Practical analysis for investment professionals
13 January 2014

Making Sophisticated Financial Models Available to All: Thinknum

Whether you are a seasoned fundamental investor struggling to keep on top of mountains of disparate global data sources, a quantitative analyst looking for distributed computing power to run your models, or a candidate for the CFA charter, chances are you will appreciate Thinknum  and its radical new business model: making sophisticated financial models available to everyone on an open, distributed computing platform. Traditional data providers should take notice as the Internet and its creative possibilities encroaches on yet another old business model.

Here is the unedited transcript of an interview I conducted with Thinknum’s co-founders, Justin Zhen and Gregory Ugwi, who both make a compelling case for why you may want to investigate Thinknum.

CFA Institute: What is Thinknum?

Justin Zhen and Gregory Ugwi: At its core, Thinknum is a platform for non-programmers to create financial models. There is an abundance of information and data on the web today, but it is scattered and loosely organized at best.

Thinknum has aggregated data from over 2,000 sources and will continue to do so, consolidating and presenting it to our users in an intuitive format. All of Thinknum’s financial models update automatically when companies publish earnings.

How does Thinknum benefit investors?

Thinknum’s software runs on a distributed computing system. For example, traders running sophisticated simulations on their local machines have to wait hours for the result; with Thinknum, this process is cut down to minutes.

Thinknum is radically open. Currently, most existing financial software prices out millions of financial analysts and investors, missing out on their respective insights. Thinknum is a platform for a domain expert in China to communicate with an investor in New York. We are indexing the world’s financial information.

Interesting. So Thinknum benefits from the collaborative insights of global investors, both professional and amateur alike?

Exactly. We believe that with a deeper pool of information and insights, the best ideas will inevitably bubble to the top. For example, Michael Burry and the guys at Cornwall Capital were able to identify a shorting opportunity in subprime mortgages long before established players on Wall Street caught on.

What kind of models are you talking about? Macroeconomic? Project financing? Equity valuation? Fixed-income valuation? Yield curve calculators?

We are talking about models for pricing financial securities. Thinknum has built a platform to create and share equity valuation models. We also have fixed income valuation models that our paying clients use to backtest strategies on a distributed computing system.

Research analysts currently publish estimates of companies and produce price targets along with a few other metrics, but their underlying financial models are not released. Thinknum places heavy emphasis on the analytical part; an investor should always show how he arrived at a valuation.

Thinknum takes advantage of the recent explosion of information sharing on the web, collecting different insights from investors and contributing better financial analysis as a result.

You mentioned earlier that Thinknum has aggregated data from more than 2,000 sources. Tell us more about that feature and about some of the data sources utilized.

We collect market data from exchanges, company filings on XBRL EDGAR, and macroeconomic data from government agencies like FRED, EUROSTAT, Ireland’s CSO, and others. These agencies are independent, often territorial, and have little incentive to ensure their data releases play well to each other. By bringing all these diverse data sources together on one platform, Thinknum enables investors to make interesting connections.

For example, we collected data on mortgage put-back requests (ABS-15G filings) and created an open database. Distressed credit investors used this information to find the bonds where investors have made the largest repurchase claims.

Thinknum is committed to indexing all of the web’s data that is useful for conducting investment analysis.

I am guessing that the success of Thinknum relies on attracting a large and diverse user community, yes? How are you planning on attracting this community? Also, talk about how you can ensure that the quality of your user base is very high.

We will attract a large user community because we have seen a major need for a platform like Thinknum. Thinknum was partially motivated by an observation we made on Goldman Sach’s trading floor during earnings season. We watched as hedge funds scrambled to get updated financial models from research analysts, who were in turn frantically updating them by hand.
We also observed the viral rise of user-generated content and crowdsourcing in finance.

Current platforms are focused on showing results generated by their users, while Thinknum is focused on showing the analysis behind each user’s conclusion.

To uphold user quality, we have been doing extensive research on the most successful crowd voting systems to filter through the models. We also designed an algorithm on top of this system to optimize the ranking of users and their respective analyses. We continue to refine our ranking algorithm as we collect more data.

What current businesses do you expect to be most threatened by Thinknum? How will you cope with their response?

Just like Web 2.0 is using collaboration and crowdsourcing to create a more trusted alternative to traditional media, we are building a collaborative platform for investors to create and share analysis with people they trust instead of relying on regurgitated analysis.

Our audience is among the most sophisticated in the investment business, and that is a PR-like answer.

Do you really expect no competition from interested parties?

We expect traditional data analysis providers to be most threatened by Thinknum. Current providers have primarily built their infrastructures on closed networks, such as local desktop software programs. Whereas Thinknum is taking advantage of Web 2.0’s movement towards open communication and building a platform for collaborative financial analysis.

Finally, what are your recommended next steps for people who may be interested in Thinknum?

Sign up now at thinknum.com and join our community to keep tabs on the new features we are rolling out.

Thank you for taking time out of your busy start-up schedule to share the Thinknum story!

 


Please note that the content of this site should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute.

Photo credit: ©iStockphoto.com/retrorocket

About the Author(s)
Jason Voss, CFA

Jason Voss, CFA, is a content director at CFA Institute, where he tirelessly focuses on improving the ability of investors to better serve end clients. He is the author of the Foreword Reviews Business Book of the Year Finalist, The Intuitive Investor. Jason also ran a successful blog titled What My Intuition Tells Me Now. Previously, Voss was a portfolio manager at Davis Selected Advisers, L.P., where he co-managed the Davis Appreciation and Income Fund. He holds a BA in economics and an MBA in finance and accounting from the University of Colorado.

Ethics Statement

My statement of ethics is very simple, really: I treat others as I would like to be treated. In my opinion, all systems of ethics distill to this simple statement. If you believe I have deviated from this standard, I would love to hear from you: jason.voss@cfainstitute.org

6 thoughts on “Making Sophisticated Financial Models Available to All: Thinknum”

  1. Tyler Lahti says:

    This is a cool concept and exciting. I’ll watching, now.

    1. Hi Tyler,

      Thank you for your feedback. I am certain the gentlemen from Thinknum would also be thrilled to know you are watching.

      With smiles,

      Jason

  2. The given concept is about to very useful for me. From the last few years, financial modeling courses have become a first choice of students.

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