Investment Strategies Research Articles

Impact of Inflation Stock Market Returns

This research paper examines the impact of inflation on stock market returns, focusing on the Indian stock market. Utilizing a comprehensive methodological approach, we analyse the relationship between inflation and stock market performance through various quantitative techniques including trend analysis, regression analysis, histograms, growth percentage models, and descriptive statistics. The trend analysis reveals patterns and shifts in stock market returns in response to changing inflation rates. Regression analysis is employed to quantify the strength and nature of the relationship between inflation and stock market returns, providing insights into the degree to which inflationary pressures affect market performance. The findings of this study indicate a multifaceted relationship between inflation and stock market returns. While high inflation periods generally correlate with increased market volatility and lower returns, the impact varies across different sectors and economic conditions. Our analysis also includes the significance of monetary and fiscal policies in moderating the effects of inflation on the stock market. This research contributes valuable insights for investors, policymakers, and financial analysts, by having a detailed analysis of Inflation-Stock market study. The study's results can inform investment strategies and policy decisions aimed at mitigating the adverse effects of inflation on stock market performance and enhancing market resilience. The purpose of the study is to investigate he correlation, understand the investor behaviour and recommend the insights from the study.

AI-ENHANCED PORTFOLIO MANAGEMENT: CRAFTING OPTIMAL INVESTMENT STRATEGIES FOR DIVERSE FINANCIAL MARKETS

This study investigates the transformative potential of artificial intelligence (AI) in portfolio management across diverse financial markets. It aims to develop a comprehensive framework that enhances investment strategies in developed, emerging, and frontier markets through AI techniques. The research will employ a mixed-methods approach, combining quantitative and qualitative methodologies. Initially, extensive secondary data will be collected from market reports, financial databases, and previous studies. This data will encompass market-specific characteristics, AI-based portfolio strategies, and financial indicators. AI methods, including neural networks, machine learning models, and natural language processing applications, will then be utilized to analyze portfolio performance across various market scenarios. The anticipated outcome of this study is a versatile AI-driven framework that accommodates the unique aspects of different financial markets. By optimizing asset allocation, risk management, and predictive analytics, this framework will provide portfolio managers with evidence-based recommendations to enhance the stability and performance of their investments. The study aims to demonstrate how AI can mitigate risks associated with market volatility, liquidity issues, and regulatory constraints, leading to a more resilient and data-driven approach to portfolio management on a global scale KEYWORDS: AI integration in finance, Portfolio performance metrics, Compounded Annual Growth Rate (CAGR), Volatility reduction

Optimized Investment Strategy and Proportional Reinsurance of Insurance Company under Ornstein–Uhlenbeck and Geometric Brownian motion Models

Optimized Investment Strategy and Proportional Reinsurance of Insurance Company under Ornstein–Uhlenbeck and Geometric Brownian motion Models

Impact of the 100 days mission for vaccines on COVID-19: a mathematical modelling study

The COVID-19 pandemic has underscored the beneficial impact of vaccines. It also highlighted the need for future investments to expedite an equitable vaccine distribution. The 100 Days Mission aims to develop and make available a new vaccine against a future pathogen with pandemic potential within 100 days of that pathogen threat being recognised. We assessed the value of this mission by estimating the impact that it could have had on the COVID-19 pandemic. Using a previously published model of SARS-CoV-2 transmission dynamics fitted to excess mortality during the COVID-19 pandemic, we projected scenarios for three different investment strategies: rapid development and manufacture of a vaccine, increasing manufacturing capacity to eliminate supply constraints, and strengthening health systems to enable faster vaccine roll-outs and global equity. Each scenario was compared against the observed COVID-19 pandemic to estimate the public health and health-economic impacts of each scenario. If countries implemented non-pharmaceutical interventions (NPIs) as they did historically, the 100 Days Mission could have averted an estimated 8·33 million deaths (95% credible interval [CrI] 7·70-8·68) globally, mostly in lower-middle income countries. This corresponds to a monetary saving of US$14·35 trillion (95% CrI 12·96-17·87) based on the value of statistical life-years saved. Investment in manufacturing and health systems further increases deaths averted to 11·01 million (95% CrI 10·60-11·49). Under an alternative scenario whereby NPIs are lifted earlier on the basis of vaccine coverage, the 100 Days Mission alone could have reduced restrictions by 12 600 days (95% CrI 12 300-13 100) globally while still averting 5·76 million deaths (95% CrI 4·91-6·81). Our findings show the value of the 100 Days Mission and how these can be amplified through improvements in manufacturing and health systems equity. However, these investments must be enhanced by prioritising a more equitable global vaccine distribution. Schmidt Science Fellowship in partnership with the Rhodes Trust, WHO, UK Medical Research Council, Coalition for Epidemic Preparedness Innovations.

