Portfolio Strategies Research Articles

Bidirectional Risk Spillovers between Chinese and Asian Stock Markets: A Dynamic Copula-EVT-CoVaR Approach

This study aims to investigate bidirectional risk spillovers between the Chinese and other Asian stock markets. To achieve this, we construct a dynamic Copula-EVT-CoVaR model based on 11 Asian stock indexes from 1 January 2007 to 31 December 2021. The findings show that, firstly, synchronicity exists between the Chinese stock market and other Asian stock markets, creating conditions for risk contagion. Secondly, the Chinese stock market exhibits a strong risk spillover to other Asian stock markets with time-varying and heterogeneous characteristics. Additionally, the risk spillover displays an asymmetry, indicating that the intensity of risk spillover from other Asian stock markets to the Chinese is weaker than that from the Chinese to other Asian stock markets. Finally, the Chinese stock market generated significant extreme risk spillovers to other Asian stock markets during the 2007–2009 global financial crisis, the European debt crisis, the 2015–2016 Chinese stock market crash, and the China–US trade war. However, during the COVID-19 pandemic, the risk spillover intensity of the Chinese stock market was weaker, and it acted as the recipient of risk from other Asian stock markets. The originality of this study is reflected in proposing a novel dynamic copula-EVT-CoVaR model and incorporating multiple crises into an analytical framework to examine bidirectional risk spillover effects. These findings can help Asian countries (regions) adopt effective supervision to deal with cross-border risk spillovers and assist Asian stock market investors in optimizing portfolio strategies.

The impact of conventional energy markets on connectedness dynamics among eco-friendly assets

ABSTRACT This study examines the connectedness among eco-friendly assets and how volatility in fossil energy markets affects this connectedness. Using a broad coverage for eco-friendly assets such as a clean energy index, a clean cryptocurrency (Cardano), a green bond index, and a sustainability-leaders index, our results show that the connectedness is higher in the extremities (tail-risk spillovers) and more significant during essential world events such as a pandemic or war. Further, we employ nonparametric causality in quantiles, quantile regression, and kernel-based regularised least squares methods to investigate the influence of fossil energy markets (crude oil, natural gas, and gas oil) volatility on eco-friendly assets’ connectedness. Our results imply that fossil energy market volatility has a positive and causal effect on connectedness dynamics among eco-friendly assets. Our study offers important implications for investors and policymakers in crafting portfolio strategies and policy decisions.

Monte carlo simulation with bilstm for day-ahead stock portfolio management

<div>Predicting stock price movement and optimizing day-ahead stock portfolios are challenging tasks due to the inherent complexity and volatility of financial markets. This study proposes a novel approach that combines bidirectional long short-term memory (BiLSTM) neural networks with monte carlo simulation (MCS) to enhance day-ahead stock portfolio management. In the proposed methodology, historical data of the top-performing 10 stocks from different sectors of the National Stock Exchange of India (NSEI) is obtained from 1 January 2004 to 30 June 2023 and utilized to train a BiLSTM model. This model effectively extracts intricate patterns and trends from the time series, leading to more accurate and robust stock price predictions. MCS generates different scenarios, considering various market conditions and uncertainties. These scenarios provide a comprehensive view of the portfolio’s performance under different conditions, thus mitigating the risk of relying solely on a single prediction. The study evaluates the proposed framework and compares its performance against traditional portfolio management strategies. Results demonstrate that the MCS with the BiLSTM approach outperforms traditional methods in terms of risk-adjusted returns and portfolio stability.</div>

Global de-diversification and stock returns

Using international firm-level data, we show that when a multinational corporation reported zero total sales made by its foreign operations, it constitutes a salient signal of global de-diversification. A simple long-short strategy portfolio that buys stocks of multinational firms and sells stocks of ex-multinational firms that report zero foreign sales earns up to 85 basis points per month (over 10% per year). Further examination suggests that investors’ inattention and limits to arbitrage could explain this return predictability. This paper is among the first to document evidence of the impact of global de-diversification on stock returns.

