Financial Objectives Research Articles

Catalyzing Sustainable Investment: Revealing ESG Power in Predicting Fund Performance with Machine Learning

AbstractIn today’s dynamic financial landscape, the integration of environmental, social, and governance (ESG) principles into investment strategies has gained great significance. Investors and financial advisors are increasingly confronted with the crucial question of whether their dedication to ESG values enhances or hampers their pursuit of financial performance. Addressing this crucial issue, our research delves into the impact of ESG ratings on financial performance, exploring a cutting-edge machine learning approach powered by the Extreme Gradient algorithm. Our study centers on US-registered equity funds with a global investment scope, and performs a cross-sectional data analysis for annualized fund returns for a five-year period (2017–2021). To fortify our analysis, we synergistically amalgamate data from three prominent mutual fund databases, thereby bolstering data completeness, accuracy, and consistency. Through thorough examination, our findings substantiate the positive correlation between ESG ratings and fund performance. In fact, our investigation identifies ESG score as one of the dominant variables, ranking among the top five with the highest predictive capacity for mutual fund performance. As sustainable investing continues to ascend as a central force within financial markets, our study underscores the pivotal role that ESG factors play in shaping investment outcomes. Our research provides socially responsible investors and financial advisors with valuable insights, empowering them to make informed decisions that align their financial objectives with their commitment to ESG values.

COMPARATIVE STUDY OF DEBT AND EQUITY FUNDS IN INDIA

The purpose of this study is to examine the features of debt and equity funds in the Indian financial market, including their suitability, liquidity, tax implications, risk-return profiles, and other relevant factors. Through the purchase of fixed-income assets such as government bonds, corporate bonds, and money market instruments, debt funds look for investments that have low volatility and consistent returns. Equity funds, on the other hand, have a significant amount of their assets invested in equities, which can result in increased volatility but may also result in higher returns over the long term. According to the findings of the study, equity funds have the potential for bigger returns, while debt funds have a lower risk profile. This is the conclusion reached after comparing the two types of funds. Furthermore, the study investigates the tax consequences of both types of funds, which is crucial because taxes play a large influence in the decisions that are made regarding investments. Debt funds are subject to the short-term capital gains tax if they are kept for a period of time that is shorter than three years. The tax rate on long-term capital gains, on the other hand, is twenty percent, and indexation advantages are included. For equities funds that have been held for more than a year, the tax rate is 10% without indexation on profits that exceed ₹1 lakh. However, the tax rate for gains that are held for a shorter period of time is 15%. This study also investigates liquidity, which is another important factor to consider. Due to the greater liquidity of debt funds in comparison to individual fixed-income instruments, investors have more flexibility to withdraw their money from debt funds when they require it. This is because debt funds have better liquidity. It is possible for the underlying stocks of an equity fund to have an effect on the liquidity of the fund. In general, large-cap funds have superior liquidity than small-cap funds or sector-specific funds. In addition, the various investor profiles and the ways in which debt and equity funds are incorporated into their portfolios are discussed. Those who are retired, conservatives who are looking for returns with a reduced level of risk, or investors who have time horizons that range from short to medium are suitable candidates for debt funds. On the other hand, equity funds are more suitable for long-term investors who are willing to take on a greater degree of risk in the expectation of potentially bigger returns. Additionally, they are an excellent choice for individuals who are interested in riding the stock market's growth wave. Although debt and equity funds are complementary additions to any investor's portfolio, they perform distinct functions and have distinct risk-return profiles. Nevertheless, they are both beneficial to the investor. Prior to making decisions on investments, investors should do a comprehensive evaluation of their level of risk tolerance, investment horizon, and financial objectives. They ought to also give some thought to the possibility of diversifying their investments across a variety of asset classes. A comparative analysis like this one will prove to be a very useful resource for individuals who are considering making investments in India's financial markets. Keywords:- Debt funds, Equity funds, Risk-return profiles, Taxation, Liquidity, Investment suitability, Indian financial market.

