I offer a probability-based framework that captures the true nature of investment reward and risk.
This is the third of my four-part empirical research into the fallacy of the random walk view of investment reward and risk. Part 1 and Part 2 discussed the random walk's failure in portraying asset returns. A random walk depicts risk as volatility.
Part 1 of this series focused on the failure of the random walk to depict the Dow Jones Industrial Average. In this article, I expand the study to include six diverse asset classes including large-caps (the S&P500), small-caps (the Russell 2000), emerging markets, gold, the dollar and the 10-year Treasury bond.
This is the first of a four-part empirical research study into the fallacy of the “random walk” view on investment reward and risk.
In Part 1, I introduced my Primary-ID model that identifies major price movements in the stock market. In Part 2, I presented Secondary-ID, a complementary model that tracks minor price movements. In this article, I combine these two rules- and evidence-based models into a composite called Cycle-ID and discuss the virtue of a single model.
In Part 1 of this series, I presented Primary-ID, a rules- and evidence-based model that identifies major price movements, which are traditionally called cyclical bull and bear markets. This article debuts Secondary-ID, a complementary model that objectively defines minor price movements, which are traditionally called rallies and corrections within bull and bear markets.
I present a modeling approach to spot primary market cycles.
Conventional wisdom dictates that equity markets adhere to long-term secular cycles and that investors should adjust their allocations based on whether valuation metrics, such as the Shiller CAPE, are relatively high or low. But what if the notion of secular market cycles is misguided because, for example, the sample size of past cycles is insufficient to attach any statistical significance?