Competitive Resilience and Innovation: Fonterra's Strategic Approach to Thriving in the Dynamic Chinese Dairy Market

In the Chinese market, Fonterra faces fierce competition with competitors such as Mengniu, especially with the increasing demand for functional, personalized, and healthy products. In response to the challenge of product line similarity, Fonterra should adopt a diversified product portfolio strategy and continue to launch new products, including special functional ingredients, personalized flavors, and innovative products that meet health needs. By reducing reliance on a single product, the company can reduce the impact of market volatility and increase its differentiation against competitors. This strategy will appeal to a wider consumer base, enhance customer loyalty, and effectively mitigate the threat of substitutes. By constantly monitoring market dynamics and competitors' actions, as well as making regular adjustments to its product portfolio, Fonterra will be more flexible in adapting to changes in the marketplace, enhancing its competitiveness and meeting evolving consumer needs, laying a solid foundation for the Company's sustainable growth.

A novel grey relational clustering model under sequential three-way decision framework

In the context of addressing clustering problems with small samples, grey relational clustering (GRC) plays a crucial role. Currently, a three-way GRC has gained popularity, particularly for its ability to handle the uncertain relationship between objects and classes. However, explicit clustering for some objects may not be achievable when employing the one-step decision, which is often a coarse-grained approach. In such cases, re-clustering these objects with a finer granularity becomes necessary. This is where the sequential three-way decision (STWD) proves to be an effective solution. Therefore, this paper is dedicated to designing a novel GRC under the STWD framework, namely GRC-STWD. Specifically, recognizing that some existing threshold calculations in GRCs can be overly subjective and that a pair-wise global threshold may not efficiently leverage available information for accurate object clustering, we introduce a novel threshold algorithm to address these limitations. Furthermore, we propose a conditional probability calculation method combining TOPSIS and grey relational degrees for clustering data lacking decision attributes or class labels. As the development of GRC within portfolio environments remains relatively nascent, we apply the proposed model to portfolio strategy construction. Finally, comparative and experimental analyses validate the model's effectiveness and feasibility. Notably, the developed model serves as a generalization of GRC, contributing to the broader advancement of GRC and STWD methodologies.

Portfolio Optimization: Application and Comparison of Markowitz Model and Single Index Model on LQ45 Stocks in Indonesia Stock Exchange

This paper examines the optimization of an Indonesian stock portfolio using two models: the Markowitz Model (Mean-Variance Model) and the Single Index Model. The data comprises historical returns of LQ 45 stocks from January 2016 to December 2021. The focus is on selecting stocks for the portfolio and determining their weights based on the two models. The study compares the performance of both optimized portfolios with the LQ45 Index benchmark, IHSG market, and each other using Sharpe and Treynor measurements. The paper tests whether the stock composition of the optimized portfolio from both models successfully and consistently generates a better performance in the future (1 January 2022 – 31 December 2022) and (1 January 2023 – 31 December 2023) compared to both LQ45 and IHSG. The results reveal a notable contrast in portfolio performance between 2022 and 2023. In 2022, both the Markowitz and Single Index portfolios exhibited remarkable returns, surpassing LQ 45 and IHSG. However, in 2023, both portfolios experienced substantial underperformance, with negative returns and unfavorable risk-adjusted metrics. These findings underscore the dynamic nature of financial markets and the need for continuous portfolio monitoring and adaptation. Investors are encouraged to reevaluate their portfolio strategies in response to changing market conditions. The study contributes valuable insights into the temporal variability of optimized portfolios and their sensitivity to evolving market dynamics.

