Evidence Based Finance

Part 3: Positioned for Opportunity

“Active portfolios can be structured to invest in securities and sectors that offer greater potential growth than the market as a whole. Active managers can add value by identifying and buying companies that are undervalued or out of favour, or those that offer above-average growth prospects.“ - CI asset management

This implies that active managers have sufficient skill to consistently outperform their counterparts and the benchmark. Although the idea makes sense, smart people should be able to find good deals consistently in the stock market… does this work in reality?

In 2010, Eugene Fama (2013 Nobel prize winner for economic sciences) and his colleague Ken French published “Luck vs Skill in the Cross Section of Mutual Fund Returns” which concluded that on average US equity fund managers do not demonstrate evidence of skill. If there were uncommon skill involved in stock picking, you would expect that skill to be demonstrated over a period of time, or it could be perceived as just luck.

S&P do a Persistence Scorecard, that reviews how fund managers perform relative to their peers over time and their ability to stay in the top quartile consistently. Were they able to consistently outperform, it would be a clear sign of skill.

In their 2020 scorecard, S&P research “reinforce the notion that choosing between active funds on the basis of previous outperformance is a misguided strategy. After all, there remains a 98.5% chance that a top-quartile fund will not stay in the top quartile for the next four years.”

U.S. Persistence Scorecard Year-End 2021 - SPIVA | S&P Dow Jones Indices S&P Indices

The theoretical framework for evidence-based investing

The first economic model that seemed to realistically capture what was going on in financial markets was the Capital Asset Pricing Model (CAPM). Proposed by Nobel laureate William Sharpe in 1962, CAPM gave a baseline for how investors could expect to be compensated for taking risk with stocks.

What the CAPM model captured is called “beta” – it is a measure of risk / volatility of a single stock or portfolio compared to the market as a whole. If something is more risky or volatile, it has a beta greater than the market (=1), you would expect a higher return than the market for the risk you take to own it. The other side of that is a portfolio or stock with a beta less than 1, it is considered less risky and you’d expect to get less return than the market for owning it.

It is also helpful in identifying systemic risk and unsystematic risk. Systematic risk is the risk of the entire market moving in one direction together, an example might be 2008, when regardless of the portfolio of stocks you owned and where, you realized a significant correction.

Unsystematic risk is risk or volatility that you can diversify away by owning many different stocks. Unlike systemic risk, which one cannot control, Investors can control how much diversity they take on, so they can take actions to mitigate unsystematic risk.

This model is particularly useful to index investors, who take on maximum diversification to control their market risk.
Efficient market hypothesis (EMH) – The reason you index

In 1970, Eugene Fama, who would be awarded the Nobel Prize in Economic Sciences in 2013, published “Efficient Capital Markets” in the Journal of Finance. It created the basis for the efficient market hypothesis, which in its simplest form, theorizes that asset (stock market) prices reflect all available information, so it is impossible to consistently beat the market.

So with these two facts, the evidence based investor would likely remove all unsystematic risk through maximum diversification and own the entire market.

As the efficient market hypothesis (EMH) was tested, anomalies began to show issues in CAPM’s pricing model for stocks – the model was only explaining about 70% of diversified portfolio returns. In 1993, Fama and his colleague Ken French published, “Common risk factors in the returns on stocks and bonds” – which created the Fama-French 3 factor model for explaining stock returns that was potentially able to be a better explanation of market returns.
Fama-French Factor model – the reason to look beyond market indexes

Using EMH as a basis, Fama and French found that there were stocks with similar characteristics that were showing excess return over time – these are identified as “factors”. This is interesting because if EMH is correct and prices reflect all information, additional returns above the market cannot reliably be achieved by finding mispriced stocks. So how were these groups gaining additional return in their data?

What it posits is that these similar characteristics create groups of more risky assets and for taking additional risk, investors should expect outsized returns. The factors are:

Market Risk – “beta” as explained by CAPM
Size factor – they found that small sized companies tended to perform as a group better than larger companies.
Value – using price-to-book ratio, they found that lower price-to-book ratios tended to perform better as a group than higher price-to-book. Simplified, this means that lower priced companies are considered riskier than higher priced ones, so you should expect greater return long term for owning them.

When you include these factors together, they can explain closer to 90% of diversified portfolio returns, from 70% of just CAPM. They have since gone on to identify two more factors, profitability and investment, that can provide additional explanatory power to the model.
Why does this matter to investors?

It is important to know why you invest the way you do. You invest in broadly geographically diverse low-cost index funds because:

You believe that stock pickers cannot outperform the market, as all information is already incorporated into the price (efficient market hypothesis)
You are looking to capture market returns through broad diversification and are willing to take the risk (beta) to do so. (Capital asset pricing model)

This single factor is relatively easy to capture at a low cost, it is easy for investors to understand -allowing them to do it themselves and maintain the investment strategy over long periods.

The underlying basis for this thesis is very easy for us to understand and we experience it daily, you are betting long term that capitalist companies whose primary goal is to increase shareholder value, will do that overall.

This is where most evidence-based investing stops. The best portfolio tends to be the low-cost one, the one you can understand, and the one you can stick with through good times and bad – and the simplicity of index-based investing will do this for most.

However, the same people that created the framework for us to understand why index investing can provide returns in excess of active managers also identified evidence-based ways for investors to gain additional return above the market – by capturing other independent risk factors.