Stock Cycles: Why Stocks Won't Beat Money Markets Over the Next Twenty Years

by Michael A. Alexander

"Important reading for serious investors."-InvestorsInsight.comFor most Americans, a 401k plan is their first exposure to investing. Many of us are relying on the stock market to provide for us in our retirement yet at the same time, most of us are afraid of the stock market. It's a valid concern. How can something so important to our financial future be so completely unpredictable? When Michael Alexander first started investing in the stock market, he noticed that few analysts seemed to have much knowledge of what the market has done in the past. While no one can give precise answers to questions about the future of the market and be right all the time, Alexander feels that it's possible to gain an understanding of the future of the stock market by studying its past.Analyzing years of historical data for patterns of behavior that might repeat in the future, Alexander provides strong statistical evidence for a cyclical pattern in the stock market. These Stock Cycles show that long periods of poor stock returns have always followed long periods of good returns. Are we in for good times or is the party over?

Genres: Finance, Nonfiction

216 pages, Paperback

First published October 12, 2000

Reviews

[Dominik · Rating 5 out of 5]

One of the best reads on the subject of long cycles theory.
The book uses widely researched Kondratiev cycles theory as a backbone of the analysis. It adds the innovation cycle as a possible explanation (Kondratieff never really determined what caused the cycle) which seems to fit right into the theory. This idea is very refreshing and adds a new dimension to the whole analysis. If carried forward past the book publication date, it might also be used as an explanation for recent Kondratiev wave misbehaving.
Along with "The Kondratieff Waves" by Nathan H. Mager this is a must-read for anyone interested in long cycle theory.

[Daniel Cunningham · Rating 4 out of 5]

Even if parts of this are wrong, they are wrong in interesting ways. And everything is described well enough and in enough detail that, especially 20 years laters, I can probably verify what the author did in a Jupyter notebook in an hour or two or five (depending in large part on accessing all the relavent data.) Which... I just might do.