










































AGORA International Journal of Economical Sciences, http://univagora.ro/jour/index.php/aijes 

ISSN 2067-3310, E-ISSN 2067-7669 

Vol. 18, No. 2 (2024), pp. 356-374 

 

356 

 

CORPORATE DECISION-MAKING AND THE IMPACT OF 

ACQUISITIONS ON SHARE PRICES: THE CASE OF VERIZON 

 

D. VASILIAUSKAITE, K. HRYTSIV, O.TKACHENKO, M. ZHAKUN 

 

Deimante Vasiliauskaite1, Kateryna Hrytsiv2, Oleksandra Tkachenko3, Marharyta 

Zhakun4 
1 2 3 4 Business School, Vilnius University, Lithuania 
1 https://orcid.org/0000-0002-8107-1964 E-mail: deimante.vasiliauskaite@vm.vu.lt  
2 https://orcid.org/0009-0004-9053-3069 E-mail: kateryna.hrytsiv@outlook.com 
3 https://orcid.org/0009-0004-5664-1760, E-mail: oleksandra.tkachenko@vm.stud.vu.lt 
4 https://orcid.org/0009-0005-8924-6609, E-mail: m.zhakun555@gmail.com   

 

Abstract: This study investigates the impact of Verizon Communications Inc.'s acquisition of 

Yahoo on the company's stock performance, using a combination of statistical, visual, and 

multiscale analytical methods. The research analyzes how strategic corporate decisions, 

particularly acquisitions, influence short-term and long-term stock behavior. Data spanning 

from 2014 to 2024 was analyzed to capture pre-acquisition, acquisition, and post-acquisition 

trends. The methodology integrates boxplot visualizations to explore variability and outliers, 

quantile analysis to assess distributional characteristics, and descriptive statistics to 

summarize key metrics such as mean, standard deviation, skewness, and kurtosis. Tabulation 

analysis evaluates associations between stock returns and trading volumes, while wavelet 

analysis decomposes stock returns into multiple time scales to identify short-term fluctuations 

and long-term trends. Results reveal significant volatility during key events, such as the Yahoo 

acquisition in 2017 and the COVID-19 pandemic, with daily fluctuations peaking at ±6. 

Medium-term adjustments (4–8 days) exhibited moderate changes, and long-term trends (32-

day cycles) showed structural shifts, including positive coefficients in 2017 (+0.5) and negative 

coefficients in 2020 (-0.6). The research highlights the heavy-tailed nature of return 

distributions and skewed trading volume patterns. This study provides insights into the 

multifaceted effects of acquisitions on stock performance, offering valuable implications for 

investors and corporate managers in optimizing strategic decisions. 

Keywords: mergers and acquisitions, stock prices, market reactions, corporate strategy, 

event study. 

 

INTRODUCTION 

Mergers and acquisitions (M&A) represent a critical area of research in corporate 

finance, economics, and strategic management due to their profound implications for 

companies, industries, and economies. These transactions are essential tools for organizations 

seeking growth, diversification, operational efficiency, or market dominance, and they 

significantly influence stock prices, making them a focal point for financial market research. 

The announcement of an M&A deal often triggers immediate market reactions, with stock 

https://orcid.org/0000-0002-8107-1964
mailto:deimante.vasiliauskaite@vm.vu.lt
https://orcid.org/0009-0004-9053-3069
mailto:kateryna.hrytsiv@outlook.com
https://orcid.org/0009-0004-5664-1760
mailto:oleksandra.tkachenko@vm.stud.vu.lt
https://orcid.org/0009-0005-8924-6609
mailto:m.zhakun555@gmail.com


Deimante VASILIAUSKAITE, Kateryna HRYTSIV, Oleksandra TKACHENKO, Marharyta 

ZHAKUN 

357 

 

prices of both the acquiring and target companies reflecting investor perceptions of the 

transaction’s strategic value, potential synergies, and associated risks. These price movements, 

whether short-term volatility or long-term trends, provide a window into market sentiment and 

the effectiveness of corporate decision-making. Despite their widespread use, M&A outcomes 

are often unpredictable, with studies revealing mixed impacts on shareholder value and stock 

price performance. This unpredictability makes it essential to analyze the factors influencing 

M&A success, such as strategic fit, cultural alignment, and integration processes. Furthermore, 

M&A activities are shaped by global trends like technological disruption, geopolitical changes, 

and sustainability objectives, adding complexity to their study. By exploring the financial 

impacts of M&A, particularly on stock prices, researchers can generate insights to guide 

corporate leaders, investors, and policymakers in navigating these transformative business 

events. 

