Portfolio Theory Research Articles

Role of Financial Literacy in Household Investment Decisions in Iraq

Purpose: The aim of the study was to assess the role of financial literacy in household investment decisions in Iraq. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study indicated that households with higher levels of financial literacy tend to make more informed and effective investment choices, which lead to better financial outcomes. These households are more likely to participate in financial markets, diversify their investments, and utilize a broader range of financial instruments, which helps in mitigating risks and enhancing returns. Financially literate individuals are also more adept at understanding complex financial products, assessing investment risks, and planning for long-term financial goals such as retirement. Conversely, a lack of financial literacy can result in poor financial decisions, leading to lower participation in the stock market, inadequate savings, and suboptimal investment portfolios. Thus, enhancing financial literacy is essential for empowering households to make sound investment decisions, ultimately contributing to greater financial stability and wealth accumulation. Implications to Theory, Practice and Policy: Behavioral finance theory, portfolio theory and agency theory may be used to anchor future studies on assessing the role of financial literacy in household investment decisions in Iraq. In terms of practical recommendations, developing and implementing targeted financial education programs is essential. These programs should focus on improving specific aspects of financial literacy relevant to investment decisions. From a policy perspective, advocating for policies that support the integration of financial literacy education into school curricula at all levels is essential.

Effect of Interest Rate Changes on Stock Market Volatility in Congo

Purpose: The aim of the study was to assess the effect of interest rate changes on stock market volatility in Congo. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Changes in interest rates have a notable impact on stock market volatility. When central banks adjust interest rates, it influences investor sentiment and economic forecasts. Typically, an increase in interest rates makes borrowing more expensive, which can dampen corporate profits and economic growth, leading to higher market volatility as investors reassess the value of stocks. Conversely, a decrease in interest rates generally lowers borrowing costs, potentially boosting corporate earnings and economic expansion, which can initially reduce market volatility. However, the long-term effects might differ as lower rates can also lead to overvaluation concerns, eventually increasing volatility. Additionally, interest rate changes signal monetary policy shifts and broader economic conditions, further influencing investor behavior and stock market dynamics. Overall, interest rate fluctuations are a critical factor contributing to stock market volatility through their direct and indirect effects on economic activities and investor perceptions. Implications to Theory, Practice and Policy: Efficient market hypothesis, portfolio theory and behavioral finance theory may be used to anchor future studies on assessing the effect of interest rate changes on stock market volatility in Congo. For practical implications, investors and financial institutions should develop risk management strategies that account for the short-term volatility spikes following interest rate announcements. From a policy perspective, policymakers should consider the lagged effects of interest rate changes when formulating monetary policy to ensure smoother market transitions.

Understanding Investment Decision-making: A Qualitative Inquiry into High-Frequency Trading, Investment Strategies, and Portfolio Performance in the Financial Market

This qualitative study explores investment decision-making in the context of high-frequency trading (HFT), investment strategies, and portfolio performance within the financial market. The research aims to provide insights into the complex dynamics influencing investment decisions and their implications for market participants. Adopting a qualitative research design, the study conducts a comprehensive review and analysis of existing literature from academic journals, books, and conference proceedings. The data collection process involves synthesizing insights from diverse sources to uncover underlying themes, patterns, and contradictions surrounding investment decision-making. Through thematic analysis and constant comparison, the study identifies key findings related to the role of HFT, diversity of investment strategies, and evaluation of portfolio performance. The results highlight the transformative impact of HFT on market liquidity, efficiency, and stability, while also raising concerns about market fragmentation and systemic risks. Additionally, the study explores the evolution of investment strategies, ranging from traditional approaches like fundamental analysis to emerging techniques such as algorithmic trading and quantitative strategies. It underscores the importance of tailoring investment strategies to individual preferences and market conditions for optimizing portfolio performance. Furthermore, the study evaluates alternative frameworks such as post-modern portfolio theory (PMPT) and factor investing, offering promising avenues for enhancing portfolio resilience and risk-adjusted returns. Overall, the research contributes to a deeper understanding of investment decision-making processes and informs stakeholders in the financial industry about effective strategies for navigating the dynamic landscape of the financial market.