Testing and Expanding the Concept of Traditional and Contemporary Localism in Rural Local Food Systems with Ozark Wild Harvesters☆

AbstractWhile research shows the potential benefits of local food systems to improve community economy and quality of life, there is a critique that these studies overlook how informal, non‐market food access practices contribute to local food systems, especially in rural places. McEntee promoted the concepts of traditional and contemporary localism in his work with rural food systems, arguing that the motivations of participants define the categories. Using narrative research with wild harvesters in the Ozark Highlands, we propose that while McEntee's definitions are useful for expanding the conversation about why people may choose local food or not, more efforts should focus on valuing and welcoming the broader intersection of priorities and strategies that people use to engage in local food systems in their communities. Promoting a wider portfolio of local food access strategies is important to communicate that there are a variety of ways to participate in localized food systems, some in regular market transactions and some in informal non‐market ways, and that all are potentially valuable in building sustainable food systems in rural areas.

What are tenants demanding the most? A machine learning approach for the prediction of time on market

PurposeThis is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.Design/methodology/approachThe random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.FindingsResults show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.Practical implicationsThe findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.Originality/valueAlthough machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.

Dynamic volatility spillover relationships between the Chinese carbon and international energy markets from extreme climate shocks

The immense attention to climate change risk has stimulated increasing interest in carbon markets and their linkages to other markets. This study investigates the dynamic volatility spillover relationships between the Chinese carbon and international energy markets, and the impact of extreme weather events on the relationships. Empirical results show that the risk is transferred from the international gas (GAS) and crude oil (OIL) markets to the Chinese carbon market (GDC). The total volatility spillover among markets and the net spillover of GDC with other markets both have obvious periodicity. The shocks from extreme weather events and extremely high temperatures can amplify the trading volume of GDC, and strengthen the fluctuations of GDC, ultimately turning GDC from a risk receiver to a risk transmitter. The portfolio strategies based on the reversal phenomenon can achieve higher average returns compared to the Chinese and EU carbon markets. In addition, the shocks of the Sino-US trade war, the Covid-19 outbreak, the COP 26 and the Russo-Ukrainian war have different effects on the volatility spillovers among markets. Economic and policy implications for investors and regulators are also provided along with these findings.

Optimal portfolio strategy of wealth process: a Lévy process model-based method

This paper is concerned with the optimal portfolio problem for a company that can invest in two risky assets, where a novel Lévy-process-driven model is constructed to describe the dynamics of the wealth process by using a constant elasticity of variance model and a jump-diffusion process. A delicately designed value function is proposed under the mean–variance criterion to reflect the optimal portfolio for the stochastic volatility model. By using the verification theorem, the desired optimal portfolio strategy is proposed by the solution to certain Hamilton–Jacobi–Bellman equations. Furthermore, the corresponding expressions are achieved by using the stochastic analysis theory. Finally, a numerical simulation example is provided to verify the effectiveness of the proposed optimal portfolio strategy.

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.

Best practices and future challenges in the treatment of oral cancer.

Oral cancer is among the most common tumour worldwide. Due to the anatomical peculiarities of the head and neck region, the treatment of oral cancer is a major challenge with regard to the preservation of aesthetics and function. The aim of the present study is to analyze currently practiced therapeutic strategies as well as current and future challenges in the therapy of oral cancer. A Pubmed-based selective literature search was performed considering literature predominantly from 2021 to 2022. Search terms were "oral cancer," "oral cavity cancer," and "head and neck cancer." Head and neck tumours are the seventh most common cancer worldwide. The suspected diagnosis of oral cancer is often made by outpatient dentists during routine examinations. With the outbreak of the 2020 COVID 19 pandemic, risk behaviour has changed with regard to the development and diagnosis of oral cancer. The gold standard of therapy is surgical resection. The need for adjuvant therapy measures depends on the histopathological TNM stage and other defined risk factors. Recurrences occur frequently and should be evaluated with regard to renewed surgical therapy. Future treatment strategies are aimed at early diagnosis, precision in resection, the use of digital technologies, and aspects of quality assurance. The economic importance in the treatment of oral cancer is currently given little consideration. The study presents a selective portfolio of treatment strategies currently practiced in Germany and in many parts of the world. In addition, future challenges in the therapy of oral cancer, in particular squamosa cell carcinoma, are presented.

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.