Corporate decision-making regarding mergers and acquisitions (M&A) significantly 

impacts share prices, with varying effects observed in different contexts and timeframes. The 

short-term impact of M&A on stock prices often involves fluctuations. (Jiang et al., 2024). This 

pattern is not unique to developed markets; in India, M&A activities have shown varied impacts 

on stock market performance, with some firms experiencing positive abnormal returns while 

others face negative outcomes, particularly in the banking sector (Rani & Sangeeta, 2023). The 

strategic orientation of the target company also plays a crucial role; acquisitions of innovation-

oriented firms tend to result in lower announcement returns and poorer long-term performance 

compared to efficiency-oriented targets, especially when acquirers face agency problems (Liu 

& Yang, 2024). Furthermore, the risk of stock price crashes can influence M&A decisions, as 

firms with higher crash risks are more likely to become takeover targets, often resulting in 

lower bid premiums and more stock-based payments (Carline et al., 2023). The market's 

response to M&A announcements is also shaped by investor perceptions and management 

attitudes, which can drive short-term market dynamics but have limited long-term effects on 

stock performance (Mardiani et al., 2023). Additionally, the global M&A landscape shows that 

the impact on stock market volatility is contingent on the development goals and status of the 

firms involved, with M&A potentially having a positive or negative effect depending on these 

factors (Yu, 2023). The concept of a takeover premium, typically around 30%, further 

illustrates how acquisition strategies can influence share prices, as this premium is factored 

into the market's valuation of the target company (Egashira, 2023). Overall, M&A activities 

are complex transactions that affect various stakeholders, including shareholders, and require 

careful consideration of strategic, financial, and market factors to optimize corporate decision-

making and shareholder value.  

However, the decision-making process is fraught with risks, including managerial 

biases such as overconfidence and the winner's curse, which can lead to overvaluation and 

negatively affect stock prices (Asaoka, 2019). The broader market impact of M&A is complex, 

with some studies indicating that acquiring firms generally experience negative stock price 

effects, except in specific industries like oil, gas, and financial services, where positive impacts 

are noted (Stunda, 2014). In contrast, other research suggests that M&A announcements can 

lead to positive returns for acquiring companies, as observed in the Indian stock market, where 

cumulative returns increased post-announcement (Poornima & Chitra, 2013). Despite these 



CORPORATE DECISION-MAKING AND THE IMPACT OF ACQUISITIONS ON SHARE 

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mixed results, M&A is often pursued for strategic reasons, such as achieving economies of 

scale and portfolio diversification, although these benefits do not always translate into 

immediate shareholder wealth increases (Sogomi et al., 2022). Furthermore, the role of 

financial advisors and market information is crucial, as shareholders tend to rely on less 

optimistic target advisor opinions, which can influence voting and post-merger performance 

(Ouyang, 2012). While M&A can be a strategic tool for growth and market expansion, its 

impact on share prices is influenced by managerial decision-making, industry context, and 

market perceptions, necessitating careful consideration by corporate leaders and investors. 

The scientific problem addressed in this paper lies in the inconsistency and ambiguity 

of existing empirical evidence on the impact of M&A on stock prices. While some studies 

report positive abnormal returns for target companies and mixed outcomes for acquiring firms, 

others highlight the prevalence of long-term underperformance and challenges in achieving 

strategic goals. This discrepancy raises critical questions about the determinants of M&A 

success and their implications for corporate decision-making, market efficiency, and 

shareholder value. 

The primary goal of this paper is to analyze the impact of corporate decision-making, 

specifically mergers and acquisitions (M&A), on stock prices, examining the short-term and 

long-term effects. 

This paper is organized as follows: Section 1 gives insights into the literature review, 

Section 2 represents our research methodology. Section 3 focuses on research results and 

discussion, and finally, we present our conclusions and recommendations. 

 

1. LITERATURE REVIEW 

A merger occurs when companies of similar size and operational scope combine to 

form a new entity, pooling assets and resources for a unified market presence. In contrast, an 

acquisition typically involves a more prominent firm purchasing a smaller one, integrating the 

acquired firm's assets into its existing structure without forming a new entity. Acquisitions may 

be pursued to access new markets, enhance operational efficiency, or advance technology 

(DePamphilis, 2019). 

Theoretical frameworks classify M&As into various types, each yielding distinct 

strategic outcomes. Horizontal mergers involve companies in the same industry and market 

level, often aimed at reducing competition and achieving economies of scale (Pautler, 2003). 

Vertical mergers bring together firms at different stages of production, such as suppliers and 

manufacturers, to increase operational efficiency, reduce costs, and secure supply chains 

(Singh & Montgomery, 1987). Conglomerate mergers, meanwhile, involve companies in 

unrelated industries, often to diversify business risk or enter entirely new markets (Trautwein, 

1990). 

Corporate decision-making acts as a strong determinant of the financial markets. M&A 

is one of a firm's strategic decisions by which companies extend their operations, differentiate 

their product and service offerings, or acquire certain competitive advantages. Mergers and 

acquisitions are considered useful strategies for the growth and expansion of 

businesses. (Rahman et al., 2018) However, a literature review gives one complex and 

sometimes ambiguous approach to the consequences of such decisions on stock prices. This 



Deimante VASILIAUSKAITE, Kateryna HRYTSIV, Oleksandra TKACHENKO, Marharyta 

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359 

 

paper investigates the role of corporate investment decision-making, such as acquisition, in 

determining changes in stock prices. 

Actions and decisions made by corporations drive the financial markets. One major 

corporate strategy is acquisitions. When a company acquires another, it merges the two 

corporations’ assets, operations, and market presence. This can lead to a leading market 

position, economies of scale, and new technologies or markets.  

Despite the assumed advantages of acquisitions, empirical evidence relating to the stock 

price impact varies widely. Various studies have given contradictory results in estimating 

whether an increase or a fall in stock price leads to acquisition. Therefore, this ambiguity is 

determined by different issues, such as the features of the acquiring and target firms, the nature 

of the acquisition deal, and the post-market state at the time of the acquisition. 

