Traditional Financial Metrics Research Articles

Dynamic web-based GIS tool for pre-feasibility evaluation of renewable energy projects

The high energy consumption and the large amount of emissions associated with roads life cycle makes it necessary for the administrations involved to invest in the implementation of renewable energy technologies on adjacent lands so that the net balance between CO2 emitted and CO2 saved is zero, thus helping to decarbonize the transport networks. To facilitate the first step in the long process leading up to the construction of wind and solar farms, a user-friendly tool is here presented that allows an early-stage evaluation of renewable energy projects. The ENROAD web-based GIS tool, through a functional, modern, intuitive, and ergonomic graphical user interface aimed at non-specialized users, optimizes the use of photovoltaic and wind technologies based on shading and wake losses, respectively, taking into account the solar radiation and wind conditions of the location and the geometry of the area selected by the user. In addition, the tool provides a thorough financial analysis, incorporating the effects of user-defined long-term interest rates, inflation rates, loan costs and efficiency losses on the Levelized Cost of Energy as well as traditional financial metrics, enabling users to conduct insightful if-then simulations. While this tool cannot have the precision of the in-depth study needed for the design of complex renewable energy facilities, it can facilitate the decision making by the administrations. Finally, a case study and a validation section have been included to demonstrate the technical and financial evaluation provided by ENROAD and ensure that the results provided are reasonable and accurate enough.

Bridging sentiment and finance: An exploration of BOD statements and bank ratios in Jordan

Purpose: This study explores the intricate interplay between sentiment extracted from Board of Directors (BOD) statements and the traditional financial metrics of banks. It investigates the relationship between a bank’s financial performance, measured by various financial ratios, and the sentiment expressed in BOD statements.Design/methodology/approach: Drawing from a dataset encompassing BOD statements from 15 Jordanian banks spanning 2017 to 2021, sentiment analysis techniques are employed to derive sentiment scores. These scores are then juxtaposed with principal components distilled from financial metrics to examine their predictive relationship.Findings: Preliminary findings reveal significant non-linear patterns between Hu and Liu’s (2004) sentimental analysis (Lui_sen), the Data Science Lab’s (n.d.) multilingual sentiment tool (multi_sen), and the principal components. A subsequent random-effects panel regression further elucidates these relationships, highlighting the significant influence of specific principal components on sentiment scores. This study provides a compelling case for integrating sentiment analysis with traditional financial indicators, offering a more comprehensive toolkit for evaluating bank performance.Research limitations/implications: Future research should explore broader datasets across multiple sectors, to validate the generalisability of the findings beyond the banking sector in Jordan.Practical implications: This research holds implications for investors, analysts, and stakeholders by combining sentiment analysis with traditional financial metrics. It offers deeper insights into the health of banks and the intentions of management. This integrated approach allows stakeholders to make informed decisions and mitigate risks based on a more comprehensive view of banks' financial situations. Additionally, the possibility of real-time sentiment analysis enables the dynamic monitoring of banking institutions' financial health as updated data becomes available.Social implications: The study’s results are valuable to users of financial information, aiding the Association of Banks and the Central Bank of Jordan in formulating policies for the banking sector. Auditors, consultants, and regulatory bodies in Jordan can also benefit from these insights.Originality/value: This is the first study exploring the relationship between sentiment extracted from BOD statements and traditional financial metrics in Jordan. It provides novel evidence of this relationship, contributing to the literature on the integration of textual data analysis and financial performance.

A Study on Antecedent and Consequential Relationship of Company's CSR Initiatives, Benefits to the Company and Society

: In an era where businesses are increasingly scrutinized for their societal impact, this study delves into the transformative power of Corporate Social Responsibility (CSR) by examining the initiatives of Godawari Power and Ispat Ltd. (GPIL) and Jayswal Neco. By looking beyond traditional financial metrics, the research seeks to uncover the broader benefits of CSR initiatives, focusing on their influence on employee awareness and societal well-being. Data was collected through surveys of employees and local community members, allowing for a comprehensive analysis of the connections between CSR efforts and their outcomes. Employing robust statistical tools such as the Statistical Package for the Social Sciences (SPSS) and Exploratory Factor Analysis (EFA), the findings reveal that both companies are not only committed to CSR but are also making meaningful strides toward societal improvement. This research highlights the essential role of CSR in fostering sustainable business practices that resonate far beyond the bottom line. Firstly, due to the company's exhibited evenhandedness, the companies' socially responsive initiatives reduce employee turnover. Secondly, the company's initiative to meet customers' justice needs tends to increase customer satisfaction. Above all, CSR initiatives' implementation in a healthy way also builds the company's reputation.