MUTUAL FUNDS AND INVESTORS BEHAVIOUR

Mutual funds have been widely acknowledged as a popular investment tool for individuals who are looking to increase their wealth and accomplish their financial objectives for a considerable amount of time. Over the past few years, the study of mutual funds and investor behavior has garnered a substantial amount of attention due to the fact that it is a complex and ever-changing topic. It is essential for both financial professionals and regular investors to have a solid understanding of the complexities of mutual funds and the factors that influence investor behavior. A intriguing connection between mutual funds and investor behavior is going to be investigated, and this preface will lay the context for that investigation. The purpose of this section is to provide a complete overview of the topic, focusing on the most important ideas and topics that will be discussed in later chapters. By delving into the world of mutual funds, we are able to learn about the many investing techniques that are utilized by fund managers, the risks and rewards that are associated with the various types of funds, and the influence that market conditions have on the performance of funds. An investigation into the actions taken by investors in relation to mutual funds is also of equal importance. The judgments, biases, and emotions of investors are critically important factors that contribute to the formation of the dynamics of the mutual fund sector. Exploring the psychological aspects of investing can offer light on the reasons why specific investment choices are made, the role that information processing plays, and the influence that market movements have on investor emotion. We hope that by the end of this investigation, readers will have a full grasp of mutual funds and investor behavior, which will enable them to make educated decisions on their investments and confidently traverse the complex world of finance. Keywords: Mutual Funds, Investor Behaviour, Investment Psychology, Portfolio Management, Risk Tolerance, Asset Allocation, Market Sentiment, Behavioural Finance, Fund Performance, Market Volatility.

Data-Driven Sustainable Investment Strategies: Integrating ESG, Financial Data Science, and Time Series Analysis for Alpha Generation

In today’s investment landscape, the integration of environmental, social, and governance (ESG) factors with data-driven strategies is pivotal. This study delves into this fusion, employing sophisticated statistical techniques and Python programming to unveil insights often overlooked by traditional approaches. By analyzing extensive datasets, including S&P500 financial indicators from 2012 to 2021 and 2021 ESG metrics, investors can enhance portfolio performance. Emphasizing ESG integration for sustainable investing, the study underscores the potential for alpha generation. Time series analysis further elucidates market dynamics, empowering investors to align with both financial objectives and ethical values. Notably, the research uncovers a positive correlation between ESG risk and total risk, suggesting that companies with lower ESG risk tend to outperform those with higher ESG risk. Moreover, employing a long–short ESG risk strategy yields abnormal returns of approximately 4.37%. This integration of ESG factors not only mitigates risks associated with environmental, social, and governance issues but also capitalizes on opportunities for sustainable growth, fostering responsible investing practices and ensuring long-term financial returns, resilience, and value creation.

Exploring Gender Dynamics and Environmental Sustainability in Family Firms

Exploring Gender Dynamics and Environmental Sustainability in Family Firms

From ethics to efficiency: Understanding the interconnected dynamics of ESG performance, financial efficiency, and cash holdings in China

This study examines the impact of Environmental, Social, and Governance (ESG) principles on firm efficiency and cash holdings in China's non-financial sector from 2010 to 2022. Through fixed effect and system GMM regression analyses, it finds that higher ESG scores enhance operational efficiency, leading to decreased cash reserves. The research highlights that sustainable practices contribute to ethical standing and financial resilience, encouraging firms to integrate ESG strategies with financial objectives. The corporate firms are encouraged to view sustainable practices not merely as compliance measures but as strategic imperatives that contribute to both ethical standing and financial resilience. By exploring the linkage between sustainable practices and financial strategies, this study enriches the dialogue on sustainable finance and responsible corporate governance, demonstrating the financial benefits of ESG adherence.

Enhancing Economic Development through Inventory Management Optimization in Agro-based Industries in Bihar: A Comparative Study of EOQ and EPQ Models

Inventory management is crucial for optimizing operational efficiency and cost-effectiveness in agro-based industries, particularly in regions like Bihar,known for their agricultural resources. This study compares the effectiveness of two inventory management models, the Economic Order Quantity (EOQ) and Economic Production Quantity (EPQ), in addressing the unique challenges faced by Bihar's agro-based industries. Data from Taruwar Agro Industries Private Limited in Ashiyana, Patna, use to calculate EOQ and EPQ values for three product categories: Tomato Chili, Peri Peri, and Punjabi Tadka. The study employs relevant formulas to compute optimal order quantities and total inventory costs, assuming constant demand, quality, price, holding costs, and ordering costs. Both the EOQ and EPQ models offered viable approaches to inventory optimization. EOQ focused on minimizing holding and ordering costs, while EPQ integrated production-related costs to optimize production flow. The study underscored the importance of aligning inventory management strategies with operational capabilities, market demand, and financial objectives. This study contributes to advancing theoretical understanding and practical applications of inventory management in agro-based economies like Bihar. Recommendations for empirical validation, contextual adaptation, technology integration, and long-term impact assessment are provided to enhance inventory management practices. Integration of the BCG matrix into decision-making processes offers a comprehensive approach to prioritizing inventory management strategies and guiding strategic implications.

