EXECUTIVE SUMMARY This study aims to evaluate the relationship between non-financial news and the financial performance of gaming companies on a global basis, with a focus on determining
INTRODUCTION
Background of the study
In recent decades, the global gaming industry has transformed from a simple pastime into a multi-billion-dollar powerhouse, significantly influencing both culture and the economy As gaming companies strive for technological advancements and innovative communication strategies, public perception and news coverage have become crucial to their success In today's fast-paced digital landscape, where information is easily accessible, the way corporate decisions are communicated through media can greatly affect business outcomes and investment choices.
In today's gaming industry, assessing a company's efficiency and profitability requires more than just financial figures; it necessitates a comprehensive understanding that includes non-financial factors Events such as game releases, partnerships, and legal matters significantly influence a company's performance Positive news can enhance financial outcomes by boosting interest and engagement, while negative information can adversely affect the bottom line.
The influence of non-financial news on the reputation of companies is well-recognized, yet its effect on the financial performance of gaming companies remains largely unexamined Given the gaming industry's rapid technological advancements and extensive customer base, there is a pressing need to explore the link between non-financial news and profitability While theoretical and empirical studies have addressed non-financial information's impact on technology companies, specific research focusing on the gaming sector is scarce This study aims to empirically analyze how non-financial news affects profitability ratios, such as Return on Assets (ROA) and Return on Equity (ROE), among gaming companies globally.
This research, derived from the paper "The Impact of Online Media Coverage on Corporate Performance" by Haiqing Qin et al., published on November 18, 2020, explores the non-linear relationship between online media coverage and corporate performance, utilizing reputation theory and agenda-setting theory Analyzing data from 1,445 Chinese listed companies between 2013 and 2018, the study identifies a nonlinear increase in the correlation between media coverage duration and corporate performance Furthermore, it reveals an unexpected inverted U-shaped relationship between the volume of online media coverage and firm performance, indicating that the relationship is not linear or directly proportional.
This also draws certain theories regarding non-financial news from the paper
“Stock Price Reaction to Nonfinancial News in European Technology Companies”
In their 2010 study, Beatriz Cuellar Fernández, Yolanda Fuertes Callén, and José Antonio Laínez Gadea examined the impact of voluntary non-financial information shared via media platforms on the stock market performance of 145 European ICT firms Their findings reveal that the market responds to this non-financial information, demonstrating its value relevance and indicating that such information plays a crucial role in the valuation of companies.
Objectives of the study
This study aims to analyze the connection between non-financial news and the financial performance of global gaming companies from Q1 2018 to Q4 2022 It is designed to provide an in-depth examination of the complex relationships within the gaming industry, focusing on various critical aspects The objectives are structured to facilitate a thorough understanding of these dynamics.
This study explores the characteristics of non-financial news within the gaming industry, focusing on its classification and impact Non-financial news encompasses diverse topics such as product and service updates, stakeholder relations, management strategies, legal and regulatory matters, marketing and public relations, as well as crisis management.
This study highlights the growing significance of non-financial information alongside traditional financial data, examining its overall influence on the financial performance and profitability of gaming companies.
This study explores the impact of non-financial media news on profitability ratios, specifically Return on Equity (ROE) and Return on Assets (ROA), in the global gaming industry By examining the relationship between non-financial information and profitability, the research aims to quantify how media coverage influences financial performance in this sector.
This study evaluates how effectively non-financial news is managed and distributed in the gaming industry, focusing on successful strategies used by leading companies to leverage such news for improved financial performance The findings aim to offer practical recommendations that can enhance profitability for gaming companies Additionally, the research seeks to provide valuable insights to gaming companies and stakeholders on the optimal use and benefits of non-financial news releases within the sector.
Questions of the research
This research aims to address the following questions in order to comprehensively examine the relationship between different types of non-financial news and the profitability of gaming companies
Does news on product and service launches or updates have impact on the profitability of gaming companies?
Does news on product and service delays or cancellations have impact on the profitability of gaming companies?
Does news on product and service issues have impact on the profitability of gaming companies?
Does news on client and customer activities have impact on the profitability of gaming companies?
Does news on stakeholders have impact on the profitability of gaming companies?
Does news on mergers and acquisitions have impact on the profitability of gaming companies?
Does news on changes in management or core departments have impact on the profitability of gaming companies?
Does news on strategic decisions have impact on the profitability of gaming companies?
Does news on alliance and partnership activities have impact on the profitability of gaming companies?
Does news on disposals have impact on the profitability of gaming companies?
Does news on accusations, allegations, and legal matters have impact on the profitability of gaming companies?
Does news on promotional and marketing activities have impact on the profitability of gaming companies?
Does news on awards and distinctions have impact on the profitability of gaming companies?
Does news on cultural and social activities have impact on the profitability of gaming companies?
Does news on issue management have impact on the profitability of gaming companies?
Contribution of the research
This study enhances existing literature in finance and the gaming industry by examining the link between non-financial news and the financial performance of gaming companies The primary goal is to investigate how non-financial news influences the global financial performance of gaming businesses.
This study enhances the academic conversation surrounding non-financial news by examining a broad spectrum of news articles related to corporate activities, rather than focusing solely on individual events By doing so, it aims to provide a more comprehensive understanding of the corporate disclosure environment, thereby reducing the potential bias found in research that concentrates exclusively on single news items.
1.4.1 Exploring new perspectives on non-financial news items impacting the financial performance of gaming companies
This study investigates the connection between non-financial news and the profitability of gaming firms, offering a fresh perspective on how intangible factors can impact tangible financial results.
This study aims to deepen the understanding of financial performance in the gaming sector by examining how non-financial news events impact the profitability of gaming companies Specifically, it investigates the effects of such news on key performance indicators, including Return on Equity (ROE) and Return on Assets (ROA), to identify the determinants influencing financial success within the industry.
1.4.2 Offering tangible contributions to the administration and use of non- financial news
This study provides valuable insights for managers, investors, and stakeholders in the gaming industry by analyzing different types of non-financial news It seeks to identify strategies that can be used to leverage or reduce the impact of these news items.
Understanding non-financial news is crucial for improving risk management in businesses, as it enables organizations to proactively identify and respond to potential risks and opportunities This research highlights the importance of integrating non-financial factors into strategic planning and decision-making processes, particularly for gaming companies By aligning their operations, strategies, and marketing efforts with the evolving landscape of non-financial information, these organizations can enhance their overall effectiveness and adaptability.
1.4.3 Providing a comprehensive examination of the impact of non-financial news on the profitability of firms at a worldwide level
The extensive worldwide scope of the research assures that its conclusions possess significant applicability and relevance across many markets and cultural contexts
The selected time frame of January 2018 to December 2022 has been deliberately chosen to reflect current trends and developments, ensuring that the study's conclusions are relevant to today's industry challenges and opportunities.
Structure of the research
This study is structured into five main chapters, beginning with an introductory chapter Chapter 2 provides a thorough analysis of existing literature, while Chapter 3 details the sample, dataset, and the empirical design and methodology used Chapter 4 evaluates the findings, and Chapter 5 summarizes the key results obtained from the research.
LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT
Theoretical background of the study
According to Palepu, Healy, and Peek (2022), Return on Equity (ROE) is a vital metric for assessing a firm's performance, reflecting how effectively managers utilize shareholder capital to generate profits ROE is determined by two main factors: the efficiency of asset utilization in profit generation and the relative size of the company's asset base compared to shareholder investments.
Return on Assets (ROA) is a key metric that indicates a company's profitability relative to its total assets, with a higher ROA reflecting more efficient asset utilization This measure is particularly useful for comparing profitability across various industries, especially when asset usage differs significantly The calculation of ROA is essential for assessing financial performance.
Return on Equity (ROE) is derived by multiplying Return on Assets (ROA) by the equity multiplier, which indicates the amount of assets a company can acquire for every euro invested by its shareholders.
ROE = ROA × Equity multiplier = Profit or loss
A high Return on Equity (ROE) can indicate strong profitability, but it may also stem from excessive debt, posing risks In contrast, Return on Assets (ROA) offers a less risky view of profitability by excluding debt from its calculations While shareholders often focus on ROE, both shareholders and debt holders should consider ROA for a comprehensive assessment of firm performance, independent of financing methods A significant disparity between ROE and ROA may suggest that debt is a major factor in profitability, which could alarm risk-averse investors.
