Stephen Leeb

Stephen Leeb, Ph.D. is the Chief Investment Strategist of The Complete Investor and Real World Investing.

Dr. Leeb’s books have been notable for predicting the secular bull market that started in the 1980s (Getting in on the Ground Floor, Putnam, 1986); the tech stock crash and rise of real assets, including oil and gold (Defying the Market: Profiting in the Turbulent Post-Technology Market Boom, McGraw-Hill, 1999); and the surge in oil prices (The Oil Factor: Protect Yourself and Profit from the Coming Energy Crisis, Warner Books, 2004). His national bestseller, The Coming Economic Collapse: How You Can Thrive When Oil Costs $200 a Barrel (Warner Books, 2006), co-authored with Glen Strathy, outlined the biggest challenges facing the US economy, and accurately predicted the 2008 sub prime mortgage crisis as well as the vicious subsequent economic cycle requiring massive infusions of government stimulus, near zero interest rates and much higher federal debt levels. Game Over: How You Can Prosper in a Shattered Economy (Business Plus, 2009) predicted a permanent peak in global commodity production. Dr. Leeb’s eighth and latest book, Red Alert (Hachette, 2011), outlined China’s growing prosperity and the ways in which its demands on increasingly scarce resources threaten the American way of life.

Among his many speaking engagements, he has been the keynote speaker at both a JPMorgan Chase energy conference and a Royal Bank of Canada commodities conference.

Dr. Leeb received his bachelor’s degree in Economics from the University of Pennsylvania’s Wharton School of Business. He then earned his master’s degree in Mathematics and Ph.D. in Psychology from the University of Illinois in just three years, an academic record that stands to date. He is frequently quoted in the financial media, including Investors Business Daily, USA Today, Business Week, The New York Times, NPR and The Wall Street Journal. In addition, Dr. Leeb is a regular guest on Fox News, Bloomberg, CNN and Neil Cavuto.

Analyst Articles

Sell to close the Energy Select Sector SPDR Fund (NYSE: XLE) January 19, 2018 $68 put option.  We recommend closing the position for now. Read More

Believe it or not, VIX – the most widely followed measure of market volatility – has actually risen a bit over the past week. The reason this may seem hard to believe is that moves of one or two points on the S&P 500 are now considered an advance, while… Read More

Buy to open the Energy Select Sector SPDR Fund (NYSE: XLE) January 19, 2018 $68 put option.  More details to come in the Weekly Update. Read More

Sell to close the SPDR S&P 500 ETF (NYSE: SPY) January 19, 2018 $255 call option. Sell to take the gain on the SPY January 19, 2018 $255 call option. Leave the other half of the trade, the SPY December 15, 2017 $250… Read More

Our indicators are in neutral territory, but we know that you’re all itching for a new trade. Our solution? To create a new indicator for something that is not neutral. And so we welcome a new indicator to our fold, one that’s a bit different from our others. It measures… Read More

Buy to open the SPDR S&P 500 ETF (SPY) December 15, 2017 $250 put option. Buy to open the SPDR S&P 500 ETF (SPY) January 19, 2018 $255 call option. Treat these two trades as one trade. In other words, however much money you normally would… Read More

Sell to close the VanEck Vectors Oil Services ETF (NYSE: OIH) April 20, 2018 $25 call option. Although the oil-stocks indicator isn’t fully in sell territory yet, it has become less bullish. The downward trajectory suggests that it is best to sell the VanEck Vectors Oil Services ETF (NYSE: OIH) April… Read More

Sell to close the Applied Materials (Nasdaq: AMAT) January 19, 2018 $50 call option. As noted in yesterday’s update, the stock indicator had become less bullish. Today, it has continued downward. We recommend taking the gain in the AMAT trade. Read More

There are many ways of constructing an indicator. Foundational in almost any construction is that you have relationships that make sense—easier said than done. It is all too easy to throw together massive amounts of data and instruct a computer to find an optimal relationship among the data and something… Read More