AstraZeneca sets ambition to deliver $80 billion Total Revenue by 2030

Introduction

AstraZeneca is a multinational pharmaceutical company headquartered in Cambridge, United Kingdom. The company specializes in developing, manufacturing, and distributing medications globally, with operations in more than 100 countries (Adrian Kemp, 2024). In a report filed by Statista on the revenue performance, the company recorded growth for 6 consecutive years in 2023 by recording approximately 44 billion U.S dollars in revenue (Statista, 2024). In one way, this could be credited to its strategic innovation in drug development, taking advantage of the COVID-19 pandemic. According to Yuyan (2023), companies in the healthcare sector are considered more stable due to consistent demand for their service. With this note, this report ventures into understanding how stock prices have consistently changed for AstraZeneca in years 2021,2022, and 2023 and their impact on the trading volume on the London Stock Exchange.

The underlying research problem is associated with understanding the relationship between stock prices, opening, closing, and adjacent prices, and evaluating how each impacts the volume of trade activities specifically, helping to understand the underlying factors that affect investors’ decisions to trade on AstraZeneca stock. More importantly, the research helps know how each variable relates to trading volume and propromotesecasting on the end of the month volume based on the stock’s opening price. Consequently, findings result in informed decision-making for investors and managers and financial analysis for comparing marketing dynamics (Majidi & Khorshidi, 2024).

Research question

1. Is there a significant relationship between opening, closing, and adjacent stock price and its monthly trade volume?

Research objective

1. Identify price changes that trigger higher trading volume.
2. To determine the extent to which fluctuation of stock prices impacts monthly trade volume.
3. Analyze the impact of stock prices on trade volume.

Literature review

Apart from this research, multiple studies have been inline building into the position of AstraZeneca in the market position while others have enriched knowledge on the factors affecting trading activities. Research Journal by Yuyan (2023) analyzes AstraZeneca’s financial performance, noting that its revenue growth, profitability, and strategic investment are essential players in supporting its market share. Consequently, Goodell (2020) notes that the value of the stock price is closely related to financial health and future potential to provide returns for investors. This aspect connects stock price and amount of trading activities. In addition, Chiah and Zhong (2020) explore further other factors that impact trading volume. Chiah and Zhong’s findings explained that higher trading activities are closely connected to stock prices, and a volatile price market accompanies volatility in the volume. To justify these changes, the efficient market hypothesis proposes that the stock price reflects all available information, including public and internal management decisions (Sánchez-Granero et al., 2020). This connects the consistent growth of revenue and stock prices to the investments in drug development and innovation on COVID-19 vaccines. Overall, the context of existing literature is based on general factors that impact stock prices. According to Goodell (2020 and Yuyan (2023), the standard issue relates to market sentiments, economic indicators, market liquidity, and companies’ performance. These factors are prone to generalization, which creates a gap in narrowing to specific stock prices, impacting trade volume. Accordingly, future research can look at specific economic indicators or investors’ behavior and how they affect trading volume.

Methodology and Data Description

Research approach and design

The research approach and design describe the research’s overall framework in terms of data type data and techniques used in the analysis. According to Saunders et al. (200), two primary methods are: qualitative and quantitative. This research uses a framework of qualitative design, relying on numerical data. In addition, this approach is guided by the positivism philosophy to understand the causal relationship between the volatility of stock prices and the trade volume. In support of this design, Hennink and Kaiser (2022) posited that the quantitative method is more effective due to its precise and reliable measurement technical. More importantly, the technique allows hypothesis testing, allowing more reliable findings. On this note, the research question assesses the relationship between stock price and trade volume. More importantly, given the opening price, it helps to derive an equation that can be used to predict trade volume in a month. Therefore, the qualitative approach is the best suited for this research.

Data collection

There are two significant research data sources: primary and secondary (Hennink & Kaiser, 2022). While primary data is most preferred for its real-time and specific data, sometimes it could be more time-consuming and cost-efficient. On the other hand, secondary data also refers to research and research. This research considered the use of secondary data due to the sensitivity and availability of the variables. As noted above, the data used for their research are all numeric, representing significant variables: opening, closing, and adjacent price, which are independent, and trade volume transactions as dependent variables. This data was organized monthly for the last three years (2021-2023). This represented 36 months, a good sample for statistical analysis (Hennink & Kaiser, 2022). The data was collected from Yahoo Finance and downloaded in an Excel format. Table 1 presents a summary of the data collected:

The table shows that the mean opening price was 97112, the closing price was 9806, and the adjacent price was 9406. The mean volume of trading activities was 50,763,676. Accordingly, more data was sourced from the company’s annual financial statement, Statista, and journals from academic sites to maintain the reliability and validity of the findings.

