In a previous article, I outlined both the purpose and construction of the Simple Stock Model. Keep reading for a quick run-down if you're new to the model, otherwise you can skip down to "Technicals" for the updated data.
Investors are constantly exposed to sound bites and data points presented without any proper context. You might have read an article about how stocks have historically bounced when sentiment has reached a negative extreme. Or that you should be out of the market if it's trading below its 200-day moving average.
When I come across articles like that, I always thought it was shortsighted to base an opinion on the S&P on only one indicator without also considering a wide variety of other inputs.
The goal of the model is to help you form a data-based outlook on the S&P. Additionally, at the end of this article, I showcase a composite model that incorporates all of the indicators I use, so your view can be comprehensive, as opposed to having tunnel vision on only one indicator.
How the Model Works
Each article is broken down into four main sections: technicals, sentiment, rates and macro. Each section includes a number of different indicators. For each indicator, there's a "filter rule" for when to be out of the market. In the spirit of simplicity, the filter rule is always binary, dictating either 100% long exposure to the S&P or a 100% cash position. The S&P is represented by the SPDR S&P 500 Trust ETF (NYSEARCA:SPY). Let's dive in to an example graph. All graphs are from Simple Stock Model.
The above data is from Yahoo Finance. The graph shows the price momentum indicator within the technicals section. The bottom portion plots the momentum metric over time, and the top portion plots the historical performance of following the filter rule.
For each indicator, new data each weekend is used to generate a long SPY or cash position for the next week. For the above momentum example, SPY's dividend-adjusted close as of Friday is the main input. Using this, I calculate the 12-month total return. For each indicator on this site (except for the macro data), I take a four-week average of the main indicator input.
So, for this example, I'm taking the four-week average of 12-month total return momentum. Why four weeks? To reduce false positives and whipsaws when an indicator is bouncing slightly above or below its filter rule. There's nothing special about a four-week average. You could use two or eight weeks and reach similar results.
Data is compiled as of Friday's close. Buying or selling decisions occur on Monday's close. I do this, as opposed to making trades at Monday's open, simply because I had a more reliable data source for dividend-adjusted close data. It's also important to reflect realistic transaction costs. Each simulated historical performance graph factors in a $10 trade commission and a 0.02% spread on SPY for each buy or sell. Commissions and spreads are lower now, but considering SPY started in 1993, I chose to use these above-average numbers.
Now you understand the methodology behind the model. Each week, I'll cover a handful of indicators, especially those that have changed positioning over the past week. Let's get started with some technicals.
Growth in margin debt occurs when investors pledge securities to obtain loans from their brokerage firm. The New York Stock Exchange releases margin debt data on a monthly basis.
It's important to avoid looking at the nominal amount of margin debt outstanding, as any credit-based indicator will steadily grow over time with the economy. Instead, I like to look at the yearly change in margin debt.
October's data revealed a fall in the amount of margin debt outstanding. Historically, positive annual growth in margin debt has actually been a positive sign for future short-term S&P returns. Data is from the NYSE.
The S&P also has positive short-term price momentum in its favor. The momentum effect is one of the strongest and most pervasive financial phenomena. Researchers have verified its efficacy as an alpha generating strategy with many different asset classes. My rule for momentum is as follows: If the four-week average of 12-month total return momentum as of Friday's close is greater than 0%, be invested in SPY. Short-term momentum is firmly positive. Data is from Yahoo Finance.
The NAAIM Exposure Index measures how bullish or bearish active investment managers are on the S&P. The index has quickly risen as active managers have gotten more bullish after the election. Data is from the National Association of Active Investment Managers.
State Street launched SPY, the ETF that this article runs all of its analysis on, in 1993. State Street started providing shares outstanding data for SPY in 2006. The number of SPY shares outstanding grows or shrinks based on the creation and redemption activity of authorized participants. Basically, when SPY is in hot demand and the number of shares outstanding is rapidly increasing, that's typically been a sign of excess optimism. Vice-versa for redemption and pessimism. The number of SPY shares outstanding has recently increased. Data is from State Street.
A weekly sentiment survey has been conducted by the American Association of Individual Investors for decades. The AAII asks participants if they are bullish, neutral, or bearish on stocks over the next six months. Survey results are typically used as contrarian indicators, meaning extreme bullishness is perceived as bearish and vice versa.
There are a number of ways to analyze AAII data, and I choose to use the spread between the percentage of bulls and percentage of bears (as opposed to just looking at bulls or bears in isolation). The spread has recently turned up as many retail investors have gotten bullish. Data is from the AAII.
The TED spread is frequently cited as a measure of credit risk in the overall economy. The spread reflects the difference between two short-term interest rates: 3-month USD LIBOR and the 3-month U.S. Treasury yield (NYSEARCA:BIL). LIBOR reflects the rate at which banks borrow between each other on an unsecured basis. The perceived risk in the banking sector grows as the spread between LIBOR and T-bills widens out.
The current TED spread of 0.45% is below my cut-off filter of 0.75%. Data is from the St. Louis Federal Reserve Economic Database.
Retail sales reflect the total value of sales at the retail level. It's a primary measure of consumer spending which accounts for the majority of economic activity in the U.S. I like to look at real retail sales data that is 1) adjusted for inflation and 2) doesn't exclude food service sales. Real retail sales have risen by roughly ~2% per year for the past few years. Data is from the St. Louis Federal Reserve Economic Database.
Last week's ISM PMI data beat expectations. The ISM PMI is a diffusion index based on surveyed purchasing managers in the manufacturing industry. It's a leading indicator of economic health. A PMI reading above 50 indicates expansion in the manufacturing sector, below 50 indicates contraction. The current ISM PMI is 53.2. Data is from the Institute of Supply Management.
That wraps up the weekly update on some of the individual indicators. Now for the composite model.
Think of each indicator as a building block that helps form an overall opinion. One study might say current sentiment has historically been bullish for stocks. Who cares? That's just one data point in isolation. I'm interested in a bigger-picture view with more context. A picture that also factors in what's going on with macro data, interest rates, etc. The composite model does just that.
Here's how it works: Each indicator is given a score of 1 or 0 depending on its current reading relative to its filter rule. If S&P earnings are down over the past year and the filter rule for that metric is to be out of the market if yearly earnings growth is below 0%, then that indicator gets a 0. The table below summarizes data from all the previous sections and assigns a 1 or 0 to each indicator based on its current reading.
All 22 indicators are averaged to form the composite score. If the composite score is greater than 0.6, the model is invested in SPY. Think of 0.6 as the overall filter rule for the composite model.
There's nothing special about 0.6 - it results in being invested in SPY about 80% of the time. I could have used a higher filter rule like 0.75 to only be exposed to the S&P when more indicators are saying to be invested, but this results in less time exposed to the market since it's a "stricter" cut-off. The charts below plot each individual category average score and the overall composite score.
So where do we stand? Technical data is very strong. Momentum and trend measures are positive, buyback programs are active, it's a seasonally strong period of the year, and we're within the historically positive FOMC drift period.
Some sentiment measures show more optimism in the markets. Notably, there are more bulls in the AAII survey and active managers (per the NAAIM Exposure Index) have raised their long exposure to stocks. The number of SPY shares outstanding has also increased.
Last week's ISM PMI data came in better than expected, and the unemployment rate substantially dropped. Retail sales are chugging along, but industrial production is still down over the past year.
Overall, the composite model is long. This is because the composite score is 0.86, above the cut-off filter of 0.60. I hope this article can help you out in your own investing endeavors. Do let me know in the comments below if you have any questions!
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.
Additional disclosure: The author does not make any representations or warranties as to the accuracy, timeliness, suitability, completeness, or relevance of any information prepared by any unaffiliated third party, whether linked in this article or incorporated herein. This article is provided for guidance and information purposes only. Investments involve risk are not guaranteed. This article is not intended to provide investment, tax, or legal advice. Performance shown for each indicator is of a simulated hypothetical model. Performance is simulated and hypothetical and was not realized in an actual investment account. Performance includes reinvestment of all dividends. All risks, losses and costs associated with investing, including total loss of principal, are your responsibility.