The purpose of an event study is to evaluate the presence of abnormal or excess returns for security holders during specific events, such as earnings announcements, merger announcements, or stock splits An abnormal return is defined as the difference between the observed return and the expected return based on a specific return-generating model These studies typically focus on events that involve the dissemination of information to market participants, whether through financial media or corporate disclosures, as well as particular corporate or government actions (Peterson, 1989).
Event study theory primarily focuses on understanding how events affect firm performance, extending beyond just abnormal returns to include various financial metrics This study specifically investigates the influence of non-financial news events on companies' financial performance, rather than stock price movements By adapting event study theory, it allows for the analysis of both the immediate and long-term effects of these non-financial events on business performance.
21 on a company's profitability It expands upon the conventional emphasis on rapid stock market responses by incorporating broader effects.
The increasing relevance of non-financial news
Recent studies indicate a decline in the relevance of financial news due to changing business models, highlighting the growing importance of non-financial assets in evaluating company performance As a result, the presentation of non-financial information is becoming increasingly prevalent in both academic and commercial sectors, offering deeper insights than traditional financial data Scholars are actively exploring the relationship between non-financial information and the financial performance of companies.
A study by Graham, Cannice, and Sayre (2002) revealed that non-financial factors play a more significant role in determining the market values of companies compared to traditional financial indicators like net income, which showed less impact on firm value.
A study by Poroy Arsoy, Bora, and Karabiyik (2014) revealed a positive correlation between non-financial information subcategories and financial indicators of companies Furthermore, the research indicated that companies exhibiting stronger financial performance are likely to achieve higher scores in non-financial disclosures.
A study by Borodin et al (2019) revealed that publishing non-financial information significantly affects financial metrics, particularly demonstrating a long-term positive impact on Return on Assets (ROA).
The influence of different non-financial news items on firm
2.2.1 News on Products and Services
2.2.1.1 News on Product and Service Launches or Updates
Introducing new products or upgrading existing ones is crucial for companies, particularly in high-tech sectors, as technological innovation serves as a significant source of competitive advantage.
Bayus, Erickson, and Jacobson (2003) examined how new product releases influence the personal computer industry, revealing that these introductions positively impact profit rates and firm size, ultimately enhancing overall profitability for companies.
A study conducted in 2008 highlighted the positive perception surrounding the promotion and launch of new products to customers Additionally, research by Palmer and Truong in 2017 demonstrated a positive correlation between the introduction of new products and profitability in technology-focused green companies.
A study by Chaney, Devinney, and Winer (1991) analyzed 1,101 public announcements of new product releases from 1975 to 1984, revealing a significant market appreciation for innovation Their research indicated that the introduction of new products is linked to an average of 0.74% abnormal daily returns.
Research shows that introducing new products in high-tech industries can lead to significant failures in company performance Rajgopal and Shevlin (2002) highlight that the market anticipates risks associated with new product launches by competitors, which can increase the likelihood of product failures This anticipation can subsequently lead to a decline in the stock performance of these firms.
The first hypothesis for the study is:
H1: News on Product and Service Launches or Updates positively impacts gaming companies’ profitability
2.2.1.2 News on Product and Service Delays and Cancellations
Numerous studies highlight the diverse effects of product delays and cancellations on a company's performance While the immediate impacts on stock prices and operational costs are well-established, the long-term implications for profitability require further investigation.
In one study focusing on a comprehensive sample of 450 publicly traded companies that had delays in introducing their products, Hendricks and Singhal
A study from 2008 revealed that delays significantly harm firms' profitability, particularly affecting smaller companies more adversely than those with higher profitability levels before the delay.
A study by Korkofingas and Ang (2011) revealed that product delays or cancellations significantly undermine customer trust and brand perception, with the effects being more severe for established brands than for lesser-known companies Additionally, the severity of the recall issue and the timeliness of the recall notification play crucial roles in shaping customer evaluations of brand equity, ultimately influencing their decisions following a product recall.
The second hypothesis for the study is:
H2: News on Product and Service Delays and Cancellations negatively impacts gaming companies’ profitability
2.2.1.3 News on Product and Service Issues
Research indicates that customer satisfaction and brand equity diminish when products or services are faulty, leading to a decrease in customers' intentions to repurchase (Roehm and Brady, 2007; Palmer, Beggs, and Keown-Mcmullan, 2000).
A recent study by Mackelprang, Habermann, and Swink (2015) examined the link between product failures and financial performance in companies, revealing that product reliability issues lead to unexpected expenses and increased costs, particularly in innovative firms The study found that these failures contribute significantly to selling, general, and administrative costs, ultimately diminishing the financial performance of these organizations.
The third hypothesis for the study is:
H3: News on Product and Service Issues negatively impacts gaming companies’ profitability
2.2.2.1 News on Client and Customer Activities
For companies in the information and communication technology (ICT) sector, acquiring new clients is crucial for success Each emerging technology requires a strategy for gaining widespread acceptance to build a solid market presence and drive profitability Additionally, firms in rapidly evolving industries must quickly attract customers, often prioritizing speed over cost in their acquisition strategies.
25 secure an early lead in the market and establish potent network influence (Cuellar- Fernández, Fuertes-Callén and Laínez-Gadea, 2010)
Research indicates that customer-related information significantly influences firm performance, challenging the traditional focus on financial data Studies, such as one by Smith and Wright (2004), reveal that metrics like customer satisfaction, loyalty, and acquisition efforts are strongly correlated with revenue growth and profitability This underscores the importance of recognizing the intangible value customers bring to a business.
The fourth hypothesis for the study is:
H4: News on Client and Customer Activities positively impacts gaming companies’ profitability
The increasing pressure from stakeholders has compelled companies to deepen their awareness of social responsibilities As a result, organizations are now providing more detailed disclosures about their ethical practices in their operations Numerous event studies have been undertaken to assess the impact of these disclosures on corporate performance.
Research by Berman et al (1999) indicates that an organization's financial performance is significantly impacted by its relationships with stakeholders Therefore, fostering positive connections with key stakeholders can enhance a company's financial success Additionally, the study reveals a strong link between stakeholder relationships and resource allocation decisions, highlighting that managerial approaches to these relationships directly influence resource distribution.
26 distribute resources inherently impacts the strength of stakeholder relationships Moreover, these two sets of variables interact with each other, ultimately influencing the financial performance of the firm
The current research on the impact of ethical activities on stakeholders lacks definitive conclusions Hamilton, Jo, and Statman (1993) found that ethical engagement might reduce return rates, while Kreander et al (2005) noted no significant performance difference between ethical and non-ethical funds.
The fifth hypothesis for the study is:
H5: News on Stakeholders affects gaming companies’ profitability
2.2.2.3 News on Mergers and Acquisitions
RESEARCH METHODOLOGY
Sample and data collection
This study analyzed publicly listed gaming companies worldwide from 2018 to 2022, utilizing data from CompaniesMarketcap to identify the 200 largest firms by capitalization Companies not listed on the stock market during this period were excluded, along with those solely in the media and entertainment sector The final sample consisted of 52 companies involved in developing or distributing digital games and manufacturing video game consoles, after eliminating firms with insufficient ROA and ROE data.
(17), Poland (8), China (4), South Korea (6), United States (6), Taiwan (4), Sweden
(4), Singapore (1), Finland (1), Italy (1), Thailand (1) If the company has multiple listings, the study selected its main market
Data on the corporate performance of gaming companies is sourced from the Orbis database, encompassing quarterly Return on Assets (ROA) and Return on Equity (ROE) ratios from January 2018 to December 2022 This study focuses on ROA and ROE metrics derived from net income, providing a comprehensive overview of the profitability of selected gaming companies.
Non-financial news data were meticulously collected using advanced filtering tools on the Google search engine This process involved gathering news articles from online gaming news sites to establish a comprehensive database of pertinent non-financial events As illustrated in Table 1, these news items are categorized into 17 distinct groups.
