S&P Summary: Strong Macro Data And Technicals, But Overly Bullish Sentiment

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Includes: BIL, CRF, DDM, DFVL, DFVS, DIA, DOG, DTUL, DTUS, DTYL, DTYS, DXD, EEH, EPS, EQL, FEX, FWDD, GBIL, GSY, HUSV, HYDD, IEF, IEI, ITE, IVV, IWL, IWM, JHML, JKD, OTPIX, PPLC, PPSC, PSQ, PST, QID-OLD, QLD, QQEW, QQQ, QQQE, QQXT, RISE, RSP, RWL, RWM, RYARX, RYRSX, SBUS, SCAP, SCHO, SCHR, SCHX, SDOW, SDS, SFLA, SH, SHV, SHY, SMLL, SPDN, SPLX, SPSM, SPTS, SPUU, SPXE, SPXL, SPXN, SPXS, SPXT, SPXU-OLD, SPXV, SPY, SQQQ, SRTY, SSO, SYE, TALL, TBX, TBZ, TNA, TQQQ, TUZ, TWM, TYD, TYNS, TYO, TZA, UDOW, UDPIX, UPRO, URTY, USA, USSD, UST, USWD, UWM, VFINX, VGIT, VGSH, VOO, VTWO, VV, ZLRG
by: Movement Capital

Summary

Technicals: We're outside of the historically positive FOMC drift period, the trend is up, and seasonality is strong.

Sentiment: AAII survey respondents have gotten a tad more bearish recently, but the number of SPY shares outstanding has risen over the past three months.

Rates: The Treasury yield curve has steepened slightly relative to twelve months ago.

Macro: Housing prices are up over the past year, but S&P earnings are still down over the same time period.

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.

Technicals

The next FOMC meeting is on March 15, 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 ~75% of the time. Data is from Yahoo Finance.

Short-term price momentum is positive for the S&P. 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. Momentum is currently positive (and accelerating) for the S&P. Since the S&P bottomed almost exactly twelve months ago, my momentum measure should star to "roll over" in the coming weeks. Data is from Yahoo Finance.

Sentiment

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 widened out after the election as people got more bullish. Since then though, optimism among survey respondents has receded. Data is from the AAII.

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. The number of SPY shares outstanding has significantly increased over the past few months. Data is from State Street.

Rates

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 ten-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 Treasury yield curve hasn't steepened that much from twelve months ago. Data is from the U.S. Treasury.

Macro

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. People view EPS growth as a sign of the improving profitability of American companies. S&P earnings per share have fallen by -1.7% over the past twelve months. My rule for EPS is as follows: If the twelve-month change in S&P EPS is greater than 0%, be invested in SPY. Data is from Standard & Poor's.

There are a variety of indices that monitor housing prices, and I choose to use the long-running 10-city index that Karl Case and Robert Shiller developed. The S&P/Case-Shiller 10-City Composite Home Price Index measures the change in value of residential real estate in 10 metropolitan areas of the U.S. The index up 4.5% over the past year. Data is from Standard & Poor's.

That wraps up the weekly update on some of the individual indicators. Now for the composite model.

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 strong. The S&P is in an uptrend, companies are actively buying back stock, and seasonality is positive. Sentiment is the main headwind for the market. NAAIM's Exposure Index is high, spot VIX is very low, and the number of SPY shares outstanding has risen quickly.

The yield curve has steepened slightly over the past twelve months but it's nothing to get worried about. The macro environment looks good. U.S. data on industrial production, housing prices, ISM PMI, and the unemployment rate is all quite positive. S&P earnings are still negative over the past year though.

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.