Fuzzy–Rough Analysis of ESG Ratings and Financial and Growth Ratios on the Stock Returns of Blue-Chip Stocks in Taiwan

This study uses fuzzy–rough analysis to investigate the influence of Environmental, Social, and Governance (ESG) ratings, along with critical financial and growth ratios, on the stock returns of blue-chip companies in Taiwan. The growing importance of ESG factors in investment decisions underscores the need to understand their impact on stock performance. By integrating the fuzzy–rough set theory, which accommodates uncertainty and imprecision in data, we analyze the complex relationships between ESG ratings, traditional financial metrics (such as ROE, return on equity), and stock returns. Our findings provide insights into how ESG considerations, alongside financial indicators, drive the returns of Taiwan’s blue-chip stocks. Three public-listed companies were evaluated using this approach, and the results are consistent with the actual stock performance. This research contributes to the field by offering a robust methodological approach to assess the nuanced effects of ESG factors on financial performance, thus aiding investors and management teams in making informed decisions.

Remote work economics: A conceptual review of financial and HR metrics

This review paper examines remote work's economic implications by evaluating financial and HR metrics in remote work environments. It provides an overview of remote work arrangements, identifies key economic drivers behind the shift to remote work, and discusses the impact of remote work on traditional financial metrics such as cost savings and productivity. Additionally, it explores HR metrics specific to remote work environments, including employee engagement, retention, and communication effectiveness. The paper highlights challenges in measuring HR metrics in remote setups and proposes solutions. Furthermore, it discusses the impact of remote work on employee well-being and organisational culture, emphasising the importance of supporting employee success and cultivating a positive remote work culture. The implications of financial and HR metrics in remote work economics are discussed, along with suggestions for future research directions and potential areas for improvement in evaluating remote work economics.

Value Relevance of Fair Value Measurement on Investment Property: Malaysian Evidence

Objective - This research investigates the disclosure practices for fair value measurement of investment property and examines the value relevance of fair value information reported by Malaysian public listed companies. Methodology/Technique - The study focuses on annual reports from 2018 of companies listed on the Main Market of Bursa Malaysia, excluding firms in the financial and unit trust sectors. The sample includes 71 companies that reported fair value gains on investment property. Findings - Findings reveal that none of the companies used Level 1 inputs, 51% used Level 2 inputs, 39% used Level 3 inputs, and 10% used a combination of Level 2 and 3 inputs. The regression analysis indicates that fair value gains are not significantly related to stock returns, suggesting that Malaysian investors do not find this information useful. However, net income before fair value gains is significantly related to share returns, highlighting the importance of traditional earnings metrics. Novelty - This study contributes to the literature by providing empirical evidence from Malaysia, revealing a gap in the perceived usefulness of fair value disclosures and underscoring the continued relevance of traditional financial metrics for investors. Type of Paper: Empirical JEL Classification: M41, M49. Keywords: Fair Value Gain, Investment Property, Value Relevance, Disclosure Reference to this paper should be referred to as follows: Shaari, H. (2024). Value Relevance of Fair Value Measurement on Investment Property: Malaysian Evidence, Acc. Fin. Review, 9(1), 42 – 53. https://doi.org/10.35609/afr.2024.9.1(2)

The Impact of Tax Planning, Profitability, and Leverage on the Value of Listed Logistics Companies

The COVID-19 pandemic significantly impacted businesses worldwide including in Indonesia. The COVID-19 pandemic's unprecedented impact on businesses has prompted a surge in research examining its effects on firm value. This study aims to analyse the value of the company after before and after the COVID-19 period and analyse the impact of tax planning, profitability and leverage. The logistics sector has an important role in business activities, including the delivery of health products during the pandemic. There are 36 transport and logistics companies sampled using purposive sampling method in this study with the research years 2018 to 2022. Using multiple linear regression analysis, the results of this study prove the effect of tax planning on firm value, while profitability and leverage cannot be proven. These results offer valuable insights for investors in the transportation and logistics sector. By incorporating tax planning information alongside traditional financial metrics, investors can make more informed investment decisions. Kata Kunci : Perencanaan Pajak, Profitabilitas, Leverage, Nilai Perusahaan

