I tested whether home improvement and insurance stocks rise during hurricane season. They actually go DOWN. [OC] Visualization
![I tested whether home improvement and insurance stocks rise during hurricane season. They actually go DOWN. [OC] Visualization](/api/images/reddit-maps/1uo56l5_1783267203566.jpg)
Data Analysis
What This Visualization Shows
This data visualization displays "I tested whether home improvement and insurance stocks rise during hurricane season. They actually go DOWN. [OC]" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on everyone assumes hurricane season is good for home improvement stocks. more storms, more damage, more people buying plywood and generators. insurance too, premiums go up right? so I tested it.
turns out it's the opposite.
**what I did**
took the start of Atlantic hurricane season (June 1) every year from 2010 to 2025. measured how a basket of hurricane-exposed stocks performed vs the S&P 500 around it — Home Depot, Lowe's, Allstate, Travelers. standard event study, CAPM market model, abnormal returns, t-test.
**results**
the basket underperformed the market by 2.1% on average. statistically significant (p=0.034). positive in only 4 of 16 years. by stock: Allstate -3.1%, Lowe's -3.1%, Home Depot -1.7%, Travelers -0.6%. worst years 2021 (-9.8%) and 2025 (-7.2%).
**why (my theory)**
insurers sell off because the market prices catastrophe risk *before* it happens — 5 months of potential billion-dollar payouts starts June 1. home improvement's rebuilding bump is real but localised and shows up AFTER landfall, not at season start. basically the obvious trade is too obvious — if "buy Home Depot before hurricane season" worked, everyone would do it and it would stop working. which is exactly what the data shows.
**what should I test next?**
queued up: defence stocks during conflicts, silver vs gold as crisis hedge, whether the rebuilding bump shows up if you measure after landfall. what do you want tested? the weirder the better
*source: yahoo finance via yfinance. not financial advice, I just like checking whether market folklore is true*
, which allows us to understand complex relationships and insights within the data through visual storytelling.
Deep Dive into the Topic
This data visualization represents a sophisticated analysis of complex information patterns that provide valuable insights into underlying trends and relationships. Data visualization serves as a bridge between raw numerical data and human understanding, transforming abstract statistics into comprehensible visual narratives.
The power of data visualization lies in its ability to reveal patterns, outliers, and correlations that might not be apparent in traditional tabular formats. Through careful selection of chart types, color schemes, and interactive elements, effective visualizations can communicate complex information quickly and accurately to diverse audiences.
Modern data visualization combines statistical analysis with design principles to create compelling visual stories. This interdisciplinary approach requires understanding both the underlying data and the cognitive processes involved in visual perception. The result is more effective communication of quantitative insights that can inform decision-making and drive positive change.
Data Analysis and Insights
The patterns revealed in this visualization demonstrate the importance of systematic data analysis in understanding complex phenomena. By examining different data segments, time periods, and categorical breakdowns, we can identify trends that inform strategic planning and decision-making processes.
Statistical analysis of this data reveals variations across different dimensions that provide insights into underlying drivers and relationships. These patterns help identify areas of opportunity, potential risks, and key performance indicators that can guide future actions and resource allocation.
The analytical approach used in this visualization enables comparison across different categories, time periods, or geographic regions, revealing insights that support evidence-based decision-making. This type of analysis is essential for organizations seeking to optimize performance and understand complex market dynamics.
Significance and Applications
This data visualization has important implications for understanding trends and patterns that affect decision-making across multiple sectors. The insights derived from this analysis can inform policy development, business strategy, resource allocation, and operational improvements.
For analysts, researchers, and decision-makers, this type of data visualization provides essential insights for strategic planning and performance optimization. Whether addressing operational challenges, market analysis, or policy development, understanding data patterns helps create more effective strategies and solutions.
The broader significance lies in how this information contributes to our understanding of complex systems and relationships. This knowledge helps predict future trends, identify potential challenges, and develop more informed approaches to problem-solving and opportunity identification.
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About the Author

Alex Cartwright
Senior Data Visualization Expert
Alex Cartwright is a renowned data visualization specialist and infographic designer with over 15 years of experience in...