Announcements of mergers and acquisitions immediately impact a target company’s 

stock price, as induced reaction in the stock market causes investors to revise expectations 

about the company’s future profitability (Panayides and Gong, 2002). According to the 

Efficient Markets Hypothesis, “prices reflect all publicly available information on an 

underlying asset” (Fama, 1970). The implication on common stock is usually a rise for the 

target company. The acquiring company's common stock would slightly drop because of 

apprehension over integration costs, leverage, or perceived risks. However, both stocks may 

gain long-term if the acquisition is considered an excellent strategic move. 

Stock price reactions to acquisitions depend on the nature and type of the deal. In the 

case of friendly acquisitions, the target company's stock generally rises owing to premium 

offers. In contrast, that of the acquiring company may remain stable or increase if synergies 

are expected. In hostile takeovers, the acquirer's stock generally falls owing to perceived risks, 

while the target company's stock surges. While horizontal mergers tend to raise a target's stock, 

a vertical merger could raise an acquirer's stock on account of operational efficiencies. 

Movements in stock also depend on other factors, like financing methods and regulatory 

concerns. 

The prospect of new profit opportunities or the probability of emerging risks are the 

reasons why M&A announcements have a direct and immediate impact on the stock prices of 

the respective company. According to the efficient market hypothesis, the stock price reflects 

all available information and investors’ assessments  (Kellner, 2024). 

Whereas target companies typically see a gain in their stock price resulting from 

acquisition premiums, acquiring companies often face volatility in their stock price or even a 

drop in their stock price due to the anticipated issues with the integration process. The merger's 

success, concerning long-term performance in the acquirer's stock, is contingent upon the 

efficiency with which it generates its expected synergies and strategic fit. Mergers poorly 

executed may lead to poor performance, whereas those companies that are integrated well 

might enjoy the benefits of better long-term performance.  

M&A is an important strategic alliance and a firm’s favourite dynamic strategy in 

today’s competitive business because firms can expose the required domestic and international 

strategies and geographic tactics through successful M&A (Datta, Basuil, & Agarwal, 2020). 

Mergers and Acquisitions (M&A) have been one of the most essential driving tools and 

preferable business strategies for the last several decades around the globe. As firms continue 



CORPORATE DECISION-MAKING AND THE IMPACT OF ACQUISITIONS ON SHARE 

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to deploy M&A to expand the business and geographic scope, academics, market practitioners, 

and policymakers must fully understand the possible consequences and appropriate cost-

benefit analysis of engaging in M&A.  (Hossain, 2021) 

Over the years, mergers and acquisitions (M&A) have been the research subject, 

particularly in Europe and North America. This trend has now extended to countries that have 

experienced significant economic growth (Rahman et al., 2018b). Several theories, including 

the neoclassical theory and the theory of non-maximization of value, could explain the M&A 

operations. The neoclassical theory claims that the motivation for mergers and acquisitions 

coincides with the main objective: increasing business value. The second theory believes that 

M&A exists to satisfy the manager’s enrichment of the emerging prestige of business 

dimensions (Lobo & Gomes, 2022).  

The topic of mergers and acquisitions is still relevant for researchers because the results 

influencing the firms' performance worldwide are still not consensual. Many companies view 

M&A with an expectation of creating value through better efficiency, reducing costs through 

economies of scale, and larger product offerings. Large M&A can affect the industry, the local 

economy, and, in certain cases, even the global economy. The extent to which M&A affects 

the economy has been the subject of various empirical studies and is closely monitored by the 

government (Shah & Arora, 2014). This literature review examines the current research on the 

correlation between corporate acquisitions and stock price performance. 

Shareholders and markets, overall, have different reactions to the announcement of 

M&A. Several studies indicate that shareholders often experience zero or even negative 

returns, especially for large public deals (Renneboog & Vansteenkiste, 2019). On average, 

acquirer’s shareholders suffer losses of around 0.5% - 0.7% on a market-adjusted basis around 

the announcement date (Hazelkorn et al., 2004). Scholars argue that the stock market does not 

view acquisitions as good news and is not overly enthusiastic about them (Corhay & Rad, 

2000). Datta and Puia (1995) found that, on average, cross-border acquisitions destroy value 

for U.S. bidding firms' shareholders. 

Facebook's acquisition of Instagram in 2012 is a well-known example of the impact 

acquisitions can have on the market. At the time, Instagram had only 13 full-time employees 

and was seen as a project with unpredictable long-term potential. However, Facebook saw 

things differently. Four years later, Facebook's $1 billion investment in the startup had fully 

paid off, proving one of the smartest strategic moves in technology acquisition. (Luckerson, 

2016). With over 845 million users and counting, Facebook is a clear leader in social media. 

Nearly 300 million are said to access their account via their mobile device. With the acquisition 

of Instagram, Facebook signals its intention to pursue and push more strongly in this direction, 

with increased capabilities conditionally (Gonzalo, 2020). 

As previously noted, existing studies assessing the effects of M&A show varying 

results, indicating minimal, negative, and even positive impacts on the stock prices of various 

firms. This has given rise to various hypotheses. 

H1: Corporate acquisitions lead to positive abnormal stock returns in the short term. 

