Investment Portfolio Management Research Articles

A Study on Stock Price Prediction Using LSTM and RNN

Stock price prediction remains a critical task in financial markets, influencing investment decisions and portfolio management. This research paper explores the efficacy of Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) in forecasting stock prices.

Does the Performance of Global Commodities Exhibit Co-movement and Long-run Relationship with the Palm Oil Companies?

While numerous papers investigate on the co-movement of global commodities and stock indices, this paper extends to previous papers by focusing on the individual stock price of palm oil companies namely Sime Darby Plantation, Wilmar International, and Astra Agri Lestari. Weekly data spanning from January 2018 to September 2023 was selected and Autoregressive Distributed Lags (ARDL) model employed to demonstrate the possible co-movement and long-run relationship. Empirical results provide sufficient evidence of co-movement between global commodities with Sime Darby Plantation and Astra Agri Lestari in the long-run. This will allow fund managers, individual investors, and stock traders to make investment decisions, trading strategies, and portfolio management based on the global commodity markets.

Analysis of Financial Market Efficiency

This comprehensive study delves into the intricacies of financial market efficiency, anchored around the Efficient Market Hypothesis (EMH) as postulated by Eugene Fama. It scrutinizes the hypothesis across its weak, semi-strong, and strong forms, incorporating a broad spectrum of empirical evidence and theoretical discourse. In light of recent advancements in technology and the increasing complexity of global financial markets, this paper also explores the impact of high-frequency trading, artificial intelligence, and blockchain technology on market efficiency. Through a meticulous examination of both supportive and critical perspectives on the EMH, the analysis extends to consider the implications of market efficiency on investment strategies, portfolio management, and regulatory frameworks. By juxtaposing traditional financial theories with contemporary market phenomena, this study seeks to offer a nuanced understanding of the dynamic interplay between market efficiency, technological innovation, and investor behavior. The ultimate objective is to provide a balanced viewpoint that acknowledges the merits of the EMH while also recognizing the evolving challenges and opportunities within global financial markets.

Comparative Analysis of Private and Public Mutual Funds: A Performance Evaluation

Abstract: The mutual fund industry is a popular investment avenue that offers investors the opportunity to invest in a diversified portfolio of securities. The industry has witnessed significant growth in recent years, with investors seeking to maximize their returns and reduce risk. This paper presents a comprehensive comparative analysis of private and public mutual funds in terms of their performance. The study aims to evaluate the risk-adjusted returns, expense ratios, fund size, asset allocation, and other relevant factors of both private and public mutual funds to determine which type of fund offers better performance. To conduct the analysis, a sample of four private and four public mutual funds was randomly selected, and their performance was evaluated using various performance metrics such as Sharpe ratio, Treynor ratio, and Jensen's alpha. The data was analyzed using statistical tools such as U test, t-test, and regression analysis. The study found that private sector mutual funds performed comparatively better than public sector mutual funds in terms of risk-adjusted returns, expense ratios, and asset allocation. Private sector mutual funds had higher Sharpe ratios and Treynor ratios compared to public sector mutual funds, indicating that they generated higher returns per unit of risk taken. Private sector mutual funds also had lower expense ratios, indicating that they charged lower fees to investors. In terms of asset allocation, private sector mutual funds had a higher allocation towards equity securities compared to public sector mutual funds, which had a higher allocation towards debt securities. The study also identified significant differences in the investment strategies, portfolio management, and investor base of private and public mutual funds. Private sector mutual funds had a more aggressive investment strategy, with a higher allocation towards equity securities, whereas public sector mutual funds had a more conservative investment strategy, with a higher allocation towards debt securities. Private sector mutual funds were also found to be more actively managed compared to public sector mutual funds. Private sector mutual funds had a higher percentage of assets under management from retail investors, while public sector mutual funds had a higher percentage of assets under management from institutional investors. The study provides valuable insights to investors, fund managers, and policymakers regarding the performance of private and public mutual funds. The findings suggest that investors may benefit from investing in private sector mutual funds due to their higher risk-adjusted returns, lower expense ratios, and higher allocation towards equity securities. Fund managers may also benefit from the study by gaining insights into the investment strategies and portfolio management practices of private and public mutual funds. Policymakers may use the study to design policies that promote the growth and development of the mutual fund industry. Overall, this study contributes to the existing literature on mutual funds by providing a comprehensive comparative analysis of private and public mutual funds in terms of their performance. The study adds to our understanding of the factors that affect the performance of mutual funds and provides insights into the investment strategies and portfolio management practices of private and public mutual funds.

