MBA FPX 5008 Assessment 2 Using Analytic Techniques to Add Meaning to Data

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Capella University

MBA-FPX 5008 Applied Business Analytics

Prof. Name

Date

MBA FPX 5008 Assessment 2 Using Analytic Techniques to Add Meaning to Data

Analyzing stock market data helps businesses, investors, and analysts understand financial performance, identify trends, and evaluate uncertainty. Express Inc., a fashion retailer known for its modern clothing and versatile wardrobe options, operates in a competitive retail environment where consumer preferences and economic conditions can influence financial results. By using scatter plots, histograms, and descriptive statistics, analysts can examine Express Inc.’s historical stock prices and trading volume to identify patterns, assess variability, and understand potential market risks. The analysis of the historical data provided indicates a downward trend in stock prices, relatively limited variation in adjusted closing prices, and substantial volatility in daily trading volume.

Company Background and Competitive Environment

Express Inc. is a fashion retailer founded in Columbus, Ohio, with a mission centered on confidence, individuality, and self-expression. The company offers contemporary clothing designed to help customers build flexible wardrobes with modern styles, suitable fits, quality fabrics, and attention to detail (Express, n.d.).

The fashion retail industry is highly competitive, with companies responding to changing consumer expectations, economic conditions, and evolving shopping habits. Express competes with retailers such as American Eagle, Gap, and Forever 21, although these businesses differ in size, product positioning, target audiences, and financial performance. Understanding this competitive environment is important when interpreting Express’s historical stock performance.

One way Express has differentiated itself is through an emphasis on personal style and versatile clothing rather than relying exclusively on short-lived fashion trends. Research on fashion consumption suggests that consumer preferences and the values associated with clothing can influence purchasing behavior and fashion-related communities (Cervellon & Wernerfelt, 2012). However, a style-focused strategy does not eliminate the need to respond to changes in customer demand.

MBA FPX 5008 Assessment 2 Using Analytic Techniques to Add Meaning to Data

The COVID-19 pandemic significantly affected the retail sector by changing shopping habits, work arrangements, and clothing preferences. The growth of remote work increased demand for comfortable and casual clothing among some consumers, while economic uncertainty influenced discretionary spending. These conditions created challenges for retailers that depended on traditional shopping patterns or demand for specific clothing categories.

For Express, analyzing historical stock prices and trading volume can provide insight into how the company’s shares performed during a period of economic and industry change. However, stock market movements alone cannot establish that the pandemic or any single business decision caused a particular change in share prices.

Graphical Representations of Express Inc. Stock Data

Graphical analysis helps transform historical stock market data into visual information that is easier to interpret. For this assessment, historical stock data from Yahoo Finance were used to examine Express Inc.’s daily high and low prices, adjusted closing prices, and trading volume. Microsoft Excel was used to organize the data, calculate statistical measures, and create visualizations.

Scatter Plots of Daily High and Low Stock Prices

Scatter plots display the relationship between two variables and help analysts identify patterns, clusters, and possible trends. In this analysis, scatter plots were used to examine the highest and lowest daily stock prices over the selected historical period.

Trendlines were added to make the overall direction of stock prices easier to identify. The resulting plots indicated a downward trend in both daily high and low prices. This pattern suggests that Express Inc.’s share price weakened during the period under review.

Microsoft Support (n.d.) explains how scatter charts and related chart types can be used to present data and illustrate relationships. In financial analysis, trendlines provide a useful visual summary, but they do not predict future prices with certainty. A more complete interpretation should also consider relevant company announcements, industry conditions, market sentiment, and broader economic developments.

Histograms of Adjusted Closing Prices and Trading Volume

Histograms show how frequently observations fall within specified numerical intervals. They are useful for evaluating the distribution, concentration, and spread of financial data.

The histogram of adjusted daily closing prices showed how frequently Express shares closed within different price ranges. The distribution appeared relatively concentrated, which is consistent with the small difference between the reported mean and median. However, a histogram covering a period of declining prices can still show a concentrated distribution. Therefore, the shape of the distribution should be considered alongside the time-based trend shown in the scatter plots.

A separate histogram was used to examine daily stock trading volume. Trading volume measures the number of shares traded during a particular period and helps analysts understand the level of market activity. The histogram showed a broad spread of trading volumes, with some observations substantially higher than others. These high-volume observations may represent unusually active trading days and can contribute to a wide overall distribution.

Excel was used to organize the data into appropriate intervals, or bins, and improve chart readability through labels and gridlines. Selecting suitable bin widths is important because overly broad intervals can hide meaningful patterns, while overly narrow intervals can make the distribution difficult to interpret (Capella University, n.d.).

Descriptive Statistics for Express Inc. Stock Data

Descriptive statistics summarize the central tendency and variability of a dataset. In this analysis, the mean, median, mode, and standard deviation were calculated for adjusted daily closing stock prices and daily trading volume. These measures help explain typical values, identify potential skewness, and assess how widely observations vary.

The mean represents the arithmetic average, while the median identifies the middle observation when values are arranged in order. The mode is the most frequently occurring value, and the standard deviation measures how far observations typically vary from the mean. Together, these measures provide a useful overview of the dataset (Lind, 2021).

Adjusted Daily Closing Stock Prices

The reported descriptive statistics for adjusted daily closing prices were:

Statistical measure	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 relatively small difference between these values suggests that the center of the distribution was not strongly displaced by extreme observations. However, the mean and median alone are insufficient to establish that the distribution is symmetrical or that prices were stable over time.

The standard deviation was approximately $1.09, indicating that daily adjusted closing prices varied around the mean. Whether this represents low or high volatility depends on the share price level, the period examined, and comparisons with other periods or securities.

