Name
Capella University
MBA-FPX 5008 Applied Business Analytics
Prof. Name
Date
Analyzing historical stock market data helps investors, business leaders, and financial analysts identify price trends, evaluate market activity, and understand financial uncertainty. For Express Inc., a fashion retailer operating in a competitive industry, scatter plots, histograms, and descriptive statistics provide useful methods for examining historical share prices and trading volume. The reported findings indicate a downward trend in daily high and low stock prices, a mean adjusted closing price of approximately $3.98, and substantial variation in daily trading volume. These results help explain patterns within the selected dataset, although they do not independently establish the causes of stock-price movements or predict future performance.
Express Inc. is a fashion retailer associated with contemporary clothing, versatile wardrobe options, and personal style. Founded in Columbus, Ohio, the company developed its brand around confidence, individuality, and self-expression. Its products have included clothing and accessories designed for customers seeking modern styles and adaptable wardrobe choices (Express, Inc., n.d.).
The fashion retail industry is highly competitive because companies must respond to changing consumer preferences, economic conditions, seasonal demand, and evolving shopping habits. Express competes within a market that includes retailers such as American Eagle, Gap, and Forever 21. Although these businesses differ in their target customers, product offerings, and financial circumstances, they face similar pressures involving pricing, brand differentiation, and consumer spending.
Understanding this competitive environment is important when interpreting Express’s historical stock performance. A company’s share price reflects more than its retail strategy; it can also respond to investor expectations, financial results, industry developments, and broader economic conditions.
Express has historically emphasized personal style and versatile clothing rather than relying exclusively on short-lived fashion trends. Research on fashion consumption indicates that consumer preferences and the meanings associated with clothing can influence purchasing behavior and participation in fashion communities (Cervellon & Wernerfelt, 2012). Nevertheless, a distinctive brand identity does not eliminate the need to respond to changing customer expectations and market conditions.
The COVID-19 pandemic disrupted the retail industry by changing shopping habits, work arrangements, and consumer spending. The shift toward remote work increased interest in comfortable and casual clothing among some consumers, while economic uncertainty affected spending on discretionary products.
These developments created challenges for fashion retailers whose sales depended on traditional shopping patterns or particular clothing categories. Companies also had to respond to changes in store traffic, online shopping, inventory management, and operating expenses.
Analyzing Express’s historical stock prices and trading volume can help illustrate how its shares behaved during a period of economic and industry change. However, historical price movements alone cannot establish that COVID-19, remote work, or any single business decision caused a particular change in the company’s share price. Additional evidence from financial statements, company announcements, and market comparisons would be necessary to evaluate those relationships.
Graphical analysis converts numerical information into visual representations that make patterns and distributions easier to understand. For this assessment, historical stock data obtained from Yahoo Finance were used to examine daily high and low prices, adjusted closing prices, and trading volume. Microsoft Excel was used to organize the observations, calculate descriptive statistics, and prepare charts.
These visual techniques provide complementary information. Scatter plots can reveal patterns in stock prices over time, while histograms show how frequently observations fall within particular numerical ranges.
Scatter plots display the relationship between two numerical variables. When dates are placed on the horizontal axis and stock prices on the vertical axis, they can also help illustrate how prices change over time.
For this analysis, scatter plots were created to examine Express Inc.’s daily high and low stock prices over the selected historical period. Trendlines were included to make the overall direction of the observations easier to interpret.
The reported charts showed downward trends in both daily high and daily low prices. This pattern suggests that Express shares experienced declining prices during the period examined. Comparing the high and low prices also provides a view of the range within which shares traded on individual days.
Microsoft Support (n.d.) explains how scatter charts can be used to display relationships between numerical variables. In financial analysis, trendlines help summarize historical patterns, but they should not be interpreted as reliable predictions of future stock prices.
A more complete assessment would consider daily returns, company announcements, industry developments, investor sentiment, and movements in relevant market indexes. These additional measures could help determine whether the observed decline was specific to Express or reflected broader market conditions.
Histograms display the frequency of numerical observations within defined intervals, known as bins. They help analysts understand the distribution of data, including its concentration, spread, and potential skewness.
The histogram of Express’s adjusted daily closing prices showed how frequently shares closed within different price ranges. The reported mean and median were relatively close, suggesting that the central values of the distribution were similar. However, this relationship alone does not prove that the distribution was symmetrical or that prices remained stable throughout the period.
A histogram summarizes the frequency of observations without preserving their chronological order. Consequently, a dataset may show a concentrated distribution of closing prices even when the share price declines over time. The histogram should therefore be interpreted alongside the time-based scatter plots.
A separate histogram was prepared for daily trading volume. Trading volume represents the number of shares exchanged during a trading session and provides information about the level of market activity.
