Is there a quantitative approach to


introduction

Investors from all over the world have always had a secret dream: to make investing automatically and effectively. Making large sums of money with little or no effort has always drawn students, economists, and mathematicians, not just because of the likely higher returns, but also because of the exciting challenge that a working system or formula brings with it, like it feels like finding the holy grail.

But it was only when databases of corporate financial data became more widely available that the search for a workable formula became a real science rather than an obscure subject.

Quantitative investing (or quantitative analysis) is an approach that uses mathematical or statistical methods to determine the value of securities and ultimately determine which securities are being sold below their (presumed) intrinsic value.

Different methods

One of the pioneers of this approach was the late Benjamin Graham, the father of value investing, who once described a formula for determining the intrinsic value of a company.

The formula is:

Intrinsic value = profit multiplier * EPS

Where:

Earnings multiplier = 8.5 + 2 * g and

g = the company’s minimum future earnings growth rate.

The formula is based on the assumption that the P / E ratio of a no-growth company is 8.5.

This is only an approximation, of course, but for some time it just worked fine as the underlying assumptions were, on average, valid and measurable. Another reason it worked is because a few decades ago not many people approached investing (and especially valuation) this way. Graham was one of the first to rationalize the concept of value as linked to profits and cash flows.

When

Warren Buffett (trade, Portfolio) was a young man who eagerly read thick stock manuals filled with financial data for thousands of companies. Since these manuals were actually the only “databases” available at the time, one should not be surprised that, with enough time, patience and effort, one could discover, for example, a company that was sold under its receipt and it benefited from what at that time must have been like finding buried treasure.

When

Joel Greenblatt (trade, Portfolio) was a young student at Wharton reading a Forbes article on Graham’s net-net stock selection formula. The method was to buy a stock only when it was sold at less than two-thirds of its net asset value (the NCAV can be calculated by subtracting all long-term debt from working capital).

At the time, he was studying the efficient market theory, but became increasingly disappointed that the theory didn’t resonate with him, so he began to verify that selecting stocks that matched Graham’s formula actually produced higher returns than he predicted. It has, and the rest is history.

Fast forward to 1992, the famous Professors E. Fama and K. French expanded the popular formula for the price model for investments by adding size and value risk factors to the existing market risk.

Specifically, their model found that small-cap and value stocks routinely outperformed the markets compared to stocks that lacked these characteristics. By value stocks, they meant stocks with higher book market values ​​(basically stocks with low price-to-book ratios).

Small caps’ overperformance can be explained by the fact that small companies can easily fly under the radar, making them more likely to be undervalued compared to larger cap companies.

With regard to the value component, there is growing evidence that choosing stocks with a low price-to-book ratio versus stocks with high multiples is not necessarily a predictive factor of stock price outperformance. This is understandable when you consider that most companies in the past generated their cash flows through the intensive use of tangible assets, but today the most profitable companies usually have few tangible assets and are therefore no longer capital-intensive.

Quantitative analysis and its limits

Let us now return to the original question: does a quantitative investment approach still make sense or should we discard it in favor of a deep safety analysis?

The answer is not easy, but here’s my take: Quantitative investing is no longer enough simply because almost everyone now has access to a computer.

But if this approach doesn’t produce the great results it has in the past, why is everyone still using it? Because it’s better than throwing darts. Having a powerful quantitative tool is not the same as knowing how to use it. You still need to set the right inputs and conditions for the tool to filter out companies with poor prospects.

That is why, for example, Greenblatt’s magic formula, which basically tries to buy good companies at good prices, still gives you an advantage over many actively managed funds (at least the advantage was clearly recognizable until a few years ago). Greenblatt’s quantitative method still selects potentially healthy companies and filters out most of the troubled companies. Yes, you can easily fall into a value trap with this method without additional analysis, but on average you will likely do well over the long term. As Greenblatt explained in The Little Book That Beats the Market, this advantage would quickly disappear if everyone followed his method.

Another good reason for quantitative analysis is that it works quite well as a sieve filter. Even the best investors use it to narrow the investment universe to a number of companies that are more likely to generate higher returns over the long term. It’s not the only way they research stocks, of course, but how

Charlie Munger (trade, Portfolio) once said: “The first rule of fishing is: fish where the fish are.”

Now let’s try to list what we can and cannot achieve with the above approach.

The quantitative analysis can:

  • Filter out (most of) bad companies.
  • Identify good trends in financial metrics in the past.
  • Combine multiple quality metrics in a single tool.
  • Reduce our investable universe and thus reduce the search time.
  • Increase the likelihood of finding a good company.

But it cannot:

  • Estimate future cash flows as they are based on past data.
  • Spot a company’s moat or tell us how solid it is.
  • Tell us how good the management team is.
  • Pick a future winner who doesn’t already have a good track record.
  • Correctly assess the intrinsic value of a company.

As we can see, a quantitative tool cannot do exactly what a good value investor is supposed to do.

Finally, we can’t entirely rule out the possibility that some shrewd investors (like

Jim Simons (trade, Portfolio) by Renaissance Technologies) can create a quantitative method that works consistently and generates above-average returns, but is the exception rather than the rule.

Here’s what Terry Smith, Founder and CEO of Fundsmith, said during a recent one interview:

interviewer: What do you think of the increasing use of computers for stock selection? And do you think that the classic fund manager will be replaced by computers in the future?

Terry Smith: See, there is no doubt that you can use computers for this. The entire passive industry is essentially being driven by the use of computers to actually prohibit stock picking. But beyond that in the active area [ …] I think humans will probably play a role in active management forever, certainly for a very long time.

The best way I could put it is this, look, you can do a lot of this stuff mechanically, but the human element comes in when you have someone who is intelligent and very experienced [ …] and you listen to the management present at meetings, conferences, and you meet them and so on. If you haven’t done this, you won’t understand.

diploma

Many investors have tried to come up with a quantitative formula that can be used to predict either the value of a security or future stock returns.

Some of them have been brilliantly successful by taking advantage of huge inefficiencies in the market due to overlooked inventory levels, poor research, or a lack of financial databases or computing power.

Nowadays, good quantitative analysis can be conveniently used to get slightly above average market results and for inventory screening purposes, thereby reducing research time.

Unfortunately, because this method is based on past data rather than the company’s current business dynamics, it cannot (usually) be used to identify future market winners.

Developing a deep knowledge of the company and the sector, understanding its business model, studying the management’s track record, and studying its competitors and customers to gauge future cash flows and growth is still the best (and most difficult) way to to outperform in the market. As Thomas Edison once said, “There is no substitute for hard work.”

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