Investment Portfolio Research Articles

Foreign Investments and Market Capitalization in Nigeria: A VECM Analysis

A healthy capital market promotes the overall economic wellbeing of a country. However, Nigeria’s capital market has witnessed both internal and external pressures leading to high rate of volatility. This study examines the impact of foreign investments on market capitalization in Nigeria’s capital market for the period 1981 to 2022. The study is anchored on the theoretical framework of capital movement across regions and seeks to explain why investors invest in foreign countries and the impacts of such investments on the host country’s capital market. We adopted a vector error correction model (VECM) as our analytical tool. The ontrol variables: exchange rate, infrastructure, interest rate, and the target variables: foreign direct investment (FDI) and foreign portfolio investment (FPI) were regressed on market capitalization. Results indicate that FDI and FPI had positive and significant impact on market capitalization in Nigeria’s capital market and that FDI and market capitalization are the major contributors to the variations in market capitalization in Nigeria’s capital market. We conclude that FDI and FPI play important roles in Nigeria’s capital market performance and recommend incentives such as tax reduction and fee waivers which are capable of encouraging foreign enterprises listing. These will make Nigeria’s capital market globally attractive to foreign investors and hence ensure a continuous and impactful inflow.

Economic Effects of Simplified Procedures for Claiming Cross-Border Tax Reliefs

Abstract We examine the effect of compliance frictions in reclaiming foreign withholding taxes on Foreign Portfolio Investments (FPI) using a comprehensive panel of FPI stocks of 83 countries, including EU Member States, between 2005 and 2019 and country-pair specific withholding tax rates. We find a negative and statistically significant elasticity of the FPI stock of equity and debt holdings to “overwithheld” withholding taxes. The estimated elasticities imply that a 1 percentage point reduction in “overwithheld” withholding taxes increases the FPI stock of equity holdings by 1.5%. In a second step, we employ a general equilibrium model to quantify the macroeconomic implications of compliance frictions. In absence of costs in the withholding tax refund process, average GDP in the EU countries would increase by 0.26% (equivalent to EUR 46 billion in 2024), while capital and wages would rise by 0.72% and 0.26%, respectively, suggesting noticeable macroeconomic costs arising from such compliance frictions.

A Multi-model Analysis of Climate-related Financial Risk Disclosure and De-carbonization Investment Portfolios Piloting to the TCFD

This research explores climate related financial risk disclosure by quantifying climate related transition risk and physical risk, creating cost-benefit analysis for businesses, industries and financial institutions with business as usual model (BAU) and alternative models of an updated carbon emission reduction scheme, constructing carbon emission-induced cost function and benefit function models, as well as multi-dimensional climate damage function models. This research integrates frameworks based on carbon shadow price, carbon emission abatement costs and climate damage costs, social costs of carbon with climate change scenarios for assessing climate financial risk, as well as its asymmetric and nonlinear impact on financial equilibrium. The heterogeneity of climate financial risk scenarios are built up on implementing national determined commitment (NDC) for a predefined temperature target 1.5-2 0C above pre-industrial levels under carbon neutrality target of Paris climate agreement and the United Nations Framework Convention on Climate Change (UNFCCC). The model functions, selected parameters and cost-benefit simulation construct a cost-effective optimal pathway for driving future carbon emissions reduction and building climate resilience to manage and mitigate climate related financial risk.

Advancing Financial Risk Prediction and Portfolio Optimization Using Machine Learning Techniques

This study explores the application of machine learning models for predicting financial risk and optimizing portfolio management. We compare various machine learning algorithms, including Random Forest, Gradient Boosting, Long Short-Term Memory (LSTM), and Transformer networks, to assess their effectiveness in forecasting asset returns, managing risk, and enhancing portfolio performance. The results demonstrate that machine learning models significantly outperform traditional financial models in terms of prediction accuracy and risk-adjusted returns. Notably, LSTM and Transformer models excel at capturing long-term dependencies in financial data, leading to more robust predictions and improved portfolio outcomes. Feature selection and preprocessing were crucial in maximizing model performance. Portfolio optimization using machine learning models, when combined with traditional optimization techniques, resulted in superior Sharpe and Sortino ratios. These findings highlight the potential of machine learning to enhance real-time financial decision-making, offering more adaptive and resilient strategies for managing investment portfolios in dynamic market environments. This research provides valuable insights into the integration of machine learning for financial risk prediction and portfolio management, with implications for future advancements in the field.

