Bearish Market Calls For Strategic ETF Higher Risk Tolerance
ETF PORTFOLIO – VALENTINE
Volatility & Polarized Fractal Efficiency
Assessing your mathematical underlying price formation patterns is key to probability of profits at any given time. Volatility is fundamental because it refers to your amount of uncertainty or risk to the statistical measure of dispersion of returns.
Based on intrinsic value and time value of the DJI and S&P 500 statistical calculations, the momentum right now will continue as a bearish trend for the next two weeks.
Currently, time value for DJI is – 465, statistical volatility is 4.73; targeting a support level near or at 14749. S&P 500 time value is -71.25 with a Statistical Volatility of 2.52; targeting a support level at or near 1675.
For this two week time frame we are investing in an ETF portfolio to capitalize on this downward trend. (Our initial beta ETF was built on January 26th, when the bearish trend broke out. We have been waiting for statistical confirmation.)
SPXS, SPXU, SMDD, HDGE, RWM, PSQ, ERY, DBA, EUM, FXY, BRZS
Daily expected return is 5.5%, correlated to a continuing bearish market performance. Volatility is 11.44% (high risk) given the correlations are not as diversified. Portfolio Alpha is 5.31; Beta (2.58) and Sharpe Ratio is 0.24. VaR is -789. This portfolio is approximately 2% more efficient compared to the S&P 500.
Our statistical volatility filter is set between 45% to 95%; Implied Volatility filter is 15% to 45%. A higher Statistical Volatility means the asset’s value can be spread out over a larger time value, but can dramatically changes the value for the short term.
Consequently, our hypothesis is to offset this by a lower Implied Volatility since it reflects the immediate market performance relationship, thus this provides for further upward price movement versus time decay of the current options month that will expire on February 14th. This smooths out the volatile fluctuations of a higher Statistical Volatility. All of the selected ETFs have an Implied Volatility of between 15% to 35%. Target date to take profits: February 14th, Valentine’s Day.
ADDING TO YOUR STATISTICAL ANALYSIS SOLUTION SET
Polarized Fractal Efficiency
Predictions at best can be based on probability outcome that minimizes your risk based on your investment time horizon: Point A to Point B. That “movement” is not linear, but more of a random walk, fluctuating in choppy segments that appear as geometrical fractals; easily visualized by the Line on your technical chart.
Forex currency trading exemplifies fractal price movement. For example, EUR/USD pip movement is typically 6 to 8 pips in one direction. The fractal component is clearly illustrated, from a microscopic insight expanded to a macroscopic trend trajectory price target, simply based on key resistant and support prices.
One must take into consideration:
- “Markets are risky.”
- “Trouble runs in streaks.”
- “Markets have a personality”
These are quotes from Professor Benoit Mandelbrot, who has spent forty years analyzing space and natural patterns; as you may recall seeing what appears to be psychedelic cosmic supernova looking geometric images.
Considering our continuing bearish trend with the major indexes, mean reversion identification or being contrarian has its challenges but present excellent set ups to come.
Of the formulary sets we use based on the core competency of volatility, the corner stone of all asset price analysis is finding the intrinsic value relative to the time value. One can derive this through statistical analysis of the underlying integrated into the asset’s option (derivative) strike price’s Implied Volatility.
You can learn more about Implied Volatility and how it drives the market based on Brownian motion at Tastytrade’s archives.
What we want to incorporate into our excel spreadsheet formulary is reflective of Mandelbrot’s fractal theory that puts into question the widely accepted modern portfolio theory (MPT).
Here is a visual example from The (Mis)Behavior of Markets, A Fractal View of Risk, Ruin & Reward by Benoit Mandelbrot & Richard L. Hudson: