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 into 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.
Most people dismiss the saying "sell in May and go away." Surprisingly enough, the strategy has worked well over the past few decades (and over the past few centuries in the UK stock market). February is within the historically best six months of the year for the market. If Monday falls between November 1 and April 30, my filter rule says to be in the market. Data is from Yahoo Finance.
The age-old trend following approach is to have long exposure to the S&P if the index is above its 200-day moving average. That works, but you get whipsawed with a lot of false signals. That's why I use a 4-week average of SPY's distance relative to its 200-day moving average. It's a bit slower on catching big moves but signals fewer false positives. The S&P is currently in a solid uptrend well above its 200-day moving average. Data is from Yahoo Finance.
The Chicago Board Options Exchange reports three different put/call ratios: Total, index and equity. The total put/call ratio combines both measures. I choose to analyze the total put/call ratio since it gives the most comprehensive view of options market sentiment. Historically, it's actually paid to cut exposure to the S&P when the ratio is low, meaning investors are optimistic and buying relatively few puts. The 4-week average of the total put/call ratio is currently 0.92, just above my cut-off filter of 0.9. Data is from the CBOE.
The NAAIM Exposure Index measures how bullish or bearish active investment managers are on the S&P. My preferred measure of the index (a four-week average) has rarely been this high. Data is from the National Association of Active Investment Managers.
The difference between the interest rate of a high yield (NYSEARCA:HYG) bond and a Treasury of comparable maturity is called a high-yield spread. The narrower the spread, the more optimistic investors are about the probability of risky U.S. corporations being able to cover their interest payments and eventually pay off their debts.
When investors grow more uncertain, they will demand a higher rate on high-yield bonds and cause spreads to widen. High-yield spreads have imploded since last February, reach low levels not seen for years. Data is from the St. Louis Federal Reserve Economic Database.
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 TED spread is below my cut-off filter of 0.75%.
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. We just got new PMI data last week, and at 56.0 the current outlook is very bright for U.S. manufacturing. Data is from the Institute of Supply Management.
We also got fresh data on the unemployment rate last week. The unemployment rate is the percentage of the total workforce that is unemployed and actively seeking employment during the previous month. It is a lagging economic indicator, but a persistently rising unemployment rate indicates a weak labor market and thus potentially weak consumer spending. The current unemployment rate is 4.8%, just below its 12-month average of 4.9%. Data is from the St. Louis Federal Reserve Economic Database.
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 on 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 chart below plots each individual category average score and the overall composite score.
So where do we stand? Technical data is super strong. The S&P is in an uptrend, companies are restarting their buyback programs, and we're in a seasonally positive period of the year. Sentiment does give me some hesitation. CBOE's total put/call ratio is very low and the NAAIM Exposure Index is elevated, meaning active investment managers are very bullish on the S&P.
The macro environment looks good. Last week's ISM PMI print beat expectations. Industrial production, retail sales, and housing prices have all risen over the past year.
Overall, the composite model is long. This is because the composite score is 0.77, 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.