What’s a financial security worth? A smart trader might tell you, “Whatever somebody else is willing to pay for it.” While that might technically ring true, a more academic answer might be, “By observing the present value of its cashflows.” This results in what is known as the fair value or theoretical value of a financial security, which may serve as an anchor point when negotiating a trade or provide a good opening bid/ask for a new security that doesn’t have a trade history. This is great because now we know how to price a security; all we have to do is generate those future cashflows and discount them back to the present using the appropriate interest rate. So how do you project those cashflows?

There is no shortage of opinions as to what the market might look like tomorrow. Research analysts devote their careers to understanding how a given company, industry, or whole market might react to changing business landscapes. Quantitative models can be leveraged to perform rigorous projections of how market factors might move with respect to their historical, statistical properties, and co-movements with other factors. In short, we just need to leverage a set of financial models trusted by the industry to generate cashflows for each type of security.

For decades, IBM Algorithmics has helped some of the world’s largest financial institutions manage their risk using a curated collection of financial models. We’re bringing that same capability to the cloud through a new set of IBM Cloud services. The Instrument Analytics service leverages the powerful IBM Algorithmics security valuation models to compute analytics on financial securities. What makes these models so powerful is that they project the behavior – usually the cashflows produced throughout the life of the security – in order to arrive at a value, even if no current valuation exists. The Instrument Analytics service supports the computation of a theoretical or market-calibrated valuation and all relevant associated analytics for investment securities. This type of analysis would appeal to:

  • Front-office traders searching for acceptability in an offer made by a broker.
  • Fintech developers looking to answer quantitative questions within investment management.
  • Risk managers looking to understand sensitivities to various market movements in order to hedge or insure investment portfolios.
  • Small to medium financial institutions struggling to perform the analysis required by financial regulations.

A new developer pattern titled “Compute analytics on an investment portfolio” demonstrates the next step in these financial models’ evolution. Whereas previously, the burden lay in data selection and curation, model configuration, and supporting the vast computational infrastructure required, we can now leverage the power of these models in a single line of code: a RESTful API call. When you complete this pattern, you’ll have a good understanding of how to:

  • Use the Investment Portfolio service to store and retrieve investment holdings over time, using the CSV file format used by production clients today.
  • Request a series of analytics from the Instrument Analytics service on the holdings in your portfolio.
  • Integrate those results into a GUI or download them directly to CSV for further analysis or integration.

With this set of tools, we expect this robust analysis to become more pervasive. By reducing friction and making the analysis easier to access, we hope to raise the standard for the industry at large as the next generation of financial technology is pioneered.

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