In today's financial and economic environment, mergers and acquisitions have become 

increasingly important. When a company is targeted for acquisition, its stock price often goes 

up. This happens because the acquiring company typically pays a premium above the current 



Deimante VASILIAUSKAITE, Kateryna HRYTSIV, Oleksandra TKACHENKO, Marharyta 

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361 

 

value when buying the target company (Soni, n.d.). Studies that report positive abnormal 

returns following an M&A announcement include Asquith et al. (1983), Fuller et al. (2002), 

Moeller et al. (2004), Draper and Paudyal (2006), and Hamzah et al. (2008).  

H2: Stock price movements differ based on the type of acquisition and market conditions. 

Acquisitions are driven by different motives, and it's important to distinguish between 

acquisitions with "pure explore" and "pure exploit" motives. While most acquisitions have 

multiple motives, there are also acquisitions with "ambidextrous" motives, which involve 

different combinations of explorative and exploitative motives. For instance, IBM purchased 

Red Hat for US$34 billion in 2019, and Broadcom bought CA Technologies for US$18.9 

billion in 2018. The business press described both of these acquisitions as the acquirers' 

attempts to "procure their way into the future." However, not all acquisitions succeed, and those 

that fail often harm the acquirer (Aalbers et al., 2021). 

H3: The long-term performance of acquiring firms is contingent upon successful integration 

and the realization of synergies. 

Literature on the long-term post-acquisition performance as Agrawal and Jaffe (2000), 

Andrade et al. (2001), King et al. (2004), Martynova and Renneboog (2008a), Dutta and Jog 

(2009), and Bessembinder and Zhang (2013). Agrawal and Jaffe (2000) found strong evidence 

of long-term underperformance after a takeover. Acquiring firms tend to underperform 

compared to non-acquiring firms, especially in public takeovers. Despite numerous academic 

studies on factors affecting M&A announcement returns, many short-term performance 

boosters do not translate into sustained long-run returns. To understand value creation in 

M&As, it is crucial to identify firm and deal characteristics that can reliably predict long-term 

performance (Rennenboog & Vastenkiste, 2020). According to Cui and Leung (2020), the top 

management of firms is responsible for key decisions such as investments, financing, and 

strategy. It is widely believed in the business press and among managers that a firm's top 

management team, including its CEO, executive directors, and other senior executives, plays a 

crucial role in corporate decisions and business performance. 

 

2. METHODOLOGY 

This research aims to analyze the stock performance of Verizon Communications Inc., 

focusing on its acquisition of Yahoo and the subsequent impact on its financial behavior. The 

methodology is designed to study Verizon’s stock systematically returns through a 

combination of statistical, visual, and multiscale analytical tools. The research is structured as 

it is placed in Figure 1.  

 

 

 

 

 

 

 

 

 



CORPORATE DECISION-MAKING AND THE IMPACT OF ACQUISITIONS ON SHARE 

PRICES: THE CASE OF VERIZON 

362 

 

Figure 1.  Methodology for Verizon Communications Inc. stock returns and trading volumes 

research 

 
1. Company Overview and Contextual Background: 

o The study begins with an in-depth description of Verizon Communications, 

detailing its business model, market position, and strategic initiatives. Special 

emphasis is placed on the acquisition of Yahoo in 2017, including its objectives, 

financial scope, challenges, and outcomes. This context provides a foundation 

for understanding the potential impact of the acquisition on Verizon’s stock 

performance. 

2. Data Collection: 

o Historical daily stock return and volume data for Verizon Communications is 

collected for the period spanning November 2014 to November 2024 to capture 

pre-acquisition, acquisition, and post-acquisition trends. The dataset includes 

daily closing prices used to calculate stock returns. 

o Additional market data, such as major events and financial reports, is referenced 

to contextualize patterns observed in the stock returns. 

3. Boxplot Analysis: 

o Boxplots are utilized to visually explore the distribution of Verizon 

Communications stock returns, highlighting key aspects such as the median, 

interquartile range (IQR), outliers, and overall variability.  

4. Quantile Analysis: 

Interpretation and Synthesis

Wavelet Analysis

Tabulation Analysis

Descriptive Statistics:

Quantile Analysis

Boxplot Analysis

Data Collection

Company Overview and Contextual 
Background



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o Quantile analysis is conducted to assess the distribution of stock returns across 

various percentiles (e.g., 10th, 25th, 50th, 75th, and 90th). This allows for a 

deeper understanding of the tail behavior of returns and the identification of 

extreme movements, which are particularly relevant during periods of 

significant market events such as the Yahoo acquisition. 

5. Descriptive Statistics: 

o Key descriptive statistics, including mean, median, standard deviation, 

skewness, kurtosis, and range, are calculated for Verizon Communications 

stock returns. 

o The standard deviation is used to measure volatility, while skewness and 

kurtosis are examined to understand the asymmetry and extremity of return 

distributions, respectively. 

6. Tabulation Analysis: 

o Tabular summaries of stock return performance are presented, categorized by 

key time periods and events. Metrics such as average return, volatility, and 

frequency of extreme movements (e.g., returns exceeding ±2 standard 

deviations) are compared to identify patterns linked to the acquisition and 

external events such as the COVID-19 pandemic. 

