def about(name)
    puts "#{name} utilizes data from unique public and third party sources to significantly
    reduce the risk profile on invested capital"
end
about('Volmanac')
#=> prints 'Volmanac utilizes data from unique public and third party sources to significantly reduce the risk profile on invested capital.' to STDOUT.

Volmanac is a fundamental / value investor who utilizes public and third-party data combined with proprietary data collection and aggregation strategies to monitor and deploy capital. We believe that small increases in confidence intervals translate to exponentially improved asset risk profiles.

We have combined our deep investing knowledge with a state of the art technology backend to make discovering investment catalysts a repeatable process. Learn more about the process HERE.

Volmanac Advantages:

  • Monitor More Assets and Themes - We leverage computing power rather than an army of analysts
  • Idea Generation – Data is often applicable to multiple assets or will lead to a more efficient risk reducing trade
  • Capital Deployment - We can size larger trades given our enhanced probability distributions from data analysis
  • Repeatable Methods – Larger data sets and asset coverage create more investment opportunities
  • Portfolio Advantages - Assets and Sectors are increasingly correlated and information is often applicable to a broader industry
  • Enhanced Fund ROI - Incremental ROI of a security Analyst at a fund quickly diminishes. Programmer ROI remains high longer
  • Exponential Data growth - New data sources and APIs coming out every day
  • Uncrowded Strategy - Tremendous amount of low hanging fruit. Most fundamental and distressed investors view data analysis and programming as a cost center (and do not have technical knowledge) and are not investing in these strategies.

Common Misconceptions:

Your strategies are only applicable to consumer equities.

While our strategies are beneficial in evaluating consumer and retail equities, our strategies extend much further. For example, many investments in our research section discuss fairly exotic distressed credit trades (i.e. predicting bankruptcy filings).

Information does not discriminate based on asset class, and frequently the best way to express our thesis is not through an equity position. In fact, the most asymmetric and levered assets tend to be built upon multiple tiers of liabilities based on very simple consumer data such as FICO scores. The ability to measure changes in aggregated data such as personal bankruptcy creditors, florida property insurance rates, and cigarette purchases has many more applications than just to retail equities.

You can’t deploy enough capital for your strategies to be effective.

We are a fundamental / value investor who utilizes data to validate and monitor positions. If anything we should be able to deploy more capital than the traditional value fund for various reasons:

  1. After a certain point at a traditional fund, the marginal return of hiring a new analyst approaches zero as there is a limit to the number of investment professionals who can cover the same name. However, at Volmanac the marginal benefit of hiring an additional programmer should remain high for a very long time as the amount of data available is increasing almost exponentially.
  2. As Volmanac ingests more data and is able to even slightly increase confidence intervals, asset risk profiles improve dramatically, allowing us to size positions even larger.
  3. There is currently much more low-hanging fruit using data analysis to discover and monitor investments. There are thousands of funds investing using roughly the same fundamental strategies, yet very few are leveraging data to assist in investment decisions similar to Volmanac’s approach.

Additionally, one of Volmanac’s goals in publishing this site is to make data analysis a more integral (hopefully mandatory) aspect of the investment process. Please contact us us with any questions, data sources, or investment ideas.