Investment Portfolio Research Articles

Innovation in Portfolio Optimization through the Use of Genetic Algorithms for Sustainable Entrepreneurship in Volatile Markets

Context. In the global financial landscape, marked by heightened economic volatility and constant transformations, entrepreneurs face the challenge of identifying sustainable investment strategies to ensure effective long-term risk management. Purpose. This study aims to develop a model for optimizing investment portfolios through financial and technological tools to maximize returns in highly volatile environments. Moreover, it aligns with the principles outlined in the Oslo Manual and the United Nations Sustainable Development Goals (SDGs). Methodology. Markowitz portfolio optimization using a classic genetic algorithm applied to data from 10 companies selected from the technology, health, and finance sectors. This data, obtained from Yahoo Finance, covers the period from 2020 to 2023. The reliability of the models was rigorously validated through internal consistency analysis, ensuring their robustness. Theoretical and Practical Findings. Theoretical results confirm the applicability of genetic algorithms in optimizing diversified portfolios. In practice, their potential to encourage investments in sustainable companies is evident, aligning with the SDGs by fostering key areas such as industrial innovation. Originality. This study adopts a multidisciplinary approach by integrating finance and technology in the selection of investment portfolios. The literature review highlights how the synergy between these two fields promotes sustainable development. Conclusions and limitations. Findings underscore the potential of genetic algorithms to perform in highly volatile contexts. However, the reliance on historical data analysis alone highlights the need for additional studies in real-world environments. These could focus on comparing other optimization models and exploring their impact in regions with diverse market structures.

Does It Pay Off to Integrate ESG Performance into Bank Investment Portfolio Selection? Empirical Evidence in the European Energy Sector

There is a growing awareness of the need to integrate non-financial information arising from environmental, social, and governance (ESG) factors into corporate strategies, processes, and credit risk assessment to generate long-term value. Our paper aims to develop, through a Data Envelopment Analysis (DEA)-based approach, a credit risk assessment tool that could be used by banks in constructing an efficient and sustainable investment portfolio, able to maximize banks’ probability contemporaneously minimizing corporate inefficiency. This study was carried out on a sample of publicly traded energy companies in Europe, with the energy sector being highly environmentally sensitive. Our portfolio selection model proves to be a valuable tool for building an efficient and sustainable investment portfolio because it leads, within a budget constraint, to selecting both the most efficient companies in absolute terms and those for which ESG scores significantly improve corporate financial efficiency. Additionally, our results show that ESG ratings at high or low levels do not affect overall company efficiency, but at a middle level, they increase it. Findings contribute (and provide suggestions) to policymakers, credit risk managers, and academics.

Analyzing Alibaba's Stock Price Volatility Before and After COVID-19 with ARIMA

This study investigates how the COVID19 pandemic affected the stock value of Alibaba by examining fluctuations in its market performance to and following the outbreak using the ARIMA models. The research utilizes Alibabas closing stock prices from December 2019 to August 2024 to cover both pre-pandemic and post pandemic situations comprehensively. By employing the ARIMA model for analyzing stock performance patterns and projecting market trends the study aims to shed light on the impact of the pandemic, on Alibabas stock value over time. The research results show that Alibabas stock price went through ups and downs along with growth over time; However, the COVID-19 pandemic brought about a lot of unpredictability into the mix, highlighting the necessity for improved forecasting methods that can adapt to sudden market changes effectively. These findings hint at the idea that while the ARIMA model is good at identifying trends in data patterns; it should be combined with techniques to be more prepared for unexpected situations. The study emphasizes how having a diverse investment portfolio is crucial, during times and stresses the importance of keeping up with regulations and making strategic investment choices wisely.