Enhancing eco-efficiency in hemp-based construction boards: environmental and economic strategies for sustainability

ABSTRACT Hemp-based construction materials have increasingly captured attention in recent years as viable alternatives to carbon-intensive materials. A benefit of using hemp (Cannabis sativa L) in construction is its ability to sequester carbon dioxide during plant growth, thereby providing the potential to create carbon-negative materials. However, hemp production can contribute to other environmental impacts such as acidification and eutrophication. Additionally, hemp-based materials usually involve higher costs compared to traditional materials. This study presented an application of an eco-efficiency framework to explore options for improving the environmental and economic performance of non-load-bearing hemp-based construction boards at the production stage in Australia. The study integrated environmental life cycle assessment, multi-criteria analysis, environmental mitigation strategies, life cycle costing and eco-efficiency portfolio analysis in order to identify eco-efficiency improvement opportunities for non-structural hemp-based construction boards and to compare them with conventional alternatives. The analysis showed that the use of solar electricity during the post-farm stage of hemp-based board production can reduce their environmental impact by 26 per cent and production costs by 0.4 per cent. The proposed framework can assist hemp growers and hemp-based composite manufacturers in Australia in developing cost-effective environmental mitigation strategies to assist sustainability in the construction sector.

Evaluating the Effectiveness of Reflective Teaching Practices in Cultivating Diversity Awareness in Teacher Education

This study evaluates the effectiveness of reflective teaching practices in cultivating diversity awareness among pre-service teachers in the UK. Employing a qualitative research design, the study examines the implementation and outcomes of reflective practices in selected teacher education programs through interviews, focus groups, classroom observations, and the analysis of reflective journals and portfolios. The findings indicate that structured reflection sessions, diverse teaching materials, and robust mentorship and peer feedback significantly enhance pre-service teachers’ understanding and appreciation of diversity. However, challenges such as resistance to change, time constraints, and lack of resources hinder effective implementation. The study contributes to the field by highlighting key components of effective reflective practices and providing empirical evidence of their impact on diversity awareness. Recommendations for future research include exploring the long-term effects of reflective practices and conducting comparative studies across different educational contexts.

Portfolio Analysis of NIH Research on Ultra-Processed Foods

Portfolio Analysis of NIH Research on Ultra-Processed Foods

Didactical Transposition in Designing Social Arithmetic Learning Vlogs in Tijarah Context

Change of thinking starting from the references used to new knowledge that will be taught and learned. In every design or learning process there will definitely be a didactic transposition process. This research is a phenomenological research that explains what didactic transpositions occurred when designing the arithmetic learning vlog script in the Tijarah context which was prepared by one of the students at UIN Raden Fatah Palembang. The data used is in the form of learning resources, results of expert discussions and 4 drafts of arithmetic learning vlog scripts in the Tijarah context. All data is explained and combined into a table that is easier to understand. Data collection took the form of group discussions and also portfolio analysis carried out on learning vlog scripts. From the results of the analysis, it was found that didactic transposition occurred in the preparation of the Arithmetic learning vlog script in the Tijarah context, which was seen from 1) changes in context and the problems raised; 2) the form of constructivist learning and 3) the relationship between learning and the Tijarah context. This shows that in the process of preparing open materials, didactic transposition can occur.