The reported histogram showed a broad distribution of trading volumes, including some observations substantially higher than others. These high-volume days may have contributed to the wide spread of the distribution. However, determining why trading volume increased on particular dates would require examining company announcements, market developments, and other relevant information.
Microsoft Excel can be used to group numerical observations into intervals and present the results through histograms. Selecting appropriate bin widths is important because overly broad intervals may conceal meaningful differences, while overly narrow intervals can make a chart difficult to interpret. The choice of bins should reflect the range and characteristics of the dataset (Capella University, n.d.).
Descriptive statistics summarize the central tendency and variability of numerical observations. For this assessment, the mean, median, mode, and standard deviation were reported for adjusted daily closing prices and daily trading volume.
The mean represents the arithmetic average, while the median identifies the middle observation when the values are arranged in ascending order. The mode represents the most frequently occurring value, and the standard deviation measures the dispersion of observations around the mean. These measures provide a concise overview of financial data and help identify patterns that may warrant further investigation (Lind, 2021).
The reported statistics for Express Inc.’s adjusted daily closing prices were as follows.
| Statistical measure | Reported value |
|---|---|
| Mean | $3.976996047 |
| Median | $3.86 |
| Mode | $5.37 |
| Standard deviation | $1.089687076 |
The mean adjusted closing price was approximately $3.98, while the median was $3.86. The difference between these values was relatively small, indicating that the average and middle observations were similar in magnitude.
However, the mean and median alone cannot establish that the distribution was symmetrical or that the stock price was stable over the entire period. Because the data represent observations collected across time, the distribution may include both higher prices from earlier dates and lower prices from later dates.
The standard deviation was approximately $1.09. This measure indicates the degree of variation in adjusted closing prices around their mean. Whether this represents relatively high or low volatility depends on the average price, the observation period, and comparisons with other securities or periods.
The reported mode was $5.37. This value represents the most frequently occurring adjusted closing price if the original dataset contains repeated observations at that exact value. Since stock prices may include many distinct values, the mode should be verified against the spreadsheet to ensure that it was calculated correctly.
Taken together, these statistics provide a summary of the central values and dispersion of Express’s adjusted closing prices. They should be interpreted alongside the scatter plots to distinguish the overall price trend from the distribution of individual observations.
The reported descriptive statistics for Express Inc.’s daily trading volume were as follows.
| Statistical measure | Reported value |
|---|---|
| Mean | 3,847,462.055 shares |
| Median | 3,047,700 shares |
| Mode | Not available |
| Standard deviation | 2,688,317.05 shares |
The mean daily trading volume was approximately 3.85 million shares, compared with a median of approximately 3.05 million shares. Because the mean exceeded the median, the distribution may have been positively skewed, meaning that some high-volume observations pulled the average above the middle value.
The standard deviation was approximately 2.69 million shares, indicating considerable variation in daily trading activity. Some trading sessions may have involved substantially more shares than a typical session.
Higher trading volume can coincide with company announcements, changes in investor expectations, broader market developments, or other events. Nevertheless, the descriptive statistics do not identify the causes of the observed variation. A more detailed investigation would compare unusually high-volume dates with relevant company and market information.
The mode was reported as unavailable. This can occur when no observation occurs more frequently than the others or when the spreadsheet’s statistical function cannot identify a single mode. When many trading-volume values are unique, reporting that no mode is available may be appropriate.
Overall, the trading-volume statistics indicate substantial variation in market activity during the period examined. Comparing the histogram with the dates of high-volume observations would provide additional insight into the timing and potential significance of these changes.
The graphical and statistical findings provide several insights into Express Inc.’s historical stock performance.
First, the scatter plots indicated a downward trend in daily high and low prices. This suggests that the market price of Express shares declined during the selected period. Although the trend is an important finding, it does not establish why the decline occurred or whether the same pattern would continue.
Second, the adjusted closing price statistics showed a mean of approximately $3.98 and a median of $3.86. The relatively small difference between these measures indicates that the average and middle observations were similar. The standard deviation of approximately $1.09 provides additional information about price dispersion, although it should be considered in relation to the share price level and the period studied.
Third, trading volume displayed substantial variation. The mean exceeded the median, and the standard deviation was approximately 2.69 million shares. These results are consistent with a distribution in which some high-volume observations may have influenced the average.
The results also demonstrate why graphical analysis and descriptive statistics should be used together. Scatter plots illustrate the direction of stock prices over time, histograms summarize the distribution of observations, and descriptive statistics quantify central tendency and variability. Each technique provides different information, and no single method fully explains the performance of a publicly traded company.