A novel Sigma-Mu multiple criteria decision aiding approach for mutual funds portfolio selection

A Sigma-Mu approach is proposed for mutual funds portfolio selection. The mean and variance of the overall performance of each asset are considered, according to an additive aggregation model, subject to weights’ preferences provided by the decision maker. These preferences concern two independent sets of weights, i.e., those pertaining to the investment indicators and those pertaining to the time periods associated with the estimation of the indicators. For the first time in the Sigma-Mu framework, a weighting matrix is exploited, assisting on the development of a method to appraise the sources of variance, due to the weighting scheme of either the indicators or the periods. The Mu's, Sigma's and covariances estimated according to the Sigma-Mu approach, enter as inputs to mixed-integer quadratic programming (MIQP) mean-variance portfolio optimization models, in order to implement an empirical testing procedure, for a period of 8 years. The underlying MIQP models are equipped to consider non-convex investment policy constraints, such as the number of securities to be included in the portfolio, specific binary buy-in thresholds, the desired exposure of the portfolio to each investment advisor etc. The dataset that has been chosen for the empirical testing includes European mutual funds, that offer a broad exposure to the whole span of investment strategies and styles. The results document that the suggested approach may effectively be utilized in mutual funds investment management, since the portfolios constructed by the suggested methodology are associated with superior absolute and risk-adjusted performance against benchmarks.

Evaluating the effectiveness of elastic net model in predicting stock closing price

Abstract. Accurate stock price prediction is important for financial investors, as it directly influences investment strategies and decision-making. In this study, the author focuses on evaluating the effectiveness of the Elastic Net model in forecasting the closing prices of Apple Inc.'s stock. The dataset, spanning from 2014 to 2023, is sourced from Kaggle and includes both historical prices and a range of technical indicators, thereby ensuring the accuracy and reliability of the research data. To enhance the predictive accuracy of the model, this paper carefully selects the 14-day moving average of the closing prices along with a set of relevant technical indicators as the key input variables. This selection aims to capture the underlying trends and patterns in the stock's price. The predictive performance of the Elastic Net model is evaluated using multiple evaluation metrics, including Mean Squared Error (MSE) and R-squared values, which quantify the model's accuracy and explanatory power. Additionally, this paper utilizes the Autocorrelation Function (ACF) plot to analyze the residuals and further validate the model's effectiveness. The results demonstrate that the Elastic Net model is well-suited for handling high-dimensional financial data, exhibiting strong performance in predicting Apple Inc.'s stock prices. However, it also underscores the model's potential problem as a valuable tool for investors seeking to make proper decisions in real-life conditions.

Dissecting E, S, and G for risk mitigation: insights from the term structure of CDS

ABSTRACT In this study, we utilize a comprehensive dataset of US-traded credit default swaps (CDS) to assess the impact of environmental, social, and governance (ESG) adoption on firm credit risk. Our study examines corporate bonds with key maturities from 0.5 to 30 years, covering data from 2007 to 2022. We apply fixed effects regression to analyse the impact of ESG scores on CDS spreads. The results are validated through a battery of robustness checks primarily the 2SLS method. The study finds that ESG scores, including the individual pillars, play a significant role in mitigating credit risk. The intriguing aspect of the finding is the decline in risk mitigation as the term-to-maturity increases. Further, the social pillar causes the highest sensitivity to credit risk, followed by the environmental and governance pillars. The effect of ESG adoption is slightly greater for subordinated bonds compared to senior tranches, however, the effects diverge for individual pillars. The study offers practical insights as understanding the relationship between sustainable practices and CDS term structure helps refine investment strategies and specifically pricing of credit risk.