Enhancing Stock Market Forecasts with Double Deep Q-Network in Volatile Stock Market Environments

Stock market prediction is a subject of great interest within the finance industry and beyond. In this context, our research investigates the use of reinforcement learning through implementing the double deep Q-network (DDQN) alongside technical indicators and sentiment analysis, utilizing data from Yahoo Finance and StockTwits to forecast NVIDIA’s short-term stock movements over the dynamic and volatile period from 2 January 2020, to 21 September 2023. By incorporating financial data, the model’s effectiveness is assessed in three stages: initial reliance on closing prices, the introduction of technical indicators, and the integration of sentiment analysis. Early findings showed a dominant buy tendency (63.8%) in a basic model. Subsequent phases used technical indicators for balanced decisions and sentiment analysis to refine strategies and moderate rewards. Comparative analysis underscores a progressive increase in profitability, with average profits ranging from 57.41 to 119.98 with full data integration and greater outcome variability. These results reveal the significant impact of combining diverse data sources on the model’s predictive accuracy and profitability, suggesting that integrating sentiment analysis alongside traditional financial metrics can significantly enhance the sophistication and effectiveness of algorithmic trading strategies in fluctuating market environments.

Impact of Social Media Sentiments on Stock Market Behavior: A Machine Learning Approach to Analyzing Market Dynamics

Social media has become a valuable tool for informed decision-making. This research delves into the influence of Twitter sentiments on the stock market’s movements and price fluctuations, specifically focusing on Tesla Inc and the tweets of Elon Musk. A combination of deductive and inductive reasoning approaches is used to explore the intricate relationship between the social media platform and the stock market. Methodologically, the Twitter data undergoes rigorous processing to derive features for the machine learning predictive model, and the sentiments are extracted using the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool. This study emphasizes the usefulness of social media in predictive modeling while underscoring the importance of evaluating data reliability considering challenges such as spam tweets and geographical relevance. Multiple machine learning models are tested against four distinct datasets addressing the high stock price volatility. XG Boost and Random Forest Regressor emerge as the most effective performers, particularly when moving averages are included, showing enhanced performance. This research establishes an evident correlation between social media sentiments and stock market movements, however with limited predicting power. It is also noted that integrating traditional financial metrics enriches the understanding of stock market dynamics while enhancing the model’s predictability.

Management’s tone change in MD&A and tax avoidance

Purpose The purpose of this paper is to explore how variations in management’s tone within management’s discussion and analysis (MD&A) sections of 10-K reports can serve as an indicator of tax avoidance and highlight the complex relationship between such linguistic shifts and the tax avoidance decisions within firms. Design/methodology/approach The paper uses a textual analysis approach to identify linguistic cues in MD&A sections of 10-K filings related to tax avoidance, going beyond traditional quantitative measures. The study uses differences in negative word occurrences in MD&A to measure management’s tone change and examines various measures of tax avoidance. The sample covers the period from 1993 to 2017 and comprises all firms with 10-K filings available on EDGAR, totaling over 30,000 firm-year observations. Findings The findings indicate a complementary relationship between tax avoidance and other drivers of firm performance. When firms have more negative management’s tone, they are less willing to engage in tax avoidance and vice versa. The study’s approach with management’s tone change provides a different and statistically significant improvement in model fit for detecting tax avoidance. Practical implications This paper provides actionable insights for detecting tax avoidance through the analysis of management’s tone in corporate disclosures, offering a new tool for researchers, investors and tax authorities. It highlights the importance of linguistic cues as indicators of tax avoidance behavior, complementing traditional financial metrics. Originality/value The paper contributes to the literature by using management’s tone change as a time-varying factor to explain tax avoidance behavior. It uncovers a larger set of linguistic cues in MD&A that can be used to detect tax avoidance. This research provides a complementary approach to traditional quantitative tax avoidance measures and offers insights into the overall relationship between tax avoidance and firm performance, going beyond one-dimensional measures typically used in prior literature.

