Consulting

Economic analysis and valuation

I did an economic analysis for a prominent economic forecaster explaining why the historical relationship between money and inflation has become even more tenuous: the diminished importance of traditional bank deposits and the role of the dollar as world money.

A California casino considering a $100 million upgrade wanted to know if their location was desirable enough to make this upgrade a financial success. I used a proprietary model to estimate the casino’s “location value," which takes into account the proximity to potential customers and the location of casinos competing for their business.

A mining and construction company wanted to build a quarry near a thriving community. The company’s economic consultant estimated that the quarry would bring $150 million in annual benefits to the city with no costs whatsoever. I found that the economic benefits were a fiction and that there were many substantial costs, including lower property values, reduced tourism, and increased driving times.

A quarry was proposed on open ranch land with no apparent economic costs. I used a proprietary model to show that there were substantial costs and minor benefits.

A windmill farm was proposed on open desert land. I analyzed the data and found that the promised economic benefits to the region were greatly exaggerated.

I prepared an economic analysis of a commuter suburb showing that new home construction depends on home prices relative to the prices of homes that are closer to where people work. One implication was that builder fees that substantially increase local home prices would reduce construction activity by a significant amount.

A company was going to use the internal rate of return (IRR) to choose between constructing a one-story or two-story building on some property it owned. My analysis showed that the IRR was misleading.

A private company was preparing a bid for a municipal water company and esimated the internal rate of return (IRR) to be over 100%. I used a valuation model to clarify the analysis.

In a divorce case, I estimated the probability that a person of a given age and with a specified amount of investable funds could sustain various levels of monthly spending for the remainder of his/her life.

In a divorce case, I constructed reasonable estimes of the annual rate of inflation and the average annual returns from stocks and Treasury bonds over the next 20 years.

A new restaurant was considering raising cash to complete construction by selling “gold cards” that would give card owners free meals when the restaurant opened. I analyzed whether this made financial sense.

I prepared an analysis for an investment adviser of the stock performance of companies that have been recognized as best places to work.

I prepared an analysis for a fund manager of the stock performance of companies that are selected annually as Fortune's top-10 most admired companies.

Statistical Analyses

A disputed Congressional election hinged on the discovery of a box of uncounted ballots. I found that it was highly improbable that randomly discovered ballots would differ so dramatically from the other ballots.

A private elementary school’s reputation for academic excellence was based on its students’ high standardized test scores. I reanalyzed the data taking into account turnover in the student body over time and the anticipated regression to the mean in scores, and concluded that its reputation was unwarranted.

I constructed a model and wrote a software program for categorizing a person’s personality based on answers to dozens of test questions. This model has two valuable features: (a) the weights given to individual questions depend on how effectively the question differentiates personality types; and (b) the test results give the probability that a person has a personality type.

I have done several analyses of the housing market arguing that the intrinsic value of a home can be estimated in the same way as the intrinsic value of stocks—by looking at the income and expenses.

I identified the factors that made some communities the most resistant to the 2005 to 2010 meltdown in residential home values. One of the most important factors was whether 2005 home prices were above intrinsic values.

I constructed a model and wrote a software program for an investment manager who wanted to help his clients quantify the tradeoff between shortfall risk (the probability that real wealth would drop below a specified floor during the investor’s lifetime) and the level of wealth at the end of one’s lifetime.

I found that earnings forecasts are systematically too extreme—too optimistic for companies predicted to do well and too pessimistic for those predicted to do poorly. The accuracy of these forecasts can be improved consistently and substantially by shrinking them toward the mean. In addition, portfolios of stocks with relatively optimistic earnings predictions underperform portfolios of stocks with relatively pessimistic predictions.

Speaking

Many talks in small venues. Here are some talks to large audiences:

At the BrainBar conference in Budapest, I debated whether computers would soon be more intelligent than humans.

At a FreedomFest conference in Las Vegas, I debated whether bitcoin is a Ponzi scheme or a legitimate investment.

I gave the keynote talk on regression to the mean at a major CFO conference.

I gave the keynote talk on the housing market to a meeting of the Mortgage Banker's Association.

I gave a talk to the Brookings Institution on whether there was a housing bubble

I gave a keynote talk on algorithmic investing to the Los Angeles chapter of the American Association of Individual Investors.

I gave a talk on the dangers of AI algorithms at the Atheneum at Claremont McKenna College

I have given dozens of radio and television interviews that include (I hope) compelling stories and memorable lessons.

Among my favorite topics are regression to the mean, the AI Delusion, value investing, and the pitfalls of data mining.

Expert Witness

"Gary, you are amazing. So glad you are part of our team.”—Betsy Lowrey, Senior Management Analyst, City of Temecula