UCB and Thompson Sampling Algorithms in Long-Term Investing

In the rapidly evolving financial markets, traditional investment strategies are increasingly being replaced by advanced algorithms. Among these, the Upper Confidence Bound (UCB) and TS algorithms, which originate from the multi-armed bandit framework, have demonstrated significant potential across various sectors. This study utilizes historical stock data from companies during the period from 2019 to 2024 to evaluate the performance of these algorithms in long-term investment strategies. The research aims to identify how these algorithms manage investment risks and maximize returns under varying market conditions. The results reveal distinct behaviors of UCB and TS in the financial markets: UCB performs better in markets with lower volatility, while TS exhibits stronger adaptability in high-volatility environments, thereby enhancing potential returns. Through a detailed comparison of UCB and TS in long-term investment scenarios, this study provides valuable insights into the strategic application of these algorithms. It offers investment strategy guidance for investors and financial strategists and supplies effective information for economic decision-making in relevant fields.

Banks’ financial performance analysis: an experience from an Islamic economy

Purpose This study aims to analyze the performance efficiency of private commercial banks in Iran as an Islamic economy. Design/methodology/approach A meticulously chosen sample comprising 15 private banks from 2020 to 2023 has been examined. This study uses data envelopment analysis (DEA) models, specifically the Constant Returns to Scale (CCR) with input orientation and the Variable Returns to Scale (BCC) with input orientation, to scrutinize performance efficiency relative to the banking sector’s average efficiency ratio. Findings The findings indicate that the performance of decision making units is superior in BCC models when determined with CCR models. The CCR-I analysis spanning 2020–2023 reveals that only two banks consistently demonstrated full efficiency performance, attaining a 100% efficiency score across all years. The observed fluctuations in banks’ efficiency performance are attributed to disparities between the growth or reduction in inputs and the corresponding augmentation or diminution in outputs. Research limitations/implications This paper has some limitations. First, selecting inputs and outputs for the DEA models is crucial, as this study’s results are sensitive to these choices. Second, this research focused exclusively on the efficiency of private banks and did not assess the efficiency of governmental banks. Practical implications The insights from this study can be valuable for bank managers in evaluating performance, regulators in maintaining financial stability and investors and market analysts in enhancing their investment strategies through bank performance analysis. Originality/value To the best of the authors’ knowledge, this study is the first to propose a framework for evaluating and forecasting commercial bank performance in an Islamic background. This model for prediction performance would be helpful for future research on the banking sector. In addition to improving Islamic banking literature, this research contributes to the literature regarding the analysis methods usually used.

The Impact of ESG Ratings on Investment Decisions in the Energy Sector During Uncertain Times

With increasing global awareness of sustainability, the financial sector must inevitably adapt. Our research investigates the role of Environmental, Social, and Governance (ESG) ratings in investment decision-making, particularly in the energy sector, by examining their impact on portfolio performance. The study aims to assess the impact of ESG integration on portfolio risk and returns. Using data from the SP500 index, econometric models and Monte Carlo simulations are employed to evaluate how ESG considerations influence portfolio optimization and risk management. The study reveals that integrating ESG criteria into investment strategies, especially during market volatility, may not always directly affect returns, they provide significant benefits in terms of risk mitigation and ethical investment alignment.