In a comprehensive analysis of non-financial news, a total of 2,565 items were categorized and recorded The predominant category was News on Product and Service Launches or Updates, accounting for 1,180 items, followed by News on Promotional and Marketing Activities with 475 items Other notable categories included News on Strategic Decisions (138 items), Alliance and Partnership Activities (137 items), Mergers and Acquisitions (102 items), and Product and Service Delays or Cancellations (101 items).
Research design and variable definition
This study focuses on corporate profitability as the dependent variable, measured through two key indicators: Return on Equity (ROE) and Return on Assets (ROA).
The Return on Assets (ROA) metric measures how effectively a corporation utilizes its assets to generate profits It is calculated by dividing net income by the average total assets over a specified period, offering a clear insight into both profitability and asset efficiency.
Return on Equity (ROE) measures how effectively a corporation uses its equity capital to generate profits, calculated by dividing net income by shareholders' equity This key financial metric provides valuable insights into shareholder value and the overall financial health of the organization.
Table 1 outlines the variables utilized to explore the connection between corporate non-financial news and profitability responses To effectively evaluate how non-financial news influences the profitability of gaming companies, these variables are classified into various typical categories of non-financial information disseminated by the media within the gaming sector.
Panel A displays Product-Related News, including News on Product and Service Launches or Updates, News on Product and Service Delay or Cancellation, and News on Product and Service Issues
Panel B displays Stakeholder Relations News, including News on Clients and Customers, News on Stakeholders, and News on Mergers and Acquisitions
Panel C displays Management and Strategy News, including News on Changes in Management or Core Departments, News on Strategic Decisions, News on Alliance and Partnership Activities, and News on Disposals
Panel D displays Legal and Regulatory Affairs The sole examined topic is News on Allegations and Legal Matters
Panel E displays Marketing and Public Relations News, including News on Promotional and Marketing Activities, News on Awards and Distinctions, and News on Cultural and Social Activities
Panel F displays Crisis Management News, including News on Issue Management of gaming companies
Detailed descriptions of each variable, with their symbols for the model, and news title examples for each variable are listed in Table 1
Table 1: Description of Independent Variables
Variables and Description Symbol Examples in the dataset Panel A: Product-Related News
News on Product and Service Launches or Updates
Counts of news stories focusing on new product or service launches, or updates of existing products or services
News on Product and Service Delays or
Counts of news stories highlighting delays or cancellations in product or service offerings
PSD Blizzard and NetEase cancel World of Warcraft mobile game
News on Product and Service Issues
Counts of news stories discussing issues such as bugs, server downtimes, or other functional problems
PSI CD PROJEKT RED gaming studio hit by ransomware attack
News on Client and Customer Activities
Counts of news stories related to the company’s acquisition of new clients or customers
Counts of news stories discussing company activities with employees and other stakeholders
Cyberpunk 2077 crunch than devs endured for The Witcher 3
News on Mergers and Acquisitions
Counts of news stories discussing company mergers, acquisitions, or spin- offs
Panel C: Management and Strategy News
News on Changes in Management or
Counts of news stories announcing changes in management structure or core departments
Compliance Officer Frances Townsend Has Stepped Down
Counts of news stories discussing short- term or long-term strategic plans, changes
Developers to Create 2-Hour Game Trials
39 in accounting or commercial policy, or improvements in management systems
News on Alliance and Partnership
Counts of news stories announcing changes in alliances, partnerships, or collaborations
AP EA and Koei Tecmo Are
Working Together to Make a Hunting Game
Counts of news on sales of a strategic unit of the business, a product line and assets, or shares in other companies
DS Square Enix sells its western studios and hits such as Tomb Raider for
Panel D: Legal and Regulatory Affairs
News on Accusations, Allegations, and
Counts of news stories highlighting legal battles, controversies, or other allegations against the company
AAL Activision sues TikTok creator following legal threat
Panel E: Marketing and Public Relations News
News on Promotional and Marketing
Counts of news stories derived from advertising campaigns, promotions, or other marketing activities
News on Awards and Distinctions
Counts of news stories on product or company award or special distinction
AD ‘Elden Ring’ Wins Big At
News on Cultural and Social Activities
Counts of news stories discussing the company’s involvement in social or cultural events and activities
CS Nintendo details their employee efforts for diversity and inclusion
Counts of news stories discussing how the company manages issues and controversies, including public relations responses
IM CD Projekt Red Offers
Apology and Refunds for Cyberpunk 2077
The analysis incorporated two control variables, including Enterprise Value (EV) and Time Dummies
Enterprise Value (EV) represents the comprehensive valuation of a company, encompassing both its stock and debt while omitting its cash and cash equivalents
As the study incorporates gaming companies in different countries around the world, meaning that there are firms of different sizes and capital structures, using
EV as a control variable can be useful in the comparison process
The model utilizes Time Dummies to capture time-specific effects that other variables may overlook, effectively isolating the impact of time from other influencing factors This function serves as a control mechanism, managing data variations and enabling a more accurate analysis of the relationships between variables.
This study aims to evaluate how various types of non-financial news affect the profitability of gaming companies Utilizing the regression method, the research analyzes the interconnected impact processes, specifically employing a pooled OLS regression model to assess the relationship between these variables and financial performance.
FP = α + β 1 PSL + β 2 PSD + β 3 PSI + β 4 CC + β 5 SH + β 6 MA + β 7 MC + β 8 SD + β 9 AP + β 10 AL + β 11 PM + β 12 AD + β 13 CS + β 14 IM + β 15 EV + β 16 Time_Dummy + ε
Where FP indicates the corporate financial performance, the study chose ROA and
The study examines Return on Equity (ROE) as the dependent variable, analyzing its relationship with 15 independent variables These include counts of news related to Product and Service Launches or Updates (PSL), Product and Service Delays or Cancellations (PSD), and Product and Service Issues (PSI), as well as counts of news concerning Client and Customer interactions (CC).
Activities), SH (counts of News on Stakeholders), MA (counts of News on Mergers and Acquisitions), MC (counts of News on Changes in Management or Core
Departments, SD (counts of News on Strategic Decisions), AP (counts of News on Alliance and Partnership Activities), DS (counts of News on Disposals), AAL
(counts of News on Accusations, Allegations, and Legal Matters), PM (counts of
News on Promotional and Marketing Activities), AD (counts of News on Awards and Distinctions), CS (counts of News on Cultural and Social Activities), and IM
(counts of News on Issue Management) The model also incorporates EV
(Enterprise value) and Time_Dummy (time dummies) as control variables
EMPIRICAL RESULTS
Descriptive statistics and correlation analysis
Table 2 presents the descriptive statistics for the variables used in our regression model, based on a sample of 1,040 observations This study analyzes the news counts of various non-financial articles collected quarterly from January 2018 to December 2022.
The analysis of the gaming companies shows a mean Return on Equity (ROE) of approximately 5.96, reflecting a moderate profitability level The ROE values range from -428.41 to 86.653, highlighting significant outliers, especially on the negative side A high standard deviation of 31.87 indicates considerable variability in ROE among these companies Additionally, the average Return on Assets (ROA) is around 11.15, with values ranging from -85.253 to 308.74, and a standard deviation of 20.3, demonstrating substantial dispersion in the ROA metric.
To address the significant differences in the frequency of various non-financial news types in the gaming industry, the research utilized min-max normalization, also known as min-max scaling, to standardize the counts This method was chosen to ensure a consistent comparison across the diverse occurrence rates of the observed news categories.
The mean values of the unscaled independent variables generally remain below 1, with most variables having a lower bound of 0 However, the upper bounds vary, suggesting that some variables have broader ranges and potentially greater influence Additionally, most standard deviations are low, reflecting minimal variability among the data.
The values for news related to product and service launches or updates range from a minimum of 0 to a maximum of 15, with an average of approximately 1.13 and a standard deviation of about 1.64 In contrast, news on promotional and marketing activities shows a substantial range, indicating diverse reporting in this area.
22 The mean value of these news articles is approximately 0.46, with a standard deviation of 1.3
After scaling the independent variables, the mean values significantly decrease, confirming the effectiveness of the scaling process The standard deviations also show a reduced scale while maintaining their proportional relationship with the mean, allowing for a comparison of variability among the variables Notably, the highest scaled mean value of around 0.076 is found in the category of News on Product and Service Launches and Updates, which has a standard deviation of approximately 0.11.