Portfolio Insurance, Portfolio Theory, Market Simulation, and Risks of Portfolio Leverage

Portfolio Insurance, Portfolio Theory, Market Simulation, and Risks of Portfolio Leverage

이해중심 교육과정에 기반한 국어과 의사소통 역량 단원 실행 연구

Objectives This study aims to implement the unit of Korean Language Education Communicative Competency, focusing on discussions using generative AI. Methods This study formed a theoretical background by examining the characteristics of understanding by design, the concept and process of discussions, the utilization of generative AI for discussions, Critical Reflection on the Discussion Unit in the Korean Language Textbook, and the structuring of units based on Korean Language Education Communicative Competency. After establishing a research procedure based on the ADDIE model, constructing an implementation model, selecting measurement tools, organizing a research group, and establishing a plan for effectiveness verification, this study was conducted by applying the unit design developed by Ryu (2023). Results Utilizing the Wilcoxon signed-rank test, the study confirms a significant improvement in the study group's communicative competency in all sub-factors from pre-test to post-test. Through reflective discussions and analysis of learner portfolios using analytical rubrics, it is confirmed that by engaging in discussion activities utilizing generative AI based on inclusiveness and initiative, the communicative competency and discussion skills of the research group have positively improved. Conclusions Hopefully, this study will have implications for the role of Korean Language Education to respond to changes in the future educational environment and contribute to the research and practice of competency-based Korean language education.

A Systematic Review of Heterogeneous Catalysis Applied to the Treatment of Pharmaceutical Wastewater: Operational Conditions and Statistical Analysis

The use of heterogeneous photocatalysis has garnered significant attention, mainly due to its remarkable efficacy in degrading recalcitrant compounds. The main objective of this research was to investigate this process applied to pharmaceutical treatment. For that, an analysis of a Final Bibliographic Portfolio (FBP), using the systematic review of the PRISMA and the ProKnow-C method, and a meta-analysis study in a historical series from 2010 to 2020, were performed for scientific works published in indexed journals from the Scopus and Web of Science databases and fully available in English. The works were filtered after a careful reading of the titles, followed by the exclusion of repeated documents and those that were not aligned with the research from 3498 articles, 40 of which were chosen to compose the FBP that addressed the classes of antibiotics, antihypertensives, analgesics, and anti-inflammatory drugs after scientific recognition and exclusion due to not fitting into one of the four FBP structured stages: (1) identification, (2) triage, (3) eligibility, and (4) inclusion. The following gaps were highlighted: (i) a limited number of studies working with interactions of the interfering variables; (ii) a large number of experiments not considering the natural constituents of wastewater; (iii) the use of drug concentrations high above the values found in aquatic matrices; (iv) little applicability of the process at the real scale. In this meta-analysis study, operational parameter optimization was fundamental to guarantee degradation efficiencies above 80% with a variety of pharmaceutical pollutants, the main representatives studied of which were tetracycline, nimesulide, diclofenac, ibuprofen, and atenolol. However, there is still a need to determine the best conditions for this technique when using real effluents, which have the utmost importance for the process on a large scale.

Old age care fund helps to manage their funds In easy way

Abstract: A Old Age Care Funds is a sort of investment vehicle in which the combined capital of several participants is used to purchase a diverse range of bonds, equities, and other securities. Funds, which are professionally managed, give people the chance to invest in a variety of assets without having to handle them themselves. The value of the Fund shares that investors purchase varies according to how well the underlying assets perform. Funds are well-liked by both rookie and seasoned investors because they offer diversity, liquidity, and expert management. Diverse mutual fund categories, such as stock, bond, money market, and hybrid funds, are available to suit varying investment goals and risk tolerances. Investors can select funds according to their risk tolerance, time horizon, and financial objectives. Securities regulators supervise mutual funds to maintain transparency and safeguard the interests of investors. They are now an essential instrument for people to develop a diversified investment portfolio and engage in the financial markets. Old Age Care Fund portfolio analysis is an important component of investment management that includes a variety of approaches for evaluating the performance, risk, and composition of investment portfolios. The efficient market hypothesis (EMH) states that markets are efficient and reflect all available information, making it difficult for fund managers to regularly outperform the market, according to Malkiel and Fame (1970). Jensen (1968) proposed the Capital Asset Pricing Model (CAPM), which emphasizes risk-adjusted returns and the identification of alpha, which indicates a fund manager’s capacity to create excess returns above the market. IndexTerms – Python, Powet-BI, Data Analysis.

