In a previous article, I outlined both the purpose and construction of my 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 the Simple Stock Model website.
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 at 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 classic 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 above its 200-day moving average, meaning it's in an uptrend.
Following this trend strategy would have kept you invested in the market since last March. The main benefit of long-only trend-following strategies is not necessarily to increase returns, but instead to lower volatility. Data is from Yahoo Finance.
We're currently trading within the best six months of the year. 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). If Monday falls between November 1 and April 30, my filter rule says to be in the market.
The last positive six-month period was particularly strong for the S&P. Data is from Yahoo Finance.
The market is now out of the buyback blackout period. During this time, companies suspend their share repurchase programs in advance of earnings announcements.
Companies typically suspend buybacks (and insider transactions) in the five-week period before their scheduled earnings announcements. It's important to note that this buyback blackout window is different for each company, and my period covers the five-week period before a majority of companies report earnings. As a percentage of total NYSE volume, corporate buybacks have increased over the past few years. Now that the majority of buyback programs are active, you can interpret that as a bullish sign. Data is from Yahoo Finance.
Growth in margin debt occurs when investors pledge securities to obtain loans from their brokerage firm. The NYSE releases margin debt data on a monthly basis.
It's important to avoid looking at the nominal amount of margin debt outstanding, like most margin debt charts do. Any credit-based indicator will steadily grow over time as the economy expands. Instead, I like to look at the yearly percentage change in margin debt.
Historically, positive annual growth in margin debt has actually been a positive sign for future short-term S&P returns. Note how excessive margin debt growth was in 2000 and 2007. NYSE margin debt has grown by 16% over the past year. Data is from the NYSE.
The NAAIM Exposure Index measures how bullish or bearish active investment managers are on the S&P. My preferred measure of the index is a four-week average. This indicator has recently dropped, meaning active managers have recently gotten less bullish on US stocks.
The following chart goes to show how excessive optimism, by itself, can be a faulty signal. Data is from the National Association of Active Investment Managers.
The Chicago Board Options Exchange reports three different put/call ratios: total, index and equity. The total put/call ratio combines the latter two. I analyze the total put/call ratio since it gives the most comprehensive view of options market sentiment. Historically, it's worked out well to cut exposure to the S&P when the ratio is low.
A low put/call ratio means that few investors are buying puts, and are therefore not worried about a sell-off in stocks. The 4-week average of the total put/call ratio is currently 0.91, just above my "complacency" cut-off filter of 0.90. Data is from the CBOE.
The VIX (NYSEARCA:VXX) 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 happens when people expect volatility to mean-revert and drop over time.
The VIX futures curve is solidly in contango. Data is from the CBOE.
A weekly sentiment survey has been conducted by the American Association of Individual Investors (AAII) for many years. 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. The AAII survey is considered more of a "retail" survey, vs. the more institutional NAAIM survey.
There are a bunch of ways to analyze AAII data, and I like to examine the spread between the percentage of bulls and the percentage of bears. This spread has recently increased as retail investors have grown more optimistic. Data is from the AAII.
The yield curve is a popular tool used 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 how the difference between the two-year Treasury yield (NYSEARCA:SHY) and the ten-year yield (NYSEARCA:IEF) has shifted over the past twelve months.
A lot has been written about the yield curve the past few weeks since it's quickly flattened as short-term rates have risen. This had made people wonder if a flattening yield curve is an ominous sign for U.S. stocks.
Historically, a rapidly steepening curve has actually been more detrimental for stocks than a flat or inverted curve. In a steepening 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.
This portion of the yield curve has flattened by 70 basis points over the past twelve months. My cut-off filter is steepening of +0.50%.
This indicator might be of less use given the current level of interest rates. Rates would have to substantially drop for my +0.50% filter to trigger. Data is from the U.S. Treasury.
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 higher risk U.S. corporations being able to service their debts.
When investors grow more uncertain, they will demand a higher interest rate on high-yield bonds and cause spreads to widen. High-yield spreads are tight, meaning that investors aren't demanding a substantial interest rate premium to lend to high yield borrowers.
High yield spreads are extremely low, but it should be noted that they stayed near these levels for years in the mid-2000s. 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 from each other on an unsecured basis.
The perceived risk in the banking sector grows as the spread between LIBOR and T-bills increases. The current TED spread of 0.18% is at multi-month lows and is well below my cut-off filter of 0.75%. Data is from the St. Louis Federal Reserve Economic Database.
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.
Last week's ISM PMI data came in at 58.2, which was a high reading but was also slightly below expectations. Data is from the Institute of Supply Management.
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 adjusted for inflation.
Real retail sales are up 2.5% over the past year. Data is from the St. Louis Federal Reserve Economic Database.
Earnings growth for the S&P 500 is largely driven by sales growth and profit margin expansion. Additionally, share buybacks are a contributing factor in earnings per share growth as buybacks shrink the number of shares outstanding. People view EPS growth as a sign of the improving profitability of American companies. My rule for EPS is as follows: If the twelve-month change in S&P EPS is greater than 0%, be invested in SPY.
S&P EPS has risen by 19.7% over the past twelve months. Data is from Standard & Poor's.
Industrial production measures the total value of output for all manufacturing, mining, and electric and gas utility facilities located in the United States. It's a key economic indicator and is a good way to quickly gauge how the manufacturing portion of the U.S. economy is doing.
Industrial production is up 2.9% over the past year, above my cut-off filter of 0%. Data is from the St. Louis Federal Reserve Economic Database.
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 strong. The trend is up, seasonality is strong, we're within the FOMC drift period, margin debt growth isn't excessively high, and most buyback programs are active.
Sentiment data is mostly optimistic. Spot VIX is low, CBOE's total put/call ratio is low, VIX futures are in steep contango, and the AAII survey reveals that retail investors are quite bullish. On the flip side, NAAIM's Exposure Index points to less bullishness among institutional investors.
The Treasury yield curve has massively flattened over the past year, high yield spreads are historically low, and the TED spread is low, indicating a lack of stress in the U.S. banking sector.
Macro data is very strong. Industrial production, retail sales, housing prices, and S&P earnings have all risen over the past year. The ISM PMI is well above 50 and the unemployment rate is low and trending lower.
Overall, the composite model is long. This is because the composite score is 0.91, above the cut-off filter of 0.60.
I update all of the individual indicators and the composite model each week, so be sure to follow me to track future updates!
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.