The control variable, Enterprise Value, averages 35 million USD, with a minimum of -529,213.4 USD and a maximum of 2.470 billion USD, highlighting the presence of significant corporations in the industry The standard deviation of 209 million USD reflects a substantial variability in the sizes of the enterprises within the sample.
Variable Obs Mean Std dev Min Max
Independent Variables (min-max scaled)
The study analyzed the relationship between key variables, revealing a significant positive correlation of moderate magnitude (r = 0.397) between Return on Assets (ROA) and Return on Equity (ROE) This finding suggests that organizations with higher ROA are likely to demonstrate greater ROE levels.
Many independent variables show modest but statistically significant associations with Return on Assets (ROA) and Return on Equity (ROE) Notably, there is a strong correlation of 0.405 between news related to product and service issues and news regarding promotional and marketing activities While some of these relationships may demonstrate a low magnitude, they remain statistically significant.
Negative correlations have been identified between Return on Assets (ROA) and specific variables, notably a correlation of -0.184 with news related to promotional and marketing activities Additionally, Return on Equity (ROE) shows negative correlations of -0.039 with news concerning product and service delays or cancellations, and -0.057 with news related to accusations, allegations, and legal matters.
Table 3: Correlation Analysis of Key Variables
The analysis of financial performance indicators reveals critical metrics such as Return on Assets (ROA) and Return on Equity (ROE), which are essential for evaluating company profitability Additionally, the scaled performance indicators (PSL, PSD, PSI, CC, SH, MA, MC, SD, AP, DS, AAL, PM, AD, CS, IM, EV) provide a comprehensive overview of various aspects of financial health and operational efficiency Understanding these metrics is vital for investors and stakeholders aiming to make informed decisions regarding company performance and potential growth.
Testing assumptions of the regression model
4.2.1 Breusch-Pagan test for Heteroskedasticity
The study utilized a pooled Ordinary Least Squares (OLS) regression analysis to estimate models for Return on Equity (ROE) and Return on Assets (ROA), with results detailed in Appendices 1 and 2 Subsequent Breusch-Pagan tests for heteroskedasticity indicated significant evidence of this issue, as shown in Table 4, where the ROE model yielded a chi-square statistic of 248.99 and a p-value below 0.0001, while the ROA model showed a chi-square value of 65.92, also with a p-value under 0.0001 This confirms the presence of heteroskedasticity, which poses a risk of inefficient estimations and invalid hypothesis test outcomes To mitigate these concerns, the study applied linear regression analysis with robust standard errors.
Table 4: Breusch–Pagan/Cook–Weisberg test for Heteroskedasticity
Assumption: Normal error terms Assumption: Normal error terms
Variable: Fitted values of ROE Variable: Fitted values of ROA
H0: Constant variance H0: Constant variance chi2(1) = 248.99 chi2(1) = 65.92
4.2.2 Variance Inflation Factor test for Multicollinearity
The study evaluated multicollinearity among key variables, finding no substantial evidence of collinearity Variable Inflation Factors (VIFs) provide additional insights into high bivariate associations, with a VIF above 5 warranting caution and a value over 10 indicating significant multicollinearity According to the results in Table 5, the average VIF values for all models were below 5, confirming that collinearity is not a significant concern in the analysis.
Table 5: Variance Inflation Factor for Multicollinearity diagnosis
The analysis of variable inflation factors (VIF) reveals that several time dummy variables and scaled metrics exhibit varying degrees of multicollinearity Notably, time_dummy20 has a VIF of 1.98, indicating a moderate level of correlation, while PSI_scaled shows a VIF of 1.43 Other significant time dummies, such as time_dummy17 and time_dummy19, both register a VIF of 1.95 Among the scaled variables, AAL_scaled has a VIF of 1.35, and SH_scaled is at 1.30 Lower VIF values are observed in variables like PSD_scaled (1.16) and MC_scaled (1.13), suggesting less multicollinearity Overall, the data indicates that while some variables exhibit higher VIF values, most remain within acceptable limits for regression analysis.
4.2.3 Hausman test for Fixed Effect and Random Effects Regression Model
The Hausman test was utilized to determine the appropriate regression model between fixed effects and random effects for both ROE and ROA The results for these models are detailed in Appendices 3 to 6, with the test findings summarized in Table 6 For the ROE model, the Hausman test statistic shows a chi-square value of 29.86 and a p-value of 0.6709, indicating no significant difference in coefficients between the two models, as the p-value exceeds the 0.05 threshold Conversely, the ROA analysis reveals a negative chi-square value of -29.54, suggesting the model does not meet the Hausman test's asymptotic assumptions and indicating a need for a more generalized testing approach due to potential model shortcomings.
Table 6: Hausman Test for Fixed Effect and Random Effects Regression Model
Test of H0: Difference in coefficients not systematic
Test of H0: Difference in coefficients not systematic chi2(34) = (b-B)'[(V_b-
4.2.4 Chi-square test with Seemingly Unrelated Estimation
The research utilized a generalized chi-square test, integrating seemingly unrelated estimation from two pooled OLS regression models focused on Return on Equity (ROE) and Return on Assets (ROA) The test aimed to compare the coefficients of these two dependent variables against various independent factors and time dummies With a chi-square value of 102.34 and 35 degrees of freedom, the p-value was found to be below 0.0001, providing strong statistical evidence to reject the null hypothesis that all coefficients across the models are equal This indicates significant disparities in the coefficients for ROE and ROA, suggesting that independent variables exert different influences on these metrics and that the two models reflect distinct underlying phenomena.
Regression results
This study utilizes pooled Ordinary Least Squares (OLS) regression analysis for model estimation, recognizing that conventional OLS standard errors may produce inaccurate results due to cluster effects in panel data analysis To mitigate this issue, robust standard errors are employed alongside linear regression analysis Furthermore, the research integrates time dummies to control for time-specific impacts affecting all organizations, allowing for a comprehensive evaluation of additional variables while considering the influence of temporal factors.
The regression study of non-financial news counts influencing ROE is presented in Table 7 Four variables exhibit statistical significance among the observed
A statistically significant association exists between an increase in news on product and service launches and a rise of approximately 22.58 units in Return on Equity (ROE), with a p-value of 0.007 Similarly, coverage of news related to client and customer activities correlates with a notable increase of about 19.46 units in ROE, supported by a p-value of 0.009 Conversely, news on promotional and marketing activities negatively impacts ROE, where a single-unit increase leads to a decline of 105.62 units, with a p-value nearing zero Additionally, news regarding issue management positively affects ROE, potentially increasing it by 23.03 units per news count, with a p-value of 0.022 However, the overall model explains only about 7.59% of the variability in ROE, as indicated by an R-squared value of 0.0759 and an F-statistic of 3.11, highlighting the need for cautious interpretation of practical implications The model's prediction accuracy is further quantified by a Root Mean Square Error (RMSE) of 31.169.
Table 7: Estimated results for ROE by pooled OLS regression
PSI_scaled -43.72525 30.18325 -1.45 0.148 -102.9547 15.50424 CC_scaled 19.45655 7.462853 2.61 0.009 4.81197 34.10112 SH_scaled 18.63023 17.11968 1.09 0.277 -14.96422 52.22469 MA_scaled -15.72268 10.66768 -1.47 0.141 -36.65619 5.210828 MC_scaled -17.0748 15.79102 -1.08 0.280 -48.06199 13.91238 SD_scaled 4.805559 5.877938 0.82 0.414 -6.728892 16.34001 AP_scaled -10.74244 7.343823 -1.46 0.144 -25.15344 3.668558 DS_scaled -4.533766 8.167849 -0.56 0.579 -20.56178 11.49425 AAL_scaled -25.71949 27.83786 -0.92 0.356 -80.34655 28.90756
67.50261 AD_scaled 8.275237 6.521192 1.27 0.205 -4.521491 21.07197 CS_scaled 1.63001 3.080776 0.53 0.597 -4.415488 7.675507 IM_scaled 23.03375 10.01993 2.3 0.022 3.371349 42.69615
EV 5.50e-09 2.21E-09 2.48 0.013 1.15e-09 9.85E-09 time_dummy Yes Yes Yes Yes Yes Yes
Table 8 reveals the results of the pooled ordinary least squares (OLS) regression analysis for the variable Return on Assets (ROA) Significant variables include News on Mergers and Acquisitions and News on Alliance and Partnership Activities, both positively impacting ROA Specifically, an increase of one count in News on Mergers and Acquisitions correlates with an approximate 11.99-unit rise in ROA, with a statistically significant p-value of 0.009 Similarly, a one-unit increase in News on Alliance and Partnership Activities results in a notable 9.85-unit increase in ROA, supported by a p-value of 0.019.
