The quest to unlock the secrets to becoming a successful investor and the science behind the theory of portfolio construction has a long and varied history. Over the last 60 years, the level of sophistication that has been applied to the topic has steadily increased.
The Early Years of Modelling Stock Returns
Central to that discipline has been the Capital Asset Pricing Model, CAPM, which has shone a spotlight onto the question of when is it optimal to add an additional security to a well-diversified portfolio. Among the many researchers that developed these ground-breaking ideas were William F Sharpe & Harry Markowitz (see Ref 1), and so successful were they in stamping their authority on the field that it is now commonplace to talk about the level of ‘Beta’ of a stock’s returns when representing how that stock compares to the market’s returns.
The Onset of Index Tracking & ETFs
As investors’ interest in the stocks market grew, so did the fascination with index investing, which in the 1990s gave rise to the concept of Exchange Traded Funds, ETFs.
One can’t really imagine an investing world without the S&P 500 Index. Indeed, since its launch in March 1957, the growth of indexing across all asset classes, and the whole professionalization of the investment landscape has gone hand in hand. This growth has been accompanied by the in-lockstep growth of the ETF industry. I guess it’s hardly surprising that ETFs, which are often known as Index Trackers, were invented to provide the investor with a simple investment product delivering market returns – less fees.
Style Based Strategies & The Arrival of Factor Investing
Fast forward another decade or so and Factor Investing became the framework that would go on to supersede the success of CAPM which treated the market as being driven by a single factor. Back in 1934, Benjamin Graham and David Dodd already knew differently as they introduced the world to Value & Growth investing with their tour de force book on stock picking called “Security Analysis” (see Ref 2).
The basic premise why an investor should care about factor investing is the idea that by grouping stocks according to these different style factors, one could gain an insight into what will drive future returns – which is a useful thing to know if you are trying to understand how to increase the returns of your portfolio. Over time, the range of factor-based stock picking strategies grew and on the back of it the range of ETFs that provide products on one or several factors. The following factors are the most popular amongst market professionals, but as a caveat it is worth highlighting that not all researchers agree what that list should comprise (see Ref 3).
Value / Growth – Strategies that focus on a stock’s fundamental metrics such as dividend payments, price to earnings or price to book ratios. With that in mind, value stocks are in a classic sense deemed to be undervalued. Whereas growth stocks are the ones deemed to have the possibility to provide above market returns.
Momentum – The essence of a Momentum strategy is to select stocks based on their past performance. The way this process works involves calculating the performance for each stock in a given target universe over a particular time period. Having ranked the stocks, an investor might choose to invest in the top ten stocks on that list.
Low Volatility – Whenever talking about a stock’s performance it’s better to talk about risk-adjusted returns. On that basis, the idea behind this approach to investing is based on the view that stocks exhibiting lower returns volatility deliver higher returns.
Quality – In a similar vein to the value factor of a stock, as a metric quality is constructed from several financial statements about the company in question. These range from the return on equity and other balance sheet related data. The topic becomes more nuanced as one takes on board information relating to the company’s corporate governance.
The Arrival of Smart Beta ETFs
During the same period that these ideas took hold, indexing & portfolio benchmarking would come to dominate the investing world. As the increase in interest in all things ETFs continued to grow, it was only natural that the index providers would themselves design indices that were based around the various factors of interest. A brief overview of the family of factor-based index products that S&P offer (see Ref 4), provides a glimpse of how developed this field has become. With index names like S&P 500 Pure Growth, S&P SmallCap 600 Pure Value and S&P 500 Minimum Volatility Index, one can readily understand what the investment idea is. Once one adds the S&P SmallCap 600 Momentum – Lowest Quintile Index, then one can see how far the factor investing revolution has come. In general, a Momentum factor tries to invest in companies that have had the best returns recently, whereas the ‘Lowest Quintile’ index bets on a reversal of that trend. The skill, as with all things investing, lies in when to pick which factor.
For many of these benchmarks expect there to be an ETF that tracks them (see Ref 5), and as a direct consequence, never in the history of investing have there been so many tools available to slice and dice one’s portfolio. Note, we didn’t even venture into the topic of ESG factors, or the blending of multiple factors to produce what became known as Smart Beta ETFs. Thus far this article has focused on Style factors that are considered to drive the returns of single stock, but what about Fixed Income or Commodity investments? In these asset classes, it is more appropriate to talk in terms of Macro-Economic drivers such as the level of real interest rates, credit and inflation, topics which are central to multi-asset class investing, a topic best left for another day.
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Ref 1 – https://www.nobelprize.org/prizes/economic-sciences/1990/markowitz/biographical/
Ref 2 – https://www.amazon.co.uk/Security-Analysis-Foreword-Buffett-Editions/dp/0071592539
Ref 3 – https://www.sciencedirect.com/science/article/abs/pii/S0304405X14002323?via%3Dihub
Ref 4 – https://www.spglobal.com/spdji/en/index-family/strategy/factors/#overview
Ref 5 – https://www.algo-chain.com/