Impact of accelerated review policy on portfolio planning of vaccine companies

BackgroundWith the introduction of the accelerated drug review policy in China, the clinical research and development time and the review and approval time of drugs have been shortened accordingly. Especially under the influence of the COVID-19 pandemic, the vaccine formulations released through the accelerated review policy are springing up, and the question of how the accelerated review policy affects the investment portfolio of vaccine enterprises has also attracted more and more attention.Aims and methodsThe article uses mixed-integer linear programming to develop a new model on portfolio planning for vaccine companies based on the accelerated review policy context. The model is constructed using the Gurobi extension class of .NET, and the investment decision is made and simulated by the Gurobi solver to investigate the portfolio planning decision of a vaccine company maximizing the net present value of its vaccine production portfolio with the increase of available capital over a 20-year time horizon.ResultsThe NPV under the accelerated review policy is significantly higher than the net present value under the standard review policy when the available capital exceeds RMB 900 million. And the difference between the two of them peaks at RMB 1.87 billion when the available capital is RMB 1.9 billion; break-even occurs about 1.3 years earlier in the accelerated review policy than in the standard review; and when the available capital is the same, firms in the accelerated review policy choose to produce four products earlier and make the decision to invest in facility construction earlier; scenarios in the accelerated review policy are not as sensitive to changes in model parameters as they are in the standard review.ConclusionThe accelerated review policy is effective in providing incentives for commercialisation. The results of this study will provide an effective reference for vaccine companies to make scientific portfolio planning under the accelerated review policy.

The Investment Puzzle: Unveiling Behavioral Finance, Risk Perception, and Financial Literacy

Abstract Contemporary financial theory upholds the market efficiency hypothesis, asserting that stock prices inherently embody all available information. In contrast, behavioral finance theory challenges this premise by proposing that psychological and emotional factors can wield influence over stock prices. This study delves into the repercussions of behavioral finance factors on investment decisions within the Saudi equity markets, utilizing risk perception and financial literacy as mediating variables. An online questionnaire was disseminated to 350 individual investors, yielding 315 completed responses deemed suitable for analysis. Employing Structural Equation Modeling (SEM), the collected data underwent rigorous scrutiny. The findings reveal that herding, the disposition effect, blue-chip bias, and overconfidence significantly and positively impact both risk perception and financial literacy. Furthermore, all four behavioral finance factors exhibit a substantial positive indirect effect on investment decision-making through the intermediaries of risk perception and financial literacy. It is imperative to acknowledge the study’s specific focus on these factors while recognizing the potential influence of other elements on risk perception, financial literacy, and investment decision-making. The research underscores the critical importance of incorporating an individual’s perception of risk into investment decisions, as it markedly shapes their proclivity for risk-taking and, consequently, the performance of their investment portfolio. The findings highlight the necessity for investors to recognize and address their behavioral biases. Additionally, the study advocates for advisors and policymakers to formulate strategies aimed at mitigating the impact of these biases. The comprehensive exploration of behavioral finance factors and their implications for investment decisions yields valuable insights for individual investors and stakeholders in financial advisory and policymaking roles.

Business risks in managing the economic development of an agro-industrial complex enterprise

Relevance. This scientific article examines the key aspects of financial risk management in the agro-industrial complex. The study is based on an analysis of the price volatility of grain, fertilizers and fuel over the past five years. The results obtained make it possible to identify effective risk management strategies, including the use of financial instruments, and provide practical recommendations for agricultural enterprises. The conclusions of the study can contribute to increasing the stability of the profitability of agribusiness with the variability of prices for raw materials.The purpose of the scientific article is to conduct a comprehensive study to assess financial risks in the agroindustrial complex. The research is aimed at analyzing the price volatility of key raw materials, developing effective risk management strategies, as well as providing practical recommendations for agricultural enterprises.Objectives: development of a model for assessing financial risks in agribusiness based on the analysis of price volatility of grain, fertilizers and fuel over the past five years; research on the effectiveness of financial instruments, such as grain options, to mitigate risks when investing in agricultural production; development of practical recommendations for agricultural enterprises, including diversification of the investment portfolio and the use of financial instruments based on calculations and analysis;Methodology. The methods used in the course of the study include a variety of bibliographic sources, data collection tools, as well as expert approaches.Results. The results of the study include a comprehensive assessment of financial risks in agribusiness based on the analysis of price volatility of key raw materials. This made it possible to identify effective risk management strategies, such as the use of grain options, to ensure stable profitability while reducing the level of financial risk.Conclusions. The results obtained in the course of the study provide practical recommendations for agricultural enterprises on effective financial risk management. Developed strategies, such as the use of grain options, can help reduce the impact of price volatility on profitability, ensuring a more stable financial position of the enterprise.

