Cy's Data Lab

By Cy Seeley, M.S. Student, Applied Data Science, Syracuse University

Abstract

This study analyzes 846,404 stock trading records to uncover patterns, anomalies, and key insights into market behavior. Metrics such as closing prices, trading volumes, and percentage price changes were examined using statistical and visualization techniques. Key findings include the stock with the highest closing price, trends in trading volume over time, and significant correlations among key variables. Additionally, the analysis identifies potential anomalies and provides recommendations for further research.

Introduction

The stock market is a dynamic and data-rich environment, reflecting investor sentiment, economic conditions, and market activity. This study explores a dataset of over 846,000 stock trading records to uncover patterns and relationships between key metrics, such as opening and closing prices, trading volumes, and percentage price changes. With detailed information on stocks and their trading behavior over time, the analysis provides insights into market dynamics and identifies anomalies worthy of further investigation.

This paper aims to answer critical questions: Which stocks exhibit the highest trading activity? How do price trends evolve over time? What correlations exist among key variables, and how might they inform trading strategies? Using statistical analysis and visual tools, this study aims to provide actionable insights for investors, analysts, and researchers.

Key Findings

• Price variables (OPEN, HIGH, LOW, CLOSE) exhibit strong interdependence, driving stock performance analysis.
• Trading volumes are dominated by highly active stocks, with noticeable anomalies suggesting irregular patterns.
• A minor downward trend in stock prices reflects broader market caution or bearish sentiment.

Conclusion

This study demonstrates the potential of stock market data to uncover meaningful insights and inform trading strategies. By analyzing metrics such as closing prices, trading volumes, and percentage changes, the findings provide a comprehensive overview of market behavior.

The results highlight the importance of price metrics in predictive modeling while identifying areas for improvement, such as incorporating macroeconomic indicators to contextualize stock trends. Further investigation into anomalies in trading volumes could reveal unusual market behaviors or data inconsistencies.

By combining statistical analysis with visual tools, this study underscores the value of data-driven decision-making in the stock market, offering valuable insights for investors and researchers alike.

Link to the Paper