Leveraging FinTech Compliance to Mitigate Cryptocurrency Volatility for Secure US Employee Retirement Benefits: Bitcoin ETF Case Study

The integration of cryptocurrencies, particularly Bitcoin, into retirement savings plans has recently garnered significant attention. This interest has been amplified by the U.S. Securities and Exchange Commission's approval of Bitcoin Exchange-Traded Funds (ETFs) in January 2024 and Fidelity Investments' decision to include Bitcoin in their 401(k) plans. These landmark developments represent a paradigm shift in retirement investment strategies, merging traditional financial planning with the dynamic and volatile world of cryptocurrencies. The entry of Bitcoin introduces novel challenges, including increased volatility and regulatory uncertainty, necessitating a comprehensive examination of its impacts on retirement savings. The study sought to explore the role of Financial Technology (FinTech) in managing these risks and assess the adequacy of current regulatory frameworks. Employing a quantitative research approach, the study collected data from 386 participants, including FinTech practitioners and investment portfolio managers through a survey that combining closed-ended and open-ended questions. Multiple regression was used to analyze the relationships between variables such as FinTech integration, regulatory compliance, and the risk associated with cryptocurrency-inclusive retirement portfolios. The study revealed that the inclusion of Bitcoin significantly increases portfolio volatility. It also found that advanced FinTech data management techniques significantly enhance risk mitigation, while current regulatory frameworks are inadequate for governing the inclusion of cryptocurrencies in retirement plans. A comprehensive framework combining FinTech and regulatory compliance was shown to effectively reduce investment risks. The study recommends cautious consideration of cryptocurrencies in retirement portfolios, with an emphasis on assessing the risk appetite of participants. It advocates for dynamic regulatory frameworks and enhanced use of FinTech for real-time risk management. The study suggests that retirement plan providers should adopt an integrated approach, combining technological innovations with regulatory oversight, to navigate the complexities of cryptocurrency investments effectively.

Factor Affecting Investment Success of Individual Investors at NEPSE

This study examined the relationships between investment success and three key variables: investment education, portfolio management, and risk management. The data were analyzed using descriptive statistics, correlations, and regression analysis. The findings revealed that investment education had a significant positive impact on investment success, suggesting that individuals with higher levels of investment education were more likely to achieve successful investment outcomes.

Robo Advising and Investor Profiling

The rise of digital technology and artificial intelligence has led to a significant change in the way financial services are delivered. One such development is the emergence of robo advising, which is an automated investment advisory service that utilizes algorithms to provide investment advice and portfolio management to investors. Robo advisors gather information about clients’ preferences, financial situations, and future goals through questionnaires. Subsequently, they recommend ETF-based portfolios tailored to match the investor’s risk profile. However, these questionnaires often appear vague, and robo advisors seldom disclose the methodologies employed for investor profiling or asset allocation. This study aims to contribute by introducing an investor profiling method relying solely on investors’ relative risk aversion (RRA), which, in addition, allows for the determination of optimal allocations. We also show that, for the period under analysis and using the same ETF universe, our RRA portfolios consistently outperform those recommended by the Riskalyze platform, which may suffer from ultraconservadorism in terms of the proposed volatility.

Determinants Of Investment Portfolio Performance: A Systematics Literature Review

A thorough grasp of the variables influencing portfolio performance is necessary given the significance of investment portfolio management in the face of shifting financial market dynamics. This study uses a method called the Systematic Literature Review (SLR) approach with an emphasis on Prefered Reporting Item for Systematic Review and Meta-Analysis (PRISMA) 2020, which has not been discovered in prior research on the same issue. The study highlights the importance of ratios like Sharpe, Treynor, Jensen, and Sortino in assessing investment portfolio success. It also highlights the impact of non-financial variables like household preferences on performance. Return is crucial, and diversification can lower risk and boost returns. The study emphasizes the use of Value-at-Risk (VaR) for dynamic risk evaluation.

ФУНДАМЕНТАЛЬНЫЙ АНАЛИЗ РОССИЙСКИХ КОМПАНИЙ: ОЦЕНКА СТОИМОСТИ И ПЕРСПЕКТИВ ДЛЯ ИНВЕСТОРОВ

The article presents a fundamental analysis of Russian companies, considering methods for assessing their value and prospects for potential investors. In Russia’s dynamic financial market, understanding the importance of fundamental analysis becomes critical for investors seeking to make informed decisions. The article emphasizes that sound fundamental analysis not only allows one to determine the current value of shares, but also provides in-depth analytical views on the long-term prospects of companies. This analysis is an important tool for forming investment strategies and portfolio management, increasing the likelihood of successful financial decisions on the Russian stock market. In times of uncertainty and market volatility, investors with a deep understanding of fundamental analysis can identify promising opportunities and minimize financial risks. Analytical insights obtained through fundamental analysis become a key factor in making investment decisions aimed at achieving sustainable portfolio growth.