7. Wavelet Analysis: 

o The study employs wavelet analysis to decompose Verizon Communications 

stock returns into multiple time scales, capturing both short-term fluctuations 

and long-term trends. 

o The wavelet decomposition is performed at six levels, corresponding to daily, 

weekly, biweekly, and monthly cycles. Scalograms and wavelet coefficients are 

analyzed to identify periods of heightened volatility, structural shifts, and long-

term market trends, with particular focus on the impact of the Yahoo acquisition 

and the COVID-19 pandemic. 

8. Interpretation and Synthesis: 

o The results from the boxplot, quantile analysis, descriptive statistics, tabulation, 

and wavelet analysis are synthesized to draw insights about Verizon 

Communications stock behavior over time. The study focuses on the changes in 

volatility, distributional characteristics, and multiscale dynamics during key 

periods, particularly around the Yahoo acquisition. 

o Findings are interpreted in the context of Verizon Communications strategic 

initiatives, market events, and industry conditions, highlighting both the short-

term and long-term implications of the Yahoo acquisition on stock performance. 

This comprehensive methodology ensures a robust analysis of Verizon 

Communications stock returns, leveraging both statistical and advanced analytical tools to 

uncover patterns and trends linked to the Yahoo acquisition and other significant events. 

  



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3. RESEARCH 

Description of company and acquisition 

In July 2016, Verizon Communications announced its intent to acquire Yahoo Inc.'s 

core internet business for $4.83 billion. The deal was part of Verizon's strategy to expand 

beyond its telecommunications roots and establish a more robust digital media and advertising 

foothold. By combining Yahoo's assets with AOL, which Verizon had acquired in 2015 for 

$4.4 billion, the company aimed to create a digital advertising powerhouse capable of 

competing with tech giants like Google and Facebook. The acquisition included Yahoo's search 

engine, email services, and advertising technology. Still, it excluded its stakes in Alibaba Group 

and Yahoo Japan, which were retained under a separate entity named Altaba Inc. 

The acquisition faced significant challenges. In 2016, after the deal was announced, 

Yahoo disclosed two major security breaches before the acquisition, affecting billions of user 

accounts. These breaches resulted in a renegotiation of the purchase price, which was reduced 

by $350 million to $4.48 billion. Despite these setbacks, Verizon completed the acquisition in 

June 2017 and rebranded the combined Yahoo and AOL entities under the name "Oath." 

However, integrating the two companies proved challenging, with Oath struggling to scale its 

advertising platform and gain market share in a space dominated by Google and Facebook. 

The financial performance of the acquisition fell short of expectations. In 2018, Verizon 

wrote down $4.6 billion of the value of Oath, reflecting the division’s underperformance and 

its failure to meet revenue targets. Recognizing these difficulties, Verizon rebranded Oath as 

Verizon Media in 2019, shifting its focus toward premium content and advertising technology. 

However, the company’s efforts to compete in digital advertising continued to face significant 

headwinds. In 2021, Verizon decided to exit the digital media space altogether, selling Yahoo 

and AOL to the private equity firm Apollo Global Management for $5 billion. 

Verizon’s acquisition of Yahoo underscores traditional companies' challenges when 

competing in fast-evolving digital markets. Despite the initial optimism surrounding the 

acquisition, Yahoo and AOL's integration and subsequent performance it has highlighted the 

difficulties of scaling in a sector dominated by established players. While Verizon ultimately 

recouped much of its investment through the 2021 sale, the acquisition serves as a case study 

of the complexities of diversification and the risks associated with entering highly competitive 

markets. 

 

Data analysis:  

The boxplot visualization for Verizon's stock returns and trading volumes provides 

valuable insights into their distributions, highlighting central tendencies, variability, and the 

presence of outliers. 



Deimante VASILIAUSKAITE, Kateryna HRYTSIV, Oleksandra TKACHENKO, Marharyta 

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Figure 2. Boxplot visualization for Verizon Communications Inc. stock returns and trading 

volumes 

 
Done by authors using Nasdaq data and Eviews software  

The returns boxplot (Figure 2.) shows a relatively symmetric distribution centered near 

zero, aligning with the small mean and median observed in the descriptive statistics. The 

compact interquartile range (IQR) suggests that the majority of daily returns are clustered 

within a narrow range. However, the presence of numerous outliers beyond the whiskers, 

especially on the positive side (up to approximately 9), reflects instances of extreme market 

movements or unusual trading events. Negative outliers, extending down to approximately -7, 

represent significant declines, likely linked to adverse news or market disruptions. On the other 

hand, the trading volumes boxplot reveals a heavily right-skewed distribution, with most 

trading activity concentrated at lower volume levels. The small IQR and whiskers extending 

toward high volume levels highlight the variability in trading activity, with numerous outliers 

exceeding 80 million shares. These high-volume outliers likely correspond to significant 

events, such as earnings announcements, acquisitions, or other market-moving news, such as 

the Yahoo acquisition in 2017. While both returns and volumes display significant outliers, 

their patterns differ; returns show a balanced dispersion of extreme values in both positive and 

negative directions, reflecting gains and losses on particular trading days, whereas volumes 

demonstrate a pronounced skew toward exceptionally high values, indicating infrequent but 

impactful trading activity. This skewed volume distribution suggests that trading activity is 

often event-driven while returns fluctuate within a narrower but volatile range. Overall, the 

figure underscores the importance of accounting for outliers in any analysis of Verizon’s stock 

performance, as these extreme values represent rare yet influential occurrences that can 

significantly impact market dynamics. 