The broader fashion retail environment should also be considered. Consumer spending, changing clothing preferences, competition, inventory decisions, and economic uncertainty can influence retail performance. However, attributing Express’s historical share-price decline directly to the pandemic or changing consumer behavior would require additional supporting evidence.
Several limitations affect the interpretation of these results. First, the analysis uses historical data from a defined period. The findings therefore describe the selected observations rather than the company’s current financial condition or future performance.
Second, stock prices reflect multiple influences, including company earnings, investor expectations, economic developments, and market-wide movements. The scatter plots and descriptive statistics cannot isolate the contribution of each factor.
Third, the reported statistics do not establish the cause of unusually high trading volume. Additional analysis could identify high-volume dates, compare daily returns with a relevant market index, and examine company announcements or financial disclosures around those dates.
Finally, the calculations and charts should be checked against the original spreadsheet. This verification should include the selected dates, adjusted closing prices, daily trading volumes, chart labels, histogram intervals, and formulas used to calculate the descriptive statistics. Confirming these details is important for maintaining accuracy in an academic assessment.
Analyzing Express Inc.’s historical stock data through scatter plots, histograms, and descriptive statistics provides a clearer understanding of its share-price patterns and trading activity. The reported results indicate a downward trend in daily high and low prices, a mean adjusted closing price of approximately $3.98, and substantial variation in daily trading volume.
The analysis demonstrates how statistical measures and graphical techniques can transform raw financial data into useful information. Scatter plots help reveal historical trends, histograms show how observations are distributed, and descriptive statistics summarize central values and variability.
However, these findings should not be treated as predictions of future stock prices or definitive explanations for the company’s historical performance. A more comprehensive assessment would combine the reported results with verified financial statements, market benchmarks, company announcements, and relevant economic indicators.
Using these techniques together supports a more evidence-based understanding of stock market data and demonstrates the value of analytical methods in business decision-making.
MBA FPX 5008 Assessment 2, Using Analytic Techniques to Add Meaning to Data, focuses on using analytical methods to interpret data and communicate meaningful findings. In this stock analysis, scatter plots, histograms, and descriptive statistics are used to examine Express Inc.’s historical share prices and trading volume.
The analysis used the mean, median, mode, and standard deviation. The mean and median summarize central tendency, the mode identifies the most frequently occurring value when one exists, and the standard deviation measures the dispersion of observations around the mean.
A difference between the mean and median may indicate that a distribution is skewed or influenced by unusually high or low observations. For Express, the reported mean of approximately $3.98 and median of $3.86 were relatively close. However, these values alone cannot establish the distribution’s shape or the stability of stock prices over time.
Standard deviation measures the variability of observations around their mean. In stock analysis, it can help describe the dispersion of prices or trading volume. When comparing volatility across securities with different price levels, analysts should also consider percentage returns and other appropriate risk measures.
The reported mean trading volume was approximately 3.85 million shares, while the median was approximately 3.05 million shares. The standard deviation of approximately 2.69 million shares indicates substantial variation in daily trading activity. Additional research is needed to identify the reasons for unusually high-volume trading sessions.
Scatter plots show relationships between numerical variables and can illustrate stock-price changes over time when dates are included. Histograms show the frequency of observations within numerical intervals. Using both methods helps analysts examine trends and understand the distribution of financial data.
Historical data can help analysts identify patterns and assess past market behavior, but it cannot predict future prices with certainty. Future performance may depend on company financial results, investor expectations, competitive conditions, economic developments, and other factors.
COVID-19 changed shopping habits, work arrangements, and consumer spending across the retail industry. These developments may have affected demand for clothing and the operating conditions facing retailers. However, determining their specific effect on Express’s share price requires additional evidence beyond descriptive statistics and graphical analysis.
Capella University. (n.d.). Using analytic techniques to add meaning to data walkthrough. https://media.capella.edu/coursemedia/mba5008element19233/wrapper.asp
Cervellon, M.-C., & Wernerfelt, A.-S. (2012). Knowledge sharing among green fashion communities online: Lessons for the sustainable supply chain. Journal of Fashion Marketing and Management, 16(2), 176–192. https://doi.org/10.1108/13612021211222860
Express, Inc. (n.d.). About us. https://www.express.com/g/about-us
Lind, D. A. (2021). Basic statistics for business and economics (10th ed.). McGraw Hill.
Microsoft. (n.d.). Present your data in a scatter chart or a line chart. Microsoft Support. https://support.microsoft.com/en-us/topic/present-your-data-in-a-scatter-chart-or-a-line-chart-4570a80f-599a-4d6b-a155-104a9018b86e
Yahoo Finance. (n.d.). Express, Inc. (EXPR) historical data. https://finance.yahoo.com/quote/EXPR/history/
