Traditional Financial Metrics Research Articles

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

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.

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.

Has the Recession Influenced the Way in Which Senior Managers Use Marketing and Financial Metrics? Towards a Research Agenda

The severe consequences of the recent recession have led to a heated debate on the suitability of traditional financial metrics both among academics and practitioners (Ambler, 2010; Danielson et al., 2008; Davidson, 2009; McDonald, 2010). It is believed that as a result of the recent economic downturn, top-level managers have become increasingly aware of the malpractices resulting from the reliance on purely financial numbers only (see van Doorn et al., 2010). What is missing is a study that thoroughly explores and defines the phenomenon of financial and marketing metrics in the context of the recent recession. A research agenda is suggested to study whether senior-level managers are guided by a set of marketing metrics or whether the traditional financial metrics still dominate in organisations. In particular, this agenda lists five research issues that deserve the attention of current and future marketing research.

We Are What We Measure

We Are What We Measure

You Learn From What You Measure: Financial and Non-financial Performance Measures in Multinational Companies

According to recent studies in international management, relationships between headquarters and subsidiaries in the global economy are characterised simultaneously by elements of organisational interdependence and local autonomy. These characteristics challenge the traditional design and use of performance measurement systems (PMSs). Contemporary approaches to PMSs emphasise the role of performance indicators for strategy implementation and advocate the adoption of non-financial indicators in addition to traditional financial metrics. We investigated the factors associated with the inclusion of non-financial indicators in PMSs used in relationships between headquarters and subsidiaries. Our empirical study is based on 141 questionnaires collected from Italian subsidiaries of foreign companies and 13 interviews with respondents. Non-financial performance indicators contained in PMSs refer equally to customer, internal processes and people measurement perspectives. Furthermore, we find that the inclusion of non-financial indicators is positively associated with relative performance evaluation, interactive use of PMSs, subsidiary size, headquarters nationality and subsidiary participation in the design of PMSs. Information gathered through interviews provides explanations of our results, and suggests that the inclusion of non-financial perspectives in PMSs contributes to the strategic alignment of international organisations by supporting learning and dialogue within relationships between headquarters and subsidiaries.

Customer-Based Corporate Valuation - Integrating the Concepts of Customer Equity and Shareholder Value

Purpose - Synthesis of the customer lifetime value and the shareholder value (SHV) approach in order to develop an integrated, marketing-based method for corporate valuation. Design/methodology/approach - Discusses the limitations and assumptions of existing methods to estimate customer value components and examines the limitations of the SHV concept. By linking the customer equity (CE) and the SHV approach, a formal model to calculate corporate value is developed. The discounted cash flow method is used for modelling the profit streams. Findings - Provides formulas for the estimation of both the individual lifetime value of a customer and CE. Provides a comprehensive model to estimate corporate value based on customer-related cash flows and traditional financial metrics. Introduces typical cases, in which the use of a customer-based valuation seems beneficial. Illustrates how our approach can be applied by using a simple case study on M&A in the telecommunication industry. Gives suggestions on how to obtain the necessary data, partially even from publicly available sources. Research limitations/implications - Advancement of the quantitative techniques for modelling the customer value components would allow for relaxing some restrictive assumptions. The explicit modelling of the future growth of the customer base (the acquisition rate) would increase the applicability of the model. Additionally, taking into account heterogeneity within the customer cohorts is a task for future research. Finally, our model needs to be applied more extensively using real data for the input variables. Practical implications - A CE-based valuation approach can guide marketing investments and helps to avoid misallocation of resources. Based on an example in the field of M&A, we demonstrate the usefulness of the approach for obtaining a realistic indicator of firm value. It helps to assess whether an acquisition is economically sensible. We provide evidence for the superiority of a customer-based approach over traditional financial methods. Originality/value - While the traditional SHV method considers cash flows at a highly aggregated level, our approach employs disaggregated cash flows on the level of individual customers. Thereby we do incorporate the lifetime values of future customers by considering different cohorts. We do capture customer defection by incorporating retention rates. Our model enables a more detailed and valid estimation of corporate value by accounting for the single customer activities that drive marketing actions. This enables a better forecasting of the free cash flow. Incorporating customer-related drivers into financial valuation models makes easier to assess the return on marketing investments.

