Many funds currently are exploring using data to assist in validating investment ideas, but these funds either do not possess the engineering acumen to perform the job properly or the programmers that are hired are given extremely specific data sets and theses to validate, which often cause the engineers to miss diamonds in the rough. To perform the job properly, engineers must posses financial knowledge as well as technical knowledge.

Asset managers still view data-analysis as a cost center and therefore are very reactionary when it comes to how the job is performed. They purchase data sets which have been popular for years and use this data incorrectly (such as aggregated credit card transactions) or subscribe to data-aggregation and research services such as yipit data, therefore becoming three times removed from the original thesis (A smart fund will work with a service such as Volmanac or build tools internally, Yipit will later copy this idea and write about it, then their subscriber funds finally learn about the idea and view the data months or potentially years later). This doesn’t mean these aggregation services aren’t helpful, but there is much less chance of finding very market moving information which can lead to a very asymmetric trade.

It is very difficult to come up with unique and new methods of acquiring relevant, marketing moving data and information. For example, our WHOIS bankruptcy search required technical knowledge and understanding of the bankruptcy and restructuring process - we are currently the only service able to perform this complex search. Other creative methods include tracking the amount of natural gas into plants owned by ethanol producer GPRE to estimate ethanol revenues, estimating foot-traffic at Restoration Hardware (RH) using cell phone signals, and monitoring Planet Fitness (PLNT) churn with app downloads and new credit card transactions.

Additionally, understanding when information is important and when information is just noise is much more difficult than simple statistical models would suggest. False positives and false negatives and both very costly, and differentiating often requires advanced investing knowledge. For example, the website ‘’ was recently registered.


This website previously was used to reference the bankruptcy of Orchard Supply Hardware, according to Wayback Machine:


Lowe’s bought Orchard Supply out of bankruptcy for $204MM in cash in June 2013. To understand if this website could reference the same Orchard Supply, it is important to understand if Orchard Supply Hardware is in fact a subsidiary of Lowe’s, or if it is included within Lowe’s Companies, Inc. If it is a subsidiary, does it provide any guarantees to any of the $15BN in bonds Lowe’s has outstanding (Orchard Supply Company, LLC was the entity Lowe’s used for the asset purchase out of bankruptcy in 2013)? Could it reference another Orchard, or maybe it is just noise. Understanding what data is important and what data is noise is the most important aspect of this process, and often the part which is overlooked by many funds. It requires in depth financial knowledge as well as extensive mathematical and engineering experience, all of which Volmanac possesses.

Volmanac works with some of the largest funds and asset managers to monitor existing investments and discover new ideas. Please contact us if you have an investment thesis or idea you believe could benefit from data analysis.