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Wille Faler is an experienced software developer, architect and agile coach with experience across a number of different industries as an independent consultant. Wille specializes in backend, integration, and Java technologies, but has more recently found a passion for Scala and text mining/analysis. Wille is a DZone MVB and is not an employee of DZone and has posted 39 posts at DZone. You can read more from them at their website. View Full User Profile

Pseudo-Science: The Dark Side of Big Data

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Over the last couple of months I have read up on volumes of Technical Analysis (“TA”) information, I have back tested probably hundreds of automated trading strategies against massive amounts of data, both exchange intraday- and tick data, as well as other sources. Some of these strategies have been massively profitable in back testing, others not so much. 

Some of the TA patterns, I’ve discarded before they even left the book, because they did not stand up to any sort of scientific scrutiny because they lacked a clear predictive thesis, where riddled with forward-looking bias (“Head and Shoulders patterns”), and in some cases where just plain bulls**t (“Elliott Wave Principle” comes to mind).

The outcomes of my testing has made me think about the implications of large scale data analysis in general: it is very easy to get fooled by randomness. In many cases in my testing results have been amazing, but I cannot come up with a plausible causal explanation as to why, and when I gently nudge the parameters just ever so slightly, outcomes can look entirely different. 

Taking a step back from the data, looking at it in a larger perspective, I’m inclined to conclude that if data across multiple parameter variations looks like a random walk and lacks a plausible causal explanation, then it is a random walk.

If I cannot say “X is caused by A and B”, I’m inclined to believe that the actual reason is “X is the result because A and B fit the historical data D, but may not do so in the future”.

And herein lies the crux of the matter: how many data scientists are inclined to take a step back, rather than just assume that there is a pattern there? How many are prepared to do so if their livelihood is largely based on them finding patterns, rather than discarding them because they do not hold up to deeper scrutiny? I’d say very few.

My conclusion to this is that the age of Big Data will see a radical increase of pseudo-scientific “discoveries”, driven out of an interest in announcing new great “patterns”. This pseudo-science will pervade both academia, public sector and private sector, God knows I’ve seen a fair number of academic research papers already that simply do not hold if you investigate their thesis in a deeper manner.

I suspect we will arrive at a point much like with any new technology whereby people will tire of the claims made by “Big Data Scientists”, because at least half of what they say will have been proven to be hokey and pseudo-science in the pursuit of being able to make even more outlandish claims in a game of one-upping the competition. Some of this will be driven by malice and self-interest, but I suspect in equal parts it will be driven by ignorance and perverted incentives putting blinders on people in the business.

Published at DZone with permission of Wille Faler, author and DZone MVB. (source)

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