I just read a very good article on how software models were partly responsible for the Great Crash of 2008. Basically, the article talks about how software programming models, based on conventional statistical theory and probabilistic techniques, cannot really predict the outcome of financial markets either short term or short term.
I think that it can get quite difficult for people to understand the deep concepts behind the article so let me explain my understanding in simpler words:
Everyone knows that stock markets are as unpredictable as any other random event in our lives. For example, I don’t know exactly what I will be doing 1 year from today. Based on my future plans, and my control over personal decisions, I can roughly predict the kind of work I’ll be doing, and the place I’ll be living. But if someone asks me “who I’ll be dating after 1 year from now” or “what clothes I’ll be wearing on a day exactly 6 months from now”, then it is almost impossible for anyone to even predict such random outcomes.
Now, mathematics can help us here. Using statistics and probability equations, one can give a possible answer based on different input parameters like: my behavioral patterns, my favorite color, my basic lifestyle etc., etc. But, this answer would only give us the odds of the event happening, and can never be 100% accurate. And, there will always be some percentage of error which these models tell us to ignore. Also, this answer will not and cannot take into account completely un-related factors like who will be the Prime-Minister of India during that time. Statistical models were derived from logical understanding of events, and as per the standard knowledge a particular event’s occurrence can only be affected by related events. Probability has no place for accommodating completely un-related events. But, since we do not have the complete understanding of chaos and fractals, statistical models were the only option then.
Chaos Theory told us that nothing happens by chance, even random events have pattern in them. There is an unseen order in chaos. The importance of non-linearity is the basic tenet of Chaos Theory. Relatively small and negligible events can cause unbelievable changes in future and un-related events also play a big role in the outcome of a particular event. So the “conventional” mathematical financial models were flawed to start with. According to the Chaos Theory, not only did they ignore the importance of other events but also the cumulative errors in such models could have disastrous effect on the predicted outcome. Here is an excerpt from the same article I read:
“As Benoit Mandelbrot, the fractal pioneer who is a longtime critic of mainstream financial theory, wrote in Scientific American in 1999, established modeling techniques presume falsely that radically large market shifts are unlikely and that all price changes are statistically independent; today’s fluctuations have nothing to do with tomorrow’s—and one bank’s portfolio is unrelated to the next’s.
So, these same “flawed” models were used everywhere in the financial industry for predicting future fund values, stock market fluctuations, financial risk calculations for a portfolio, etc. And, the biggest reason which accelerated the Great Crash was the over-reliance on such software models.
Do we have enough knowledge of this new Chaos Theory and fractals to help us in such random predictions and can they be used commercially? The answer is Yes! Here is an article which talks about how fractals can make current models more predictable by taking into account not only related events, but also the principle of self-similarity:
How Fractals Can Explain What’s Wrong with Wall Street
Here is another interesting book to read on the same topic:
The Predictors: How a Band of Maverick Physicists Used Chaos Theory
There is a lot for the mathematicians and programmers to learn from Chaos Theory & fractals, and it is not only the financial industry which can benefit from these new theories, but also other domains like HR, logistics, ERP, traffic management etc. Chaos is new, raw and we are still un-raveling its mysteries. But it is only this theory which has opened our eyes to a new previously “un-seen” dimension, and it is time we start imbibing Chaos not only in our software models, but also in our daily life.
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