Portfolio Theory Research Articles

Is gold a safe haven for the U.S. dollar during extreme conditions?

This paper examines whether gold can act as a hedge/safe-haven asset for the U.S. dollar exchange rate risk against a broad range of currencies. Findings imply the important exchange rate hedging role of gold. Moreover, gold acts as a safe-haven asset during the COVID-19 pandemic and the Russia-Ukraine war for some exchange rate pairs examined. Analysis of gold-currency portfolios reveals that including an optimized amount of gold reduces portfolio variance during the COVID period but produces mixed findings during the war. These findings are robust against additional tests accounting for different holding periods for gold and asymmetries in the relationship between gold and exchange rate returns. Our results offer important implications for international investors and policymakers concerned with the U.S. dollar's exchange rate risk.

The anatomy of government bond yields synchronization in the Eurozone

Abstract We investigate the synchronization of the Eurozone’s government bond yields at different maturities. For this purpose, we combine principal component analysis with random matrix theory. We find that synchronization depends on yield maturity. Short-term yields are not synchronized. Medium- and long-term yields, instead, were highly synchronized early after the introduction of the Euro. Synchronization then decreased significantly during the Great Recession and the European Debt Crisis, to partially recover after 2015. We interpret our empirical results using portfolio theory, and we point to divergence trades as a source of the self-sustained yield asynchronous dynamics. Our results envisage synchronization as a requirement for the smooth transmission of conventional monetary policy in the Eurozone.

A Report on Investment Differs from Gambling: A Comprehensive Study on Technical Analysis in Investment Decision-Making

This research paper conducts a thorough examination with primary objectives focused on acquiring a comprehensive understanding of scrutinizing portfolio analysis, navigating the complexities of the stock market, addressing risk management considerations, and exploring the psychological facets of trading, the study aims to provide valuable insights for investors and researchers. Employing a descriptive research design, the paper establishes a structured framework for data collection, analysis, and interpretation, ensuring the reliability and validity of the study's outcomes. The research emphasizes technical analysis, specifically delving into candlestick patterns, and systematically evaluates indicators, oscillators, and trading strategies prevalent in financial markets. Distinguishing investing from trading and illuminating the divergence from speculative activities like gambling are additional focal points. In terms of data collection, the paper opts for a secondary method, utilizing existing information from literature, articles, and databases. This pragmatic choice aligns with considerations of accessibility, cost-effectiveness, and time efficiency, facilitating an in-depth analysis of the company's financial landscape and investment dynamics. Ultimately, the research contributes a nuanced understanding of financial markets and investment strategies, offering insights crucial for investors, analysts, and researchers navigating the complex terrain of corporate finance.

Portfolio Theory and COVID-19: A Quantitative Analysis of Investment Products

This study examines the analysis of different investment products after the COVID-19 pandemic, using portfolio theory, with the aim of exploring an optimal investment allocation, assuming that investors are rational and that there are no other contingencies that could affect the upward or downward trend of the investment products. Risk may be quantified and varies in practice. Investment risks are higher for financial instruments with higher volatility, but potential gains and losses are also more significant. Therefore, a person must take more risks to get a larger return on investment, and taking more risks should be rewarded proportionally with higher returns. This research can create a preliminary investment portfolio by thoroughly calculating and analyzing the real situation of various investment goods and by assessing various assets using the portfolio theory.

Utilizing the Mean-Variance Model to Optimize Pension Investment Portfolios: A Detailed Analysis of Key Industries

Pension investment portfolios play a pivotal role in ensuring financial security for retirees. With an aging global population, the importance of optimizing these portfolios to withstand market fluctuations and ensure steady returns becomes paramount. This study delves into the complexities of pension investment portfolios using the time-tested mean-variance model, aiming to strike a balance between risk and expected returns. Focusing on key industries like technology, entertainment, automotive, resources, and air travel, the research critically analyzes a diverse set of stocks. Methods employed include a deep analysis of expected returns, variances, covariances, and the overarching risk-return trade-off. Preliminary results underscore the nuanced nature of portfolio management and the indispensability of incorporating modern portfolio theory in pension fund management. The outcomes of this study not only aid in making informed investment decisions but also shed light on the broader social implications of robust pension fund management, emphasizing its significance in securing retirees' financial futures.