Case Study – Developing a National Research and Evidence Base for The Health and Wellbeing Chapter of The Welsh Government’s 2023 Innovation Strategy for Wales

This paper presents research informing the Welsh Government’s (WG) Health and Wellbeing chapter of the new Innovation Strategy for Wales (ISW). WG recognises the need for a cross portfolio Innovation Strategy to bring greater coherence and alignment to our existing Welsh health and social care innovation ecosystem, particularly in a post EU environment. This approach aims to support greater collaboration between healthcare, industry, academia and the third sector to deliver greater impact and value by developing, sharing and adopting innovative practice, leadership and skills development and supporting new technology development. As part of the development process, key stakeholders were identified as part of an innovation ‘push’ and innovation ‘pull’ nexus approach. Organisations were categorised into domains of health and social care providers, innovation infrastructure, technology platforms, innovation centres, and partnerships across Wales’s health and life sciences ecosystem. A qualitative, action research led approach was used to derive the key themes supporting development of the ISW health chapter. Multi-sector consultations were conducted with experts from various fields, including health, social, care, research, and technology. Our analysis of qualitative data collected through an extensive consultation process led to the emergence of areas of prioritisation for the ISW. Three areas of prioritisation were identified in the analysis: a) Creating coherency across the innovation ecosystem, where every partner understands their role; b) Focusing innovation activity on NHS Wales organisational needs/priorities, generating ‘Innovation Pull’; c) Creating an ‘adoption ready’ pipeline of innovation externally into health and social care at scale, creating an ‘Innovation Push’ offer. The ISW aims to create the optimal conditions for an innovation ecosystem underpinned by clear infrastructure and a robust policy framework, which has the full support from health and social care leadership, to create a culture of innovation at all levels of the health and social care system. The ISW was launched on 27th February 2023, with full political support from the Welsh Government Cabinet; a majority Labour party government supported by the Plaid Cymru party, as part of the Welsh Government’s 2021 election cooperation agreement.

Short-Term Versus Long-Term Portfolio Management Strategies and the Selection of Securities

Purpose: This study examines the effectiveness of short-term versus long-term portfolio management strategies and the selection of securities. It aims to provide insights into how different strategies impact investment performance, considering risk tolerance and financial goals. Research Design and Methodology: The study synthesizes findings from academic journals and empirical research using a qualitative literature review approach. The methodology involves thematic analysis to identify key themes, patterns, and insights related to portfolio management strategies and security selection. Findings and Discussion: The research highlights that short-term strategies like momentum trading can capitalize on transient market inefficiencies but entail higher transaction costs and volatility. Conversely, long-term strategies, such as value investing, focus on fundamental analysis and offer more stable returns over time. The integration of ESG criteria into security selection is shown to enhance portfolio performance and align investments with sustainability objectives. Behavioral biases and technological advancements also significantly influence portfolio management decisions. Implications: The study underscores the importance of balancing short-term and long-term strategies based on investor risk tolerance and financial goals. Financial practitioners can leverage these insights to design diversified portfolios and offer tailored advice. Future research should explore the dynamic interplay between these strategies and the impact of technological and regulatory changes on portfolio management. Integrating ESG considerations is crucial for sustainable investing and aligning with evolving market dynamics.

Statistical analysis of stable fundamental anomalies in stock pricing on the Russian market

Subject. The article considers stable anomalies in the dynamics of stock prices on the Russian stock market, which are caused by specific characteristics of issuing companies. Objectives. The aim is to perform a cross-sectional analysis of excess returns of stock portfolios reflecting new stable fundamental anomalies of the Russian stock market. Methods. The study employs the data of accounting (financial) statements of issuers. I considered the groups of indicators related to operational efficiency, expectation of slowdown in operational efficiency, threat of insolvency (bankruptcy) of the issuer, and the size of capital investments of issuers. The groups of indicators identified four working hypotheses on consideration of corresponding systematic risk factors in the prices of financial instruments. Analytical procedures were carried out in the RStudio development environment. The sample population covers the period from June 2012 to December 2021. Results. I tested the working hypotheses and formed special portfolios, the excess profitability of which simulates systematic risk factors associated with specific characteristics of issuing companies. The test of risk factors for redundancy, using auxiliary regressions and the Sharpe ratio for various combinations of risk factors, demonstrated that the risk factor associated with operational efficiency of issuers can be considered as redundant, since it is fully explained by other risk factors. The risks of slowing the growth of efficiency and the risk of bankruptcy of the issuer in the Russian stock market are investigated for the first time. Conclusions. The revealed price anomalies contribute to the development of theoretical and empirical prerequisites for factor investment strategies in the Russian stock market. The findings may help investors to increase portfolio strategy’s profitability in the Russian stock market.

