Prattle AnalyticsA Boston/St. Louis-based startup is hiring financial engineers. Prattle Analytics, founded by a Harvard PhD., is a unique financial data and information company that utilizes its proprietary methods and algorithms to forecast global capital market movements based on central banking communications.

Candidates with investment banking and/or hedge fund backgrounds and aspirations are welcome to explore career opportunities with the company. Compensation is negotiable for highly qualified individuals with the opportunity to earn an equity stake in the venture over time for top performers.

Should you wish to apply, e-mail your resume (cover letter optional) and inquiry to


Exclusive Interview with the CEO

I asked the founder and CEO of the company, Mr. Evan Schnidman, a series of questions; his answers are below:


What is Prattle Analytics?

Prattle Analytics is a financial data company. We use proprietary text analysis technology to analyze the content of central bank communications. This method of analysis is unlike any existing “Fed Watching” methodology because it relies on examining central bank communications in a completely comprehensive and unbiased way. The result is the first ever quantitative Fed watching data.


How / why / when did you decide to pursue this idea?

As a PhD student I began researching how central bank communications impact financial markets. I quickly became a financial news junkie watching far too much CNBC and Bloomberg TV. In my hours of news watching I became increasingly annoyed with how these networks covered central bank communications: they focused on a single sentence or phrase in a larger communication and simply ignored the rest of the text. They would then proceed to debate one phrase for hours. This method of analysis struck me as biased and incomplete; they were leaving data (in this case words) unanalyzed. So I set out to develop a better method for analyzing these communications.


Who are your core customers?

Our customers are predominantly hedge funds and mutual funds, though almost any portfolio manager can easily find a use for our data. Global macro funds and algorithmic trading funds tend to be the most enthusiastic about integrating our data into their existing strategies.


What has been the feedback you’ve received so far on your research/products?

Most of our customers backtest our historical data prior to purchasing. A few have been kind enough to share that backtesting information with us and thus far, the response has been overwhelmingly positive. In fact, a futures fund recently informed us that in their return to volatility trading strategy they tested our data as a trading signal in comparison to buy and hold and trend following only to find that our central bank sentiment data yields more than double the equity market return generated by either strategy. Further, they found that our data has low correlation to these commonly used strategies, so it can be used in concert with them to generate even greater returns. We see this as only one small application of our data and our internal testing shows that the opportunities in Forex are potentially more lucrative because we have data on central banks around the world, thus data on both sides of many currency pair trades.


Who are the key members of your team?

Aside from myself, my business partner and CTO, William MacMillan is also indispensable. Bill has a PhD from University of Michigan and he is a brilliant statistician and a good enough programmer that he recently won a hackathon. His skills have allowed us to scale my initial idea into a viable business. In addition to Bill, our Lead Developer, Jermell Beane has been extremely valuable in setting up our database systems and optimizing our data analysis and delivery for speed. We have also recently added a Director of Operations, Natty Hoffman. She will be taking much of the operational burden off me so that I can further focus on sales. I should also take a moment to acknowledge our dedicated team of interns who have done an incredible job helping us develop much of the data in our database.


Why should a financial institution, investment bank, or asset management firm choose you?

As I noted earlier, our data is the only comprehensive, unbiased and quantitative central bank data available. But our data is more than unique, it is also an incredible trading signal across asset classes. So, a financial institution should be interested in our data product if they want to make more money. Perhaps more precisely, since our data is a superb sell signal ahead of down markets, they should want our data if they want to preserve their returns.


Who are your competitors?

Although nobody else provides comparable central bank sentiment data, there are several broader sentiment data products sold. These macro data products can be informationally interesting, but they are rarely a direct trading signal.


What makes your approach different?

Our methodology is far more sophisticated than that of other financial sentiment data firms. We often get customers telling us that they won’t buy sentiment data because it is not tradable and historically that has been true, our data changes that. While prior methods relied on scoring good buzzwords minus bad buzzwords to derive sentiment, our methods are far more comprehensive and thus much more accurate. The result is precise data that serves as a tradable signal designed to be input directly into existing multi-factor models.


What is it about your background that makes you an expert in this field?

I have a PhD in Political Economy from Harvard University with extensive experience in finance and consulting. My business partner, Bill MacMillan, is a PhD Applied Statistician with extensive experience in data science across industries. Our combined skills not only make us experts in this field, but the ideal type of experts because of the subject matter of our academic research. I focused on analyzing the market effects of central bank transparency and communications while Bill used text analysis to analyze federal regulatory information. These combined research skills allowed us to develop a methodology that pulls together four disparate strands of academic literature and create a completely unique, patent pending, method of analyzing the text of central bank communications.


What is the future of your company?

We are presently finishing development on our central bank sentiment data; by midyear we expect to have data on 20 central banks around the world. Given the diminishing returns of analysis on central banks in smaller economies, we plan to begin applying our text analysis algorithm to a variety of other subjects. In particular, we are working on a data product that examines the sentiment of corporate and regulatory communications to provide valuable data on individual equities.


What is your view on financial markets today?

Markets today are surprisingly subdued for the growing turmoil we are seeing globally. In particular, disinflationary pressures in Europe and slowing growth in China pose real threats to global growth, even as the U.S. economy appears to be normalizing. Although 2015 looks like it could be a neutral to slightly positive year for markets, I suspect that by year-end we will be concerned about down markets persisting through 2016.


If there is one thing we should know about your company above all else, what would that be?

Our data is the only commercially available – tradable source – for comprehensive, unbiased, quantitative data on central bank communications.


About the Founder

Evan SchnidmanEvan A. Schnidman is the founder and CEO of Prattle Analytics, a financial data company. Evan holds a Ph.D. from Harvard University as well as Bachelors and Masters Degrees from Washington University in St. Louis.

Evan has been featured in Bloomberg News and on The Deal. Evan’s financial research has been featured in The Wall Street Journal, Bloomberg View and Seeking Alpha while his academic research has been published in journals and edited volumes. Notably, Evan’s first article in Bloomberg, “Strong Dollar Advocates Make a Weak Case” (Jan. 25, 2012), showcased a mathematical model he designed to successfully predict every intermediate S&P 500 market bottom and top since 2008. Evan’s financial research will be further showcased in his forthcoming book titled “How the Fed Moves Markets.”

In his consulting capacity, Evan has vetted the political, economic and financial risks of major infrastructure investments for large corporations. Evan has also vetted finances, management structures and community engagement of small and midsize financial institutions to maximize relationships, tax status and grant opportunities from the government. From 2010-2014 Evan taught courses in economics, public policy and political science at Harvard University and Brown University.

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