The Impact of Robo-Advisors and Risk Tolerance on Retail Investor Portfolio Performance

The Impact of Robo-Advisors and Risk Tolerance on Retail Investor Portfolio Performance

The Impact of the VIX Volatility Index on the ISX Index of the Iraq Stock Exchange under Portfolio Investment for the Period 2016-2023

The research aimed to identify the volatility index (VIX) and its trend path during 2016-2023, as well as clarify the relationship between the (VIX) and ISX indices using the ARDL model, and test the stability of both indices. The research concluded that the ISX index for the Iraqi market is selected in a way that allows the index to reflect the state of the capital market. It is calculated through price changes, which does not reflect the reality of the market. The results of the ARDL model also showed the insignificance of the relationship in the short and long term, as the probability P (0.5280) appeared, which is higher than 0.05, which means that the VIX index did not have any effect on the ISX, whether the VIX rose or fell, and this indicates the isolation of the Iraqi Stock Exchange from international markets and its progress in modern trading tools and methods. Among the recommendations for the purpose of developing the ISX index, statistical methods and formulas should be adopted that reflect the actual market reality and simulate international markets and indices. Adopting advanced and modern methods in trading and integrating with international financial institutions and markets through gaining experience, field visits and keeping up with developments.

Risk Management Practices and their Implications for Performance and Market Share in Indonesian Islamic Banks

This paper analyzes Indonesian Islamic banks' risk management and market share. The mixed-methods research includes a comprehensive literature review and a qualitative case study of three of Indonesia's major Islamic banks. The case study finds that risk management best practices helped the chosen Islamic banks prosper. These steps include some common practices such as diversification of the financing portfolio, efficient internal control systems, and strict compliance with Shariah principles. Additionally, the study suggests that the implementation of risk management practices should be tailored to the specific needs and characteristics of each Islamic bank. practices such as diversification of investment portfolios, strict adherence to Shariah principles, and the use of advanced risk management tools and techniques. Additionally, the study suggests that effective risk management can enhance the reputation and credibility of Islamic banks in the eyes of their stakeholders. Getting up a department for risk management, integrating risk management, and making the organization's risk culture stronger This study emphasizes risk management's role in Indonesia's Islamic banking's long-term growth. By tackling risk management challenges and creating solid risk management methods, Indonesian Islamic banks can enhance their performance, increase their market share, and help grow a vibrant Islamic banking industry

Financial contagion in the US, European and Chinese stock markets during global shocks

Under globalisation, integration, and financialisation of national economies, the financial markets’ interdependence tends to swell, which increases the probability of financial disturbances spreading between countries, especially during global shocks, and calls for the development of new standards of financial regulation. The article studies the financial contagion among stock markets of different countries during global shocks associated with the COVID-19 pandemic, the energy crisis, and the special military operation of Russia in Ukraine (SMO). The concept of financial contagion underlies the methodology of the research. The financial contagion is diag nosed based on the construction of DCC-GARCH models and the calculation of dy namic conditional beta coefficients. Causal relationships in the interaction of indices are established using the Granger test. The data are average daily indices: the Ameri can S&P 500, the European STOXX 600, and the Shanghai Stock Exchange (SSE) Composite Index – for December 2018 – March 2024 obtained from the financial portal Investing.com. The study reveals a high level of connectivity in normal times between the American and European stock markets with some autonomy of the Chinese stock market. However, during the 2020 pandemic, there was a short-term strong contagion of the S&P 500 and STOXX 600 from the SSE Composite Index, as well as longer-term, moderate cross-contagion between the S&P 500 and STOXX 600. During the 2021 energy crisis and SMO, there was strong and relatively long-lasting contagion of the S&P 500 from the STOXX 600 and their much weaker contagion from the SSE Composite Index. The findings may be useful for stock market players in managing investment portfolios, and for the state in formulating financial stabilisation policies during the impact of global shocks.

