Welcome to Episode 44 of Retire Me!
In the world of enterprise technology, businesses want proof that the solutions they are buying will perform as expected. They look for research, they test solutions to make sure they are compatible with their environment, and they build business cases based on their findings. These steps help them make good decisions.
When I discovered factor investing in 2017, it made sense that investment decisions should be carried out the same way as big business decisions. Asking questions like:
1) What is the evidence that supports our decision making?
2) The data robust and reliable?
3) Can we implement it in a way that fits my life and my goals?
4) Can I expect a relatively better ROI on this approach over decades, versus other alternatives?
Over the next 4 weeks, we will:
1) Introduce factor investing more in depth
2) Explain the main factors that drive investment returns
3) Explain how factors can be implemented
4) Offer some pros and cons and reasons why you may want to either pursue or avoid a factor investment strategy
Most of the content for these episodes is inspired and heavily sourced from Larry Swedroe and Andrew Berkin's book:
"Your Complete Guide to Factor Based Investing" Your Complete Guide to Factor-Based Investing: The Way Smart Money Invests Today: Berkin, Andrew L, Swedroe, Larry E: 9780692783658: Books - Amazon.ca
The design point of these episodes is to explain factors in "Plain English". Some of the research is a little wonky, so I have done my best to condense and simplfy wherever possible.
If you have questions or clarifications, please message me or send an email to firstname.lastname@example.org
• Traditionally, most portfolios have been constructed primarily from public equities and bonds.
• Solid historical wisdom has been to diversify across stocks and bonds all over the world for your portfolio
• However, there is a non - traditional way to think about diversification. Rather than viewing a portfolio as a collection of asset classes, one can view it instead as a collection of diversifying factors.
• To determine which exhibits in the factor zoo are worthy of investment, we will use the following criteria. For a factor to be considered, it must meet all of the following tests:
○ Has to deliver a premium to returns
○ Has to be persistent - holds across long periods of time and different economic regimes
○ Has to be pervasive - holds across countries, regions, sectors, and asset classes
○ It has to be robust - it holds for various definitions (ie. Value premium exists regardless of whether you measure its definition by price-to-book, price to earnings, price to cash flow, or price to sales)
○ It has to be investible - it holds up not just on paper but it survives actual implementation issues such as trading costs
○ It has to be intuitive - it is logical, it has a risk-based explanation, and/or it has a behavioural definition
• Through this lens, the book narrows down on a short list of them and we are going to focus on:
§ Market Beta (1)
§ Small Cap Premium (1)
§ Value Premium (2)
§ Profitability Premium (2)
○ Fixed Income:
§ Credit Premium (3)
§ Term Premium (3)
• From there:
○ Implementation of a factor portfolio (4)
○ Thoughts on Factors (pros and cons) (4)
• Discovery of factors started 50 years ago with the development of the first major breakthrough in research into asset pricing - the Capital Asset Pricing Model (CAPM)
• Developed in the early 1960s by William Sharpe, Jack Treynor, John Litner, and Jan Mossin
• The point of the research was to figure out how much of an investment portfolio's performance could be explained by some common factor vs. The skill of the fund manager/stock picker
• So CAPM helps define how much an asset moves with the broad stock market
• By definition, a market portfolio consisting of all stocks has a beta of exactly one
• Investor A has a portfolio of high flying tech stocks with a beta of 1.5, when the market goes up 10%, that portfolio goes up 15%. When the market portfolio drops 10%, that tech heavy portfolio goes down 15%.
• Investor B has a portfolio of defensive stocks (drug stores, utilities, supermarkets) with a beta of 0.7, when the market goes up 10% it only goes up 7%. When the defensive portfolio drops 10%, the defensive portfolio only goes down 7%.