ФОРМУВАННЯ СТРАТЕГІЇ ПІДПРИЄМСТВА В УМОВАХ ДИНАМІЧНОГО БІЗНЕС-СЕРЕДОВИЩА

The article examines the specifics of the functioning of a dynamic business environment, which stimulates enterprises to constantly adapt to changes and form effective strategies to ensure their profitability. The process of forming the company's strategy in the conditions of constant changes in the business environment is studied. The key aspects of the formation of the company's strategy are analyzed, in particular, the identification of factors of the external environment that affect business processes, the analysis of internal resources and competitiveness, as well as the principles of determining strategic goals and action plans. Methods and tools that help enterprises effectively adapt to changes, such as SWOT analysis, PESTLE analysis, portfolio analysis and others, are considered. The importance of constant monitoring and strategic planning to ensure the conditions of sustainable development of the economic entity is determined. Recommendations for the formation of an enterprise strategy in the conditions of a dynamic business environment are offered. The importance of adapting and constantly updating the company's strategy is proven, which is defined as a key factor of successful activity in modern conditions. The need for active strategic planning of the economic activity of enterprises and their adaptation to the changes that are constantly occurring in their business environment, in order to achieve a competitive advantage and sustainable development, is determined. It has been proven that in order to achieve success on the market in conditions of significant dynamics of business processes, it is necessary to take into account not only current trends, but also possible future changes in the environment itself, which requires an enterprise to be open to innovation and ready to take risks. It was determined that innovation and flexibility are important elements of the company's development strategy, allowing it to quickly respond to changes and occupy new markets. The peculiarities of the formation of the business entity's development strategy in the conditions of a dynamic business environment, which requires it to combine traditional strategic approaches and innovative ideas, are considered. It was determined that the involvement of all levels and units of the organization in the process of strategic planning is an important aspect of the formation of the company's development strategy in the conditions of a dynamic business environment. At the same time, communication and cooperation between structural divisions, the ability to effectively exchange information become the basis for the successful implementation of the chosen strategy.

A Python-based Empirical Study of Markowitz's Portfolio Theory in the Chinese Stock Market

Markowitz's portfolio theory serves as a pivotal framework in investment science, offering investors a scientifically robust approach to asset allocation. This paper conducts an empirical analysis of the theory within the context of the Chinese stock market, utilising Python for data manipulation and computation. A selection of five stocks was made based on pre-established criteria, and Python was employed to examine their correlation for portfolio construction viability. Subsequently, Python facilitated the identification of the portfolio with the maximum Sharpe ratio and the minimum variance, as well as the delineation of the efficient frontier. Results from the empirical study confirm the applicability and utility of Markowitz's portfolio theory for Chinese investors, thereby substantiating its significant relevance in China's financial market. Based on the findings, investors are advised to diversify their portfolios following the guidelines of the efficient frontier to optimize returns while managing risks. Moreover, considering the complexities of the Chinese stock market, harnessing computational tools like Python can grant an analytical edge, enabling more informed decision-making. It's also pivotal for investors to routinely review and adjust portfolio compositions in line with market dynamics, ensuring they stay aligned with the principles of the efficient frontier.

Portfolio Theory Application and Enhancement in the Chinese Stock Market

Portfolio Theory is commonly used in funds management because it lowers risk and maximises return rate. As the research progresses, the big gap in its efficiency in the Chinese stock market must be filled. Therefore, the main topic for this paper is the Portfolio Theory's application in the Chinese Stock Market and what improvements could be made. This research method is as follows: 1) collecting relative papers through databases after filtering, 2) classifying papers' purpose and results, and 3) analysing their conclusions and summarising the current state of Portfolio Theory in China. This paper discovers the increasing suitableness of the Portfolio Theory in the Chinese stock market and derives several methods for improvements, such as taking risk attitudes, stock preferences, and skewness into consideration. In addition, variation of asset choices during crises such as COVID-19 is considered to optimise the portfolio. In conclusion, the Chinese stock market still shows immaturity in many aspects. For example, it is suggested that governments make policies to keep companies' cash flow transparent.

The secret of voice: How acoustic characteristics affect video creators' performance on Bilibili

The importance of voice has been well acknowledged in sensory decision-making. Yet, past literature on video creators' performance did not shed much light on the impact of video creators' acoustic characteristics. Building on signaling theory of portfolios, we examine how the acoustic characteristics of a video creator and the signals of video quantity affect the number of likes a video creator receives and the change in the number of the creator's followers. Using a longitudinal dataset obtained from Bilibili through automated speech recognition analytics and text analysis approach, econometrics models are employed to test the research model. Findings indicate that loudness variability of a video creator significantly increases the number of likes the creator receives, and exerts a positive effect on the change in the number of the creator's followers. In contrast, vocal pitch has a significant positive impact on the number of likes but a significant negative impact on the change in the number of followers. Results further suggest that a video creator's performance varies according to the signals of video quantity, i.e., the number of weekly published videos and the average length of videos. Our results could offer theoretical contributions and practical insights for video creators to enhance their performance by adjusting the acoustic characteristics and the signals of video quantity wisely.