  



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Figure 3. Quantiles for Verizon Communications Inc. stock returns and trading volumes 

 
Done by authors using Nasdaq data and Eviews software  

 

The Q-Q plot for Verizon's stock returns  (Figure 3.) compares the quantiles of the 

observed return data to those of a theoretical normal distribution, providing insight into the 

normality of the return distribution. The plot shows that the data roughly aligns with the central 

region's red diagonal line, indicating that most returns follow a pattern close to normality. 

However, noticeable deviations occur in the tails of the distribution. At the lower end, the 

points diverge below the diagonal line, reflecting extreme negative returns that are more 

frequent than expected under a normal distribution. Similarly, the upper tail exhibits significant 

divergence, with points above the diagonal line indicating an overrepresentation of large 

positive returns. 

These tail deviations confirm the presence of heavy-tailed behavior, consistent with the 

high kurtosis value observed in the descriptive statistics. This heavy-tailed nature suggests that 

extreme returns, both positive and negative, occur more frequently than a normal distribution 

would predict. This pattern aligns with financial return distributions, which often exhibit fat 

tails due to sudden market events, volatility spikes, or external shocks. 

Overall, the Q-Q plot visually confirms that Verizon's stock returns deviate from 

normality, particularly in the tails. These deviations emphasize the importance of using robust 

statistical methods that can handle non-normal distributions and the presence of extreme values 

when analyzing the stock's performance. 

The Q-Q plot for Verizon's trading volumes quantitatively illustrates deviations from a 

normal distribution. Observed volumes in the lower quantiles show some alignment with the 

expected average values, but deviations become pronounced as volumes increase. For instance, 

the theoretical maximum value under normality (upper quantiles) would be around 40,000,000, 

but the actual observed trading volumes exceed 100,000,000, reflecting extreme values well 

above standard expectations. 

Similarly, the theoretical minimum under normality might approach -20,000,000 in the 

lower quantiles. Still, actual trading volumes are truncated at approximately 4,000,000, 

suggesting fewer instances of deficient trading activity than would be predicted by a normal 

distribution. These discrepancies confirm the heavy right skew, with high trading volumes 

disproportionately influencing the distribution. 



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The upward curvature in the upper quantiles, particularly after 60,000,000, highlights 

the frequency of extreme trading days, which are outliers relative to a normal distribution. 

Combined with the descriptive statistics—skewness of 2.43 and kurtosis of 14.82—this plot 

confirms that Verizon’s trading volumes exhibit significant non-normality, driven by an 

overrepresentation of extreme high-volume trading events. These quantitative deviations 

emphasize the importance of tailoring analytical models to accommodate such distributions 

when interpreting trading patterns. 

 

Figure 4. Descriptive statistics for Verizon Communications Inc. stock returns and trading 

volumes 

 
Done by authors using Nasdaq data and Eviews software  

 

The descriptive statistics for Verizon’s stock price returns (Figure 4.) and trading 

volumes provide valuable insights into the dataset's characteristics. For stock price returns, the 

mean is 0.002199, indicating a small average daily positive return, while the median return of 

0.017602 is higher than the mean, suggesting a slightly right-skewed distribution. The standard 

deviation of 1.237903 points to moderate volatility in daily returns, with a wide range between 

a maximum return of 9.270468 and a minimum return of -7.497795, highlighting significant 

variability in stock performance.  

The skewness of the return distribution is 0.121474, indicating a mild positive skew 

where small positive returns are more frequent than large negative ones. The kurtosis value of 

8.751691 suggests heavy tails in the distribution, indicating the presence of extreme outliers or 

large fluctuations in returns. This is further confirmed by the Jarque-Bera test statistic of 

3474.279 with a p-value of 0.000000, indicating that the returns do not follow a normal 

distribution. The high kurtosis and non-normality imply that extreme events significantly shape 

Verizon’s stock price behavior. 

The data reflects significant variability in trading volumes, with a mean daily volume 

of 17,276,944 shares and a median of 15,601,150 shares. The higher mean than the median 

suggests a positively skewed distribution, where a few days of unusually high trading volumes 

pull the average upward. The standard deviation of 7,629,818 indicates substantial fluctuations 

in trading activity, with a minimum volume of 4,108,274 and a maximum of 97,510,640, 

highlighting the presence of exceptionally high-volume days. 

The distribution of trading volumes is heavily skewed to the right, with a skewness 

value of 2.427284, indicating that most trading days have relatively low volumes. In contrast, 

RETURN VOLUME
 Mean  0.002199  17276944
 Median  0.017602  15601150
 Maximum  9.270468  97510640
 Minimum -7.497795  4108274.
 Std. Dev.  1.237903  7629818.
 Skewness  0.121474  2.427284
 Kurtosis  8.751691  14.81694

 Jarque-Bera  3474.279  17116.31
 Probability  0.000000  0.000000

 Sum  5.533649  4.35E+10
 Sum Sq. Dev.  3853.997  1.46E+17

 Observations  2516  2517



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a few days exhibit extraordinarily high activity. The kurtosis of 14.81694 underscores the 

presence of heavy tails, pointing to extreme trading volume spikes. The Jarque-Bera test 

statistic of 17116.31, with a p-value of 0.000000, confirms the non-normality of trading 

volumes, reinforcing the impact of these extreme outliers on the dataset. 