THE IMPACT OF FINANCIAL STATEMENT FRAUD THROUGH THE FRAUD HEXAGON ON CONSUMER CYCLICALS SECTOR COMPANIES ON THE INDONESIA STOCK EXCHANGE IN 2020-2022

The objective of this study is to collect empirical evidence regarding the Factors in the Fraud Hexagon Theory that can be used to detect financial statement fraud. The case study of the Consumer Cyclical Sector on the IDX in the range of 2020-2022 is used in testing the Fraud Score model. In this analysis, 89 Consumer Cyclicals companies serve as samples, purposeful sampling is used to select samples, in this study, financial statement fraud (KLK) is the dependent variable. The independent variables used are financial targets, financial stability, external pressure, CEO education, political connections, state-owned enterprises, external auditors, rationalization, and CEO duality. EViews version 13 testing software was used for data analysis of multiple linear regression analysis. This study shows that financial statement fraud is significantly influenced by external pressure and rationalization. There is no statistically significant correlation between financial objectives, financial stability, and rationalization.

Examining the Impacts of Human Resource Management HRM-Financial Management Alignment: A Qualitative Review of Accounting Literature

This qualitative research explores the implications of aligning Human Resource Management (HRM) practices with Financial Management objectives in organizations. The study aims to investigate the strategic significance of HRM-Financial Management alignment, its impact on organizational performance, and its implications for managerial practice. Through a comprehensive literature review, the research examines existing knowledge, theories, and empirical findings related to HRM-Financial Management alignment. The methodology involves systematic data collection, thematic analysis, and synthesis of literature from scholarly articles, books, and reports. Findings indicate a positive correlation between alignment and organizational outcomes, including financial performance, employee productivity, and innovation. The strategic integration of HRM and Financial Management functions fosters collaboration, communication, and innovation within organizations, enhancing their competitiveness and long-term sustainability. The study underscores the importance of prioritizing HRM-Financial Management alignment as a strategic imperative for organizational success in dynamic and competitive business environments.

SMS SPAM DETECTION WITH MULTINOMIAL NAIVE BAYES

SMS (short messaging service) usage has increased dramatically as a result of the growth in mobile users, enabling text messaging between smartphone and landline users. But there has also been a noticeable increase in unsolicited communications, or spam, coinciding with this growth in SMS usage. Through marketing campaigns and attempts to gain private information, such as credit card numbers, these spam messages seek to further business or financial objectives. The duty of removing spam mails has therefore grown in significance. In response, a number of deep learning and machine learning methods have been used to identify SMS spam. Using data from the University of California, Irvine (UCI), this research examines the application of such strategies. Key Words: MultinomailNaiveBayes, SMS Spam Detection, NLTK, Vectorization, Feature Extraction.

Green Supply Chain Finance: A Bibliometric Review of Financing Instruments, Challenges, and Opportunities

Green Supply Chain Finance (GSCF) has emerged as a pivotal approach for businesses to reconcile financial objectives with environmental sustainability imperatives. This bibliometric review synthesizes the evolving landscape of GSCF literature, analyzing financing instruments, challenges, and opportunities. Leveraging data from 967 papers spanning 1992 to 2024, the review reveals a substantial scholarly interest with a cumulative citation count of 82,996. Through network visualizations and citation analyses, key themes such as sustainable practices, financial innovations, and technology integration are identified. The review underscores the significance of GSCF in advancing sustainable supply chain management and calls for continued research and collaboration to address emerging challenges and capitalize on opportunities.

Connectedness between central bank digital currency index, financial stability and digital assets

This study examines the interconnectedness between central bank digital currencies (CBDC) index, digital assets and financial stability. First, we use the CBDC index as a measure of financial stability and examine its connectedness with other known measures of financial stability used in the literature. Secondly, we analyse the connectedness of CBDC index with digital assets such as cryptocurrencies and non-fungible tokens and various measures of financial stability. By analysing index returns of CBDC data and applying various connectedness measures to CBDC index, cryptocurrencies, stablecoins and NFTs, we gain insights into the relationships among these assets within a framework. The findings reveal a significant level of connectedness between CBDCs index, digital assets and financial stability. Our analysis shows a weak positive connectedness between CBDCs index and digital assets, indicating that movements in the CBDC index are not closely related to the performance of various digital assets and have a very small contribution to the changes in the returns of digital assets. Furthermore, the study finds bidirectional connectedness between CBDCs and other financial stability measures, suggesting that changes in CBDC performance can influence the overall stability of the financial system, and vice versa. This highlights the importance of carefully considering the design and implementation of CBDCs to ensure they support financial stability objectives.