Dynamic asymmetric spillovers and connectedness between Chinese sectoral commodities and industry stock markets.

This study investigates the dynamic and asymmetric propagation of return spillovers between sectoral commodities and industry stock markets in China. Using a daily dataset from February 2007 to July 2022, we employ a time-varying vector autoregressive (TVP-VAR) model to examine the asymmetric return spillovers and dynamic connectedness across sectors. The results reveal significant time-varying spillovers among these sectors, with the industry stocks acting as the primary transmitter of information to the commodity market. Materials, energy, and industrials stock sectors contribute significantly to these spillovers due to their close ties to commodity production and processing. The study also identifies significant asymmetric spillovers with bad returns dominating, influenced by major economic and political events such as the 2008 global financial crisis, the 2015 Chinese stock market crisis, the COVID-19 pandemic, and the Russia-Ukraine war. Furthermore, our study highlights the unique dynamics within the Chinese market, where net information spillovers from the stock market to commodities drive the financialization process, which differs from the bidirectional commodity financialization observed in other markets. Finally, portfolio analysis reveals that the minimum connectedness portfolio outperforms other approaches and effectively reflects asymmetries. Understanding these dynamics and sectoral heterogeneities has important implications for risk management, policy development, and trading practices.

ВИДИ ІНСТРУМЕНТАРІЮ УПРАВЛІННЯ ПОРТФЕЛЕМ МІЖНАРОДНИХ БІЗНЕС-ПРОЄКТІВ ПІДПРИЄМСТВ

The construction of a classification of tools for managing the portfolio of international business projects of enterprises is important from a theoretical and practical point of view, as it allows systematizing the available tools and methods, ensuring their effective use in various situations. From a theoretical point of view, this classification contributes to the development of a scientific approach to project portfolio management, defining the main categories of tools and their purpose, which helps in a clear understanding of their application and functionality. In the process of analyzing various tools for managing a portfolio of international business projects, it was proven that effective management requires the use of a complex of quantitative and qualitative methods. It is well-founded that quantitative tools such as portfolio analysis, capital investment and budgeting, as well as monitoring and control, ensure the accuracy of measurements and the objectivity of decision-making with the help of numerical data and statistical analysis. On the other hand, it is argued that qualitative methods, such as SWOT analysis, process management, and intercultural communication, provide a deeper understanding of the contextual aspects of projects, allowing to reveal non-obvious opportunities and threats that cannot always be quantified. It is also confirmed that regular tools such as ERP systems and digital communication tools contribute to the continuous optimization of the company’s activities and support daily operations, while ad hoc tools such as portfolio analysis and risk analysis are applied for specific situations , which require special attention or a quick reaction. This indicates the importance of flexibility in the choice of tools depending on the current needs and strategic goals of the enterprise. In general, the success of managing a portfolio of international business projects depends on the balanced use of various management tools that allow for a comprehensive assessment of internal and external factors, as well as an effective response to the dynamic international environment.

Reinforcement learning for deep portfolio optimization

<p>Portfolio optimization is an important financial task that has received widespread attention in the field of artificial intelligence. In this paper, a novel deep portfolio optimization (DPO) framework was proposed, combining deep learning and reinforcement learning with modern portfolio theory. DPO not only has the advantages of machine learning methods in investment decision-making, but also retains the essence of modern portfolio theory in portfolio optimization. Additionaly, it was crucial to simultaneously consider the time series and complex asset correlations of financial market information. Therefore, in order to improve DPO performance, features of assets information were extracted and fused. In addition, a novel risk-cost reward function was proposed, which realized optimal portfolio decision-making considering transaction cost and risk factors through reinforcement learning. Our results showed the superiority and generalization of the DPO framework for portfolio optimization tasks. Experiments conducted on two real-world datasets validated that DPO achieved the highest accumulative portfolio value compared to other strategies, demonstrating strong profitability. Its Sharpe ratio and maximum drawdown also performed excellently, indicating good economic benefits and achieving a trade-off between portfolio returns and risk. Additionally, the extraction and fusion of financial information features can significantly improve the applicability and effectiveness of DPO.</p>