Spillover effects of volatility between the Chinese stock market and selected emerging economies in the middle east: A conditional correlation analysis with portfolio optimization perspective

In recent years, the rapid transmission of information and interconnectedness of global financial markets have amplified the convergence and influence among them. Consequently, the occurrence of spillover effects in one market can significantly impact other markets. Accurately identifying and understanding these spillover effects is crucial for effectively managing and controlling market fluctuations. This research aims to measure and analyze the spillover effects between China's stock market and selected emerging economies in the Middle East, with a focus on exploring diversification opportunities. The analysis encompasses three distinct time periods, including the overall period from May 1, 2005, to May 31, 2023. The sub-periods consist of the first sub-period from May 1, 2005, to October 31, 2009, and the second sub-period from December 1, 2010, to May 31, 2023. Multivariate Generalized Heterogeneous Autoregression (MGARCH) is employed in this study to examine the spillover effects between China's economy and the emerging economies under consideration. The Granger causality analysis reveals a unidirectional causality running from the Chinese stock market to Jordan, as well as from the UAE to China throughout the entire observation period. However, no spillover effects are found between China and Saudi Arabia in either direction during any of the periods. Notably, a two-way causality is detected between the Chinese and UAE markets in the second sub-period. Furthermore, MGARCH results indicate no spillover effects from China to the emerging economies during the overall period, first sub-period, or second sub-period. The findings of this research offer valuable insights for investment portfolio managers in the Chinese economy, who may consider the examined emerging economies as potential destinations for risk diversification.

Research on the Pension Investment and Asset Management

The study explored how to develop a pension investment portfolio and analyzed solutions for special cases. We first reviewed the global context of pension investment and emphasized the crucial role of pension in financial planning. Subsequently, we specifically analyzed the "firefighter case" and decided whether to choose "one-time payment" or "fixed benefits". Finally, specific results were provided and some suggestions were given. In addition, we emphasize the importance of individual risk tolerance and investment goals in formulating investment strategies to ensure that investment portfolios match individual needs. Through the guidance of this study, investors can learn how to manage their pension investments and achieve long-term financial security and robust retirement plans. The diversity and effective management of pension investment portfolios are crucial for the success of pensions, and this is worthy of careful consideration and action by every investor.

Comparative Analysis and Research of Investment Portfolio Management Models

Portfolio management plays a significant role in the world of finance and investing, offering benefits and contributions to both individual investors and institutions. Researchers use many mathematical models to make informed decisions about how to allocate and manage investments within a portfolio to optimize risk versus return trade-off based on the investors preference and constraints. This paper primarily employs the literature review methodology and comparative analysis methodology. Firstly, it collects, summarizes, and analyzes multiple papers on portfolio management models, including Markowiz Mean-Variance Model, Capital Market Line (CML), Arbitrage Pricing Theory (APT) and Capital Asset Pricing Model (CAPM) with the origin of the models, their key assumptions, components, and applications. Additionally, this paper also utilizes comparative analysis by comparing the similarities, differences, and respective downsides and benefits of the four main research models. Through this comparison, the paper investigates the relationships and application distinctions among the models. As a result, these models evolve and refine over time, with some building on others. CAPM extends the Markowitz Model by introducing the risk-free rate and market portfolio as benchmarks, simplifying the risk-return relationship, and introducing systematic risk. The Capital Market Line (CML), derived from CAPM, illustrates efficient portfolios made up of both market and risk-free assets and shows the risk-return trade-off. APT, a later model, can be seen as an extension of CAPM.

Deep learning applications in investment portfolio management: a systematic literature review

PurposeMachine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.Design/methodology/approachThis review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.FindingsThe authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.Originality/valueSeveral systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

Management of investment portfolios employing reinforcement learning.

Investors are presented with a multitude of options and markets for pursuing higher returns, a task that often proves complex and challenging. This study examines the effectiveness of reinforcement learning (RL) algorithms in optimizing investment portfolios, comparing their performance with traditional strategies and benchmarking against American and Brazilian indices. Additionally, it was explore the impact of incorporating commodity derivatives into portfolios and the associated transaction costs. The results indicate that the inclusion of derivatives can significantly enhance portfolio performance while reducing volatility, presenting an attractive opportunity for investors. RL techniques also demonstrate superior effectiveness in portfolio optimization, resulting in an average increase of 12% in returns without a commensurate increase in risk. Consequently, this research makes a substantial contribution to the field of finance. It not only sheds light on the application of RL but also provides valuable insights for academia. Furthermore, it challenges conventional notions of market efficiency and modern portfolio theory, offering practical implications. It suggests that data-driven investment management holds the potential to enhance efficiency, mitigate conflicts of interest, and reduce biased decision-making, thereby transforming the landscape of financial investment.