Understanding dynamic return connectedness and portfolio strategies among international sustainable exchange-traded funds

The rapid growth of sustainable investing has led to the global expansion of environmental, social, and governance (ESG) investment products. Existing literature on sustainable investing focuses primarily on corporate social responsibility theory and risk assessment, with relatively little research on ESG investment value and portfolio strategies. Using data from six worldwide ESG exchange-traded funds (ETFs) between 2020 and 2023, we examine return connectedness and portfolio performance by employing a time-varying parameter vector autoregressive (TVP-VAR) and portfolio approaches. The findings reveal that European ETF plays a dominant role in the worldwide ESG system due to the market size and market maturity. Specifically, the European ETF can substantially reduce portfolio volatility. Moreover, the results show that minimum variance and risk-parity portfolios outperform the other portfolio strategies during periods of geopolitical turmoil. These results provide valuable insights for improving the resilience of ESG markets and enhancing sustainable investment strategies.

Time-frequency Co-movement and Cross-quantile Connectedness of Exchange Rates: Evidence from ASEAN+3 Countries

This study investigates the time-frequency co-movement and cross-quantile connectedness of exchange rates. Using wavelet coherence and cross-quantile methods, we examine ASEAN+3 countries’ time-frequency co-movement, quantile spillover effects, and network connectedness of the exchange rate markets. Our empirical results are as follows: significant co-movement heterogeneity exists across countries over different frequency bands. Moreover, the Chinese Yuan (CNY), Japanese Yen, and South Korea Won are desirable sources of diversification for other currencies across different investment horizons. CNY and JPY exhibit good regional safe haven currency attributes in different investment horizons. Overall, these findings suggest ways for currency authorities to maintain exchange rate stability and investor portfolio decisions.

The impact of credit ratings on international investment: Evidence from an emerging market

This study examines the influence of sovereign credit ratings on international investments in emerging markets, focusing on Foreign Direct Investment (FDI) and Foreign Portfolio Investment (FPI). Using data from 22 emerging economies spanning 1999 to 2019, we employ OLS and fixed-effects regression models to analyze how credit ratings affect investment flows and market volatility. The findings indicate that sovereign credit ratings significantly impact investor decisions, as these ratings serve as a key metric in assessing investment risk and potential. This research highlights the essential role of credit ratings in enhancing financial stability and promoting economic integration through foreign investments, with implications for growth and trade. The study provides novel insights for investors, policymakers, and rating agencies on the role of credit ratings in emerging market investments, contributing to a deeper understanding of their impact on global economic dynamics.

Investment Portfolio Allocation and Insurance Solvency: New Evidence from Insurance Groups in the Era of Solvency II

This study examines the effect of the investment portfolio structure on insurers’ solvency, as measured by the Solvency Capital Requirement ratio. An empirical sample of 88 EU-based insurance groups was analyzed to provide robust evidence of the portfolio’s impact on the Solvency Capital Requirement ratio from 2016 to 2022. Linear regression and supervised machine learning models, particularly extra trees regression, were used to predict the solvency ratios, with the latter outperforming the former. The investigation was supplemented with panel data analysis. Firm-specific factors, including, unit-linked and index-linked liabilities, firm size, investments in property, collective undertakings, bonds and equities, and the ratio of government bonds to corporate bonds and country-specific factors, such as life and non-life market concentration, domestic bond market development, private debt development, household spending, banking concentration, non-performing loans, and CO2 emissions, were found to have an important effect on insurers’ solvency ratios. The novelty of this research lies in the investigation of the connection of solvency ratios with variables that prior studies have not yet explored, such as portfolio asset allocation, the life and non-life insurance market concentration, and unit-linked and index-linked products, via the employment of a battery of traditional and machine enhanced methods. Furthermore, it identifies the relation of solvency ratios with bond market development and investments in collective undertakings. Finally, it addresses the substantial solvency risks posed by the high banking sector concentration to insurers under Solvency II.

