Quantitative Investing – A Misunderstood Science

Quantitative Investing – A Misunderstood Science

We often hear about the two most shared investment methodologies and philosophies, one is basic and the other is technical. I am here to introduce, demystify and educate people on the often misunderstood philosophy of quantitative investing.

Quantitative investing seeks to reduce potentially negative human biases, and bases investment decisions solely on the data – just the facts; it is emotionless and disciplined. Quantitative investing is generally a more demanding use of science and math than basic investing. “Quants” are not trying to understand the nuances of an idea, but analyze patterns and examine thousands of data points to come up with statistical consistencies.

Now, while this might sound more “out there”, it is in fact MORE shared in our everyday lives, or in almost every aspect of our existence. Every time we get on our mobile phones, we are employing the results of mathematical rigor, every time we get in our cars, we are benefiting from strong scientific studies on everything from the grind zones that keep us safe to the angles of the car that maximize its performance. Better however, we depend on science and quants to get astronauts into space, to navigate man made machines into the great emptiness of space and stay perfectly on course! So, in my estimation, it is perfectly logical to use the same methods for investing. Yes, basic investors have a place in the investment world, and yes, they can extract hidden value by talking with management and learning about the specifics of a product or market part. But, just as robustly as mathematics can take us to the moon, it can help design a successful investment methodology.

In quantitative examination, quants like me look at price behavior, which really is the behavior of the people trading the stocks. The up and down movement of a stock over the time of a day is really the battle between buyers and sellers, each with their wide variety of reasons for making buy or sell decisions. With this in mind, what becomes applicable are things like “who is winning the fight”, which is looking at where the stock closed on its range, or “who is more aggressive”, which you can see by looking at how fast it moves in one direction or another, or “how strong are the buyers”, which one can see by studying the quantity behind the buy orders. In other words, a more granular look into the activity of buyers and sellers is really an examination of supply/need dynamics, which in almost any econometric form, can tell us more about the situation, and in particular, the greater chances of a particular outcome. Simply put, if need is steadily increasing, one wants to own that!

Let me explain further why the statistics used by quants are, in my opinion, more applicable than the ratios calculated from balance sheets and income statements. All of the information on a balance sheet or income statement is exactly what most investors (basic) are using to make their decisions on whether to buy or sell. From a quant perspective, the reasons behind the buy or sell decision are irrelevant; quants just want to see the consensus results of those decisions. If there is something fantastic on the balance sheet, millions of people will start buying a stock and the stock will start behaving better. Quants will notice this strength and jump right on it. So, in the end, I believe quantitative investing to be a comprehensive conduit to knowing what all the balance sheets and income statements are saying. With this in mind, I guess you could say that quants inadvertently are exposed to balance sheets and income statements by looking at what traders do!

Another argument I often hear against quantitative investing and modeling is that it is based on historical stock and pricing activity, and as consequence, uses a narrower source of information than say, fundamentalists. As explained above, historical trading activity is, by definition, the broadest source of information since all other information, no matter where it was gathered from, is aggregated in people’s trading decisions based on that information. Whether from conversations with management, round the world travels to each and every store, or in-thoroughness examination of balance sheets, income statements, or quarterly reports, every investment decision results in either a buy or sell at some price and with some size.

consequently, by studying price/quantity activity, quants capture ALL of this information. In a way, it’s Darwinian – that is, all information assessed by market participants is reflected in their collective buy/sell decisions (trading activity), which ultimately discloses the market’s consensus opinion. If a management conference call was attended by many, disliked by some listeners and liked by others, and I am just one of the listeners, I only have a single source of information (my personal like or dislike). But, if I watch the stock performance after the call, I see what the majority decided, which is more likely the most accurate interpretation and consequently the most likely predictor of the future outcome.

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