applications
another option. One investment bank did just this, reducing its time from concept to market for new FX micro-second trading algorithms from 3 months to 2 weeks.’
Machine learning ‘Machine learning is one of the growth areas at the moment; there is a lot of interest. We see it being applied across financial services, increasingly in risk management, but also in asset management, and internal tasks like data management,’ said Wilcockson. As datasets get larger, it becomes increasingly difficult to find relevant information: ‘It may not find you that needle in the haystack, but maybe an area of interest where the needle might be located.’ Machine learning ‘sits concurrently with the
world of big data, so datasets are getting ever larger, databases ever broader. We have seen the rise of the NoSQL database,’ he continued. A NoSQL database provides a mechanism for storage and retrieval of data that is modelled other than the tabular relations used in relational databases. According to Wilcockson: ‘People are wanting to mine those datasets for relevant, useful, business information. In those instances where you have significant amounts of data, machine-learning offers a nice entry point to effectively mining big data.’ Te origins of machine learning have been
around for a long time: in 1959, Arthur Samuel defined machine learning as a ‘field of study that gives computers the ability to learn without being explicitly programmed.’ Once a sufficiently comprehensive set of instructions has been developed, potentially a machine-learning program or algorithm can be leſt to disseminate relevant information but also, as more relevant data is available to be analysed, the machine- learning program becomes more effective as it is constantly testing its own model, validating it, and then improving upon discrepancies within the model using the information that is available. ‘So the more data, the more reliable data
you have, the likelihood is that the model will be more useful. Te example I would draw on here is fraud: it is a common theme in financial services industry at the moment – you can barely pick up the Financial Times without seeing an article about rate rigging,’ said Wilcockson. He went on to give an example of the Madoff,
and similar funds that were exposed as being fraudulent. One of the positive aspects to come out of such scandals was that ‘it gave the world some candidate dataset that regulators and risk managers within say risk management firms or banks can use to potentially detect fraudulent trades or fraudulent funds.’ He continued: ‘Tere are a number of factors that can drive a machine-learning algorithm. One that we see [in this example] is the over-use of zero returns as
26 SCIENTIFIC COMPUTING WORLD
opposed to negative returns – so fraudulent.’ Tese candidate data-sets that contain the
key characteristics and trends associated with fraudulent type investment funds can then be used by a risk manager within a financial organisation to train a decision-tree method or other machine-learning method to determine candidate fraudulent funds. ‘In this particular area, there are more datasets coming on, maybe not on a daily basis, but on a regular basis and that’s proving helping models learn to identify these issues,’ said Wilcockson. Tere are problems associated with this, such as false
THERE ARE
PROBLEMS ASSOCIATED WITH THIS, SUCH AS FALSE POSITIVES
positives: ‘You will find funds that perhaps may seem fraudulent. You know it’s unlikely that it’s going to pinpoint the exact fraudulent fund, but it can help the CRO, the risk managers, identify the likely candidates for further investigation,’ said Wilcockson. Tese applications have led to an increase in the use of Matlab: ‘For legal cooperation, we have noticed an uptake in the law community in use of our tools,’ he said.
What went wrong in 2008? Khan explained that financial services are a small but significant part of Maplesoſt’s business. ‘It’s been relatively stable as well, even given the financial turmoil over the last five years.’ He went on to explain that ‘by and large, people working in the finance industry use our soſtware to develop new pricing models that capture a wider range of effects. A lot of what precipitated the financial crisis in 2008 was the inappropriate use of risk models.’ Te ‘normal’ distribution forms the basis of classic option-pricing models
and value at risk (VAR) – a technique that quantitative analysts looked at in the 1990s to judge, or predict, the maximum loss in a portfolio to a given confidence interval within a given timeframe. But the assumption of a normal distribution is not well founded. ‘Unfortunately, extreme events happen
up to 10 times more oſten than the normal distribution would have you believe.’ Quantitative analysts have been using Maple soſtware to investigate the effects of different probability distributions on risk prediction models. ‘I think that in itself has secured our revenue stream from the finance market.’ Tis propensity for extreme events can seem counter- intuitive, but this kind of scenario could also be applied to climate change. In a volatile climate, extreme events become more frequent and this concept is relevant to the financial markets, especially in 2008, when there were a number of compounding factors that caused the instability alongside improper use of risk scenarios. ‘Tat’s analogous to Murphy’s Law –
everything that can go wrong will do, in the long term. It may not happen today, or next week, but it may happen in the next year.’ Khan added: ‘I am finding that people are trying to actually investigate the effects of extensions to standard probability distributions – extensions that capture the effect of skew and kurtosis.’ Kurtosis, from the Greek Kurtos meaning
‘curved or arching’, is a measure of the peak of the probability distribution of a real-valued random variable. In contrast, skew describes the tails in a probability distribution, ‘if you have a higher skew, it means that you capture more of these events,’ said Khan. He concluded: ‘So what I am finding is that financial mathematicians are taking standard risk projection models, like the VAR, and investigating the effects of skew. Tat would give you a more realistic sense of how much money is on the line.’
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