Data analysis

Data analysis refers to the process of deriving meanings from the data collection. In Saunders et al. (2009) research, quantitative data are analyzed using statistical methods such as descriptive statistics, correlation, regression, and other methods. On the other hand, quantitative data is analyzed through contextualization, comparative analysis, or thematic analysis. For this research, three central analysis processes were processes carried out in line with the research objective. The first involves a summary of descriptive statistics and the deriving of graphs showing the trends in the stock price and trade volume. The second was correlation analysis. According to Topuz (2021), this analysis is used to assess if there is a relationship among the variables examined. This connects to the research question, which aims to assess if there is a relationship among variables and what type of relationship. The third process involves regression analysis. In this part, regression is used to identify the extent of the relationship and at what rate changes in variable x would result in changes in variable Y (Topuz, 2021). This connects to the third research objective, which is to assess the extent to which fluctuation of prices impacts trading volume. ultimately, this is illustrated through the regression equation.

Difficulties

Maintaining accuracy and completeness took much work despite data availability on multiple platforms, such as Yahoo Finance, Statista, and Bloomberg. AstraZeneca stocks are traded in different exchange markets, including the London Stock Exchange and the Nasdaq Global Select Market. Addressing accuracy from Yahoo finance data about the two markets may take a lot of work. Also, the analysis uses correlation and regression inference, which assumes a relationship between the variables. This assumption limits the practicality of the findings, resulting in accuracy in decisions and forecasts made. In addition, the assumption of normality in data distribution where violation may lead to a lack of validity. To address these issues, the research applies triangulation for consistency and three years of monthly data to create a bigger picture or a larger sample.

Statistical analysis

Correlation

Correlation is a statistical measure that quantifies the strength and direction of the relationship between two variables. In other words, it tells the percentage level under which variable x affects variable y. Under this research, three major variables were tested: opening price, closing price, and adjacent price for the tree variables. Below is the summary output table 2:

In the above table, column six presents the correlation coefficient variable on the trade volume. Generally, this coefficient is all negative showing. A negative coefficient suggests that a decrease in variable x leads to an increase in variable y. In this research, the opening price had a coefficient of -0.38. This suggests a weak relationship between the variables and investors, who do not primarily depend on opening prices to make investments.

On the other hand, the coefficient for closing and adjacent prices is -0.44 in both. Similarly, this weak relationship suggests that investors rely on only factors to make investment decisions. However, it is noticeable that the relationship for the adjacent price, which is considered the average stoke market price, has a high impact on trading volume, fulfilling the requirement of objective one (Sukesti et al., 2021).

In line with objective two, fluctuation in the stock price impacts the monthly trading volume. About 38% of the changes are caused by the opening price, while the adjacent stock prices may cause 43%. This volatility is explained by investors’ perception of the stock’s future performance. For instance, high stock prices perpetuate optimism in the market and may promote trading activities. However, Ante (2023) adds more weight by stating that changes in market price create a wealth effect on investors and increase trading activities in a battle for more profit or returns.

A report by the Financial Times in 2018 and a journal on behavioral heterogeneity in the stock market (Li et al., 2021) provide similar conclusive. Li and colleagues say the trade volume and stock prices have a neutral relationship, showing a low margin between the two. The findings highlight that investors partially predict the stock prices for the trading volume and vice versa. Evaluating the evidence by the Financial Times article, the inverse relationship between stock and trading volume was stated in 2008 after the global financial crisis (Financial Times, 2018).

Regression

Regression analysis provides a more comprehensive answer to the relationship between variables. The output of the analysis includes an equation that can be used to predict future variables given either independent or dependent variables. Table three represents the summary output of the regression

The above table represents a multi-regression for the stock prices. Similar to the findings from correlation, the regression shows a correlation coefficient of 0.509, which indicates a neutral relationship between the variables. R squared of 0.259 represents the proportion of trade volume explained by the stock price changes. The F-statistic illustrates the validity explained by the regression model to the variability in the dependent variable, which is 2.9. on the other hand, the p-value for the analysis is 0.09297, which illustrates that the model is not statistically significant as the variables partially do not contribute to changes in the trade volume. According to Sukesti et al. (2021), a p-value less than 0.05 would suggest that the variation in the stock process influences the trading activities. Taking the coefficient of determination, the impact of the stock price on trade volumes is considered low, representing 25% of all trading activities.

Conclusion

Overall, changes in the stock prices affect the volume of trade for AstraZeneca. The above report systematically conducted a literature review and statistical analysis of ck prices in Yahoo Finance, providing insightful knowledge. In fulfillment of object one, the research identifies that adjacent stock price has the highest impact on trade volume with a coefficient of -0.44. On the other hand, the regression analysis has helped to understand that the relationship between the stock prices and trading volume is considerately neutral, with 25% of trade volume being influenced by changes in stock price. This means that volatility in the price stack causes volatility in trading activities.

However, these findings are limited by the analysis method assumption of linear relationship between variables and normality in distribution. This calls for a more comprehensive analysis with a larger sample or including inferential tests such as the Kruskal-Wallis and Wilcoxon signed test to provide a more comprehensive sample. The findings also noted that about 25% of trade volume is affected by stock prices. Therefore, there is a need to assess factors that cause the remaining 75%, taking into consideration either internal management decisions or the external environment, such as inflation or economic growth. By doing this, the overall aspect of trading volume would be covered, thus developing a framework to predict and make informed decisions.