US Funds’ returns-based ESG extraction and implementation: a multifaceted quantile regression approach

ABSTRACT This study introduces a novel ESG intrinsic-based return factor and its application in asset pricing. This factor is extracted using a parallelized rolling window estimation and extreme value-weighted quantile portfolios. It carries a positive risk premium, indicating that investors are willing to assess its risk exposure. We further show that higher returns can be obtained in the top 30% quantiles using a long-only trading strategy. We apply a Monotone Composite Quantile Regression Neural Network (MCQRNN) model to explain US fund returns and address the needs of investors seeking to optimize their investment strategies. This model surpasses traditional benchmark models by performing deep quantile estimation and considering the nonlinear relationships between fund returns and six firm-based characteristics. This approach empowers investors by explaining the core principles of impact investing and highlighting how our constructed ESG risk factor can generate competitive returns even in volatile markets when its risk is well assessed. Highlights A sustainable smart beta factor construction is ensured ESG Long-only trading strategy grants higher returns in the top 30% quantiles Using ML, we further assessed the need to implement ESG factors into asset pricing Positive ESG risk premium reveals the investors’ interest in impact investing US market remains promising for positive ESG investment signals

Enhancing investment attractiveness in wartime: a comprehensive analytical approach

The study focuses on analyzing the changes in investment attractiveness during wartime, when certain regions are occupied or destroyed, some enterprises are non-operational due to damaged production facilities, there is a significant outflow of labor force, and energy issues arise. The core issue of the study lies in the necessity of considering the complex political and economic situation’s impact on Ukraine’s investment climate. This requires developing more adaptive assessment methods that can account for both economic and socio-political risk factors. In such circumstances, reliable data is hard to obtain, which prompted the goal of substantiating the factors that characterize the country’s investment attractiveness. To provide a comprehensive assessment of investment attractiveness, an integrated indicator is proposed, which combines data reflecting investment attractiveness, including indices calculated by leading global organizations. The analysis includes such indices as the Innovation Development Index, the Corruption Perception Index, and the Judicial System Index. As a result, an average value of the changes was calculated, showing a comprehensive shift in the indicators related to investment attractiveness. The analysis using the integrated indicator demonstrated that investment attractiveness has decreased by 10 % during the war, but certain indicators, such as the Corruption Facilitation Index and the Judicial Index, have actually improved during the war. The results obtained can be used to develop risk management strategies for both international and local investors. They can also serve as a basis for creating recommendations for improving the investment climate, which in turn can attract capital and stimulate economic growth under challenging conditions

Innovation, Global Competitiveness and Human Development Index: EU Cluster Case

This study investigates the effects of innovation on global competitiveness within the framework of the EU13 and EU15 countries. Using a Pooled Driscoll and Kraay regression analysis that takes into account unit and time effects, the research illuminates the relationship between the Human Development Index (HDI), which represents Global Competitiveness, Research Expenditure, the Number of Researchers, the Number of Patents, and the Human Capital Index. The findings reveal that while the number of researchers and research expenditure lack a statistically significant effect on HDI in both groups, a significant positive correlation was found between HDI and both the number of patents and human capital. More specifically, for EU13 countries, an increase in the number of patents and human capital leads to respective increases of 0.005 and 0.04 in the HDI, while for EU15 countries, these figures stand at 0.0008 and 0.03 respectively. The study concludes that investment strategies aimed at enhancing human capital and increasing the number of patent applications can notably improve global competitiveness. For EU13 countries, in particular, greater effort in the mentioned areas is needed to narrow the gap with their EU15 counterparts. In addition, despite the indirect impact on competitiveness, it is recommended for EU13 countries to boost their R&D investments and foster technological transfer to enhance their innovation capabilities. The findings from this study underscore the pivotal role of innovation in achieving global competitiveness and suggest a need for stronger collaboration within the European Union, particularly in scientific and technological fields, to facilitate knowledge and skill exchange.