Robust portfolio selection with subjective risk aversion under dependence uncertainty

This paper solves the robust portfolio decision problem with subjective risk aversion under dependence uncertainty. We use spectral risk measures to capture investors’ subjective risk aversion while constructing mixture R-vine copula uncertainty with R-vine copula change-point detection to characterize dependence uncertainty. We introduce worst-case spectral risk measures (WSRM) and formulate portfolio decision problems with WSRM as risk measures. We conduct an empirical study using eight global stock indices. The results demonstrate that our robust portfolio outperforms two other portfolios without incorporating dependence uncertainty or considering dependence uncertainty constructed only through common economic crisis information in static and dynamic investments encompassing the full out-of-sample period and crisis periods across different risk aversion levels. The performance of our dynamic robust portfolio increases with investors’ risk aversion levels. Our robust portfolio strategy can help investors with diverse risk preferences achieve substantial profits and enable those with high-risk aversion to reap the rewards of their risk aversion.

Assessing portfolio vulnerability to systemic risk: a vine copula and APARCH-DCC approach

This study evaluates the sensitivity and robustness of the systemic risk measure, Conditional Value-at-Risk (CoVaR), estimated using the vine copula and APARCH-DCC models. We compute the CoVaR for the two portfolios across five allocation strategies. The novel vine copula captures the complex dependence patterns and tail dynamics. The APARCH DCC incorporates volatility clustering, skewness, and kurtosis. The results reveal that the CoVaR estimates vary based on portfolio strategy, with higher values for the cryptocurrency portfolio. However, CoVaR appears relatively robust across strategies compared to ΔCoVaR. The cryptocurrency portfolio has a greater overall vulnerability. The findings demonstrate the value of CoVaR estimated via the vine copula and APARCH-DCC in assessing portfolio systemic risk. This advanced approach provides nuanced insights into strengthening risk management practices. Future research could explore the sensitivity of the CoVaR to different weighting schemes, such as equal versus market-weighted portfolios. Incorporating the Gram–Charlier expansion of normal density into the APARCH specification enables a nonparametric, data-driven fitting of the residual distribution. Furthermore, comparing the CoVaR to another systemic risk measure could provide further insights into its reliability as a systemic risk measure.

A Comparison of Home Purchase and Liquid Lazy Portfolio Returns in Turkey

This study presents a comprehensive comparison between the effectiveness of home purchase and liquid Lazy portfolio strategies for generating historical returns in the Turkish market. The primary aim is to develop a liquid Lazy portfolio that outperforms residential property investments. The study covers the period from January 2010 to June 2023, focusing on high-liquidity assets including the Borsa Istanbul 100 Index, currency exchange rates, gold prices, real estate investment trusts, and major stock market indices. Methodologically, the research involves data pre-processing, logarithmic transformations, and cumulative time series creation. Portfolio optimization centers around maximizing the Sharpe ratio with three test portfolios developed. Findings indicate that all portfolios consistently outperform the benchmark Residential Property Price Index, displaying higher returns and Sharpe ratios despite increased volatility. These insights are relevant for both academic researchers and investors seeking practical applications for portfolio management. It's important to note that historical performance offers insights but not future guarantees. In conclusion, the study highlights the potential benefits of constructing scalable, liquid, and high-return Lazy portfolios as alternatives to traditional home purchases