Page 10 - Newcom
P. 10

JUNE 2020
How long
until my portfolio recovers from March’s sell-off?
Using historical returns to forecast a recovery
Markets operate in two different modes: ran- dom (or “normal,” as it’s known in the Gaussian world) and fractal (non-normal or extreme). My
work on the luck factor (i.e., sequence of returns) about 20 years ago indicated that markets are random about 94% of the time. It is fractal for the remaining 6%, split evenly between up and down directions.
In the random mode, well-known strategies such as asset allocation, diversification, rebalancing, dollar-cost averaging and buy-and-hold work perfectly well. The prob- lem is the other 3% of the time, when markets are fractally going down; then these strategies don’t help much. You might say, “Well, it only happens 3% of the time — why worry?” The answer is simple: plans can get ruined. A bad downturn can wipe out 10 years of retirement income.
After a fractal event such as the market response to Covid-19, we all hope for a quick recovery. In the past, central banks jumped in to help — but these recoveries did not come cheaply. The global total debt was US$87 trillion in 2000. Before Covid-19, it was estimated at US$253 trillion, and governments around the world have since introduced unprecedented monetary and fiscal policies in response to the crisis (see p. 12 for more).
Assuming central banks can continue to bail out the economy, how many months or years would it take the portfolio to recover its original value?
This analysis uses actual market history, which we call “aftcasting” (as opposed to “forecasting”). Aftcasting
byJimC. Otar,M.Eng.,
a retired certified financial planner
and professional engineer; he founded
displays the outcome of all historical asset values of all portfolios, on the same chart, since 1900. It gives a bird’s- eye view of all outcomes for a given scenario. It also pro- vides the success and failure statistics with exact historical accuracy because it includes the actual historical equity performance, inflation and interest rate, as well as the actual historical sequencing/correlation of these data sets.
Recovery in an accumulation portfolio
Let’s first look at an accumulation portfolio. Imagine a 30-year-old client named Keith with a portfolio worth $100,000. The asset mix is 70% equities and 30% fixed income. Half of the equities are in Canadian stocks (S&P/ TSX composite) and half are in U.S. stocks (S&P 500).
As for the fixed income portion, we use the historical interest on six-month U.S. certificates of deposit plus 0.5% as the net yield. This reflects approximately a bond ladder with an average maturity of five to seven years at current yields, assuming no defaults and no capital gains/losses.
Keith plans to add $4,000 each year to his portfolio. Then a fractal event happens and his portfolio loses 25% of its value; it’s now worth $75,000. Keith wonders when his portfolio will get back to its pre-crash value of $100,000.
Figure 1 displays the aftcast. Each of the grey lines represents one specific starting year since 1900. The blue line represents the median portfolio: half of the grey lines are above it and the other half are below. The green

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