Artificial intelligence in investments: The current state

Subject. This article discusses the issues of reducing investment risks by compiling a balanced investment portfolio using artificial intelligence. Objectives. The article aims to analyze the investment portfolio compiled by artificial intelligence for further use and profitability, and find out whether artificial intelligence can replace a professional trading participant at this stage. Methods. For the study, we used logical and statistical analyses, and also conducted an experiment with a chatbot. Results. The article presents the results of an experiment conducted using the ChatGPT generative artificial intelligence (AI) chatbot in Telegram Messenger. Conclusions. At this stage, artificial intelligence is not able to replace a professional market participant, but this should not be ruled out in the future.

Barriers for institutional investors to invest in digital financial assets

Subject. The article analyzes barriers to active involvement of institutional investors' funds in digital DFA in the Russian financial market. Objectives. The study aims at identification of the main barriers preventing the active involvement of institutional investors' funds in DFA, development of recommendations for their elimination. Methods. The study employs general scientific methods of research, logical, comparative, and statistical analysis to review foreign practice and academic publications of domestic and foreign authors. Results. We revealed three main groups of barriers. They are legal and regulatory barriers; low volume of the primary market and insufficient liquidity in the secondary market; low level of financial literacy and problems of understanding DFA by private investors. To eliminate them, we offer to amend a number of federal laws to allow institutional investors to include DFA in their investment strategies and portfolios. The problem of low liquidity of the secondary market can be solved by including the main Russian trading platforms in the register of DFA exchange operators of the Bank of Russia, and providing private investors' access to these transactions. Conclusions. It is crucial to develop a risk rating system for DFA and criteria for DFA inclusion in the portfolios of institutional investors.

STOCK MARKET PERFORMANCE: THE FIRM SIZE AND PRICE-EARNINGS RATIO OF SHARIAH AND NON-SHARIAH COMPLIANT SHARES

Stock markets serve as vital components of financial markets, providing avenues for short and long-term capital investment. The coexistence of two entities, namely Shariah Compliant Shares (SCS) and Non-Shariah Compliant Shares (NSCS) as supervised by the Shariah Advisory Council of the Securities Commission of Malaysia (SACSCM) characterizes Malaysia as a unique financial market. Stock return is a good indicator of a company’s performance, and many variables are said to have an impact on stock return. This study aims to investigate how firm size and price-earnings ratio would influence the stock return of a company. A total of 63 companies were selected consisting of 32 Shariah compliance companies and 31 conventional companies. Data from January 2018 until December 2022 were analyzed using descriptive statistics, correlation analysis, and regression analysis. This study discovered there is a significant negative relationship between firm size and stock return for SCS and NSCS indicating small companies often make higher returns than large companies. Meanwhile, the price-earnings ratio insignificantly affects the stock return, especially for NSCS. Whether the shares are Shariah compliant or Non-Shariah compliant, firm size has a more significant impact on stock return. To maximize shareholder wealth, the company will prioritize maintaining the optimal firm size, and investors will consider the firm size when planning their investment portfolios. The findings offer valuable insights for future research, companies, and investors seeking guidance on choosing shares for potentially higher returns.