Social and Environmental Responsibility in Accounting: Beyond Financial Metrics

This exploratory research study is motivated by the evolving role of accountants in contemporary business environments. It seeks to investigate the expanding responsibilities of accountants beyond traditional financial metrics to address social and environmental issues, driven by shifting societal expectations and the imperative for organizations to embrace sustainability and responsible business practices. Historically, accountants have been primarily concerned with financial transactions and economic performance, but the global business landscape's transformation demands a broader perspective. The pressing issues of climate change, social inequality, and corporate misconduct have highlighted the need for accounting professionals to engage with ethical considerations, sustainability reporting, and the integration of non-financial metrics into decision-making. The primary objective of this study is to comprehensively explore the expanding role of accountants in addressing social and environmental responsibility in accounting. It aims to analyze the ethical dimensions, sustainability reporting practices, corporate social responsibility, and the integration of non-financial metrics into the decision-making processes of organizations. To achieve this objective, the study employs a qualitative research design, incorporating case studies to provide in-depth insights into real-world practices. Data collection methods include literature review, document analysis, and interviews with accounting professionals. Data analysis techniques involve thematic analysis to identify patterns, themes, and key findings. The research reveals several key findings. Accountants are now expected to go beyond their traditional roles as record-keepers and financial gatekeepers. Ethical considerations have become integral to accounting practices, impacting transparency and reporting st

Интеграция ESG-повестки в регионы Российской Федерации

In recent years, there has been an increased interest in non-financial indicators, in particular environmental, social and managerial factors representing the concept of ESG (Environmental, Social and Governance). The ESG concept allows for a comprehensive assessment of long-term sustainability, which goes beyond traditional financial metrics. The ESG concept represents a comprehensive assessment of long-term sustainability that goes beyond traditional financial metrics. The development of indicators to quantify the impact of ESG agenda has allowed extensive empirical research at the micro level. However, there remains insufficient research on the impact of ESG at the macro level. This article examines whether the application of the ESG indicator system can affect the economic attractiveness of Russian regions. The study covers 85 regions of the Russian Federation (excluding the Donetsk People's Republic, Luhansk People's Republic, Zaporozhskaya oblast and Khersonskaya oblast) for the period from 2014 to 2022. To conduct the study, descriptive analysis and linear regression of panel data were used to investigate the relationship between ESG indicators and the economic attractiveness of Russian regions. The results of the study showed that the integration of the ESG indicators system affects the economic attractiveness of regions. Based on the proposed ESG system, it is possible to compile a regional index and an ESG rating to assess the attractiveness of regions and the quality of life. During this study, limitations were identified and directions for future research were proposed.

Sustainability and Climate Risk Data

As economic actors face increasing pressure to be socially responsible and environmentally conscious, investors look beyond traditional financial metrics and seek data to support their decision-making. Measuring and reporting sustainability and climate risks is a relatively new discipline with several interrelations to economic, financial, risk management, and other cognitive study areas such as climate sciences, and have come a long way in the last couple of years. We are in the middle of the evolutionary process, as data availability and coverage are much better than a decade ago. However, there is still a lot to do until the new metrics and indicators are fully embedded in risk management frameworks and decision-making processes, similar to traditional risk measures. In our essay, we will explore the role of third-party data vendors for sustainability, ESG, and climate risk-related information, their importance in the modern investing world, and review the major challenges. We give an insight into the evolution of the market of data vendors and also the regulatory landscape. Finally, we explore the unique roles of central banks and their challenges in implementing actions in sustainability.

Benchmarking Private Equity Fund Performance in the Mid-Market GP Universe with Insights on Social Stratification

This work examines the impact of social stratification issues, particularly the diversity of the management team, on fund performance within the middle-market GP universe. It aims to harmonize traditional financial metrics with sociological aspects to provide a comprehensive explanation of what drives fund performance. Using a quantitative analysis approach, this research investigates the Burgiss dataset, which includes well-documented financial statements for over 3,000 private equity funds from 1990 to 2020. The study focuses on common performance metrics: Internal Rate of Return (IRR), Multiple on Invested Capital (MOIC), and Cash-on-Cash return. It measures the effects of fund size, vintage year, management fees, and management team diversity on these performance metrics. The findings indicate that larger funds are generally associated with higher IRR, MOIC, and Cash-on-Cash returns. Notably, diversity indices of the management team within these funds are positively related to IRR (β = 0.50, p < 0.001) and MOIC (β = 0.45, p < 0.001), two of the most important performance metrics. While some funds initially had low I returns, many showed recovery in the longer term. The results underscore the significant role social factors play in the performance of private equity funds. This study sheds light on the broader discussion of the relationship between social science, economic performance, and social structures. It illustrates the positive effect of management diversity on fund performance, which could motivate a reconsideration of investment strategies from a sociological standpoint, making them more sustainable and inclusive.