Tail risk spillovers between Islamic sectoral equities and bond markets: a time-frequency domain approach

ABSTRACT This study examines the tail risk spillover between Islamic sectoral stock indices and country specific investable Islamic bonds in time and frequency domain and provides useful implications for portfolio management. For the analyses, Conditional Autoregressive ValueatRisk (CAViaR), Quantile Connectedness, and DCCGARCH t-Copula models are estimated utilizing daily data from 15 countries. The findings show that the connectedness and spillover of risks are much stronger at tails than at median, and in the short-term than in long-term. Whereas median risk connectedness surges during COVID-19 pandemic, the connectedness between tail risks is usually elevated even before the COVID-19 pandemic with industrial sector being consistently a significant net transmitter of shocks. Moreover, all the sectoral stock market indices are significant and consistent transmitter of left tail shocks indicating a much stronger role of sectoral stock markets in transmitting large negative shocks. A heightened connectedness is also observed during Russia-Ukraine war mainly in the short-term frequency. The portfolio analysis shows that long positions in sectoral stocks can usually be hedged by taking short positions in Islamic bonds. Hedging effectiveness and optimal portfolio weights are also calculated to provide market participants with further information.

Practice Briefing: The implications of a move towards explicit discounted cash flow (DCF) models for property investment valuations

PurposeThe primary purpose of this Practice Briefing is to examine the implications of the recommendations of the Royal Institution of Chartered Surveyors (RICS) Review of Investment Valuations to shift towards (explicit) discounted cash flow (DCF) valuations on valuers, clients, risk management and market data.Design/methodology/approachThis Practice Briefing will develop a conceptual framework to articulate the impact of the shift towards explicit modelling assumptions in valuations. A comparative analysis is conducted to contrast traditional valuation approaches with an explicit DCF approach.FindingsThe proposed shift to DCF valuations in the real estate industry carries both subtle and profound implications. While on the surface, the move appears to bring minimal change since current valuations are already DCF-based, a deeper exploration reveals a potential transformation to the understanding, analysis and measurement of pricing and risks in commercial real estate.Practical implicationsThe traditional techniques adopted in valuations have become pervasive in the industry, hindering more sophisticated modelling and analysis of individual assets and portfolios. Explicitly modelling assumptions in both the initial years of cash flow and perpetuity calculations would unveil fundamental relationships and rationales that have previously been buried within, and perhaps ignored by, an All Risks Yield (ARY).Originality/valueThis briefing identifies the cause of confusion when interpreting ARY differentials on implied target returns and risk and demonstrates how a change to explicit assumptions, such as growth and letting periods, would enhance transparency, enabling more informed client–valuer discussions, which in-turn will boost confidence in valuation accuracy, lead to more sophisticated asset modelling, market data, forecasting and market analysis.

Intelligent Text Mining for Ontological Knowledge Graph Refinement and Patent Portfolio Analysis—Case Study of Net-Zero Data Center Innovation Management

Ontological knowledge graph (OKG) is a well-formed visual representation that depicts knowledge organization in formal elements (e.g., entities and attributes) and their interrelationships. OKG is crucial for innovation management analysis as it provides a clear boundary to understand complex knowledge domain in detail. In the patent analysis field, it facilitates the definition of a well-defined patent portfolio, aiming for accurate and complete patent retrievals and subsequent analyses. In recent decade, the rapid growth of the information and communication technology (ICT) sector has rendered data centers (DCs) indispensable for data processing, storage, and cloud computing, while ensuring security and privacy during DC operations. However, their energy-intensive operations pose challenges to global efforts toward achieving net-zero emissions goals. In response, this research develops a formal OKG refinement process and uses DC net-zero technology OKG as case study for in-depth OKG refinement and application in patent portfolio analysis. The net-zero DC domain covers five sub-technologies. Utilizing the proposed OKG refinement and patent portfolio analysis framework, the 1801 most recent decade’s patents related to relevant “DC net-zero technologies” are retrieved and analyzed. Particularly in this case, DC colocation and server-as-a-service perspectives are the newly discovered sub-domains for OKG refinement. Furthermore, the research also adopts the technology function matrix and technology maturity to assess current and future technology development trends, providing crucial insights supporting strategic innovation management.

