Every day, BRS runs its algorithms and produces a signal: Green, Yellow or Red. Investors determine how to interpret the signal based on their own objectives, life circumstances, and risk tolerances to manage their risk exposures. Green means "All In," Yellow means "Lighten Up," and Red means "Red Alert," reduce risk to one's minimum. For example, for an older investor in retirement, "All In" would likely mean something quite different than it would to a 30-year-old.
From Efficient Markets Theory to Behavioral Finance
The algorithm was developed based on a paper with the title above written by Nobel laureate Robert J. Shiller. In this paper (click here, see pages 91-96), he discusses the failure of the efficient markets theory to explain stock market prices and the "blooming of behavioral finance."
In particular, he describes one of the oldest theories about financial markets, which he calls price-to-price feedback theory. Essentially, he argues that the emotions of greed and fear drive market prices far too high on the upside, and much too low in downturns.
I developed and tested hypotheses to try to quantify conditions that might make investors feel greedy and fearful. And it turns out that the size of market gains and losses, combined with the speed, does a pretty good job in determining when to be invested, when to be cautious, and when to get out.
I call my algorithm Vertical Risk Management (VRM) and have back-tested using consistent positioning program for the Utilities Select Sector SPDR ETF (NYSEARCA:XLU). The primary objective of VRM is to manage the downside risk to keep losses within a tolerable range. The thesis is that the best investment is one in which an investor can maintain a strategy that attempts to capture the upside potential as long as the losses do not exceed a loss threshold.
From May 2000 through June 2016, XLU had produced a total return of 244%. However, the maximum drawdown from maintaining a 100% long position was 52%, which is excessive.
The VRM strategy is a long/short strategy that aims to manage risk by actively adjusting position size and direction. The back-tested total return over the same period using the VRM strategy was 267% but the maximum drawdown from peak was just 20% (see graphs below).
The signal for July 12, 2016 is Green.
Important SEC Disclosures
This material is provided for limited purposes. It is not intended as an offer or solicitation for the purchase or sale of any financial instrument. References to specific asset classes and financial markets are for illustrative purposes only and are not intended to be, and should not be interpreted as, recommendations or investment advice. The opinions expressed in this article represent the current, good-faith views of the author(s) at the time of publication. The views are provided for informational purposes only and are subject to change. This material does not take into account any investor's particular investment objectives, strategies, tax status, or investment horizon. Investors should consult a financial advisor for advice suited to their individual financial needs. The author cannot guarantee the accuracy or completeness of any statements or data contained in the article. Predictions, opinions, and other information contained in this article are subject to change. Any forward-looking statements speak only as of the date they are made, and the author assumes no duty to update them. Forward-looking statements are subject to numerous assumptions, risks, and uncertainties. Actual results could differ materially from those anticipated. Past performance is not a guarantee of future results. As with any investment, there is a potential for profit as well as the possibility of loss.
The information presented in this paper relates to the creation and testing of investment models and the use of backtested performance data. Back-tested results are calculated by the retroactive application of a model constructed on the basis of historical data and based on assumptions integral to the model which may or may not be testable and are subject to losses. Hypothetical performance results have many inherent limitations, some of which are described below. No representation is being made that any account will or is likely to achieve profits or losses similar to those shown. In fact, there are frequently sharp differences between hypothetical performance results and the actual results subsequently achieved by any particular trading program. One of the limitations of hypothetical performance results is that they are generally prepared with the benefit of hindsight. Back-testing allows the security selection methodology to be adjusted until past returns are maximized. In addition, hypothetical trading does not involve financial risk, and no hypothetical trading record can completely account for the impact of financial risk in actual trading. For example, the ability to withstand losses or to adhere to a particular trading program in spite of trading losses are material points which can also adversely affect actual trading results. There are numerous other factors related to the markets in general or to the implementation of any specific trading program which cannot be fully accounted for in the preparation of hypothetical performance results and all of which can adversely affect actual trading results.
Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.
I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.