While machine learning cannot do everything people can do, the technology is finding traction in the field of finance.
Machine learning cannot do everything people can do but the technology is finding more widespread use in the field of finance.
Barry Porter talks with Gary Kazantsev, Bloomberg’s Head of Machine Learning, Gideon Mann, Bloomberg’s Head of Data Science, and Bruno Dupire, Bloomberg’s Head of Quantitative Research, about the possibilities.
Q: What is the biggest misconception about machine learning in finance?
Gary Kazantsev: That it is some sort of a magic wand that will solve hard problems in contravention of truths known from basic statistics.
No amount of machine learning will help if the problem you are trying to solve is ill-posed, or you don’t have a sufficient amount of data, or if you aren’t careful about issues like non-stationarity and bias.
Gideon Mann: One major misconception is that machine learning can do things that people cannot do — that it can magically accomplish things that tax human ability. Typically, the biggest impacts of machine learning come by automating simple and straightforward human decisions, but doing it on a cost basis that makes various processing economical. This, in turn, leads to the appearance of magic.
Q: What excites you most?
GK: The range of available problems that are now possible to tackle using machine-learning methods.
Bruno Dupire: The challenges that AI throws at us, how it forces us to question what constitutes our essence as human beings. Domains of competences formerly thought to be our unassailable kingdom are surrendering one by one, redefining ontological issues.
Two big questions are: Can machines perform all our cognitive tasks? And, if they can, are they going to perform them much better than we do?
Q: How advanced is machine learning in finance today?
GK: It depends. The range of problems being attacked and the methods used is now vast and rapidly expanding. We are familiar with organizations which do end-to-end strategy development (from portfolio selection to execution) as an ensemble machine-learning problem.
There are also plenty of firms who are now only starting to investigate this field.
The level of acceptance of new technology in financial institutions varies depending on their acceptable risk profile, specific requirements for interpretability and transparency of models, and even geographical region. This applies to machine learning even more so than many other technologies. BD:
It is still in its early stage, but catching up very quickly, avidly. Data, both structured (security price time series, fundamentals) as well as unstructured (text from news/tweets/call transcripts, net searches, satellite images) are systemically exploited and the array of methods is ceaselessly expanding.
Random forests, support vector machines, knowledge graphs, recurrent nets, LSTM (long short-term memory), convolution nets, GAN (Generative adversarial networks). It has changed a lot since I initially used neural nets to forecast financial time series in 1987.
Q: How are sophisticated clients using machine learning in their workflow, and how is it impacting investment strategies?
GK: We have seen everything from counterparty risk analysis to optimal execution, and from predicting bankruptcy risk to forecasting returns, earnings or unemployment statistics.
It’s also being used in portfolio construction, sentiment analysis of financial news and so on. Machine learning is becoming an integral part of the toolbox used in creation of systematic strategies.
Q: What is driving investment and attention in machine learning in the financial industry?
GM: Machine learning has had an enormous effect on other industries and has driven significant growth. Think Google, Amazon, Facebook. There are also an increasing number of financial firms that have been able to harness machine learning to drive value.
Finally, the pressure to trim costs has focused firms inward to see if they can do more with less, and enhancing employee productivity through augmentative technology has become more appealing.
Q: What new Bloomberg machine-learning application or tool are you most proud of and why?
BD: We are building a machine-learning prototyping suite that enables the user to access scikit-learn, TensorFlow and our own functions, in a very user-friendly interactive environment.
It offers multiple ways to visualize the data, the progress of the learning and how the algorithm operates.
GM: We have made significant investments in our neural network infrastructure, and because of our efforts have seen numerous examples of deployed neural network models.
From these, the effort in understanding tables has particularly made me proud as it demonstrates the power of these new technologies on a thorny old issue.
GK: I am particularly proud of the work we have done on question answering. We have been able to make an impact on the way clients use the Terminal despite this being a very challenging open problem.
Q: Can you give one prediction for the future?
GM: I think the future is likely to be increasingly characterized by fairly stable periods interrupted by very rapid changes as the speed at which information and technology gets disseminated increases.
GK: Sea levels will rise, markets will fluctuate, and deep learning will not give us true human-level artificial intelligence. BD: For advanced tasks, it is not enough to let data drive the learning process, one also needs to inject expert knowledge, leading to hybrid systems.