Hybrid ARMA-GARCH-Neural Networks for intraday strategy exploration in high-frequency trading

The frequency of armed conflicts increased during the last 20 years. The problems of the emergence of military disputes, not only concern social parameters, but also economic and financial dimensions. This study examines the potential impact of global geopolitical events on the stock market prices of the Dow Jones U.S. Aerospace & Defense Index and Foreign Exchange (FOREX) markets movements. We analyse whether defence stocks and exchange rate perform similarly during military incidents or geopolitical crises. We built an Autoregressive Moving Average Model with a Generalized Autoregressive Conditional Heteroskedasticity process (ARMA-GARCH) with the machine learning methods of Neural Networks, Deep Recurrent Convolutional Neural Networks, Deep Neural Decision Trees, Quantum Neural Networks, and Quantum Recurrent Neural Networks, aimed at detecting intraday patterns for forecasting defence stock market and FOREX markets disturbances in a market microstructure framework. The empirical results provide preliminary findings on the foreseeability of market disturbances and small differences are observed before and during geopolitical events. Additionally, we confirm the effectiveness of the hybrid model ARMA-GARCH with the machine learning approaches, being ARMA-GARCH-Quantum Recurrent Neural Network the technique that achieves the best accuracy results. Our work has a large potential impact on investment market agents and portfolio managers, as shocks from geopolitical events could provide a new methodology to support the decision-making process for trading in High-Frequency Trading.

Information system of analysis for segmentation of the securities portfolio of the product market

The investment market is a complex competitive environment where speed and accuracy of decision-making play a crucial role. Understanding sentiment and asset behavior is key to forecasting and successful investment portfolio management. The results of information analysis confirm the hypothesis of the effectiveness of food market assets, and this in turn proves the possibility of increasing the portfolio's profitability by including food sector assets in it. Creation of an information analysis system for the purpose of segmentation of the contained portfolio is an urgent task, which will allow obtaining real and objective information about the potential risk and profitability of various assets. The paper provides an overview of specialized applied software products that are available on the Internet and are used to analyze financial data. An overview of modern information technologies, which allow convenient processing, visualization of data and forecasting of asset price movements, was carried out. An alternative version of the own information system of analysis is proposed, which focuses on the assets of the food sector. The main task of the created information system is to provide convenient tools for market data analysis, visualization and forecasting of price movements. The main functionalities of the created system are described, including a visual comparison of historical data, price movement forecasting using machine learning, and recommendations on the optimal content of the investment portfolio. The conducted example of the analysis demonstrates how the received information will help to reduce risks and increase the efficiency of decision-making, when managing a portfolio that contains assets of the food sector. The results of the work show that in the case of a small set of assets and, accordingly, restrictions on the diversification of capital investments, the use of such information systems in the field of food products is effective and profitable. The presented technique also demonstrates that an investor, even with non-professional knowledge in the field of programming and information and communication technologies, can effectively use them to create specialized systems for data analysis, visualization and forecasting.

The Dollar Cost Averaging, Lump Sum, and Value Averaging Strategies in Mutual Fund Investments

Investment strategy and portfolio management are crucial considerations when investing in mutual funds. This research aims to analyze the effectiveness of achieving optimal returns by simulating dollar cost averaging, lump sum, and value averaging approaches. This analysis is conducted about the performance of a mutual fund portfolio, utilizing the Sharpe and Treynor methods. The research sample employs a purposive sampling technique, selecting the top 5 mutual funds in terms of performance, managed by different investment managers in the year 2022. Simulations are being conducted over three specific periods. In addition to mutual funds, a comparative analysis was conducted involving the Jakarta Composite Index (JCI) and 10-year government bond yields (SUN). The simulation results on equity funds for the three periods indicate that the Lump Sum investment method outperforms dollar cost averaging and value averaging, particularly over 6 years. This finding is further corroborated by the Kruskal-Wallis test, which highlights a significant variance in return performance. The results of the Sharpe and Treynor investment methods exhibit significant differences, as confirmed by the Mann-Whitney test. Lastly, regarding benchmark comparisons, the performance of equity funds surpasses that of the benchmark.