Research indicates that an increase in promotional and marketing news is linked to a decrease in Return on Assets (ROA), with each additional report correlating to an estimated loss of approximately 56.51 units in ROA This correlation is statistically significant, highlighted by a p-value nearing zero However, the model's explanatory power is limited, as it accounts for only 7% of the variability in ROA, reflected in an R-squared value of 0.07 The F-statistic of 3 suggests that the model fits the data better than one without predictors, with a probability of obtaining such an extreme F-value being effectively zero Additionally, the Root Mean Square Error (RMSE) is calculated at 19.916, indicating the model's prediction error.
Table 8: Estimated results for ROA by pooled OLS regression
PSL_scaled 3.232991 6.036092 0.54 0.592 -8.611812 15.07779 PSD_scaled 11.57777 6.693102 -1.73 0.084 -24.71184 1.556303 PSI_scaled 19.55001 9.836135 -1.99 0.047 -38.85175 -0.2482744 CC_scaled 6.395541 7.896818 0.81 0.418 -9.100619 21.8917 SH_scaled 2.682411 9.774355 -0.27 0.784 -21.86292 16.4981 MA_scaled 11.99986 4.616107 -2.6 0.009 -21.05818 -2.941532 MC_scaled 6.297996 11.55492 0.55 0.586 -16.37657 28.97256 SD_scaled 2.374101 4.114815 -0.58 0.564 -10.44872 5.700522 AP_scaled 9.853758 4.185482 -2.35 0.019 -18.06705 -1.640462 DS_scaled 2.613835 9.38383 -0.28 0.781 -21.028 15.80033 AAL_scaled 21.87217 20.65474 1.06 0.290 -18.65923 62.40357 PM_scaled -56.5128 12.81 -4.41 0.000 -81.65024 -31.37536 AD_scaled 5.966264 5.436086 1.1 0.273 -4.701129 16.63366 CS_scaled 1.289259 2.41418 0.53 0.593 -3.448157 6.026676 IM_scaled 8763979 5.863541 0.15 0.881 -10.6298 12.3826
EV 4.34e-09 1.55E-09 2.8 0.005 1.29e-09 7.38E-09 time_dummy Yes Yes Yes Yes Yes Yes
Discussion
This study aims to explore how various categories of non-financial news affect the financial performance of gaming companies, specifically focusing on profitability metrics such as Return on Equity (ROE) and Return on Assets (ROA) The findings reveal that certain types of non-financial news can have distinct positive or negative effects on both ROE and ROA, ultimately influencing the overall profitability of these firms.
Research indicates that news articles about product launches and updates significantly impact Return on Equity (ROE) in the gaming industry This sector is marked by frequent releases of new games and updates, generating substantial interest from the gaming community Such articles can ignite enthusiasm and attract media attention from both existing and potential customers, potentially driving higher sales and revenue Increased sales directly enhance the Net Income component of the ROE equation, thereby improving overall profitability Moreover, if the new offerings feature higher profit margins, the company's profitability can see further benefits.
Regular updates on products and services from 55 companies can significantly boost customer retention by enhancing engagement and satisfaction over time Loyal customers often yield a higher lifetime value, which greatly contributes to a company's profitability Additionally, new product releases create cross-selling opportunities, allowing for the bundling of offerings and increased transaction values Furthermore, consistent updates signal to investors that the company is committed to long-term growth, attracting those seeking promising investment opportunities, which can lead to higher stock prices and improved return on equity (ROE).
Positive updates about client and customer activities can significantly boost investor confidence, leading to increased investment and improved stock performance Such favorable news not only enhances financial arrangements but also fosters community engagement, encouraging client acquisition and retention A loyal community is more likely to recommend new customers and spend more, ultimately positively impacting return on equity (ROE).
The availability of information related to mergers and acquisitions (M&A) positively influences return on assets (ROA) This effect occurs because M&A announcements often create expectations of enhanced operational efficiency.
Acquiring companies can create significant synergies, such as cost reduction and improved efficiencies, which can enhance Return on Assets (ROA) by increasing net income from each asset These acquisitions also provide access to new geographical and demographic markets, leading to revenue growth and further boosting ROA In the gaming industry, for instance, firms often acquire others with advanced technologies or skilled talent, enhancing their competitiveness and optimizing the use of existing assets Additionally, positive M&A news tends to bolster investor confidence, increasing share valuations While ROA serves as an operational metric, strong investor backing can further facilitate asset optimization opportunities.
Research indicates that news about alliances and partnerships positively affects Return on Assets (ROA) These collaborations enable faster market entry without significant financial investment, optimizing the use of existing resources Strategic alliances can create new distribution channels, bundled offers, and co-marketing initiatives, which drive sales and improve asset utilization Moreover, partnerships allow for resource sharing—such as technology, talent, and infrastructure—maximizing asset efficiency Additionally, collaborations enhance customer engagement by offering added value through bundled services or improved features, leading to better utilization of customer-related assets like user databases and platforms.
This study reveals an unexpected negative correlation between promotional and marketing activities in gaming companies and key financial metrics, ROE and ROA While organizations typically aim to boost financial performance through marketing initiatives, the findings suggest that high-budget campaigns can lead to increased expenses that do not necessarily translate into higher revenue Ineffective targeting may waste resources, failing to expand or retain the customer base, ultimately lowering asset and equity efficiency Furthermore, if a marketing campaign does not meet expectations, it can damage customer trust and lead to decreased sales, exacerbating the negative impact on financial performance and company reputation.
This study's findings support previous research, highlighting that external factors like media coverage significantly impact financial performance metrics such as Return on Equity (ROE) and Return on Assets (ROA) However, the relationship between marketing and promotional activities and financial performance metrics diverges from traditional business beliefs that prioritize marketing strategies Further investigation is necessary to understand this complex outcome, consistent with earlier studies.
58 presented diverse results on the impact of promotional and marketing efforts on the financial success of companies
This study provides valuable insights into the connection between non-financial news counts and financial performance metrics, specifically Return on Equity (ROE) and Return on Assets (ROA) However, it is important to note the limitations, as the R-squared values of 0.0759 for ROE and 0.07 for ROA indicate that these models explain only a small portion of the variability observed Additionally, the use of pooled ordinary least squares (OLS) regression may not effectively account for unobservable heterogeneity among organizations Furthermore, the study does not consider the quality or tone of the news, which could be a significant influencing factor.
Future research can enhance this study by integrating sentiment analysis into the evaluation of news articles, allowing for a better understanding of how the sentiment of news coverage impacts financial results Additionally, employing alternative econometric models, like fixed-effects or random-effects models, could offer a deeper insight into the relationships observed in the data.
CONCLUSION
Conclusion
In the digital media era, corporate news has become increasingly accessible, significantly impacting various aspects of businesses and the financial world This article examines how online media coverage of non-financial news influences key financial performance indicators, specifically Return on Equity (ROE) and Return on Assets (ROA), in the gaming industry By analyzing a comprehensive dataset of 52 publicly listed gaming companies globally, the study employs statistical models to assess the relationship between non-financial news coverage and financial performance metrics.
The analysis reveals a positive correlation between non-financial news and performance metrics such as ROE and ROA Notably, news about new or improved products and updates on client activities significantly boosts ROE, while news on mergers, acquisitions, and partnerships positively influences ROA Conversely, an unexpected negative correlation was found between promotional and marketing news from gaming firms and both ROE and ROA, suggesting that excessive coverage may harm firm performance.