The reported mode was $5.37. This value represents the most frequently occurring price only if the dataset contains repeated observations at that exact value and the mode was calculated correctly. Because stock prices can contain many unique values, the mode should be interpreted carefully and checked against the original spreadsheet.

Stock Trading Volume

The reported descriptive statistics for trading volume were:

Statistical measure	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, while the median was approximately 3.05 million shares. Because the mean was higher than the median, the distribution may have been positively skewed, with a number of high-volume trading days pulling the average upward.

The standard deviation was approximately 2.69 million shares, indicating considerable variation in daily trading activity. Some days may have experienced much higher volume than the typical trading day, potentially reflecting changes in investor interest, market conditions, or company-related news. The available statistics do not establish the cause of these differences.

The mode was reported as unavailable. This can occur when no value appears more frequently than others or when a spreadsheet function cannot return a single mode. For a dataset with many unique trading-volume values, reporting that no mode is available may be appropriate.

Overall, the trading-volume statistics show greater absolute variability than would be suggested by the median alone. Analysts should consider both the distribution and the timing of high-volume observations when evaluating changes in market activity.

Analysis and Interpretation of Stock Performance

The combined graphical and statistical analysis provides several insights into Express Inc.’s historical stock performance. First, the scatter plots indicated a downward trend in daily high and low stock prices during the selected period. This suggests that the market price of Express shares declined over the period examined. The trend describes historical performance, however, and should not be treated as a guarantee of future price movements.

Second, the adjusted closing price statistics showed that the mean and median were relatively close. This indicates that the central values of the dataset were similar, although it does not independently prove that prices followed a consistent pattern. The reported standard deviation provides additional information about the dispersion of prices around the mean.

Third, the trading-volume data showed greater variation, with the mean exceeding the median and the standard deviation indicating substantial differences between daily observations. This pattern is consistent with a distribution influenced by high-volume trading days. Examining those dates alongside company announcements and broader market events could help determine whether particular events coincided with unusual trading activity.

MBA FPX 5008 Assessment 2 Using Analytic Techniques to Add Meaning to Data

The findings also demonstrate why graphical analysis and descriptive statistics should be used together. Scatter plots show how values change over time, while histograms reveal how frequently values fall within different ranges. Descriptive statistics summarize central tendency and variability. No single method provides a complete explanation of stock performance, but combining these approaches creates a more informative view of the available data.

The analysis should also be interpreted within the wider fashion retail environment. Changes in consumer spending, demand for casual clothing, competitive pressure, and economic uncertainty can affect retailers’ business performance. Nevertheless, attributing Express’s stock-price decline directly to COVID-19 or changing consumer preferences would require additional evidence, such as financial reports, market benchmarks, and event-specific data.

Limitations of the Analysis

Several limitations should be considered when interpreting the results. The analysis relies on historical data from a defined period, so its findings may not represent Express Inc.’s current financial condition or future performance. Historical stock prices reflect multiple influences, including company-specific developments, investor expectations, and broader market movements.

The reported summary statistics also do not establish the cause of the observed price trend or high trading-volume observations. Additional analysis could compare Express’s returns with a relevant market index, examine daily percentage changes, identify dates with unusually high volume, and evaluate company announcements during the study period.

Finally, the calculations and charts should be checked against the original spreadsheet to confirm that the selected dates, adjusted closing prices, trading-volume values, and statistical formulas are accurate. Verifying these details is particularly important when presenting results in an academic assessment.

Conclusion

Analyzing Express Inc.’s historical stock data using 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 relatively small difference between the mean and median adjusted closing prices, and substantial variation in trading volume.

These findings illustrate how descriptive statistics can help summarize financial information while graphical techniques reveal patterns that may not be obvious from a table of numbers. However, the results describe historical observations rather than predict future market performance or establish the causes of stock-price changes. For a stronger assessment, the findings should be combined with verified company financial information, relevant market comparisons, and evidence about the economic and competitive conditions affecting Express Inc.

Frequently Asked Questions

What is the purpose of analyzing Express Inc.’s stock performance?

The purpose is to identify historical stock-price trends, summarize typical closing prices, and examine changes in trading activity. These findings can help analysts understand the data and identify questions that require further financial investigation.

Which descriptive statistics were used to analyze Express Inc. stock data?

The analysis used the mean, median, mode, and standard deviation. The mean and median summarize central tendency, the mode identifies the most frequent value when one exists, and the standard deviation measures variability.

What does the difference between the mean and median stock price indicate?

A difference between the mean and median may indicate that a distribution is skewed or influenced by unusually high or low observations. In this dataset, the values were relatively close, but additional measures and visual inspection are needed to assess the distribution’s shape.

Why was the standard deviation of trading volume important?

The standard deviation measured how much daily trading volume varied around its mean. The reported value indicates considerable variation in the number of shares traded, which may reflect unusually active trading on certain days.

How do scatter plots and histograms differ?

Scatter plots help display relationships and patterns between variables, or between a variable and time when time is placed on an axis. Histograms show the frequency distribution of a numerical variable across intervals. Both provide useful but different perspectives on financial data.

Can historical stock data predict future Express Inc. stock prices?

Historical data can help identify patterns and inform financial analysis, but it cannot predict future prices with certainty. Future performance depends on company results, market conditions, investor expectations, and other factors not fully captured by descriptive statistics.

How might COVID-19 have affected Express Inc.’s stock performance?

The pandemic changed shopping habits, work arrangements, and consumer spending across the retail industry. These developments may have influenced Express, but confirming their specific impact on its stock performance requires additional evidence beyond the historical charts and summary statistics presented here.

References

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: Lesson 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

Owler. (2022). Express. https://www.owler.com/company/express

Yahoo Finance. (2022, July 10). Express, Inc. (EXPR) historical data. https://finance.yahoo.com/quote/EXPR/history/