation by portraying the complete picture of the subject in a systematic manner. Further, this study highlights that households make suboptimal financial decisions that affect their financial well-being. To reduce the adverse impacts of these decisions, policymakers and financial institutions must take steps to improve households’ use of formal financial markets. Household decisions can be reformed by enhancing consumers’ knowledge about financial products and services. Furthermore, households can be served better by offering customization in traditional financial products.Originality/valueThis study synthesizes the main findings of selected literature on HF. The expansion of studies on HF has generated the need to review the existing literature in a systematic manner. To the researchers’ best knowledge, this SLR is the first thorough study of available articles in the HF domain. This study presents the scope of future research by highlighting numerous aspects and functions of HF.

An empirical study of financial securities market based on three factor model

Recent years, with the continuous development and regulation of Chinese security market, the influence of Chinese financial security market income factor is also being improved, but how to guide the correct operation of Chinese security market and improve the stock income, has become the main analysis research content of Chinese security market. Three-factor model technology provides a certain basis for the selection, prediction, decision and evaluation of financial portfolio. The three-factor model is not only widely used in our financial security market, but also can help listed enterprises to analyze and forecast our financial security market in a convenient tool form, such as: In our country's financial security market, the stock yield of small-scale companies is obviously higher than that of large-scale companies, or even value stock is higher than that of growth stock. This paper mainly analyzes and studies the operating direction of financial securities market through the three-factor model technology.

Quantifying GHG emissions enabled by capital and labor: Economic and gender inequalities in France

AbstractMany studies have investigated the carbon footprint of households. They rely on consumption‐based responsibility and focus on how many emissions are embodied in a product. Here we open a new field by discussing the emissions that individuals enable by providing labor and capital to companies, using the framework of income‐based (downstream) responsibility. This perspective focuses on the emissions enabled by providing inputs to production processes, and is relevant for discussion of sustainable work and the carbon impact of investment and financial portfolios. We compute the downstream carbon intensity of primary inputs for 35 industries in France using the multi‐regional input–output database EXIOBASE. We provide a detailed picture of enabled emissions, disaggregating those by industry and primary inputs. On average, capital inputs are more carbon intensive than labor inputs. Finally, we couple downstream carbon intensities with an extensive national survey on wages to obtain a distribution of the income‐based emissions of employees. Income‐based emissions are much more unequally distributed than wages due to the huge variability of carbon intensity across industries: a truck driver enables far more emissions than a social‐care worker. Inequalities in emissions do not strongly interact with economic inequality. Yet they are gendered because women work disproportionately in low‐carbon‐intensive industries such as healthcare. As a result, women contribute less to GHG emissions than their wage share would seem to indicate.

Can cryptocurrency solve the problem of financial constraint in corporates? A literature review and theoretical perspective

Purpose This paper aims to attempt to examine some of the unique features of cryptocurrency and the reasons for its growing market acceptability. Given the expanding size of cryptocurrency markets, the present study strives to identify whether it can be used as an alternative financial asset in place of traditional financial assets to meet firms' financial constraints. It also provides issues for future research in the area of cryptocurrency markets. Design/methodology/approach This paper analysed 94 research papers from databases such as ScienceDirect, Proquest, EBSCO, Emerald Insight and Web of Science. Articles connected to cryptocurrency, financial assets and corporate financial constraints research were explored. VOSviewer software has been used to visualise the specified body of literature and identify eight clusters in previous literature using keyword and abstract analysis. Findings Studies reveal that cryptocurrency markets are independent of traditional financial markets and cryptocurrency returns have less correlation with traditional financial asset classes. This can be an advantage to firms, especially during times of crisis when traditional financial assets are impacted by significantly lower returns, while cryptocurrencies can serve as an alternative. Realtime data reveals that during the pandemic, cryptocurrencies had the maximum growth in returns which also happened to be a time when firms faced severe cash constraints. While accepting cryptocurrency as a means of exchange is still under review by regulatory authorities, it can be considered an alternative asset for investment purposes. Firms can take advantage of it to overcome financial constraints and thus reap the gains from holding crypto assets for precautionary reasons. Originality/value The present study investigates using cryptocurrency as an alternative financial asset to solve the financial constraint problem in corporates. The issues regarding volatility, cyber securities, gold returns, long-term and short-term returns have been some of the most prominent studies in the area of cryptocurrency. The present study uses eight theme-based clusters to identify the role of cryptocurrency as an alternative investment class and examines evidence-based research regarding the financial returns from holding cryptocurrency over certain traditional asset classes such as gold, currency or stocks. In recent years, it has been found that investors' growing interest in holding cryptocurrency as part of their financial portfolio has led to the substantial appreciation of cryptocurrency prices. To the best of the authors’ knowledge, the study will be a novel attempt to identify the role of cryptocurrency as an antidote to the companies’ financial constraints and liquidity issues.