Does Enterprise Risk Management Improve Performance and Create Shareholder Value? Empirical Evidence of Listed Firms on the Ho Chi Minh Stock Exchange, Vietnam

This study applies Agency Theory, Stakeholder theory, and Modern Portfolio Theory, which revolves around the way enterprises are organized, managed, and controlled and guides the managers’ decisions in ensuring stability and creating value based on satisfying the interests of stakeholders, to analyze the impact of enterprise risk management implementation on the firm’s performance and value creation of 285 listed firms on the Ho Chi Minh Stock Exchange, Vietnam, from 2017 to 2022. Feasible Generalized Least Squares (FGLS) estimation results show that risk management practices positively affect firm value and business performance. In addition, the study also indicates that the variables of Big 4 auditor, inspection committee presence, sales growth, firm age, firm size, and leverage have affected firm performance and value differently. The findings have several implications for investors, managers, and researchers.

Corporate Governance and Sustainability in Latin America

Purpose: Examine the relationship between corporate governance (CG) and corporate sustainability (SC), measured though Environmental, Social, and Governance (ESG) variables.Methodology: The study is explanatory in scope, and we used the feasible generalized least squares (FGLS) methodology to analyze the results.Findings:The results show that some aspects of the board of directors such as size, committees and gender diversity significantly explain corporate sustainability, while CEO duality, independence, and tenure on the board do not. Practical implications: to offer a perspective for institutions to promote companies to advance in matters pertaining corporate governance and sustainability to guarantee long-term viability of organizations, society, and the environment.Value: The results contribute to academic knowledge about the relationship between CG and SC in Latin America by offering a better understanding of the topic and enabling new areas of research in the region. Most studies have focused on the relationship between ESG and performance, portfolio analysis, corporate disclosure, or asymmetric information. Unlike these studies, this work extends the field of research in CG by identifying the variables of the board of directors that are related to corporate sustainability.Limitations: One of the limitations of the study lies in possible selection, information, and reporting biases as the ESG metrics may not fully reflect corporate sustainability. It is essential to address this gap through better adaptation of the measures and deeper theoretical exploration to better understand corporate sustainability in Latin America.

Neural Networks for Portfolio Analysis With Cardinality Constraints.

Portfolio analysis is a crucial subject within modern finance. However, the classical Markowitz model, which was awarded the Nobel Prize in Economics in 1991, faces new challenges in contemporary financial environments. Specifically, it fails to consider transaction costs and cardinality constraints, which have become increasingly critical factors, particularly in the era of high-frequency trading. To address these limitations, this research is motivated by the successful application of machine learning tools in various engineering disciplines. In this work, three novel dynamic neural networks are proposed to tackle nonconvex portfolio optimization under the presence of transaction costs and cardinality constraints. The neural dynamics are intentionally designed to exploit the structural characteristics of the problem, and the proposed models are rigorously proven to achieve global convergence. To validate their effectiveness, experimental analysis is conducted using real stock market data of companies listed in the Dow Jones Index (DJI), covering the period from November 8, 2021 to November 8, 2022, encompassing an entire year. The results demonstrate the efficacy of the proposed methods. Notably, the proposed model achieves a substantial reduction in costs (which combines investment risk and reward) by as much as 56.71% compared with portfolios that are averagely selected.

Machine Learning Approach for Predicting the Net Asset Value (NAV) of Mutual Funds based on Portfolio Holdings

Mutual funds, a cornerstone of modern investment portfolios, offer investors a diversified and professionally managed approach to financial growth. These investment vehicles collect small amount from multiple investors and invests the collective investment further in diversified portfolio of stocks, bonds, or other securities. Mutual funds are characterized by their net asset value (NAV), which represents the per-share value of the fund's assets minus its liabilities. Investors benefit from the expertise of seasoned fund managers who make investment decisions based on their analysis of market trends and company performance. By providing an accessible and diversified investment option, mutual funds have become a popular choice for both novice and seasoned investors seeking to achieve their financial goals. Apart from that, the role of AI is increasing exponentially in various areas. Such AI based tools and algorithms are utilized by various MFs expert to identify the suitable investment portfolios. This paper proposes a technique to analyse the performance of the different stock's portfolios based on the past data. Combining the isolated analysis of different stocks portfolios may be utilized to predict the net asset value prediction (NAV) of MFs. The experiments carried out in this research work uses linear regression analysis to analyse the stock's performance at sub level of the MFs. A case study for the Axis Bluechip fund's is also carried out by using the proposed ML approach. The Yahoo Finance dataset is used for the case study. Previously, machine learning algorithms like linear regression (LR), decision tree regression (DTR), and multivariate regression (MR) are taken to predict and analyse the dataset. These algorithms calculate the NAV of the mutual fund with good accuracy and analyse the best result for the test dataset. The various models are being created from different portfolios to analyse the NAV of the mutual funds. This work proposed hierarchical method to perform the linear regression analysis for the NAV predicting. This work uses R-Square (R2) statistical measures to determine the goodness of models. The obtained R2 is 0.86, implying that the created models have good accuracy and performance on the dataset.