Investigating Risk and Return Dynamics in Equities of Selected 10 Firms: A Five-Year Study (2019-2023)

This five-year study (2019-2023) delves into the intricate dynamics of risk and return within equity markets, focusing on ten selected firms listed on the National Stock Exchange (NSE). Through meticulous analysis, leveraging existing literature and empirical evidence, the research aims to empower stakeholders with evidence-based insights to optimize portfolio performance and manage risks effectively. Employing descriptive research methodology, secondary data was collected from various sources including the NSE website, publications, and journals. Statistical tools such as standard deviation and correlation matrices were utilized to assess risk and return profiles. The findings reveal diverse performance trajectories among the selected companies, with some exhibiting stable returns while others experiencing significant fluctuations. The scatter plot analysis highlights a potential positive correlation between risk and return, underscoring the importance of considering both factors in investment decisions. Additionally, the correlation matrix heatmap aids in identifying pairs of companies with complementary return patterns, facilitating portfolio diversification.The study recommends cautious consideration of the risk-return tradeoff, emphasizing the need for investors to align investment strategies with their risk tolerance and financial objectives. By bridging the gap between theory and practice, this research contributes to a deeper understanding of equity market dynamics, providing actionable insights for investors to navigate volatile market conditions effectively.

Risk and Return Analysis on Selected 10 Companies: a Five-year Study (2018-2022)

The "Risk and Return Analysis on Selected 10 Companies: A Five-Year Study (2018-2022)" delves into the intricate dynamics of risk and return within the equity market landscape over a significant period. Through meticulous examination spanning five years, this study aims to unravel the performance metrics of ten carefully chosen companies, providing investors and financial analysts with invaluable insights into optimizing portfolio strategies and maximizing returns. Employing contemporary methodologies and empirical data, the study contributes to the ongoing discourse surrounding risk-return dynamics, addressing the escalating need for evidence-based decision-making in financial markets amidst increasing complexity and volatility. Methodologically, the study employs a multifaceted approach, including the meticulous selection of ten companies based on predefined criteria, comprehensive data collection, and the application of various quantitative techniques to evaluate risk and return metrics. The analysis focuses on equity stocks over the specified five-year period, utilizing variance and standard deviation as fundamental statistical measures to quantify dispersion and volatility of stock returns. The study aims to furnish stakeholders with actionable insights to optimize investment decisions and mitigate potential risks effectively, ultimately aiding stakeholders in making well-informed investment decisions aligned with their financial objectives and risk tolerance levels.

PREFERENCE OF INVESTORS TOWARDS POST OFFICE SAVING SCHEMES

Background/Purpose:Post office saving schemes are backed by the government of the respective country, providing a high level of safety and security for investors funds.Some post office saving schemes may offer tax benefits, such as exemptions on the interest earned or deductions under specific sections of the tax code. Post office saving schemes, such as fixed deposits or recurring deposits, can be suitable for individuals with long-term savings goals, such as planning for education, marriage, or retirement.It is essential for investors to carefully evaluate their financial objectives and consider factors like inflation, liquidity needs, and overall portfolio diversification before choosing post office saving schemes as their primary investment avenue. Objective: To analyze the preference of investors towards different post office saving schemes. Design/Methodology/Approach: Primary data is collected through a structured questionnaire. The area of research is confined to Durg district only. Descriptive analysis has been done to analyze the research questions. Originality/Value: This paper outlines the perception of an individual towards the different saving schemes provided by the Post office. Findings/Results:An investment is a pledge of a persons current assets that allows him to receive future income through interest, dividends, rent, wages, pension benefits, or capital appreciation.

A conceptual framework for data-driven sustainable finance in green energy transition

As the world grapples with the urgent need for sustainable development, the transition towards green energy stands as a critical imperative. Financing this transition poses significant challenges, requiring innovative approaches that align financial objectives with environmental sustainability goals. This review presents a conceptual framework for leveraging data-driven techniques in sustainable finance to facilitate the transition towards green energy. The proposed framework integrates principles of sustainable finance with advanced data analytics to enhance decision-making processes across the financial ecosystem. At its core, the framework emphasizes the importance of harnessing vast datasets related to energy production, consumption, environmental impact, and financial performance. By leveraging machine learning algorithms and predictive modeling techniques, financial stakeholders can gain deeper insights into the risks and opportunities associated with green energy investments. Key components of the framework include data collection and aggregation, risk assessment, impact measurement, and investment optimization. Data sources range from traditional financial indicators to environmental metrics, social impact assessments, and geopolitical factors. Through comprehensive data analysis, financial institutions can assess the long-term viability and sustainability of green energy projects, while also evaluating potential social and environmental impacts. Risk assessment methodologies within the framework consider both financial risks, such as market volatility and regulatory uncertainty, and non-financial risks, such as climate change impacts and community resilience. By integrating these factors into risk models, investors can make more informed decisions that mitigate potential losses and maximize returns. Furthermore, impact measurement tools enable stakeholders to quantify the environmental and social benefits of green energy investments. By tracking metrics such as carbon emissions reduction, energy efficiency improvements, and job creation, investors can assess the contribution of their portfolios towards broader sustainability objectives. Finally, the framework incorporates investment optimization strategies that align financial goals with environmental objectives. Through portfolio diversification, asset allocation, and innovative financial instruments such as green bonds and impact investing funds, financial institutions can allocate capital more efficiently towards green energy projects. The conceptual framework presented herein offers a systematic approach to integrating data-driven methodologies into sustainable finance practices. By leveraging advanced analytics and comprehensive datasets, financial stakeholders can drive the transition towards green energy while simultaneously achieving financial returns and positive environmental outcomes.