Dynamic Connectedness Among Alternative and Conventional Energy ETFs Based on the TVP-VAR Approach

This study investigates risk transmission in the US energy instrument market to determine if certain factors, such as crude oil and natural gas, influence this market and whether stock or energy investment portfolios track their behavior. To investigate volatility spillover, the VAR-based connectedness approach is applied. This approach facilitates the measurement of interdependence across a network of variables, providing insights into aggregate, directional, and net interdependence. The use of the time-varying parameter vector autoregression (TVP-VAR) approach, as developed by Antonakakis and Gabauer, avoids the problems associated with selecting rolling window sizes and the resultant loss of observations during estimations. The analysis revealed a distinction between alternative and traditional ETFs, with lower interdependence observed among the volatility of alternative energy ETFs. While most energy ETFs transmit risk within the systems analyzed, some act as risk receivers, though their net receiving/transmitting character fluctuates. The results of this study are significant for investment portfolio managers.

Application of VAR-GARCH for Modeling the Causal Relationship of Stock Prices in the Mining Sub-sector

Accurate modeling is expected to minimize risk and maximize profit in investment portfolios, one ofwhich is in stock price modeling. This research aims to model the causal relationship between stockprices using the Vector Autoregressive - Generalized Autoregressive Conditional Heteroskedasticity(VAR-GARCH) model. The VAR-GARCH model is used to overcome heteroscedasticity and modeldynamic volatility. The data used for the modeling consists of daily stock prices from July 2023 toMay 2024 for mining sub-sector companies listed on the Jakarta Islamic Index (JII), including ADMR,ADRO, and ANTM. The results showed that the VAR(1) model is stable, but this model indicates thepresence of heteroskedasticity or ARCH effects. Therefore, the VAR(1) model was combined with theGARCH model, and the results showed that the best model is VAR(1)-GARCH(1,1). The VAR(1)-GARCH(1,1) model is appropriate and meets the homoskedasticity assumptions for modeling the stockprices of the mining sub-sector in the Jakarta Islamic Index (JII). This indicates that the VAR-GARCHmodel could successfully handle the volatility of stock price data. In general, this research is in linewith previous research, i.e., the VAR-GARCH model showed a better model for capturing the volatilitypatterns in the data.

Enhancing stock market predictions via hybrid external trend and internal components analysis and long short term memory model

When it comes to financial decision-making, stock market predictability is extremely important since it offers valuable information that may guide investment strategies, risk management, and portfolio allocation overall. Traditional methods often fail to accurately predict stock prices due to their complexity and inability to handle non-linear and non-stationary patterns in market data. To address these issues, this study introduces an innovative model that combines the External Trend and Internal Components Analysis decomposition method (ETICA) with the Long Short-Term Memory (LSTM) model, aiming to enhance stock market predictions for S&P 500, NASDAQ, Dow Jones, SSE and SZSE indices. Through rigorous testing across various training data proportions and epoch settings, our findings reveal that the proposed hybrid model outperforms the single LSTM model, delivering significantly lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. This enhanced precision reduces prediction errors, underscoring the model’s robustness and reliability. The superior performance of the ETICA-LSTM model highlights its potential as a powerful financial forecasting tool, promising to transform investment strategies, optimize risk management, and enhance portfolio performance.

A Study on Portfolio Management Strategies Adopted by Asset Management Companies Across the Globe