The Ultimate Owner of Environmental, Social, and Governance Investment

The coronavirus (COVID-19) pandemic has affected society in immeasurable ways, including investment. As the pandemic has impacted society's values, it has proven to be a major turning point for environmental, social, and governance (ESG) investment. This investment approach, which evaluates a company's ESG ratings alongside traditional financial metrics, was already “coming off a banner year,” and its reach continues to expand. Although numerous studies have investigated the impact of ESG scores on financial returns and the trend in ESG investment strategies, only a limited number of studies have attempted to capture the key players in ESG investment. Therefore, to determine the most influential investors in the ESG investment field, the cumulative impacts are calculated based on the ESG scores of invested companies, the total market price of invested companies, and the investor history portfolio report. We perform an iteration of calculation to convey the impacts that the invested companies have on the ultimate investors, and we identify the major players in the field and differences in the trend by type of investor and country.

Measuring the environmental performance of green SRI funds: A DEA approach

In this paper, we tackle the issue of evaluating an important class of green investing that integrate classical financial tasks with some environmental issues: the so-called green mutual funds.In order to do this, we first propose three measures of environmental sustainability which may give financial agents information on the greenity of their investments. These alternative indicators could serve as an overall measure of environmental sustainability and overcome the drawback that limits the evaluation of green investments to the measurement of CO2 emissions while often more than one environmental aspects may be considered.Secondly, we propose some models aimed at evaluating the performance of green mutual funds by taking into account the proposed environmental measures as well as some classical financial indicators. The analysis is carried out in a Data Envelopment Analysis (DEA) framework, since DEA models can consider strategic non-financial objective measures in addition to traditional financial metrics in order to give a more balanced view of socially responsible investments (SRI). The presented models include indicators of environmental sustainability in different ways, either by penalizing a high environmental consumption or by rewarding an environmental saving entailed by a low consumption. The environmental measures and the models proposed are applied to a set of European green funds in the periods 2012–2015 and 2010–2015 to conduct an extensive analysis.

Predicting short-term stock prices using ensemble methods and online data sources

With the ubiquity of the Internet, platforms such as: Google, Wikipedia and the like can provide insights pertaining to firms’ financial performance as well as capture the collective interest of traders through search trends, number of web page visitors and/or financial news sentiment. Information emanating from these platforms can significantly affect, or be affected by, changes in the stock market. The overarching goal of this paper is to develop a financial expert system that incorporates these features to predict short term stock prices. Our expert system is comprised of two main modules: a knowledge base and an artificial intelligence (AI) platform. The “knowledge base” for our expert system captures: (a) historical stock prices; (b) several well-known technical indicators; (c) counts and sentiment scores of published news articles for a given stock; (d) trends in Google searches for the given stock ticker; and (e) number of unique visitors for pertinent Wikipedia pages. Once the data is collected, we use a structured approach for data preparation. Then, the AI platform trains four machine learning ensemble methods: (a) a neural network regression ensemble; (b) a support vector regression ensemble; (c) a boosted regression tree; and (d) a random forest regression. In the cross-validation phase, the AI platform picks the “best” ensemble for a given stock. To evaluate the efficacy of our expert system, we first present a case study based on the Citi Group stock ($C) with data collected from 01/01/2013 - 12/31/2016. We show the expert system can predict the 1-day ahead $C stock price with a mean absolute percent error (MAPE) ≤ 1.50% and the 1–10 day ahead with a MAPE ≤ 1.89%, which is better than the reported results in the literature. We show that the use of features extracted from online sources does not substitute the traditional financial metrics, but rather supplements them to improve upon the prediction performance of machine learning based methods. To highlight the utility and generalizability of our expert system, we predict the 1-day ahead price of 19 additional stocks from different industries, volatilities and growth patterns. We report an overall mean for the MAPE statistic of 1.07% across our five different machine learning models, including a MAPE of under 0.75% for 18 of the 19 stocks for the best ensemble (boosted regression tree).