Financial Asset Allocation and Portfolio Optimization Strategies Based on Stochastic Optimization Methods

Abstract Financial market uncertainty necessitates sophisticated asset allocation and portfolio optimization strategies for enhanced returns and risk mitigation. This study explores stochastic optimization methods to overcome the limitations of traditional models in complex market environments. By employing expected utility theory and the Lagrange multiplier method, we develop an optimization model that facilitates fair equity pricing and enables dynamic allocation of financial assets, outperforming traditional approaches in simulated markets. Specifically, our method yields mean allocations for state-owned and non-state-owned enterprises at 17.4112 and 16.9297, respectively, demonstrating its superiority. Analysis of bank asset portfolios reveals significant improvements in asset structure and yields, underscoring the effectiveness of stochastic optimization in financial asset management, offering substantial benefits in efficiency and investment returns.

THE IMPACT OF ARTIFICIAL INTELLIGENCE (AI) ON PORTFOLIO OPTIMIZATION

This paper explores the transformative impact of artificial intelligence (AI) on portfolio optimization in the financial sector. It examines how machine learning algorithms have revolutionized risk management and investment strategies by analysing vast datasets more efficiently than traditional methods. The study contrasts modern portfolio theory with AI-driven approaches, highlighting the advantages of machine learning models in addressing market risks and volatility. The findings suggest that AI not only enhances predictive accuracy but also offers a more dynamic and adaptable framework for portfolio management, thereby outperforming conventional techniques in managing financial risks.

Systematic ESG risk and hedge fund

<abstract> <p>We present a framework for linking hedge funds to environmental, social, and governance (ESG) investments based on systematic ESG risk. We proposed a double-factor model to explain the co-movement of asset returns due to two systematic risk factors: Adjusted market risk and systematic ESG risk. Complementing prior work that introduces ESG characteristic indicators as determinants of agents' utility functions, this framework leverages modern portfolio theory by establishing a channel through which systematic ESG shocks affect asset risk and, in turn, equilibrium expected returns. This approach enabled us to estimate empirical market prices for systematic ESG risk from real-world observations and derive the relationship between equilibrium expected returns and the loadings on systematic risk factors. Applying this framework to U.S. equity mutual funds, we found that the loadings of systematic ESG risk were significantly correlated with the expected return of the sorted portfolios. These results suggested that it is possible to design hedge fund strategies that capitalize on systematic ESG risk that varies over time.</p> </abstract>

Balancing Profit and Morality: Confucian Insights into Financial Management and Corporate Governance

This study explores the integration of Confucian principles in modern financial management and corporate governance, offering a nuanced perspective on how ancient philosophical wisdom can inform contemporary business practices. By analyzing seminal Confucian texts and reviewing current literature, this research illuminates the alignment between Confucian ethics and ethical business conduct, emphasizing a balance between financial success and societal welfare. The findings reveal that Confucianism, with its core values of Ren (compassion), Yi (righteousness), and Li (propriety), advocates for a holistic approach to business. This approach prioritizes long-term societal benefits and ethical decision-making over immediate financial gains. The study contrasts and compares Confucian perspectives with modern financial theories, highlighting the relevance and applicability of Confucian ethics in today's business world. In financial management, Confucian principles encourage prudent investment strategies and respectful stakeholder relationships, aligning with aspects of portfolio theory and wealth maximization but with an added ethical dimension. In corporate governance, Confucianism promotes leadership based on moral integrity, advocating for a governance model that values trust, mutual respect, and social responsibility. This research contributes to the understanding of how traditional philosophical systems like Confucianism can complement and enrich modern business practices.