A systematic review on exploring the influence of Industry 4.0 technologies to enhance supply chain visibility and operational efficiency

This systematic review investigates the implications of Industry 4.0 technologies on supply chain visibility and operational efficiency. The primary aim is to discern the impact of technological integration on contemporary supply chain dynamics. Methods: A comprehensive search strategy identified 65 pertinent studies published between 2015 and 2023. The review adheres to systematic methodologies, employing the Critical Appraisal Skills Programme framework for quality assessment. Data synthesis incorporates qualitative and quantitative analyses to distill key themes and patterns. Results: The review unveils the pivotal role of information visibility in fortifying supply chain outcomes, emphasizing the need for a dual investment strategy encompassing technological solutions and a collaborative organizational culture. Regional variations in supply chain practices, insights from humanitarian supply chains, and the influence of environmental factors on agility broaden the understanding of Industry 4.0 implications. Organizations are urged to adopt a context-specific, adaptive approach, recognizing the significance of intangible assets and tailoring strategies to local contexts for optimal supply chain performance. This systematic review contributes a nuanced understanding of Industry 4.0’s transformative potential in supply chain management, emphasizing the interplay between technology, organizational culture, and regional contexts.

The Behavior of Housewives in the Management of Fixed and Non-fixed Income to Anticipate Economic Problem in the Future. Macassar, Indonesia

Type of qualitative research through phenomenological, approach as for hsl research shows that the role of housewives in managing finances and anticipating the future is increasingly recognized, especially in the context of gold investment as a strategy to protect family wealth from market fluctuations and economic uncertainty. Gold investment is seen as a stable and profitable option in the long run, which allows housewives to plan wisely for the future of their family. The majority of housewives view gold as the ultimate investment, based on a good understanding of the benefits and associated risks, as well as adopting a careful investment strategy, including setting clear goals, consistency in investment, choosing a trusted investment platform, and following market developments. With awareness of the importance of gold investment and continuing financial education, housewives can build a strong foundation for family financial stability, maintain peace of mind in the face of an uncertain future, and provide a solid foundation for future generations.

AI-driven robo-advisors on personal financial management and investment strategies

The advent of AI-driven robo-advisors has revolutionized personal financial management and investment strategies. Leveraging sophisticated algorithms and machine learning, these platforms offer personalized financial advice and automated portfolio management with minimal human intervention. By analyzing vast datasets, including market trends, economic indicators, and individual client preferences, robo-advisors can create tailored investment plans that optimize returns and manage risks effectively. This paper explores the transformative impact of AI in democratizing financial advice, enhancing decision-making processes, and making investment management accessible to a broader audience. The integration of AI in robo-advisory services not only improves efficiency but also provides a scalable solution to meet the diverse financial needs of individuals.

Cracking the Book Value Mystery: Understanding Earning Per Share, Price Earning Ratio, and Operational Efficiency in IDX80 Enterprises Indonesia

Research Aims: This study aims to elucidate the determinants of book value by investigating the effects of earnings per share (EPS), price-to-earnings (PER) ratios, and operational efficiency among companies listed in the IDX80 index from 2021 to 2023. Design/methodology/approach: The research utilizes a quantitative approach with an explanatory design, analyzing secondary data from the financial reports of 80 companies. Variables like EPS, PER, BV, and NPM are assessed using the Partial Least Squares (PLS) method via SmartPLS to uncover their relationships in the Indonesian capital market. Research Findings: Key findings highlight the significant impact of EPS on share valuation reflected in book value (BV) and the mediating role of NPM in this relationship. The study underscores the complexity of using PER alone to evaluate company worth within the IDX80 index, emphasizing the need for further exploration to enhance investment strategies and financial decision-making. Theoretical Contribution/Originality: This study enhances understanding of equity valuation in Indonesia's capital market by analyzing how EPS, PER ratios, and operational efficiency influence companies in the IDX80 index from 2021 to 2023. It integrates theoretical frameworks like signalling theory and mediation via NPM, showing that EPS significantly impacts share valuation through NPM. The research underscores limitations of using PER exclusively to evaluate company worth, emphasizing the intricate nature of stock valuation in the IDX80 index. Future studies should delve deeper into these factors to improve investment strategies and financial decision-making.