Best Interests in the Long Term: Fiduciary Duties and ESG Integration

Two persistent misconceptions continue to affect the way fiduciaries think about sustainable investing: (1) fiduciary duties block a fiduciary investor from considering environmental and social factors and (2) if a fiduciary investor engages in sustainable or responsible investing, the portfolio will suffer financially. An examination of socially responsible investing, ESG integration (an investment process that involves consideration of material environmental, social and governance (ESG) factors together with traditional financial metrics), corporate social responsibility, and impact investing, shows that neither of these assumptions is correct. Analyses of different forms of sustainable investing have found no necessary cost to a portfolio when sustainable funds are compared with traditional funds. The SEC already requires companies to report material information, and reporting standards developed by the Sustainable Accounting Standards Board (SASB) and the Global Impact Investing Network (GIIN) are improving the understanding of the financial materiality of ESG factors. Given the changes in finance, fiduciary duties also require examination. The duty to act as a prudent investor is of central importance, and the available data explains why a prudent investor should consider ESG information. Moreover, since the duty of impartiality protects future beneficiaries, that duty requires a long-term investment time horizon, increasing the need to take ESG factors into consideration. It follows that a prudent fiduciary investor not only may, but should, use ESG information in developing financial policy and decisions.

Dealing with the ‘Alignment Problem’

When companies try to become more customer-centric, one of the biggest problems has to do with aligning their internal systems, accountabilities and responsibilities with the needs and interests of individual customers. This ‘alignment problem’ comes in three different forms. ‘Alignment 1.0’ involves coordinating individual business units and divisions to ensure an enterprise-wide view of individual customers. ‘Alignment 2.0’ requires integrating the performance metrics at individual business units with customer-specific tactical goals. And ‘Alignment 3.0’, the most difficult issue, requires a company to reconcile customer-centric metrics, such as satisfaction scores, or the financial asset value of customers, to more traditional financial metrics of success, such as quarterly sales and profits, and shareholder value.

Disclosure of Long-Term Business Value: What Matters?

Sustained and superior performance depends, in part, on effective measurement, management and disclosure of traditional financial metrics. There are many other metrics such as those used to evaluate environmental, social and governance (ESG) performance; resource efficiency; business model resilience; innovative capacity; brand strength and corporate culture that can be just as informative on how a business is creating value. There is a lot to be said for building a business that outperforms on these metrics — especially when competitors miss, ignore or simply misunderstand how they can drive business value and growth opportunities. CFOs, with their unique, cross-functional vantage point, need to consider more than their results for the next quarter and a wider range of stakeholders — customers, suppliers, consumers, employees, non-governmental organizations and communities — that play an important role in an organization’s success. This research takes an in-depth look at how companies determine materiality in the ESG context, and discusses the challenges managers face and how these impact what type of data is disclosed. Today, most companies who measure these nontraditional metrics disclose most of the information in a separate sustainability report — yet this is likely to change. The challenge faced by all public and private companies is how to determine what information to disclose. Many companies are trying to apply the principle of materiality from traditional financial reporting to this new set of performance metrics, but this remains difficult terrain because there is little hands-on guidance about precisely what to do. Simply put, managers have neither the tools nor the approach to make these decisions efficiently and rigorously. The result is data that is difficult to use for making business decisions, both inside a company and externally. Materiality determination is one of the most complicated ESG-related decisions for senior management, which faces considerable uncertainty related to ESG topics. With limited resources at hand, managers should choose a small set of material performance indicators that inform on valuation impacts and consistently report data and should focus less on trying to satisfy every one of the company’s stakeholders. Furthermore, ESG materiality determination need not be a qualitative “finger to the wind” exercise. We believe that decision science methods have the potential to put corporate leaders and CFOs in a stronger position to use the intelligence that is gained from all key stakeholders using a structured approach to stakeholder engagement; and make strategic choices, including capital budgeting decisions. More importantly, the result is quantitative and based upon fundamental insights into how complex decisions are made.