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 simplestockmodel.com.
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.
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. Right now, the S&P is above its 200-day moving average. Data is from Yahoo Finance.
The next FOMC meeting is on December 14, which means we're outside of the historically positive FOMC drift period. The FOMC drift is the tendency of equity prices to rise more often than average in the days leading up to a FOMC meeting. My pre-FOMC drift period is defined as 20 trading days. Since 1993, only being long during this period has beaten the returns of a buy and hold strategy while only being invested in the S&P 2/3 of the time. Data is from Yahoo Finance.
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.
September's data revealed a big jump in the amount of margin debt outstanding. Historically, this has actually been a positive sign for the S&P. Data is from NYSE.
The Chicago Board Options Exchange reports three different put/call ratios: total, index and equity. The index ratio calculates the volume of index puts traded relative to index calls. The equity ratio calculates the same ratio but for individual stocks. The total put/call ratio combines both measures. I choose to analyze the total put/call ratio since it gives the most comprehensive view. Historically, it's actually paid to have exposure to the S&P when the put/call ratio is high. Currently, the total put/call ratio is high. Data is from the CBOE.
The VIX futures curve is made up of prices of individual VIX futures contracts. When the curve is upward sloping from left to right, the curve is said to be in contango. Contango means that market participants expect implied volatility to be higher further out in time. The VIX futures curve is typically in contango.
When the curve is downward sloping, the curve is said to be in backwardation. In this scenario, near-term VIX futures are more expensive than long-term futures meaning that investors expect volatility in the short term to be very high. This occurs when the spot VIX index spikes up and people expect volatility to mean-revert and drop over time.
Currently, the VIX futures curve is in contango. Data is from the CBOE.
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 has recently increased as a result of money markets being forced to pull their holdings of commercial paper and invest in government securities. This has caused the yield on commercial paper, and by extension LIBOR, to increase. The current TED spread of 0.52% is still below my cut-off filter of 0.75%. Data is from the St. Louis Federal Reserve Economic Database.
The yield curve is a popular tool that is used to try to forecast the direction of the economy. More often than not, people talk about how a flat or inverted yield curve is bad for markets. I choose to analyze the movement of the curve rather than its static shape. Specifically, I look at the difference between the two-year yield (NYSEARCA:SHY) and the 10-year yield (NYSEARCA:IEF).
Historically, a rapidly steepening curve has actually been more detrimental for stocks than a flat or inverted curve. In a steepening yield curve, short-term rates fall faster than long-term rates. In the past, steepening yield curves have been associated with the Federal Reserve quickly lowering the federal funds rate during a recession.
The curve has massively steepened since July as long-term rates have spiked. Data is from the U.S. Treasury.
Over the past twelve months, the growth in nominal S&P 500 earnings per share has been negative. Historically, this has been a bad sign for future short-term returns. Data is from Standard & Poor's.
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 4.9%, equal to its 12-month average of 4.9%. Historically, this has been a bad sign for future short-term stock returns. 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 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 fairly strong. We've exited earnings season and companies have restarted their buyback programs. November marked the beginning of a seasonally strong period of the year. Plus, the short-term price trend of the S&P is up.
There's still a good bit of negativity embedded in the markets. CBOE's total put/call ratio is high, the ISEE call/put ratio is low, and the NAAIM Exposure Index has recently fallen. On the optimistic side, spot VIX has come down quite a bit, the VIX futures curve is in contango, and AAII respondents have recently gotten more bullish.
The U.S. Treasury yield curve has rapidly steepened as long-term rates have increased. The TED spread is high, but it's not at nosebleed levels.
The macro picture is more negative than any other category. Industrial production and S&P earnings are both down over the past year, and unemployment is now equal to its 12-month average. On a bright note, the ISM PMI has held above 50 for a couple of months now.
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.