Building my case from last time (the fortune index), where I suggested databases should talk to themselves and if natural systems express universal laws like patterns, divergences, seasonality, and constants then the data generated or derived from these natural systems should also express this universality. And if the data also express this universality then the question to be asked here is whether the universality character lies in something common to data rather than to a natural system.

This is what we refer to as data universality. Universality can be defined as “The aspects of a system’s behavior which are independent of the behavior of its components. And even systems whose elements differ widely may nevertheless have common emergent features”, then Data universality can be defined as the “common universal behavior of any data set irrespective of its organic source of generation or derivation.” Assuming data universality is a science. What does it bring down? The elephant blows away the blindfolds and finally, psychologists, technicians, fundamentalists, statisticians, mathematicians, scientists, etc. talk the same language. What is the problem we solve? Apart from pushing stock market into the scientific domain, we could address the problem linked with all complex systems and, we could understand complexity at a unit level. Why could we not do it till now? Interdisciplinary science is relatively new. An economist or physicist never thought their paths could meet, till one physicist jumped the ship and created Econophysics.

There is a key fundamental idea of value and growth cycles and that the premium markets give to growth over value. Robert Arnott has researched extensively on the subject and has built a novel fundamental indexing approach around it. We recreated the similar value and growth divergence using derived ranking rather than using pure price or fundamental data.

We took the top composite indices of India, Japan, Austria, UK, Australia and the US, ranked their components using our data innovation approach and created two portfolios, a worst losers’ portfolio, and a best winners’ portfolio. Barring Austria and US where the worst losers matched in performance with the best winners’ portfolio, the worst losers’ portfolio beat their respective benchmark performance over a five-year rolling return for all the markets.

What does this suggest? This suggests a few things. First; a worst performers’ portfolio delivers more than a best winners’ portfolio over longer terms of investing. Second; Worst performers’ portfolio generally does better than the market i.e. the value is superior to growth over the longer term. Third; fundamental behavior between value and growth does not need just fundamental data, the same value and growth divergence can be illustrated in ranking data from any group of market assets. The question arises yet again, is the value and growth divergence owing to fundamentals or owing to data universality?

Another paper written in 2004 by “Universality in multi-agent systems”, Parunak, Brueckner, Savit on Universality talks about the universal behavior of natural systems. The three stages consist of randomness, order, and herding. Call it coincidence but stock markets have a similar three system behavior (previous article). The extreme reversion universal system explained in chapter IV ranks performance over three broad segments; worst and best (below 20 and above 80 percentile ranking), near 50 percentile, and rest of the region. The universal behavior expresses itself in rankings. Again the data representation prevails despite different group characteristics.

The universality paper also suggests that the reason agents don’t optimize our decision making is owing to time constraints. The gap here is that the authors don’t connect seasonality with universal behavior, or in other words, they don’t connect and study the confluence of universalities.

Extreme reversion comes from a confluence of universalities. Because seasonality is an essential component of extreme reversion (best becomes worst and vice versa) there is a focus on understanding timing constraints (The question of when? is addressed). Which stage is more likely after randomness, order or herding? The performance cyclicality (extreme reversion systems) is also connected to multiple holding periods. Performance is growing or decaying differently for different periods. This is why any system that assumes timing aspect to be inherent to the decision-making process could be a better model for understanding universality and hence comprehending risk better.