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Data scraping can be a valuable tool for hedge funds in informing their investment decisions and strategies. By targeting a relevant list of websites, hedge funds can track competitor growth, track prices, analyze market sentiment, and more to help create a map of the macroeconomic playing field. Internet data can provide hedge funds with a wealth of actionable information. These insights can be critical in making more informed investment decisions, developing innovative investment strategies, and managing risks. The ability to analyze large volumes of data in near real-time, often from diverse and unconventional sources, provides a significant competitive edge in the highly complex and dynamic world of hedge fund management.
A hedge fund with large stakes in some of America’s biggest retailers came to Atlas looking to help inform their investment strategies in the space. Our client aimed to assess pricing variations across thousands of similar SKUs from different retailers to gain insights into market trends and consumer behaviors.
We spoke directly with analysts at the hedge fund to identify the types of data that would be most beneficial to their investment strategy. The client selected a list of relevant retailers (Walmart, Home Depot, Lowes, Costco, TJ Maxx, Kraft, etc) which was then filtered down to Home Depot and Lowes being the most relevant for their business needs. In addition, the client narrowed down a list of relevant product types between the 2 websites that would serve as the source of price comparison to be made.
With a clearer understanding of the target business objective, along with the target websites and inputs to focus on, we began our data scraping-related work.
Our team developed custom AtlasBots to parse through the different portions of the Home Depot and Lowes websites and extract the pieces of information our client requested. Despite both companies being major retailers, navigating their web architectures proved difficult given the complexity of their website structure. That said, we successfully initiated the data extraction programs, and put together a raw data dump containing the target information our client requested.
Having organized a raw data dump of information, we meticulously cleaned, processed, and formatted data to harmonize it between the two different retailers. This involved adjusting the data to overcome the inconsistencies between how each retailer presented its information. For example, one retailer may present different parameters with different fields of unit measurement than the other, and in order to properly join the two datasets together we went column by column to ensure a full match was doable between the Home Depot and Lowes datasets.
Now, having fully sourced and cleaned our desired datasets, we pass things forward for analytic work. The cleaned data was presented in a Python-based visualization dashboard, enabling the client to examine price discrepancies between the 2 retailers. Along with price discrepancies, the client was able to slice the dataset in different ways to analyze the distributions of reviews, brands, and other qualitative metrics shared between Home Depot and Lowes. Our client now had a much clearer picture of exactly how much more expensive different SKUs and brands were for a similar set of products in the Home Depot and Lowes stores.
Along with the BI dashboard, Atlas was able to seamlessly export the cleaned data in Excel format allowing for further client analysis. The client could easily build out custom Excel models on their end to further mine the dataset for insights.
All tasks completed, the insights gained through this project allowed our client to craft tailored investment strategies. By understanding the pricing and product strategies of the retailers, they were able to predict market trends and invest accordingly. Having access to such a detailed comparative analysis of major retailers granted our hedge fund a unique edge over competitors in the market. The result was investment decisions being made with significantly more clarity.