Recommendation for managing non-financial news
The study reveals that non-financial news significantly influences market perception and corporate operations, impacting stakeholder trust, brand equity, and long-term strategy, despite financial news being the main driver of stock prices This section offers recommendations aimed at improving the management of non-financial news in the gaming industry.
5.2.1 Media monitoring and rapid reaction
To effectively manage non-financial news, it is essential to implement media monitoring and rapid response strategies This practice entails the systematic observation and analysis of relevant media content, allowing for the timely and efficient addressing of emerging issues and developments related to the research topic.
Companies should implement a robust media monitoring system that utilizes tools to track mentions across various platforms, allowing them to understand the nature and extent of non-financial news coverage Additionally, organizations should create a dedicated team to respond to inquiries, which will help mitigate the effects of negative non-financial information and promote positive media coverage.
Maintaining transparency within an organization can significantly reduce internal rumors and misinformation It's essential for employees to be informed about key non-financial developments to effectively respond to external inquiries and provide consistent messaging.
Establishing a crisis communication plan is essential for effective management during emergencies This involves creating an internal strategy that outlines specific protocols and designates accountable individuals responsible for conveying non-financial information to employees, stakeholders, and the public during crises.
Routine updates to stakeholders, including investors, partners, and key clients, are essential for maintaining trust, even when the information extends beyond financial matters Regular briefings keep stakeholders informed and engaged, while leveraging social media platforms such as LinkedIn, Twitter, and industry-specific forums allows companies to share non-financial news that aligns with their messaging and strategic goals.
Companies should create a contingency plan to manage unexpected non-financial news that could harm their reputation or operations This plan involves a predefined sequence of actions to address such situations In cases of severe and unforeseen information, enlisting the help of crisis communication professionals can provide valuable insights and tailored strategies Their external perspective offers an impartial viewpoint and expert techniques to effectively mitigate the crisis's impact.
Businesses must consistently consider the legal consequences of non-financial information, especially regarding environmental, social, and governance (ESG) issues It is essential to follow ethical guidelines when sharing non-financial data to reduce potential legal risks and maintain a positive corporate image.
Effectively managing non-financial news is crucial for a company's communication strategy By prioritizing thorough preparation, robust processes, and ethical standards, organizations can enhance the impact of positive news while mitigating risks associated with negative media coverage This study's findings suggest that companies adopting a proactive approach to non-financial information management are more likely to foster stronger stakeholder relationships and achieve sustainable long-term growth.
Limitations of the study and future research directions
While this research provides insights into the understanding of the impact of non- financial news on gaming firms’ profitability, it is not without limitations
The study's primary limitation lies in its sample size and composition, as it includes gaming companies from various countries with differing financial reporting standards and periods While data on quarterly Return on Equity (ROE) and Return on Assets (ROA) metrics were collected to evaluate profitability, many gaming companies, particularly in regions like China and Japan, were not adequately represented.
European countries report Return on Equity (ROE) and Return on Assets (ROA) on a semi-annual basis, leading to gaps in secondary data sources for this study Consequently, the absence of data from several major gaming firms, which play a significant role in the market, may result in a sample that is not representative of the broader population.
The study's temporal scope is limited to a brief five-year period from 2018 to 2022, potentially overlooking significant industry changes in other timeframes Additionally, the research period coincided with the COVID-19 pandemic, which may have introduced biases that would not occur under normal conditions.
Despite attempts to maintain objectivity, the research may still be affected by the researcher’s biases and subjectivity during the collection of primary data, specifically in the tallying of non-financial news related to various gaming companies.
Recognizing these limitations enhances the value of the research by offering a comprehensive understanding of its scope and applicability This insight benefits both scholars and practitioners, while also serving as a foundation for future research suggestions.
Appendix 1: Pooled OLS Regression Model Estimates (ROE)
ROE Coefficient Std err t P>t [95% conf interval]
PSL_scaled 22.57857 10.33559 2.18 0.029 2.296733 42.86041 PSD_scaled -4.16982 11.97918 -0.35 0.728 -27.67692 19.33728 PSI_scaled -43.7253 16.40385 -2.67 0.008 -75.91502 -11.5355 CC_scaled 19.45655 18.84367 1.03 0.302 -17.52095 56.43404 SH_scaled 18.63023 15.9362 1.17 0.243 -12.64183 49.9023 MA_scaled -15.7227 8.585384 -1.83 0.067 -32.57003 1.124674 MC_scaled -17.0748 11.03735 -1.55 0.122 -38.73371 4.584107 SD_scaled 4.805559 9.736281 0.49 0.622 -14.30023 23.91135 AP_scaled -10.7424 7.700122 -1.40 0.163 -25.85262 4.367734 DS_scaled -4.53377 11.21636 -0.40 0.686 -26.54396 17.47643 AAL_scaled -25.7195 14.94225 -1.72 0.086 -55.04112 3.602135 PM_scaled -105.617 18.52433 -5.70 0.000 -141.9679 -69.2662 AD_scaled 8.275237 13.6588 0.61 0.545 -18.52784 35.07831 CS_scaled 1.63001 9.344246 0.17 0.862 -16.70648 19.9665 IM_scaled 23.03375 12.20735 1.89 0.059 -.9210954 46.98859