A comprehensive review of treasury management practices and their impact on corporate sustainability in emerging economies: Strategic insights from the oil and gas sector

Treasury management practices are critical for ensuring financial stability and fostering corporate sustainability, particularly in the volatile oil and gas sector of emerging economies. This comprehensive review examines the relationship between treasury management practices and corporate sustainability, focusing on strategic insights that can be drawn from the oil and gas industry. The review highlights the importance of effective cash management, risk assessment, and liquidity optimization as key components of successful treasury operations. The study categorizes treasury management practices into several essential areas, including cash flow forecasting, working capital management, investment strategies, and risk management frameworks. Each category is analyzed concerning its contribution to corporate sustainability, emphasizing the need for a holistic approach that aligns financial objectives with environmental and social considerations. The findings indicate that robust treasury management practices can significantly enhance operational resilience, improve resource allocation, and support long-term sustainability initiatives. Furthermore, the review discusses the challenges faced by oil and gas companies in emerging economies, such as economic instability, regulatory uncertainty, and fluctuating commodity prices. These challenges necessitate the adoption of innovative treasury strategies that not only safeguard financial assets but also promote sustainable business practices. By integrating sustainability metrics into treasury decision-making, companies can better manage risks associated with environmental regulations and stakeholder expectations. Additionally, the role of technology in modernizing treasury management is examined, particularly in terms of digital tools and platforms that enhance data analysis and decision-making capabilities. The review underscores the potential for fintech solutions to streamline treasury operations and improve transparency, thereby contributing to enhanced corporate sustainability. In conclusion, this review provides strategic insights into the interplay between treasury management practices and corporate sustainability in the oil and gas sector of emerging economies. The insights presented herein are intended to guide industry practitioners in developing effective treasury strategies that foster sustainable growth while addressing the unique challenges of the emerging market context.

Blockchain for sustainable supply chains: A systematic review and framework for SME implementation

This paper presents a systematic review of the role of blockchain technology in fostering sustainable supply chains, with a particular emphasis on its implementation for small and medium-sized enterprises (SMEs). The objective is to explore how blockchain can address critical challenges such as transparency, traceability, and accountability, thereby contributing to more sustainable and ethical supply chain practices. Through an extensive literature review, this study examines various blockchain applications and their potential to enhance sustainability by reducing inefficiencies, ensuring compliance with environmental standards, and mitigating risks related to unethical practices. The research methodology involved a comprehensive analysis of peer-reviewed articles, case studies, and industry reports to identify the benefits and barriers of blockchain adoption in supply chains. Key findings suggest that while blockchain can significantly enhance transparency and sustainability, SMEs face unique challenges in terms of technological adoption, cost implications, and scalability. Additionally, the paper proposes a framework tailored to SME needs, outlining practical steps for blockchain implementation, including initial investment strategies, stakeholder collaboration, and integration with existing technologies. The study concludes that blockchain holds great potential for advancing sustainable supply chains, but its adoption by SMEs requires a strategic approach that addresses financial, technical, and operational barriers. This framework provides a roadmap for SMEs aiming to leverage blockchain technology to achieve sustainability goals, thus contributing to long-term business competitiveness and compliance with global sustainability standards.

The Effects of FOMO on Investment Behavior in the Stock Market

This study explores the impact of Fear of Missing Out (FOMO) on stock market investment behavior, focusing on how psychological and emotional factors influence investors' decision-making processes. The primary objective of the research is to investigate the relationship between FOMO and key behavioral trends, such as overtrading, speculative investing, and the formation of asset bubbles. A qualitative research approach was employed, utilizing a literature review of existing studies on behavioral finance, FOMO, and stock market dynamics. The findings indicate that FOMO significantly affects investors by amplifying emotional reactions to market trends, leading to irrational decisions that prioritize short-term gains over long-term financial stability. Social media and technology play a critical role in amplifying FOMO, creating herd behavior that contributes to market volatility and speculative bubbles, as seen in phenomena like "meme stocks." The study also reveals that younger investors are more susceptible to FOMO, largely due to their reliance on social media for investment advice. The results suggest that enhancing financial literacy and promoting disciplined, long-term investment strategies are essential for mitigating the negative effects of FOMO. Regulatory interventions targeting misinformation on digital platforms may also help reduce the prevalence of FOMO-driven investment behavior. Future research should further investigate the psychological mechanisms behind FOMO and explore the long-term sustainability of FOMO-driven market practices.