IMPROVING THE BUDGET PROCESS THROUGH EFFECTIVE BUDGET PLANNING STRATEGY

This research paper addresses the importance of an effective budget planning strategy in improving the budget process within institutions and organizations. Emphasis is placed on the potential benefits of this strategy through enhanced transparency, improved resource management, enhanced balance between financial and non-financial objectives, and enhanced cooperation and coordination between different sections. A set of recommendations is made for the effective implementation of this strategy, including the development of participatory and consultative processes, improved monitoring and evaluation, enhanced training and development of staff, and effective risk management. This research is aimed at continuously improving the budgetary process and enhancing the financial and operational performance of enterprises.

Elements of the modern system of domestic financial control

Subject. This article deals with the theoretical and practical issues related to the construction of a domestic financial control system and the formation of its elements (components). Objectives. The article aims to determine the main features of the functioning and development of the domestic financial control system, identify the elements (components) of the proposed system with the justification for classifying them as types of control, develop a scheme of the financial control system pointing out input and output values, the target function, as well as determine the goals, objectives and principles of building a domestic financial control system. Methods. For the study, we used the systems and logical approaches, and the general scientific cognition methods. Results. On the basis of the categorical analysis, the article clarifies the main elements (components) of the modern system of domestic financial control, its goals, objectives and principles of construction. It also formulates the need to build a modern system of domestic financial control, determines the main features of its functioning and development, identifies the elements (components) of the proposed system with the justification for classifying them as types of control, offers an author-developed scheme of the financial control system pointing out input and output values, and the target function. Relevance. The results obtained can be used in the activities of control and audit offices, forensic units, audit organizations and tax authorities during examination, control and audit.

Digital Capabilities and Social Enterprise Performance: The Mediating Effect of Digital Innovation

Social enterprises are driven by financial objectives to preserve social values and focus on attaining self-financial stability. Digital technologies increase awareness and opportunities for social entrepreneurs. The relationship between digital capabilities and social enterprise performance is associated with digital innovation in a broader social context. This study assesses the connection between digital capability and social enterprise performance. Furthermore, we consider how digital innovation mediates the way in which digital capability influences social enterprise performance. Data were gathered from 326 respondents from various small and medium social enterprises in Bangladesh. The deductive reasoning method was used, and the data were examined through structural equation modelling using IBM SPSS 25 and SmartPLS4. The results indicate that digital capability is positively correlated with social enterprise. In addition, digital innovation was determined to mediate the connection between digital capability and social enterprise performance.

The comparative financial feasibility analysis and the environmental impacts of sugarcane-based bioethanol production in South Africa

The objective of this paper was to determine the financial feasibility and environmental implications of bioethanol production from sugarcane in South Africa. The financial feasibility objectives was achieved using two capital budgeting techniques, namely, the Net Present Value (NPV) and Benefit-Cost Ratio (BCR) under four scenarios considerations: all sugarcane is used for biofuel production (I), all sugarcane destined for the sugar export market is used to produce bioethanol, II), All sugarcane is crushed into cane juice which is then transported into the refinery (III) and IV) Export designated sugarcane is transported by farmers to the refinery in the form of a cane juice. The environmental implications were determined using the Input-output and multiplier analysis. The NPV and BCR) results indicated that it is not feasible to produce bioethanol from sugarcane without a government subsidy. The production of sugarcane-based bioethanol however had environmental benefits as it was estimated to results to an average Green House Gases (GHGs) emission reduction of 28% related to petrol, of which 84% of the GHGs emission savings was from Carbon Dioxide (CO2). For bioethanol production to be financially feasible the financial support from private investors and government is recommended in subsidising the cost of feedstock.