Value-Based Multidisciplinary Optimization for Commercial Aircraft Design and Business Risk Assessment

Traditional commercial aircraft design attempts to improve performance and reduce operating costs by minimizing takeoff weight. A better design approach also takes into account factors such as aircraft demand, market uncertainty, and development and manufacturing costs. This paper presents a design methodology that integrates an aircraft performance model with a program valuation technique based on real options theory to address uncertain market demand and managerial flexibility. The coupled performance/financial framework enables an integrated approach to technical design and programmatic decisions. In addition, the methodology provides a framework for specification of design requirements and for quantification of the financial implications associated with technical and business uncertainty. The methodology is demonstrated for an aircraft design example of the blended-wing-body concept. Comparing performance-optimized and value-optimized designs, we show that use of value as a design metric leads to a trade-off between aerodynamic efficiency and reduced manufacturing costs. Key findings demonstrate that traditional financial metrics cause the decision maker to focus overly on reducing costs in the short term. The stochastic methodology shows that a willingness to spend up-front money in the design process to ensure long-term profitability is a better strategy.

The long-term value of M&A activity that enhances learning organisations

Viewing the automobile industry as being made up of independent learning-organisations may reveal some tie-ups that can generate value not easily revealed by traditional financial metrics. The key question to be answered when considering M&A activity between automakers from this perspective is how the formation of such a relationship (or its dissolution) may affect an automaker's organisational learning (e.g., the development of increasingly higher-performing organisational routines to shorten development lead times, improve a vehicle's overall customer satisfaction, improve the ease with which a vehicle can be assembled). We analyse three case studies to obtain insights into how M&A activity may be used to enhance the learning organisations of automakers. The first two cases, Renault-Nissan and Ford-Mazda, involve very significant but still minority equity stakes between Western and Japanese automakers. The third case, Chrysler-AMC, was a full acquisition involving two US firms.

Customer‐based corporate valuation

PurposeSynthesis of the customer lifetime value and the shareholder value (SHV) approach in order to develop an integrated, marketing‐based method for corporate valuation.Design/methodology/approachDiscusses the limitations and assumptions of existing methods to estimate customer value components and examines the limitations of the SHV concept. By linking the customer equity (CE) and the SHV approach, a formal model to calculate corporate value is developed. The discounted cash flow method is used for modelling the profit streams.FindingsProvides formulas for the estimation of both the individual lifetime value of a customer and CE. Provides a comprehensive model to estimate corporate value based on customer‐related cash flows and traditional financial metrics. Introduces typical cases, in which the use of a customer‐based valuation seems beneficial. Illustrates how our approach can be applied by using a simple case study on M&A in the telecommunication industry. Gives suggestions on how to obtain the necessary data, partially even from publicly available sources.Research limitations/implicationsAdvancement of the quantitative techniques for modelling the customer value components would allow for relaxing some restrictive assumptions. The explicit modelling of the future growth of the customer base (the acquisition rate) would increase the applicability of the model. Additionally, taking into account heterogeneity within the customer cohorts is a task for future research. Finally, our model needs to be applied more extensively using real data for the input variables.Practical implicationsA CE‐based valuation approach can guide marketing investments and helps to avoid misallocation of resources. Based on an example in the field of M&A, we demonstrate the usefulness of the approach for obtaining a realistic indicator of firm value. It helps to assess whether an acquisition is economically sensible. We provide evidence for the superiority of a customer‐based approach over traditional financial methods.Originality/valueWhile the traditional SHV method considers cash flows at a highly aggregated level, our approach employs disaggregated cash flows on the level of individual customers. Thereby we do incorporate the lifetime values of future customers by considering different cohorts. We do capture customer defection by incorporating retention rates. Our model enables a more detailed and valid estimation of corporate value by accounting for the single customer activities that drive marketing actions. This enables a better forecasting of the free cash flow. Incorporating customer‐related drivers into financial valuation models makes easier to assess the return on marketing investments.

Value creation efficiency in the new economy

Firms, which have recognised the significance of Intellectual Capital as the key resource of modern business, aim at developing ways to manage and also measure this key resource in order to relate their Intellectual Capital performance and bottom line results. But it has become obvious that the traditional financial metrics do not reflect reality because the core of modern business is not cost but instead value creation. Traditional indicators such as revenue, cash flow or income are often misleading and, in our opinion, hinder management's ability to measure more accurately whether or not value is created or destroyed. It has become obvious that, if a firm aims to achieve a maximum result with its existing tangible and intangible resources, management must introduce new tools for monitoring and measuring how well the resources (physical, financial and intellectual capital) have been utilised and whether they create or destroy value. In this context, the ability of employees to transform their knowledge and skills into value creating action is of vital importance. Therefore, intellectual capital has gained in importance as a new knowledge management instrument for managing, measuring and improving a company's intangible assets. It can enable any firm to launch a renewal process and transform itself into a powerful strategic weapon for the new century. Focusing on monitoring and measuring intellectual capital performance, our research shows that Value Creation Efficiency Analysis (powered by the VAICTM model) may be utilised for better measuring business activity at the firm, regional or national level. In whatever follows, after we introduce the concept of Intellectual Capital and its comprising elements, we use Value Creation Efficiency Analysis to study Croatia's banks and the country's entire economy.