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The goal was to apply the best practices from academic finance and institutional investing to the needs of individual investors, no matter what their financial balances were.

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At the time, there were two things happening in the investment landscape. The first was the shift from Defined Benefit plans (pensions) to Defined Contribution plans (401(k)s), which left the burden of investment decisions on the individual. The second was the rise of the internet, particularly in the heart of Silicon Valley, home to Financial Engines and Stanford University, the alma mater of many starting members.

Christopher Jones, former chief investment officer and now a senior advisor to 91��̳ Engines, was one of the company’s first employees, building and managing the team of investing experts from the ground up. As he recalls, “Dr. Sharpe saw the opportunity for bringing both of these seismic shifts together, by combining powerful software technologies with sophisticated financial theory, which would ultimately serve individuals – regardless of their wealth or investment expertise – to provide independent, objective investment advice.”

The first problem the team faced when building the “financial engine” was how to model a wide range of scenarios about how the economy and financial markets might evolve over time to give people a realistic view of possible future outcomes. They would rely on Dr. Sharpe’s pioneering work in financial economics, including how prices of financial assets are determined, and the link between risk and return.

To solve this, the “engine” uses what the team called the “Pricing Kernel” – a model that takes current economic conditions like inflation and interest rates and generates thousands of possible future paths for how the stock and bond markets might perform in the future. The goal was to identify possible paths that reflect how markets actually behave: How do inflation and interest rates interact? How do different asset classes move with each other? How do unexpected economic shocks play out over time?

Next, the team worked to show how a range of different asset classes might behave in each of these future scenarios. The framework divides the investing world into 16 different asset classes, each with their own behavior and patterns of performance. Each future scenario considers how each of the asset classes might perform in relation to each other under an array of possible circumstances.

From there, Financial Engines built models to capture the characteristics of more than 38,000 mutual funds, Exchange-Traded Funds and individual stocks, each incorporating a mix of exposures to up to 16 different asset classes, along with a host of characteristics that might cause their behavior to differ from the broad asset classes. This includes risk factors that might be specific to that fund or company, the impact of fees and expenses, capturing the impact of taxes, and modeling the predictable components of active manager performance. With this sophisticated security-specific forecasting tool, investors can see the full range of possible outcomes for their particular investments.

Next, the team developed the ability to analyze thousands of possible securities to build customized investment strategies, taking into consideration an individual’s time horizon, goals, preferences for risk, and even their tax situation.