Stock markets.  Bull and bear.  Trading and investing.  Lehman Brothers (the “Bank of Evil”).  Underneath these evocative terms and more is a complex, chaotic, non-linear time series of prices.  Their complexity is such that retail investors may experience feelings of disorientation with respect to exotic terms like derivatives, OTC trading, low latency trading, and dark pools that seemingly require practitioners to don black robes and chant around a mysterious circle. 

It is supposed to be straightforward.  Select any accounting and finance text and be treated to chapters on the time value of money, intrinsic monetary value, cash flow analysis, balance sheet analysis, income statements, and more to help objectively evaluate how much an asset is worth now from its likeliness of having some value in the future.  If the investor calculates the asset is worth buying now at X, then they should buy if it is on sale for less-than-or-equal to X and pass otherwise.   Further treatment on fundamental and technical trading, especially with respect to the Foreign Exchange, can be found here (updated 2020). Taking it to the other side raises the question of what the counter-party values the same asset and why.  Adding in third party sell-side analyst reports does the same for yet another dimension.  How much is the asset worth and who among us is “correct” or has the asset “mis-priced?” 

At the end of the day, one could apply this technique to buying clothing, or a car, or a horse and carriage, or anything else at the local department store or bazaar.  Indeed, markets and derivatives and hidden transactions predate by several thousands of years the terms “Adjustable Rate Mortgages, Collateralized Debt Obligations, and Credit Default Swaps” involved in the latest 2007-2008 economic pain.  Only the scale is larger, the layers a little deeper, and the jargon more heavily specialized.  The result is a long time series that may not be truly random (Lo & Mackinlay, 1988). 

To help in their analysis, investors have supporting tools such as channel and trend lines, various indicators, and pattern recognition techniques to summarize the time series.  These tools can transform a price time series from this:

to this:

which has support and resistance lines, with some uptrending bottom-to-bottom and top-to-top channel lines that implies the time series – Dow Jones Industrial Average Q1 2013 – is rising.  These are classic linear tools.  There are also a myriad of non-linear, adaptive tools.  Some of the more famous non-classical non-linear and adaptive tools are neural networks. 

Artificial neural networks, as applied here as machine learning tools, are in the broad gray-area intersection between what the brain does and how (neuroscience) and what a computational algorithm could do (computer science).  Originating as a model of low level neuron anatomy (McCulloch & Pitts, 1943) and scaling up to a model of self-programming pattern recognition (Rumelhart, et al, 1986) artificial neural networks have become a flexible and powerful tool that greatly extends statistical regression into the layered depths of non-linear and non-parametric data sets.  Various studies over the past two and a half decades mix variants of artificial neural networks with financial time series data sets. 

Bahrammierzaee (2010) compare artificial neural networks on three separate financial services tasks, including credit evaluation, portfolio management, and financial prediction.  The neural networks outperformed traditional statistical regression models in all tasks, especially where the data became highly non-linear.

Rezaiedolatabadi, et al (2013) use a hybrid mixture of different artificial neural network techniques to enhance market prediction on the Tehran Stock Market, outperforming individual techniques.

Zhang & Wu (2009) borrow a technique termed improved bacterial chemotaxis – a variant of a stochastic, genetic evolutionary algorithm – to blend with an artificial neural network to enhance its stability, efficiency, and generalization. 

These are very clever and advanced mixture models applied to a complex time series task.  It appears to make perfect sense – if the market produces stochastic volatility, the neural network prediction model counters with wavelets and Boltzmann Machines.  It could very well be that few if anyone on this planet can do better in each of these particular micro-scale techniques. 

But it must be remembered that artificial neural networks originated as core models of what the brain does and how.  The intersection with computational algorithms and subsequent merging into machine learning only arose and lasts with the need for an objective and testable expression tool.  Artificial neural networks can and do perform complex pattern recognition.  But that only scratches the surface of what they represent.  Applying neural networks to financial prediction solely because their advanced non-linear pattern recognition are among the few that fit chaotic price time series misses the main point.  This would be like an amateur chef stewing a truffle – it turns a rare, expensive, and complex ingredient into common canned goods.  Truffles happen to be mushrooms, but a mushroom is not a truffle.  A neural network can do pattern recognition, but pattern recognition is not a brain model.

This is especially poignant given that the financial time series dataset is not a set of data points.  Every tick on a stock price is in reality a buyer agreeing with a seller on the price.  The prices are an aggregate transaction log, a history of decisions and communications among the active traders and investors in the marketplace.  These are not simply arbitrary test points on a physical energy gradient or plane.  This is not the XOR problem.  This is not simply about covariance matrices and convexity of returns.  These are the beginnings and ends of hopes and dreams for the future.  These spring from the brain.  That taking a byproduct of a brain model turned neural network and applying it to the symptoms of human behavior as tracked by financial time series works is a testament to the dedication and hard work in both fields.  But there is room for something greater.

What if the core neural network of the brain can directly merge with the cause of human behavior in a higher-level model?  If human environments in real life are more about interactive marketplaces than sterile lab data points translated into computational mathematic algorithms, then perhaps the neural networks of the brain can merge with financial analysis of human decision making under uncertainty:  Neural networks paired with finance but with cognitive and neuroscience in the driver’s seat rather than machine learning and stochastic math.  Focusing on such topics as selective attention, executive attention, and mirroring neurons, such a partnership would be behavioral finance on steroids meets executive cognitive development. 

Therefore, fitting artificial neural networks with financial dataset does makes perfect sense – but very importantly, not simply for the non-linear machine learning capabilities that are advertised.