• If you add bonds to a portfolio, that changes its beta
• Investor C has a portfolio of 70% tech stocks (beta 1.5) and 30% riskless Treasury Bills - the beta of the portfolio is only 1.05 (70% * 1.5)
• When you compare beta to the standard riskless asset (one-month US Treasury Bills) to determine if there is a premium for holding the market portfolio over riskless T-Bills
• To evaluate factors, you measure by taking the difference in average annual returns between the two factors
• In the Market beta factors, we will take the annual average return of the US total stock market and subtract the average annual return of One-Month US Treasury Bills
• Also known as long-short portfolios (long US stocks, short US bonds)
• Positive in 2/3 of calendar years
• No matter how long the time horizon, there is always a chance of underperformance
• For 5 year time horizon - chance of underperformance was 18%
• For 20 year time horizon - chance of underperformance was 4%
• It is the risk of underperformance that is the reason any factor premium exists**
• In their 2011 publication “Equity Premiums Around the World,” authors Elroy Dimson, Paul Marsh, and Mike Staunton found that since 1900 market beta has been positive in virtually every country and region around the globe.
• Canada - 4.1 since 1915
• US - 5.5% since 1925
• World ex-US - 3.5% since 1915
• Market Beta is super easy to invest in
• You can get a market stock or bond index fund or ETF for a few basis points
• Stocks are much more volatile than bonds - investors command a higher return for enduring the volatility
• September 1929 - June 1932, US Stocks lost more than 83%. One-Month Treasuries returned 6% over the same period. In fact, the one month treasury bill has never experienced a negative year in nominal terms.
• Take the "double whammy" that stocks falling coinciding with negative economic cycle job loss/layoff
• CAPM was the biggest breakthrough for about 30 years
• Over time, we learned that CAPM was only able to explain about 2/3 of the returns of a diversified portfolio
• For example if Portfolio A returned 10% and Portfolio B returned 13%, the difference in Beta would only explain about 2 percentage points of the 3 percent difference in return.
• The remaining Percentage difference might be explained by luck, skill (stock selection or timing the market), or by some other factors.
• Eventually other factors were discovered
• In 1992, Kenneth French and Eugene Fama wrote a paper called "The Cross Section of Expected Stock Returns"
• They proposed that, along with the market beta factor, exposure to the factors of size and value further explained the difference in returns in diversified portfolios
• The Fama French 3-factor model improved upon the CAPM formula, accounting for more than 90% of the differences in returns between diversified portfolios
Small Cap Stocks
• Size factor is calculated by taking the annual average return of small cap stocks and subtracting the return of large cap stocks
• US - Size premium is 2.02% per year from 1927 - 2019 (according to DFA)
• Size has been persistent but not to the same extent as market beta
• Positive over 56% one year, 62% 5 year, 85% 10 year periods (1926 - 2019 in US) (DFA)
• Size combined with other factors make it more compelling
• Canada - 0.29% size premium from 1988 - 2019,
• Developed ex US - 4.91% size premium from 1970 - 2019
• Emerging Markets - 1.86% size premium from 1989-2019 (DFA)
• Risk based
○ Greater leverage
○ Smaller capital base
○ Greater vulnerability to changes in credit conditions (higher cost of borrowing)
○ Greater uncertainty of cash flow
○ Less liquidity
• More volatile than large cap sticks by about 1/3 in the US between 1927 and 2015
• smaller companies tend to perform relatively poorly in bad times, and assets that do poorly in bad times should require a risk premium.
• And in 2008, large-cap stocks lost 36.5 percent, while small-cap stocks lost 38.7 percent.
• Gerald Jensen and Jeffrey Mercer, authors of the 2002 paper “Monetary Policy and the Cross-Section of Expected Stock Returns,” examined the relationship between economic-cycle risk and the size effect.
• They found that when size is isolated, there is a significant small-firm premium only in periods of expansionary monetary policy. In restrictive periods, the size effect is not statistically significant.
• They concluded that monetary policy has a significant impact on the size effect.
• Proving this, the bounce-back in small cap stocks from last year
• 1 year ago today
○ Russell 2000 - up 110.81%
○ S&P 500 - up 54.9%