Drowning in Data

If you agree with the proposition that more information leads to more efficiency in pricing in the markets, that should probably lead to the conclusion that markets have never been as efficient. We’ve simply never had this much data and data has never been as accessible as it is now.

Gone are the days when Warren Buffett could comb through Moody’s manuals to find net-nets. Now, data-rich stock screeners are readily available to anyone. On top of that, there are armies of hedge funds other quantitative investment shops out there, crunching data and trying to advantage of any arbitrage opportunity they can find.

A Valuable Lesson of VAR

Had you argued this to me about a year ago, I would have wholeheartedly agreed. Today I’m not so sure. And here’s the reason why.

You see, I’m a football fan (soccer) and recently they have implemented something called VAR into the game. VAR stands for Video Assistant Referee. It basically means that during a game there is now an additional assistant referee who reviews decisions made by the head referee with the use of video footage and analytical technology in real-time. He is then able to communicate with the head referee during the game.

The objective of the VAR implementation is to minimize human errors causing substantial influence on match results. Previously, the referee had to make split second decisions on incidents. Now he or she can utilize VAR, which means better data. The VAR can analyse incidents by replaying it from different vantage points and use graphics to determine rulings such as offside. Sounds great, doesn’t it.

The Interpretation of Data

The really interesting thing about VAR, is that after its implementation there is still a fair amount of dispute regarding key referee decisions. Even with the additional data provided by VAR, pundits are still arguing whether decisions on offsides, penalties and such where correct or not.

It seems that more accurate data by itself doesn’t necessary lead to better decision making. The data still needs to be interpreted. In that sense, it’s not just a question of decision being subject to human error or not. Sometimes, different people will perceive the same data differently. It is in some way a matter of opinion.

Financial Data and Insights

If we apply this to the investing world, it is safe to say the following:

If you would show two analysts the same financial and operational data two competing companies, it is entirely plausible that the conclusions that those two analysts might draw from the data would be diametrically opposed.

The interpretation of the data will be subject to frameworks the analysts used to draw insights out of the data. Insight, per definition, is the power or act of seeing into a situation. But insight, is in the analyst, not insight the data.

Largest S&P 500 Single Day Drop

One of the things that has preoccupied my mind lately are the underlying differences in approach between active investing and passive investing.

Imagine the two following hypothetical money managers: One of them is an active investor. He performs bottom-up fundamental research of companies, trying to determine their “intrinsic value”.

The other investor is passive. He uses quantitative analysis in order to find factors would have lead to out performance compared to a specific benchmark (these strategies are called “smart-beta” as they are passive in nature, but still aim to outperform the benchmark).

Analytical vs Statistical Approaches

For lack of better terminology, lets say that the active investor has an analytical approach, while the passive investor has a statistical approach.

The active investor is focused on the future cash flows of the company. He is tries to understand the business model of the company he is analyzing how the company creates value. He might try to study historical transaction multiples or how similar public compare in terms of valuation ratios. But primarily, the fundamental investor is trying to analyse future events.

The quantitative investor, however, is looking at a universe of stocks. He mines datasets to find a relationship between factors and performance. He designs different strategies and uses backtesting to see how these strategies would have performed.

The Limits of History

But what is data? Data is history.

Consider the following: Suppose you ask the investors about the largest single day drop in the S&P 500. The quant tells you that the largest single daily drop of the S&P 500 occurred on October 19, 1987, when the index fell by 20.47%.

The fundamental investor, however, tells you that the largest single day drop hasn’t happened yet.