AI-Enhanced Stock Market Prediction: Evaluating Machine Learning Models for Financial Forecasting in the USA

In the USA, one of the world's largest and most liquid financial markets, the ability to anticipate market trends has deep economic implications. Precise stock market forecasting is highly instrumental for investors and analysts in making better asset allocation decisions, managing risks, and setting investment strategies. This research aimed to analyze the efficiency of some machine learning models in stock market forecast evaluation. This research project concentrated on stock market data from the USA, exploring historical price patterns, trading volumes, and relevant economic indicators to assess the performance of various machine learning models. The dataset used for this research work about predicting stock market trends is an exhaustive collection of historical stock prices, some fundamental financial indicators, and relevant news about the market, gathered from various dependable sources. Historical stock prices are retrieved from financial market databases like Yahoo Finance and Google Finance. These sources have daily records of open, high, low, and close prices, and trading volumes for thousands of publicly traded companies for extended periods, normally running into several years. The analyst selected several strategic models, namely, Random Forest, Gradient Boosting, and Logistic regression. Logistic Regression outperformed the other two models with relatively higher accuracy, while the others are just a little below. The findings of this study have implications well beyond academic curiosity into investment strategies and financial analysis. By leveraging the strengths of the best models, investors can create better-informed trading strategies. These models can also include predictive insights that give a serious edge to the risk management strategy in an investment portfolio. By using the predictive power of models like Random Forest, investors can foresee market fluctuations and adjust their portfolios to reduce potential losses.

The Role of Advanced Technologies in Automated Trading Systems and Its Influence on Investor Attitudes

This paper provides an overview of algorithmic trading (robo-trading), which replaces human stockbroker recommendations to investors for buying/selling/holding stocks in their investment portfolio, and the integration of technologies like artificial intelligence and quantum computing within automated systems. The paper covers the impacts of algorithmic trading, the specific implementations of advanced technologies, and the acceptance of these technologies among college students. The cited articles show that robo-trading has existed since the 1970s. Algorithmic trading has increased stock market efficiency. Artificial intelligence allows for predictive models to adjust to the changing financial markets. On the other hand, quantum algorithms like Variational Quantum Eigensolver (VQE) aid in portfolio optimization. The paper also presents findings from an empirical study on college students’ attitudes toward advanced technologies within robo-trading. Although a few other studies exist on investor attitudes about acceptance of algorithmic trading for stock portfolios, no other study examined the attitudes of college students on the potential application of advanced technologies within robo-trading. A statistical analysis was undertaken to see how attitudes shift based on factors like class level and previous stock market experience. The paper discusses the results and policy implications for stock brokerage firms that hope to implement advanced technologies.

Unlocking the application of AI for portfolio maximization investment decision

Purpose of research The aim of the study is to examine the transformative role of artificial intelligence (AI) in maximizing portfolio investment decision-making process. AI application tools specifically focus on data processing & analysis, portfolio optimization, algorithmic trading & automated execution, chatbots & robotic advisory services, and predictive analytics. The purpose of study is to provide how these technologies can maximize the investment strategies by improving the precision of forecasting, return optimization and risk management. Moreover, conceptual framework that shows the dynamic interaction between AI application tools, investors, and financial markets. Design/Methodology The conceptual approach evaluates the integration of AI application tools into investment decisions. The analysis of current applications of AI in finance develops detailed conceptual model that maps the flow of information between investors, AI application tools, in financial markets. The study uses secondary data from existing literature, research papers, and case studies to validate the efficiency of AI-driven investment strategies. Results/Findings The results show that AI significantly improves investment decision-making processes accuracy and efficiency. The conceptual framework developed in this study establishes how AI-driven application such as portfolio optimization and predictive analytics directly contribute to maximizing returns on investments while minimizing risks. Furthermore, the role of chatbots and robotic advisory services in providing tailored investment advice, which eventually results in more strategic investment decisions. Practical Implications Artificial intelligence (AI) can help investors, financial institution that make specific investment plans, and financial advisory services. The findings of this research indicate that AI tools significantly improve investment portfolio performance by providing real-time database management system. The conceptual framework aids in understanding the association between AI application tools and financial markets. This study concludes that combining AI application tools into investment strategies signifies a key innovation for achieving competitive edge in a dynamic financial market.