EV 5.50E-09 4.78E-09 1.15 0.250 -3.88e-09 1.49E-08 time_dummy1 0 (omitted) time_dummy2 -1.3803 6.135824 -0.22 0.822 -13.42081 10.66021 time_dummy3 1.984822 6.153119 0.32 0.747 -10.08963 14.05927 time_dummy4 -2.94097 6.167048 -0.48 0.634 -15.04275 9.160814 time_dummy5 -7.76317 6.152474 -1.26 0.207 -19.83635 4.310014 time_dummy6 -2.54362 6.148476 -0.41 0.679 -14.60895 9.521717 time_dummy7 -1.98946 6.157815 -0.32 0.747 -14.07312 10.0942 time_dummy8 6.890678 6.159423 1.12 0.264 -5.19614 18.9775 time_dummy9 -5.53666 6.149609 -0.90 0.368 -17.60422 6.530904 time_dummy10 -3.61679 6.187092 -0.58 0.559 -15.75791 8.52432 time_dummy11 3.544365 6.175216 0.57 0.566 -8.573444 15.66217 time_dummy12 10.5053 6.155487 1.71 0.088 -1.573799 22.58439 time_dummy13 2.270021 6.172652 0.37 0.713 -9.842757 14.3828 time_dummy14 2.240169 6.169085 0.36 0.717 -9.865609 14.34595 time_dummy15 4.053675 6.162044 0.66 0.511 -8.038285 16.14564 time_dummy16 1.498361 6.186755 0.24 0.809 -10.64209 13.63881 time_dummy17 -1.84602 6.195231 -0.30 0.766 -14.0031 10.31107 time_dummy18 -3.04763 6.179697 -0.49 0.622 -15.17423 9.078975 time_dummy19 2.599839 6.194051 0.42 0.675 -9.554931 14.75461 time_dummy20 1.805433 6.235313 0.29 0.772 -10.43031 14.04117 _cons 7.603234 4.417934 1.72 0.086 -1.066208 16.27268
Source SS df MS Number of obs 1,040
Adj R-squared 0.04 Total 1055486 1,039 1015.86748 Root MSE 31.2
Appendix 2: Pooled OLS Regression Model Estimates (ROA)
ROA Coefficient Std err t P>t [95% conf interval]
PSL_scaled 3.232991 6.604082 0.49 0.625 9.726394 16.19238 PSD_scaled -11.5778 7.654277 -1.51 0.131 26.59798 3.442444 PSI_scaled -19.55 10.48149 -1.87 0.062 40.11815 1.018123 CC_scaled 6.395541 12.04045 0.53 0.595 17.23179 30.02287 SH_scaled -2.68241 10.18267 -0.26 0.792 22.66417 17.29935 MA_scaled -11.9999 5.485761 -2.19 0.029 22.76473 -1.23498 MC_scaled 6.297996 7.052478 0.89 0.372 -7.54129 20.13728 SD_scaled -2.3741 6.221144 -0.38 0.703 14.58204 9.833833 AP_scaled -9.85376 4.920109 -2.00 0.045 19.50863 -0.19888 DS_scaled -2.61384 7.166862 -0.36 0.715 16.67758 11.44991 AAL_scaled 21.87217 9.547579 2.29 0.022 3.136676 40.60767 PM_scaled -56.5128 11.8364 -4.77 0.000 79.73972 -33.2859 AD_scaled 5.966264 8.727499 0.68 0.494 11.15997 23.09249 CS_scaled 1.289259 5.970647 0.22 0.829 10.42712 13.00564 IM_scaled 0.876398 7.80007 0.11 0.911 14.42991 16.18271
EV 4.34E-09 3.05E-09 1.42 0.156 1.65e-09 1.03E-08 time_dummy1 0 (omitted) time_dummy2 -0.9386 3.920577 -0.24 0.811 8.632068 6.754861 time_dummy3 -2.79544 3.931628 -0.71 0.477 10.51059 4.919708 time_dummy4 1.940716 3.940529 0.49 0.622 -5.7919 9.673331 time_dummy5 -5.40895 3.931216 -1.38 0.169 -13.1233 2.305388 time_dummy6 0.202718 3.928661 0.05 0.959 -7.50661 7.912046 time_dummy7 -1.83158 3.934629 -0.47 0.642 -9.55262 5.889457 time_dummy8 -1.08624 3.935656 -0.28 0.783 8.809291 6.636819 time_dummy9 1.537668 3.929385 0.39 0.696 6.173081 9.248418 time_dummy10 4.887863 3.953336 1.24 0.217 2.869885 12.64561 time_dummy11 1.974665 3.945747 0.50 0.617 5.768191 9.717522 time_dummy12 1.503157 3.933141 0.38 0.702 6.214963 9.221277 time_dummy13 0.763648 3.944109 0.19 0.847 6.975994 8.503291 time_dummy14 0.052981 3.94183 0.01 0.989 7.682188 7.78815 time_dummy15 -1.24373 3.937331 -0.32 0.752 8.970065 6.482616 time_dummy16 -3.45013 3.95312 -0.87 0.383 11.20745 4.307195 time_dummy17 -2.26133 3.958536 -0.57 0.568 10.02929 5.50662 time_dummy18 -3.26687 3.948611 -0.83 0.408 11.01535 4.481604 time_dummy19 -2.01064 3.957782 -0.51 0.612 -9.77711 5.755836 time_dummy20 -3.24281 3.984148 -0.81 0.416 11.06102 4.575405 _cons 13.50714 2.822906 4.78 0.000 7.967667 19.04661
Source SS df MS Number of obs = 1,040
Adj R-squared = 0.0376 Total 428206.5 1,039 412.133271 Root MSE = 19.916
Appendix 3: Fixed Effect Regression Model Estimates (ROE)
Fixed-effects (within) regression Number of obs 1,040
Group variable: Ticker_des~g Number of groups 52
ROE Coefficient Std err t P>t [95% conf interval]
6.783955 CC_scaled 5.067522 15.18523 0.33 0.739 -24.73283 34.86788 SH_scaled 15.52793 13.31259 1.17 0.244 -10.59745 41.65331 MA_scaled -1.347383 7.821396 -0.17 0.863 -16.69653 14.00177 MC_scaled -2.317401 9.130964 -0.25 0.800 -20.23652 15.60172 SD_scaled -3.936824 8.441062 -0.47 0.641 -20.50204 12.62839 AP_scaled -8.44004 6.526379 -1.29 0.196 -21.24777 4.367695 DS_scaled -4.569787 9.149499 -0.50 0.618 -22.52528 13.3857 AAL_scaled -29.85377 12.58458 -2.37 0.018 -54.55047 -5.157076 PM_scaled -21.08543 17.72991 -1.19 0.235 -55.87961 13.70875 AD_scaled 2.417914 11.36095 0.21 0.832 -19.87746 24.71329 CS_scaled -1.846921 7.683691 -0.24 0.810 -16.92583 13.23199 IM_scaled 11.55216 10.27848 1.12 0.261 -8.618903 31.72322
EV 3.73E-09 1.04E-08 0.721 -1.67e -08 2.42e- 2.42e-08 time_dummy1 -1.97825 4.923153 -0.40 0.688 -11.63972 7.683223 time_dummy2 -2.50455 4.898363 -0.51 0.609 -12.11737 7.108273 time_dummy3 1.615939 4.917464 0.33 0.743 -8.034369 11.26625 time_dummy4 -3.974761 4.88548 -0.81 0.416 -13.5623 5.61278 time_dummy5 -9.462654 4.895897 -1.93 0.054 -19.07064 1453296 time_dummy6 -4.429954 4.853578 -0.91 0.362 -13.95489 5.094981 time_dummy7 -4.001584 4.897888 -0.82 0.414 -13.61348 5.610309 time_dummy8 4.550553 4.879072 0.93 0.351 -5.024413 14.12552 time_dummy9 -7.259228 4.882611 -1.49 0.137 -16.84114 2.322683 time_dummy10 -5.65681 4.873018 -1.16 0.246 -15.21989 3.906274
67 time_dummy11 2.616995 4.861587 0.54 0.590 -6.923657 12.15765 time_dummy12 9.160729 4.869643 1.88 0.060 -.3957331 18.71719 time_dummy13 -.3133689 4.89041 -0.06 0.949 -9.910586 9.283848 time_dummy14 3115741 4.856006 0.06 0.949 -9.218126 9.841274 time_dummy15 2.574733 4.871247 0.53 0.597 -6.984876 12.13434 time_dummy16 -.5154781 4.854157 -0.11 0.915 -10.04155 9.010594 time_dummy17 -3.662059 4.85447 -0.75 0.451 -13.18874 5.864626 time_dummy18 -5.176837 4.855991 -1.07 0.287 -14.70651 4.352833 time_dummy19 1249899 4.877109 0.03 0.980 -9.446125 9.696104 time_dummy20 0 (omitted)
_cons 6.961556 3.711269 1.88 0.061 -0.32165 14.24476 sigma_u 20.844377 sigma_e 24.442321 rho 42105019 (fraction of variance due to u_i)
Appendix 4: Random Effects Regression Model Estimates (ROE)
Random-effects GLS regression Number of obs = 1,040
Group variable: Ticker_des~g Number of groups = 52
Wald chi2(35) = 51.95 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0325
ROE Coefficient Std err t P>t [95% conf interval]
PSL_scaled 21.40061 9.9415 2.15 0.031 1.915625 40.88559 PSD_scaled 8.725204 10.0311 0.87 0.384 -10.93538 28.38579 PSI_scaled -36.01529 13.79472 -2.61 0.009 -63.05244 -8.97813 CC_scaled 6.870938 15.21801 0.45 0.652 -22.95581 36.69769 SH_scaled 15.75045 13.2915 1.19 0.236 -10.30042 41.80131 MA_scaled -2.74799 7.733535 -0.36 0.722 -17.90544 12.40946 MC_scaled -3.706695 9.117354 -0.41 0.684 -21.57638 14.16299 SD_scaled -3.211266 8.393044 -0.38 0.702 -19.66133 13.2388 AP_scaled -8.66388 6.508822 -1.33 0.183 -21.42094 4.093177 DS_scaled -4.606387 9.160952 -0.50 0.615 -22.56152 13.34875 AAL_scaled -29.53542 12.54979 -2.35 0.019 -54.13256 -4.93829 PM_scaled -32.74201 17.38385 -1.88 0.060 -66.81374 1.329712 AD_scaled 2.947048 11.34337 0.26 0.795 -19.28554 25.17964 CS_scaled -1.584846 7.678759 -0.21 0.836 -16.63494 13.46525 IM_scaled 13.01808 10.24833 1.27 0.204 -7.068269 33.10443