The asset management industry has been growing drastically over the last 15 years. It grew from 45,6 trillion USD to 103,1 trillion USD from 2009 to 2020, In Asia, it has grown 483% over the 11 years. Similarly, Latin America, the Middle East, and Africa saw the most minor growth and were valued at trillions of USD. Retail investors now comprise 41% of the asset management industry in China and India and above 55% of the asset management investors in the country. Since the Healthy Growth of AMC is the same as Millions of Retailers in the respective Financial Sectors, It is necessary to study the Portfolio Management Strategies adopted by Asset Management Companies across the Globe in general and In India in Particular. Hence, an attempt is made to study the performance and strategies of global AMCs and also observe the opportunities and challenges the same across the globe with the main objective of understanding the concept of Portfolio Management Strategies and Asset Management Companies; to review the existing literature on the topic concerned; to analyze the opportunities and challenges of the same across the globe. The study is an observatory in nature and revealed that the main opportunity is to get support from the respective economies of the countries to get desired growth in AMC investments and exploit the developments in various sectors via making Portfolio investments by direct buying of financial instruments or through mutual funds. Similarly, this study seeks to shed light on how emerging markets like India would be helpful with its key govt initiatives by ease of doing business, tax concessions for Foreign Domestic Investments (FDIs) and Foreign Institutional Investors (FIIs) etc. The main suggestion is to diversify their assets based on risk tolerance which can reduce the costs of the operations by using economies of scale to minimize the costs. The survival of any AMC across the globe is mainly on the feasibility of their services which could be affordable to retail investors by the end of the day. Similarly, Technology is evolving fast, and asset management companies need to implement new tools as soon as they are released to use them to create more accurate models to predict market movements.

Capacity investment portfolio optimization considering EV charging flexibility: A heuristic algorithm-informed system dynamics approach

Orderly charging of electric vehicles (EVs) functions as a flexible tool for peak shaving to help integrate renewable energy sources effectively. Coordinating investments in charging resources and generation resources can enhance social benefits while ensuring the charging needs of EVs are met. This study aims to optimize the long-term investment strategies for these resources using a heuristic algorithm-informed system dynamic (SD) approach. Firstly, a SD model is developed to simulate the long-term interactions among generation resources, EVs, and power demand, capturing their dynamic relationships. Then, the fixed feedback functions of investment parameters in the SD model are replaced with an optimization model, enabling parameter optimization for environmental and economic objectives. Given that the process must quantify the aforementioned long-term interrelationships with potential high-order, nonlinear feedback loops, the solution is implemented using Particle Swarm Optimization (PSO). Data from a specific province in China was used for the case study. The results show that the optimized investment portfolio reduced carbon costs and total investment by 2.4% and 22%, respectively, while total social costs decreased by 17% compared to the pre-set investment strategy. Lastly, we also conducted sensitivity analysis and algorithm comparison.

The Dynamics of FPI and FDI in Indian Economy : A Correlational Study

Foreign Portfolio Investment (FPI) and Foreign Direct Investment (FDI) play distinct yet complementary roles in India's economic development. FPI is short-term and volatile, enhancing liquidity in financial markets, while FDI is long-term and supports sustainable growth through capital formation, technology transfer, and employment generation. Despite their individual contributions, FPI and FDI operate independently, driven by different factors. Policymakers must adopt differentiated approaches: FPI can be promoted through market stability and regulatory transparency, whereas FDI requires structural reforms and an investment-friendly environment. This paper also suggests directions for future research, focusing on sector-specific impacts, the influence of policy changes, and the role of macroeconomic conditions in shaping these investment inflows.

Unveiling the impact of oil price shocks on global sukuk markets: a focus on quantile coherence and time-frequency connectedness

ABSTRACT This study examines the relationship between oil price shocks and Islamic bonds in 15 global markets. We focus on how they are connected in terms of time and frequency, specifically in quantiles. Through a quantile coherency approach, we discover that oil price shocks affect sukuk indices differently depending on the type of shock. When we increase the frequency of analysis from once a year to once a month, we observe a significant increase in coherence values of oil risk shocks across the majority of sukuk indices of oil exporting nations in both the lower and middle quantiles. Furthermore, based on quantile cross-spectral coherence analysis, we notice that the extreme quantile dependence between oil price shocks and the majority of the oil exporting nations’ sukuk indices remains stable over a short-time horizon. Our research indicates that demand shocks have a significant impact on the correlation between sukuk markets during uncertain periods, such as COVID-19 and the Russian-Ukraine war. Our findings provide a credible understanding of the behaviour of sukuk indices in response to fluctuations in oil prices, which is important for investment portfolio managers and policy decision-makers.