The Importance of Market Structures

Porter's Five Forces is a widely used framework for analysing competitive forces within an industry or market. It was developed by Michael Porter in the late 1970s and has since become a cornerstone of competitive strategy analysis in business. The Fairlight team has found that Porter’s work has been quite useful in identifying businesses with sustainable competitive advantages. Often businesses we score the highest, see their industries consolidate into cozy oligopolies over time, leading to a rational competitive environment with pricing power. A trait that has become quite important over the past 3 years due to well telegraphed inflation.

To directionally test Porter’s work, we identified 18 companies either currently owned within the Fund, or which have been owned in the past, for which the top 3 competitors in their industry controlled more than 90% market share (i.e., oligopoly market structures). These businesses score well on Porter’s framework by default. Using market cap as the weighting factor, we constructed an “Index” to indicate how these businesses in aggregate have performed against the market (S&P 500).

Whilst acknowledging there is significant hindsight and survivorship bias in this analysis, over the past decade the Oligopoly Index has delivered an impressive annual return of 23%, significantly outperforming the S&P 500, which has yielded an 11% return during the same period. It’s worth noting that while the Oligopoly Index exhibits higher volatility, the Sharpe Ratio stands at an impressive 1.04x, surpassing the S&P 500’s ratio of 0.46x – indicating better returns per unit of risk (at least the academic definition of risk). Indicatively it appears Porter was onto something.

It's essential to keep in mind that the Oligopoly Index trades at a premium. When this analysis was conducted on August 1st the Index was trading on a P/E ratio of 32x in contrast to the S&P 500’s 22x. As Fairlight runs a valuation sensitive strategy (our portfolio trades closer to 20x), we are often trying to buy these wonderful underlying businesses on some sort of setback to build in a margin of safety, and hopefully enhance returns.

Buy and sell strategy

Although our strategy doesn’t have a 10 year history, we can demonstrate the Fairlight buy and sell discipline by indicating the “hit rate” of our buys and sells compared to the average price to earnings (PE) ratio of this index since the inception of the strategy in 2017.

The yellow dots denote the discipline in trading at reasonable prices, for buying it means we bought below the P/E of the Oligopoly Index and likewise selling above the P/E of the Index. Our hit rate in buying cheaper than the Index is 60% and 68% for selling more expensive than the index. To be clear, sometimes “paying up” makes sense – ie, we’ve generated returns above the market more often than not when paying a premium, however, we require an unusual level of confidence in the competitive position and growth rates of these companies to compensate for a lower margin of safety on valuation (see Figure 1).

Figure 1.

Selling fast and buying slow

Our higher hit rate on selling isn’t surprising to our team – we have carefully designed our strategy to target our sell discipline as we view the benefit of doing so as a “free kick”. We note that active managers generally do a good job of buying, but a poor job of selling. In fact, a paper by Akepanidtaworn, K. et al, “Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors” (2018) demonstrates this phenomenon well.

Within the paper the data is comprised of 783 portfolios, with an average size of $573 million. The average fund in the sample beats its respective benchmark by about 2.6% per year. 4.4 million trades (2.0 and 2.4 million sells and buys, respectively) are observed between 2000 and 2016. More simply put, it’s a large data set comprised of managers that in aggregate outperform the market.

The paper concluded that buy decisions were generally conducted after a fundamental piece of news has been analysed and incorporated into the decision, i.e. an earnings release or similar. However, sell decisions were not based on news, were random in nature and often conducted at extreme share price outcomes - both positive and negative. The performance for buy and sell decisions after the first 365 days were stark (see Figure 2).

Figure 2.

Interestingly, the data above indicates that in general, active managers sell their positions too early. In other words, they don’t let their winners run. To overcome this heuristic Fairlight has broken its sell discipline down to match the style of investments we target within the strategy. For our higher growth companies, we are more patient in the speed in which we sell to ensure we participate as much as possible in the upside, whilst also managing risk. Essentially, we sell in 50bps increments for our higher quality growth names as they transition through fair value and only sell the residual position when the business is quite obviously expensive.

The Fairlight view

Our experience indicates that the Porter framework works well, especially where an industry has consolidated into a rational oligopoly. Buying with discipline enhances a margin of safety, however research indicates a nuanced sell discipline may be a more reliable source of alpha. Technology, process and culture helps prevent us from selling wonderful businesses too early whilst simultaneously tracking potential thesis creep and heuristics that may prevent us from identifying errors efficiently.

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