Design dimensions of corporate venture capital programs—a systematic literature review

AbstractThere is a plethora of research on organisational design elements of corporate venture capital (CVC) programs. However, the fragmented nature of this vein of corporate venturing research has led to an inconsistent picture regarding the organisational design of CVC programs. The goal of this study is to provide a holistic picture for both scholars and practitioners by integrating empirical research on the design of CVC programs. Therefore, the study employs a systematic literature review approach including a sample of 41 studies published from 1987 to 2023. For a systematic assessment of design elements of CVC programs, an inductive concept development approach is used to illustrate four main design dimensions–personnel, corporate relationship management, investment operating model and portfolio relationship management–which reflect 69 descriptive design elements. While previous studies have mainly looked at individual design dimensions of CVC programs from a strategic perspective, this paper presents comprehensive view on organisational structures of CVC programs by identifying building blocks of CVC design according to chosen objectives and available typologies. By specifying and allocating design dimensions to structural types and objectives of CVC programs, this study may also serve as a foundation for further research on the concepts which prevent high rates of early abandonment of CVCs.

Theoretical and methodological fundamentals of studying franchising

Methods. The study is based on the application of the monographic method for the study of clusters and the identification of cause-and-effect relationships of their development in the study of franchising. General scientific methods are also applied, in particular: abstractions – to determine the essence of the concept of «franchising»; grouping – to substantiate the theories that form a special theoretical platform for franchising research as a model of business organization; comparative analysis – when establishing quantitative and qualitative differences in the process of highlighting research clusters in the historiography of franchising. Results. Research clusters in the historiography of franchising were analyzed, which made it possible to form a theoretical platform for its study, which includes a system of theories regarding development prerequisites (globalization, competition, «push-pull» factors, entrepreneurship, network structures) and features of franchising. It is proposed to divide the theories that substantiate the specifics of franchising into two groups according to the criterion of their reflection: attraction and use of resources (theories regarding relations (agency, signals, institutional, stakeholders), as well as decision-making (transaction costs, eclectic paradigm, entrepreneurial orientation). It was determined that the most widespread is the theory of agency, which explains the regularities of relations and the weakening of the parties’ disagreements, when one executing entity represents the relations of another customer through the delegation of powers in decision-making, execution of operations, financial planning, investment portfolio management, etc. Considering the basis of relationships in the emergence of franchising and their importance for business success, the importance of the provisions of the concept of trust is updated. It is demonstrated that it continues to develop in the system of theoretical provisions on partnership in franchising, taking into account the new challenges of global instability, which have increased after the Covid-19 pandemic, the full-scale Russian invasion of Ukraine and the exacerbation of other international conflicts. Novelty. Based on the analysis of the historiography of franchising and the identification of the most common theories, there is proposed the classification of the theoretical fundamentals of studying franchising by groups that determine the prerequisites for its development, relations, decision-making, attraction and use of resources. Practical value. The conducted research can deepen the interdisciplinary approach and adapt the developed franchising methodology to Ukrainian characteristics, which will allow analyzing the development of franchising in Ukraine through the prism of theoretical justifications of its formation in international practice.

CURRENT STATUS AND PERSPECTIVES OF THE DEVELOPMENT OF INVESTMENT ACTIVITY OF THE COMMERCIAL BANKS DURING MILITARY AGGRESSION

Because of writing the scientific work, it was established that the protracted war, terrorist attacks on the energy infrastructure and the subsequent suppression of the economy provoked by this are the main sources of risks for the financial stability of commercial banks. The state of investment activity of commercial banks was studied and it was determined that in order to increase it and reduce the risk of loss of liquidity in war conditions, commercial banks should apply methods of passive investment portfolio management. It has been proven that the combination of financial assets with different investment terms in the investment portfolio will allow taking into account the fluctuations of interest rates and the level of interest risk, which is under the influence of investment owners. It was determined that with the beginning of the war, the share of non-performing loans expectedly increased, most noticeably in the retail segment. Despite significant write-downs, the banking sector posted a quarterly profit after a loss-making first half, driven by operational efficiency. The net assets of banks for the third quarter of 2022 were analyzed, which increased by 7.5% and exceeded the pre-war level, this happened due to the increase in the volume of deposit certificates of the NBU. It was established that the TOP-10 banks of the country included banks that were able to continue working despite the introduction of martial law and hostilities and maintain their credit and investment portfolio at the proper level: Privatbank, Ukreximbank, FUIB, Ukrgasbank, Raiffeisen Bank, Oschadbank, Sens Bank, OTР Bank, Universal Bank and Credit Agricole Bank. It was established that in order to develop the investment activity of commercial banks in the conditions of military aggression, the following basic requirements must be observed: continuity of the investment process; availability of a diversified portfolio, where capital investment will be focused on 4–5 classes; one must allow the portfolio to grow with the market during upswings, but at the same time protect it during periods of general decline.