Application of Particle Swarm Optimization in Optimal Asset Allocation

Particle Swarm Optimization (PSO) is an effective tool for solving nonlinear, non-convex optimization problems, offering a quick and efficient way to identify rational asset allocations. In PSO, each particle represents a specific asset allocation and moves within the search space to optimize the defined criteria. Particles update their positions based on personal experience (individual best) and the collective experience of the swarm (global best), gradually converging toward an optimal solution. Research on applying PSO to asset allocation demonstrates that this algorithm not only optimizes expected returns but also minimizes risk in investment portfolios. With its adaptability and computational speed, PSO can become a valuable tool for investors in formulating flexible and effective asset allocation strategies in a volatile financial environment.

The US Dollar as a Prerequisite for the Crisis Potential of the Global Monetary and Financial System

The crisis of the global monetary and financial system based on the US dollar has been the focus of attention of the international academic community for a long time. At the same time, modern research does not sufficiently present a comprehensive approach to studying the causes and consequences of the dysfunctionality of the current international monetary standard. The purpose of the paper is to systematize the cause-and-effect relationships of the crisis potential of the global monetary and financial system based on the US dollar as the key reserve currency. Methods of scientific abstraction, system analysis and logical generalization were used. Factors characterizing the crisis potential of the global monetary and financial system are identified: reproduction of global imbalances, chronic capital outflow from countries of the global periphery, growth of global debt and inflation, rupture of global chains and neoprotectionism, destabilization of markets for fuel, raw materials and food products. According to the author, the conservatism of the current international currency standard is due to the lack of real alternatives to the US dollar as international liquidity due to the US control of key institutions and mechanisms of the global financial market. The restrained attitude of the United States towards the need to introduce advanced digital technologies in the sphere of monetary circulation and reluctance to participate in the transformation of the global monetary and financial system reduces the chances of its qualitative reform. It is concluded that the main impulses for the de-dollarization of the global monetary and financial system should be the coordinated demands of the IMF member countries to restore the system of multilateral settlements using a wide range of national currencies; large-scale diversification of their investment portfolios by including emerging market assets; coordinated actions of the BRICS+ countries to create innovative mechanisms for international payments based on modern digital technologies.

EVALUATION OF CONVENTIONAL AND SHARIA STOCK PERFORMANCE BASED ON SHARPE, TREYNOR, AND JENSEN INDECES

The purpose of the research is to analyse the performance of conventional and sharia stocks in the Indonesian capital market for the 2019-2023 period. the LQ45 and JII indices are used as proxies to represent each of the conventional and sharia stocks. The method used in this research is descriptive method, where the evaluation of the performance of conventional and sharia stocks is measured based on the sharpe, treynor, and jensen indices. The population in this research are all stocks included in the LQ 45 and JII indices in the 2019-2023 period. The samples in this study were 19 conventional stocks from the LQ 45 indices and 13 Islamic stocks from the JII indices. The sampling method used is non-probability sampling with a purposive sampling approach. the results of this study show several stocks that have performed well during the 2019-2023 period, in conventional stocks, namely: ADRO, ANTM, BBRI, ICBP, INCO, ITMG. While in sharia stocks, namely ADRO, ANTM, ICBP, INCO. These stocks can be considered by investors to be included in the investment portfolio. The conclusion of this study is that the performance of conventional and sharia stocks based on the Sharpe, Treynor, and Jensen, value of stocks is dominated by negative values, which means that the condition of stock analysis is quite bad compared to the portfolio, portfolio beta, and the market or benchmark used.