Comparative Analysis of SVR and LSTM in Stock Price Forecasting Across Market Cycles

This study investigates the predictive capabilities of Support Vector Regression and Long Short-Term Memory networks on stock price trends across different market conditionsbear, bumpy, and bull markets. With the ongoing evolution of machine learning technologies, their application in financial forecasting has shown substantial potential for capturing complex patterns in vast datasets, which traditional models often fail to process efficiently. This study particularly focuses on the performance of these models in forecasting stock prices from the S&P 500 index, evaluated through the lens of Modern Portfolio Theory (MPT). The models are assessed based on their ability to forecast trends and their implications when applied to constructing investment portfolios, evaluating key financial metrics such as expected returns, standard deviation, Sharpe ratio, and maximum drawdown. The findings indicate that while both SVR and LSTM exhibit competence in trend prediction, especially in bull markets, their predictions diverge from actual market performance when applied to portfolio construction under MPT. This discrepancy underscores the need for further refinement in modeling approaches to enhance accuracy and reliability in real-world investment scenarios. This research contributes to the empirical literature by demonstrating the practical implications of deploying advanced machine learning and deep learning models in dynamic market environments and suggests directions for future enhancements.

MUTUAL FUND PORTFOLIO ANALYSIS

Abstract — Mutual funds serve as popular investment vehicles due to their potential for diversification and professional management. However, choosing the right funds and optimizing one’s portfolio allocation can be complex. This project presents a comprehensive Mutual Fund Portfolio Analysis system designed to assist investors in making informed decisions. The system allows users to input their mutual fund holdings, tracking them over time to assess performance. It offers a range of analytical tools, including portfolio diversification analysis, risk assessment, and comparison against relevant benchmark indices. These features provide investors with a holistic view of their portfolio’s performance and risk profile. Moreover, the system employs advanced algorithms to generate personalized recommendations for optimizing the portfolio based on the user’s investment goals and risk tolerance. These recommendations aim to enhance returns while managing risk effectively. By utilizing this system, investors can gain deeper insights into their mutual fund investments, enabling them to make well-informed decisions. Ultimately, this project aims to empower investors to achieve their financial goals through strategic mutual fund portfolio management. Keywords – Mutual funds, Portfolio analysis, Investment decisions, Diversification, Risk assessment, Benchmark comparison, Portfolio optimization, Investment goals, Risk tolerance.

Количественный анализ финансовых инструментов на основе портфельной теории и теории игр

The article focuses on modern methods of quantitative analysis of financial instruments based on the Markowitz portfolio theory, which allows designing optimal portfolios of financial instruments under the assumption of extreme rationality of investors and game theory, which allows taking into account the factor of player interaction between those participating in financial markets. The relevance of the research topic concerns the need to improve the quality of financial decisions made in conditions of increased volatility of financial markets. An initial set of financial instruments was built, consisting of fifty alternative options for placing funds. A preliminary analysis of the initial set of financial instruments allowed us to narrow it down to ten elements that are most preferable for placing funds. The classic Markowitz portfolio model, which assumes maximizing the expected return on a portfolio at a given level of risk, is complemented by auxiliary constraints that allow taking into account individual investor preferences. The presented six modifications of Markowitz portfolios help to identify the dynamics of the quantitative characteristics of portfolios depending on the individual preferences of investors. Expected profitability and expected risk, estimated from real financial data, are accepted as quantitative characteristics of portfolios. The game model is constructed in the form of a game with nature, which allows taking into account the complex nature of player interaction, which in most cases is not characterized by antagonism. The game model is designed to select the optimal net strategy of the investor, which takes into account various financial market conditions expressed by a market index (market portfolio). The study of the game model is implemented on the basis of the integrated Hodge-Lehman criterion regarding profitability and risk. Its use made it possible to take into account the individual level of investor confidence in the available financial information. In the process of practical implementation of these techniques, results were obtained that allow us to conclude about the degree of sensitivity of optimal investment strategies to individual preferences and perceptions of investors. The constructed models can be used to update the content of professional training of students in the higher economic school system, as well as to set up courses of additional professional education.

