The following is the first of Bryce James’ quarterly communications. Bryce, the president of Smart Portfolios, employs leading edge portfolio management embracing Nobel Prize winning strategies for portfolio design based on risk management. Since his strategies are a critical component of our clients’ portfolios, we are confident that his views on the markets will be a particularly interesting read.
Wall Street isn’t like it used to be when I got into the business thirty years ago. Back then, an investor was rewarded for diligently providing sound research. Now it seems that markets merely respond to the actions of the Federal Reserve and little else matters; other than the occasional high-frequency trading model that goes rouge and creates mini-crashes. So, is Wall Street just an electronic form of Vegas or is there some edge to give investors an advantage?
In other words, is the game rigged or can you be a card counter and capitalize on the laws of probability?
Traditionally, investment research focused on one or more of the following types of disciplines: Fundamental Research, Technical Analysis, Economic Research, Sentiment Indicators and Risk Analytics. Combinations of these disciplines, combined with some Game Theory, became known as Quantitative Analytics, or Quant Models. My goal is to show you the market quantitatively so you can get a better sense of where we are in the market cycle and understand how we balance the trade-off between risk and return.
How do you know if a P/E Ratio (Price/Earnings per Share) of 22 is good or bad? It might be attractive for a biotech stock but extremely expensive for a utility; the bottom line is it’s relative. When markets are bullish they can continue rising beyond fair values for extended periods of time because of investor greed and confidence. When markets correct or go bearish, investor fear can quickly send prices far below their reasonable value.
What you see in the chart below is the P/E Ratio on the SPY* (ETF** that tracks the S&P 500 or market index) over a short, medium and long-term basis, both as a distribution of P/E’s and as an Oscillator of the historical P/E’s.
As you can see, we are near-term historical highs on the S&P 500. Prior to last month’s recent correction, the market was within 2% of all-time highs. This would suggest the market is over-priced and potentially due for a correction. Similar results would be found in comparing the S&P’s price to its cash flow, book value, and most other fundamental factors.
Technical indicators are mostly an indication of momentum or trend. The longer a trend is in place the more extreme the change around the historic mean return. In the chart below you can see the SPY has had a long bullish run and, just prior to the recent correction, was trading far above its 200 day moving average; as it is now again.
Also note how the Relative Strength Index chart (bottom) was also trading near its highs. Technical traders love this strength, but ‘the show’ can only go on so long before the music stops and the chairs get pulled out beneath them; this is known as mean reversion. The longer the market trends in one direction, the greater the probability is for a mean reversion.
I have a hard time relying on economic data because many of the core indicators provided by the U.S. Government have been changed to fit the political party line. I feel like we are in China sometimes. The definition of unemployment and under-employment has changed and I’m less confident in believing the stated inventory and production levels, the export/import levels, or most any of the economic forecasts. Several stats, such has corporate earnings upside surprises, has been good, but how much has come from decreasing inventory and staff? The bottom line is that I’m a bit distrustful of the published economic data.
There are several Bullish/Bearish Sentiment Indicators. They are all currently Bullish, but not as high as one would think as many investors have simply become distrustful of the financial markets. Suffice it to say, even the fund managers are less optimistic as the market highs would suggest.
Risk, the least understood word among investors and investment professionals alike. Is it the risk of losing money or the volatility of your account? Is it the risk you don’t meet your income or retirement needs? The truth is Wall Street’s traditional method for measuring risk poorly measures the volatility or the probability of losing money.
Examine the charts below. The green line in the top chart is the price of SPY since 2006. The black line in the lower chart is the daily price fluctuation (% up/down change). The red line in that chart is Wall Street’s estimate of daily losses. Notice the amount of times the daily loss (black line) exceeds the red line; mostly during market crashes and corrections. The red line (called VaR) estimates loss poorly, especially when you need it the most, when markets are going down.
Now notice how the blue line more accurately depicts the current level of risk. The blue line (called Expected Shortfall) is a new measure of risk and it provides a superior method of tracking the probability of loss on a daily basis. (Technical note: The blue line, Expected Shortfall, is computed using Garch modeling using fat-tail distributions. Expected Shortfall may also be computed with other distribution models.)
The take away here is that securities tend to rise when risk is low or when risk is decreasing, and decrease when risk is rising. There is a high correlation between price direction and volatility. Like technical analysis, the longer risk is low (prices usually rising) the higher the probability becomes of a larger and larger correction. The low volatility and increasing prices, throughout 2004 to 2007 led to the crash of 2008.
Likewise, we have had low volatility and increasing prices for the past several years. If you were a student of fractals or the laws of probability you would find reasons to be very cautious in the current market environment. Luckily, we have powerful computers to perform our math and analyze the risks and relationships. It’s not an exact science but we feel it’s better than the alternative solutions.
The relative value of the markets fundamentally, technically and as it relates to risk, all point toward caution in our models. We accept markets can go higher due to over-exuberance, like in 1997, 2000 and 2007 (to name a few) but we feel the risk/reward trade-off does not warrant being more aggressive at the current market valuations.
I welcome your questions and thoughts in the comments.
*SPY: ETF Index Fund for the S&P 500
**ETF: Electronically Traded Fund
Thank you to Bryce James, president of Smart Portfolios.