EV 4.02e- 8.32e-09 0.48 0.629 -1.23e-08 2.03E-08 time_dummy1 -2.024832 4.940439 -0.41 0.682 -11.70791 7.658251 time_dummy2 -2.679507 4.915799 -0.55 0.586 -12.31429 6.955281 time_dummy3 1.38669 4.93423 0.28 0.779 -8.284224 11.0576 time_dummy4 -4.149377 4.903558 -0.85 0.397 -13.76017 5.461419 time_dummy5 -9.537325 4.914864 -1.94 0.052 -19.17028 0.095631 time_dummy6 -4.47826 4.873751 -0.92 0.358 -14.03064 5.074117 time_dummy7 -4.034307 4.916338 -0.82 0.412 -13.67015 5.601538 time_dummy8 4.568867 4.899297 0.93 0.351 -5.033579 14.17131 time_dummy9 -7.330197 4.903553 -1.49 0.135 -16.94098 2.28059 time_dummy10 -5.66525 4.893899 -1.16 0.247 -15.25712 3.926616
69 time_dummy11 2.424022 4.882339 0.50 0.620 -7.145186 11.99323 time_dummy12 9.042428 4.891239 1.85 0.065 -.5442246 18.62908 time_dummy13 -.3424168 4.911849 -0.07 0.944 -9.969464 9.28463 time_dummy14 3079289 4.878791 0.06 0.950 -9.254326 9.870184 time_dummy15 2.514581 4.893768 0.51 0.607 -7.077027 12.10619 time_dummy16 -.560018 4.875873 -0.11 0.909 -10.11655 8.996517 time_dummy17 -3.668278 4.877022 -0.75 0.452 -13.22707 5.89051 time_dummy18 -5.113628 4.878547 -1.05 0.295 -14.6754 4.448148 time_dummy19 1741069 4.899956 0.04 0.972 -9.42963 9.777844 time_dummy20 0 (omitted)
_cons 7.464514 4.484758 1.66 0.096 -1.325451 16.25448 sigma_u 18.174609 sigma_e 24.442321 rho 35604284 (fraction of variance due to u_i)
Appendix 5: Fixed Effect Regression Model Estimates (ROA)
Fixed-effects (within) regression Number of obs 1,040 Group variable: Ticker_des~g Number of groups 52
Within = 0.0481 min = 20 Between = 0.0123 avg = 20 Overall = 0.0334 max = 20
ROE Coefficient Std err t P>t [95% conf interval]
The analysis of various scaled metrics reveals significant insights: MC_scaled shows a notable positive correlation with a value of 12.5007 and a p-value of 0.035, indicating statistical significance AAL_scaled stands out with a high value of 29.17775 and a p-value of 0.000, suggesting a strong relationship Conversely, PSL_scaled, PSD_scaled, and PSI_scaled exhibit negative values, with PSL_scaled at -3.807239, PSD_scaled at -6.916805, and PSI_scaled at -7.666488, indicating weaker associations Other metrics like CC_scaled and SD_scaled show minimal impact with values of 1.262418 and 1.258904, respectively The remaining metrics, including SH_scaled, MA_scaled, and AP_scaled, present mixed results, with p-values above 0.05, suggesting no significant correlation Overall, the data highlights key metrics that warrant further exploration for their potential implications.
EV 3.98e-09 6.78e-09 0.59 0.557 -9.3e-09 1.73e-08 time_dummy1 1 4.461506 3.197246 1.40 0.163 -1.812949 10.73596 time_dummy2 2 3.567847 3.181146 1.12 0.262 -2.675013 9.810707 time_dummy3 3 2.277719 3.193551 0.71 0.476 -3.989484 8.544923 time_dummy4 4 6.447136 3.172779 2.03 0.042 2206953 12.67358 time_dummy5 5 -1.245837 3.179544 -0.39 0.695 -7.485554 4.99388 time_dummy6 6 4.269116 3.152061 1.35 0.176 -1.916667 10.4549 time_dummy7 7 2.115023 3.180838 0.66 0.506 -4.127232 8.357279 time_dummy8 8 2.47313 3.168618 0.78 0.435 -3.745144 8.691405 time_dummy9 9 5.85098 3.170916 1.85 0.065 -.3718044 12.07376 time_dummy10 10 8.495243 3.164686 2.68 0.007 2.284685 14.7058 time_dummy11 11 6.158943 3.157263 1.95 0.051 -.0370466 12.35493
71 time_dummy12 12 5.797107 3.162495 1.83 0.067 -.4091506 12.00336 time_dummy13 13 4.489406 3.175981 1.41 0.158 -1.743319 10.72213 time_dummy14 14 3.570074 3.153638 1.13 0.258 -2.618803 9.758951 time_dummy15 15 2.797117 3.163536 0.88 0.377 -3.411184 9.005419 time_dummy16 16 23249 3.152438 0.07 0.941 -5.954031 6.419011 time_dummy17 17 1.409458 3.15264 0.45 0.655 -4.777461 7.596377 time_dummy18 18 3757863 3.153628 0.12 0.905 -5.813071 6.564644 time_dummy19 19 1.500158 3.167343 0.47 0.636 -4.715615 7.715931 time_dummy20 0 (omitted)
_cons 7.824414 2.410211 3.25 0.001 3.094481 12.55435 sigma_u 13.052689 sigma_e 15.873587 rho 40339841 (fraction of variance due to u_i)
Appendix 6: Random Effects Regression Model Estimates (ROA)
Random-effects GLS regression Number of obs = 1,040 Group variable: Ticker_des~g Number of groups = 52
Wald chi2(35) = 49.37 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0544
ROA Coefficient Std err t P>t [95% conf interval]
The analysis of scaled metrics reveals several key insights: AAL_scaled shows a significant positive correlation with a value of 28.42 and a p-value of 0.000, indicating strong statistical significance In contrast, PSD_scaled and PSI_scaled exhibit negative values of -7.44 and -9.37, respectively, suggesting potential areas of concern MC_scaled stands out with a positive value of 11.87 and a p-value of 0.044, highlighting its relevance Other metrics such as SH_scaled and MA_scaled show moderate values of -5.69 and -3.38, respectively, while CC_scaled and SD_scaled indicate minimal impact with values of 1.96 and 0.81 The remaining metrics, including AP_scaled, DS_scaled, PM_scaled, AD_scaled, and CS_scaled, reflect varying degrees of influence but generally trend towards lower significance These findings underscore the importance of focusing on AAL_scaled and MC_scaled for further analysis.
EV 3.69E-09 5.44E-09 0.68 0.498 -6.97e-09 1.43E-08 time_dummy1 4.292309 3.197102 1.34 0.179 -1.973896 10.55851 time_dummy2 3.372735 3.18115 1.06 0.289 -2.862204 9.607674 time_dummy3 2.032295 3.193106 0.64 0.524 -4.226077 8.290668 time_dummy4 6.257705 3.173212 1.97 0.049 0383239 12.47709 time_dummy5 -1.384478 3.18051 0.44 0.663 -7.618163 4.849207 time_dummy6 4.145721 3.153863 1.31 0.189 -2.035736 10.32718 time_dummy7 2.000255 3.181483 0.63 0.530 -4.235336 8.235847 time_dummy8 2.411799 3.1704 0.76 0.447 -3.802071 8.625669 time_dummy9 5.704618 3.173132 1.80 0.072 -.5146074 11.92384
73 time_dummy10 8.430626 3.166893 2.66 0.008 2.22363 14.63762 time_dummy11 6.004393 3.159415 1.90 0.057 -.1879463 12.19673 time_dummy12 5.652656 3.16515 1.79 0.074 -.550924 11.85624 time_dummy13 4.389515 3.178495 1.38 0.167 -1.84022 10.61925 time_dummy14 3.531906 3.157056 1.12 0.263 -2.655809 9.719621 time_dummy15 2.707591 3.166757 0.86 0.393 -3.499138 8.91432 time_dummy16 1587748 3.155193 0.05 0.960 -6.025289 6.342839 time_dummy17 1.367699 3.155914 0.43 0.665 -4.817779 7.553178 time_dummy18 3496868 3.156904 0.11 0.912 -5.837732 6.537106 time_dummy19 1.461908 3.17075 0.46 0.645 -4.752648 7.676464 time_dummy20 0 (omitted)
_cons 8.22956 2.93103 2.81 0.005 2.484847 13.97427 sigma_u 12.160672 sigma_e 15.873587 rho 36984105 (fraction of variance due to u_i)
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