Formal models are receiving increasing attention for their ability to more accurately predict outcomes than humans working without them. During the 2008 and 2012 presidential elections, Nate Silver’s mathematical modeling for "The New York Times" correctly predicted the outcomes in 49 and 50 states respectively. While no model has yet consistently outperformed the markets on its own, a growing number of fund managers and investors use mathematical models to invest smarter by automating research and identifying risks.
Models excel at predicting risk. Mitigating and managing risk allows investors to stay ahead of the market not by winning bigger and more often, but by losing less often. Insurance companies operate on the anticipation that they will frequently suffer losses; however, if their actuaries can predict the rate at which claims will be made they can set premiums at profitable levels. Similarly, an investor who has a stake in a manufacturing company might use models to predict the likelihood of a supply shortage from a troubled country and hedge against that risk accordingly.
Investors seek to gain from long-term appreciation of assets. Traders, on the other hand, scour the markets for aberrations on which they can capitalize. For example, stock in a particular company might sell for less than that of a similarly situated competitor. Unless there’s some business reason for the difference, traders might anticipate that the lower price stock is undervalued and likely to increase in value soon. Mathematical models can identify stocks that trade at lower valuation multiples, such as price to earnings, for further examination.
In light of the financial market crash of 2008, investors have spent more time examining the worst-case scenarios for their portfolios. Investors can build models to predict how different events would impact the value of their stocks, and run crash tests to look for major risk factors. By testing extreme cases, investors can make contingency plans to avoid financial ruin in the face of an unanticipated crisis.
Discounted Cash Flows
Perhaps the gold standard in valuation, discounted cash flow (DCF) models reduce the anticipated net income for the decreasing value of money over time and the risk associated with a particular venture. As could be anticipated, DCF models rely on a number of assumptions based on historical experience, and are most accurate under unchanging circumstances. However, creating a DCF model to value companies based on anticipated income provides investors with a way of setting price targets for buying and selling stocks in absolute terms.
Sean Butner has been writing news articles, blog entries and feature pieces since 2005. His articles have appeared on the cover of "The Richland Sandstorm" and "The Palimpsest Files." He is completing graduate coursework in accounting through Texas A&M University-Commerce. He currently advises families on their insurance and financial planning needs.