Portfolio Analysis of Multi-Factor Stock Selection Model Based on Investor Sentiment

Since 1990, China's securities market has been gradually improved. Domestic and foreign scholars have proposed many theoretically perfect investment models, but not many of them have been applied in practice because the market cannot satisfy the strict assumption conditions of the models. Foreign quantitative investment models are not necessarily effective in the Chinese market. Based on this, this paper takes the portfolio theory as a guide, uses the scoring method to construct a multi-factor stock selection model, and selects the relevant factors in combination with the characteristics of the Chinese market. Through empirical analyses, the validity of the multi-factor stock selection model with equal weight allocation in China's market is verified. In addition, this paper innovatively constructs an investor sentiment factor, combines the sentiment timing with the multi-factor stock selection model, optimises the timing on the basis of the multi-factor stock selection model, and proposes an investment strategy to obtain excess returns stably.

National Cancer Institute-funded grants focused on synchronous telehealth cancer care delivery: a portfolio analysis.

Telehealth use increased during the COVID-19 pandemic and remains a complementary source of cancer care delivery. Understanding research funding trends in cancer-related telehealth can highlight developments in this area of science and identify future opportunities. Applications funded by the US National Cancer Institute (NCI) between fiscal years 2016 and 2022 and focused on synchronous patient-provider telehealth were analyzed for grant characteristics (eg, funding mechanism), cancer focus (eg, cancer type), and study features (eg, type of telehealth service). Of 106 grants identified initially, 60 were retained for coding after applying exclusion criteria. Almost three-quarters (73%) of telehealth grants were funded during fiscal years 2020-2022. Approximately 67% were funded through R01 or R37 mechanism and implemented as randomized controlled trials (63%). Overall, telehealth grants commonly focused on treatment (30%) and survivorship (43%); breast cancer (12%), hematologic malignancies (10%), and multiple cancer sites (27%); and health disparity populations (ie, minorities, rural residents) (73%). Both audio and video telehealth were common (65%), as well as accompanying mHealth apps (20%). Telehealth services centered on psychosocial care, self-management, and supportive care (88%); interventions were commonly delivered by mental health professionals (30%). NCI has observed an increase in funded synchronous patient-provider telehealth grants. Trends indicate an evolution of awards that have expanded across the cancer control continuum, applied rigorous study designs, incorporated additional digital technologies, and focused on populations recognized for disparate cancer outcomes. As telehealth is integrated into routine cancer care delivery, additional research evidence will be needed to inform clinical practice.

Benchmark-based deviation and drawdown measures in portfolio optimization

Understanding and modeling of agent’s risk/reward preferences is a central problem in various applications of risk management including investment science and portfolio theory in particular. One of the approaches is to axiomatically define a set of performance measures and to use a benchmark to identify a particular measure from that set by either inverse optimization or functional dominance. For example, such a benchmark could be the rate of return of an existing attractive financial instrument. This work introduces deviation and drawdown measures that incorporate rates of return of indicated financial instruments (benchmarks). For discrete distributions and discrete sample paths, portfolio problems with such measures are reduced to linear programs and solved based on historical data in cases of a single benchmark and three benchmarks used simultaneously. The optimal portfolios and corresponding benchmarks have similar expected/cumulative rates of return in sample and out of sample, but the former are considerably less volatile.