Explaining REIT returns in emerging economies: A Fama-French approach with foreign investment and political stability

This study examines the applicability of the Fama-French 3-factor model to Real Estate Investment Trusts (REITs) in emerging economies using monthly data from January 2016 to December 2023 for 23 REITs across five emerging markets. A Generalized Method of Moments (GMM) (system) approach assesses the impact of 12 explanatory variables, including traditional factors like market, value, size, and momentum premiums, as well as emerging market-specific factors such as the Morgan Stanley Capital International (MSCI) Emerging Markets Currency Index and Bloomberg Commodity Ex-Agriculture Index. Control variables like political stability, foreign direct investment, and portfolio investment are also included. The results show that value premium, foreign direct investment, portfolio investment, and commodity prices positively influence REIT excess returns, while momentum premium and political instability negatively affect them. These findings highlight the combined importance of traditional and emerging market-specific factors, emphasizing the critical role of stable political conditions for REIT performance. This research contributes valuable insights for investors and policymakers in understanding REIT dynamics in emerging markets.

Navigating Country Risk: Impacts on Foreign Direct and Portfolio Investment in Egypt

Navigating Country Risk: Impacts on Foreign Direct and Portfolio Investment in Egypt

Evaluating the performance of investment portfolios using the information ratio

Evaluating the performance of investment portfolios using the information ratio

EMPIRICAL INSIGHTS INTO PORTFOLIO MANAGEMENT AND BANK PERFORMANCE IN KOSOVO: A GENERALIZED METHOD OF MOMENTS EXAMINATION

The purpose of this study is to analyze certain banking factors, namely the managing of the investment portfolio in the banking business of Kosovo. The data used are time series on an annual basis for a total of the last 13 years, and these data were tested using the Generalized Method of Moments. The findings of the study on the return on assets show that investments in the production sector have a negative influence, whereas, investments in the trade sector, and investments in short-term securities have a statistically important positive influence on return on assets. With the same level of importance investment in the production sector negatively influences the return on equity, whereas investment in the trade sector and investment in short securities positively affect the return on equity. The surprising result has turned out to be in the rankings in the service sector since it has a non-significant statistical value. From the aspect of innovation and originality, during the investigation of the empirical literature, we did not notice that these independent indicators were included.

Stochastic Analysis of Buy and Hold Versus Annual Rebalancing Portfolio Strategies

Numerous articles in the literature develop theoretical mathematical models that prove rebalancing investment portfolios will outperform a buy and hold strategy on a risk adjusted basis. However, a number of authors using varying asset classes and time periods have found instances where buy and hold outperforms a rebalancing strategy on a risk adjusted basis. This research shows that both can be correct. Most of the papers found in the literature use historical prices and price changes to validate their findings. The research effort detailed in this paper simulates a wide range of possible future asset pricing scenarios that do not rely on historical price patterns. These findings suggest that differences between these two strategies, at a more detailed level, favor the rebalancing strategy, but not exclusively.

AI in Wealth Management and WealthTech

The main idea of the paper is researching AI and other technologies for Wealth Management. Implementation of the AI in WealtTech helps in giving the best investment advises. Wealth Management includes retirement planning, inheritance tax, estate planning, and other activities according to investment preferences. WealthTech aims to manage investment funds and assets more efficiently, accessible, and personalized through data-driven insights and smart automation. Besides improving their efficiency, WealthTech aims to automate investment and wealth management services. WealthTech utilizes innovations like blockchain, artificial intelligence, and machine learning to improve investment decisions combining technologies for fund transformation and asset handling. These technologies include a wide range of solutions and include robo-advisors, social trading, robot-retirement, quant advisors, and micro-investing. This approach use the best technology solutions more advanced than typical analysis used in financial institutions. WealthTech AI applications are developed to create wealth data dashboards tailored to the clients needs. The applications enable clients to see full and transparent picture of their entire wealth and visualise wealth maps and data-driven analytics to uncover valuable investment insights. The aim of the applications is to convert diversified investment portfolio in clear reports and graphs. Because of the advancements in AI, the programs provide personalized advice concerning wealth management and business strategies. The Manuscript will present WealthTech solutions on the practical example and recommend the most attractive investment opportunities on the global market.