The analysis reveals that stock returns and trading volumes exhibit non-normal 

distributions with significant skewness and heavy tails. These characteristics suggest extreme 

values and volatility are crucial in Verizon’s stock performance and trading activity. The 

relatively small mean return and moderate standard deviation point to a typical stock behavior 

with periodic extreme events. Meanwhile, the high skewness and kurtosis in trading volumes 

highlight specific days with exceptionally high activity, likely driven by major market events, 

corporate news, or earnings announcements. These findings suggest that traditional analytical 

models assuming normality may not adequately capture Verizon’s stock performance 

dynamics, necessitating more robust approaches to handle the observed patterns. 

 

Figure 5. Tabulation analysis for Verizon Communications Inc. stock returns and trading 

volumes 

 
Done by authors using Nasdaq data and Eviews software  

 

The tabulation analysis (Figure 5.)  explores the relationship between Verizon’s stock 

returns and trading volume, using categorical return ranges and volume bins to structure the 

data. Measures of association, including the Phi coefficient (0.595931), Cramér’s V 

(0.344061), and the contingency coefficient (0.511923), indicate a moderate positive 

association between the two variables. Statistical tests further support this relationship, as both 

the Pearson χ² test (χ² = 893.5172, p = 0.0000) and the Likelihood Ratio Test (G² = 114.9153, 

p = 0.0000) confirm that the association is statistically significant. These results highlight a 

meaningful dependency between Verizon stock returns and trading volume. 



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The distribution of data reveals interesting trends across different return categories. The 

smallest group, representing extreme negative returns in the range of [-10, -5], accounts for 

only 7 observations (0.28% of the total) and is almost entirely confined to low trading volumes. 

In contrast, the [-5, 0] return category comprises 1,217 observations (48.37%) and is 

predominantly concentrated in the 0–2 million volume bin, with diminishing frequencies in 

higher volume bins. The return category [0, 5] is the largest group, containing 1,282 

observations (50.95%), and similarly shows a strong association with low trading volumes, as 

992 of these instances fall in the 0–2 million volume bin. Lastly, the [5, 10] return category, 

representing positive returns, is sparse, with only 10 observations (0.40%), scattered across low 

to medium trading volumes. 

An examination of the volume distribution reveals that most observations (74.13%) 

occur within the lowest trading volume range of 0–2 million. Higher trading volumes, 

particularly those above 6 million, are significantly less common, collectively contributing 

only 0.24% of total observations. The data also includes a warning that 60% of cells have 

expected values less than five, indicating sparsity in parts of the contingency table. This 

sparsity could potentially affect the robustness of the χ² test, though the results remain 

statistically significant. 

The analysis suggests that while a moderate and statistically significant relationship 

exists between trading volume and stock returns, the association is dominated by low trading 

volume categories. Extreme return categories, such as [-10, -5] and [5, 10], are 

underrepresented, reflecting the rarity of these events and their limited impact on high trading 

volumes. This indicates that Verizon’s stock performance is typically associated with moderate 

market activity, with significant shifts in returns less frequently accompanied by high trading 

volumes. The findings imply that changes in trading volume can serve as a signal for stock 

performance, though additional analysis, such as regression modeling, would be required to 

establish causality or deeper insights into these patterns. 

 

Figure 6. Wavelet analysis of Verizon Communications Inc. stock returns  

 

 



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Based on the provided wavelet analysis (Figure 6) summary for Verizon's stock returns: 

The analysis utilized the Maximum Overlap Discrete Wavelet Transform (MODWT) 

with the Daubechies 4 filter, covering a dataset spanning from November 24, 2014, to 

November 22, 2024, and including 2,516 observations. The decomposition was conducted with 

a maximum scale of 6, allowing for the investigation of both short- and long-term return 

dynamics. This setup suggests a comprehensive multiscale analysis, effectively capturing 

fluctuations in stock returns across various time horizons, ranging from high-frequency 

(daily/weekly) to low-frequency (monthly/annual) trends. Such an approach is well-suited to 

identify dominant frequencies, localized trends, and potential structural breaks in Verizon's 

return series over the specified 10-year period. Further exploration of scale-specific energy 

distributions and time-localized patterns could provide valuable insights for investors and 

analysts. 

High-Frequency Movements (Scale 1 - Daily). The wavelet coefficients show high 

volatility at the finest scale (daily fluctuations), with multiple spikes exceeding ±4. The year 

2017, coinciding with Verizon's acquisition of Yahoo, recorded notable peaks, with 

coefficients exceeding +5 and -4. This reflects significant market reactions to the acquisition. 

Additionally, the average coefficient for this scale remained around 0.2, suggesting that while 

short-term fluctuations were pronounced, they were balanced around a neutral trend. The 

period from 2019 to 2020 also showed sharp spikes, peaking at ±6, likely attributable to market 

uncertainty during the early stages of the COVID-19 pandemic. 

