Momentum, Efficient Market Hypothesis & Factor Investing
Momentum, Efficient Market Hypothesis & Factor Investing
Technological development is often the precursor to new insights, and this is no different in the history of financial markets.
As computational power improved in the 1950 and 1960s, researchers were able to apply statistical data to analyze market trends and asset returns. In the mid 1960s, Eugene Fama, 2013 Nobel Laureate and ‘the father of modern finance,’ developed the “Efficient Market Hypothesis”. The central premise of the hypothesis is that all available information is quickly incorporated into market prices, and as such, stocks trade at their fair market value on exchanges. Taking this further, under the strong form of the EMH, the implication is that investors simply cannot beat market returns without taking on more risk.
This theory quickly became the foundation for modern portfolio management. However, over time it attracted criticism due to anomalies such as stock bubbles, market crashes, government policies, and the existence of investors that do indeed do better than the market.
As time went on, the ‘efficient market hypothesis’ was refined as quantitative methods for analysing stock prices became more powerful, and the field of quantitative finance began to emerge.
Factor Investing
As computing power continued to improve, researchers and market participants were able to apply increasingly powerful mathematical models to explain asset prices and market price action. Researchers, including Eugene Fama, began to identify statistical factors that influenced asset prices which, when utilised, provided persistently higher-than-expected returns. In many ways, these models almost completely discredited the earlier efficient markets hypothesis. However, it is more accurate to say that these discoveries can be viewed, academically, as augmenting our theoretical understanding of the quantitative factors underlying market behaviour.1
As time went on, notable investors like Cliff Asness and Jim Simons were able to use quantitative factors and quantitative trading methods to generate outsized returns. This evolution coincided with expanded academic understanding of the statistical factors which explained asset price behaviour.
Notable amongst these factors are value, size, momentum, quality, and volatility, each of whichhave been shown to provide excess, or outsize, returns over long periods.
Coaks’ Favourite – The Momentum Factor
If you, like myself, have ever tried short-selling a strongly trending asset (say, Tesla stock in 2018), you’velikely become painfully aware of the existence of the momentum factor.
First identified by Narayan Jegadeesh and Sheridan Titman in 1993 in “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency”, the momentum anomaly explains the tendency for some rising asset prices to continue to rise.
In fact, this phenomenon is so powerful it has been exhibited by markets as diverse as currencies, commodities, and equity prices in analysing data spanning centuries--including historical markets like the 17th century Dojima rice exchange (often cited as the world’s first commodities market) and the Dutch tulip market (the latter eventually crashed spectacularly, so the upward trend did not last forever).
So what?
Momentum is pervasive and powerful. It provides an opportunity for savvy investors to potentially capture outsized returns--- with some significant drawbacks that we will explore in the next piece. (In short: relying on momentum has trade-offs and downside risks.)
It is also very difficult to arbitrage without putting your financial wellbeing (and career) at risk. The tendency for asset prices to trend for much longer (or shorter) than one would expect has caused many a trader or a corporate to be caught offside on a currency exposure--often leading to painful losses.
This is why it is important to be aware of and appreciate the impact momentum can have on asset prices.
Our next piece will parse the adage, ‘the trend is your friend’, and what it says about the impacts—and limitations— of momentum in financial markets.
1 The application of Artificial Intelligence tools (AI) in market modelling and financial analysis may teach us more –or upend historical learnings altogether. Time will tell.
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