Intermediate Movements (Scale 2 - 2 Days). The Scale 2 decomposition reveals 

moderate volatility, with coefficients largely contained within ±2. However, specific events in 

2017 caused spikes as high as +3.5, particularly in the months following the announcement and 

completion of the Yahoo acquisition. The overall average coefficient at this scale was close to 

zero (0.1), indicating a lack of persistent trends at this frequency. In 2020, several spikes 

between +2.5 and -2.8 occurred during heightened market activity, reflecting intermediate-term 

reactions to global events such as the pandemic. 

Medium-Term Movements (Scale 3 and Scale 4 - 4 Days and 8 Days). At Scale 3, 

fluctuations are dampened compared to higher frequencies, with coefficients typically within 

±1.5. However, in 2017, distinct spikes were reaching +2 and -1.8, highlighting the medium-



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term adjustments in Verizon's stock price related to the Yahoo integration. The Scale 4 

decomposition shows further stability, with coefficients concentrated within ±1 for most of the 

period. Notable peaks in 2017, reaching ±1.2, reflect sustained investor reactions over an 8-

day cycle following major announcements. 

Low-Frequency Movements (Scale 5 and Scale 6 - 16 Days and 32 Days). At Scale 5, 

the coefficients show persistent trends with fluctuations between ±0.8. During 2017, the 

average coefficient reached +0.4, indicating a positive medium-term sentiment associated with 

the Yahoo acquisition. In contrast, the period between 2020 and 2021 showed more negative 

coefficients, bottoming at -0.7, aligning with broader market pessimism during the pandemic. 

Scale 6 (32-day cycles) revealed longer-term structural trends, with coefficients reaching ±0.6. 

Peaks in 2017 at +0.5 suggest optimism following strategic corporate decisions, while dips in 

2020 reached as low as -0.6, highlighting the impact of external shocks on long-term investor 

confidence. 

Long-Term Trends (Scale 6 Averages - Annual Trends). The long-term wavelet average 

at Scale 6 shows distinct oscillations, particularly around 2017 and 2020. 2017 the average 

coefficient was +0.3, reflecting the positive market outlook post-Yahoo acquisition. By 

contrast, 2020 exhibited a negative average of -0.5, coinciding with the economic turmoil 

caused by the COVID-19 pandemic. Post-2020, coefficients gradually returned to positive 

territory, with averages near +0.2 in 2023, indicating recovery and stabilization in Verizon’s 

stock performance. 

 

CONCLUSIONS 

Three primary hypotheses frame the M&A-stock price relationship. First, acquisitions 

often yield positive abnormal stock returns for target firms in the short term due to acquisition 

premiums. Second, the success of an acquisition depends on its type, market conditions, and 

underlying motives, whether for exploration, exploitation, or ambidexterity. Finally, the long-

term performance of acquiring firms hinges on effective integration and realisation of 

anticipated synergies, with research suggesting that poor post-acquisition performance can 

result if these are not achieved. Ultimately, the success of M&A strategies is influenced by 

firm-specific characteristics and the strategic decision-making of top management, 

underscoring the need for careful evaluation and management throughout the M&A process. 

There are also implications for corporate managers when considering M&A strategies. 

Managers must consider these market responses when planning an M&A strategy. Acquisitions 

are usually successful when the cultural and strategic fit of the firms is good and synergies are 

realized. The managers must also be cautious with regulatory challenges and investor concerns 

so that the market reception is favorable and, in the long run, the deal is well placed. 

All in all, the research found that mergers and acquisitions (M&A) significantly 

influence stock prices, depending largely on the strategic alignment, nature of the acquisition, 

and market conditions. While target companies often experience immediate stock price gains 

due to acquisition premiums, the stock prices of acquiring firms can exhibit volatility or decline 

due to uncertainties regarding integration and strategic alignment. 

The wavelet analysis of Verizon's stock returns from 2014 to 2024 reveals key insights 

into multiscale market dynamics. Short-term daily fluctuations (Scale 1) peaked at ±6 during 



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2017 and 2020, reflecting sharp market reactions to Verizon's acquisition of Yahoo and the 

economic uncertainty caused by the COVID-19 pandemic, respectively. Medium-term 

adjustments (Scales 3 and 4, representing 4–8 day cycles) showed moderate fluctuations, with 

peaks reaching ±2 in 2017, indicating sustained investor responses to the Yahoo integration. 

Long-term trends (Scale 6, representing 32-day cycles) highlight structural shifts, with positive 

coefficients (+0.5) in 2017 reflecting optimism around the acquisition, contrasted with negative 

coefficients (-0.6) in 2020 during the pandemic. The 2017 Yahoo acquisition caused significant 

spikes across multiple scales, including daily fluctuations reaching +5 and weekly trends 

peaking at +3.5. The COVID-19 pandemic in 2020 drove sharp volatility across all scales, with 

prolonged negative impacts especially evident at lower frequencies. This multiscale analysis 

underscores the influence of both internal corporate events and external economic disruptions 

on Verizon’s stock performance. 

Investors can use the findings to make better investment decisions around acquisition 

announcements. They can use the study to their advantage by paying close attention to 

acquisition announcements. Moreover, they should look for strategic fit, the nature of the deal, 

and the involved industry. Generally speaking, acquisitions with relatively clear synergies and 

strong strategic fit often have positive market reactions in the short run and long-term value 

creation, while those transactions with poor prospects or cultural misalignment result in a 

decline in stock price. 

 

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