This article is a departure from the quantitative research that usually appears on the Robot Wealth blog. Until recently, I was working as a machine learning consultant to financial services organizations and trading firms in Australia and the Asia Pacific region. A few months ago, I left that world behind to join an ex-client’s proprietary trading firm. I thought I’d jot down a few thoughts about what I saw during my consulting time because I witnessed some interesting changes in the industry in a relatively short period of time that I think you might find interesting too. Enjoy!
Perceptions around Artificial Intelligence (AI) in the finance industry have changed signifcantly, as scepticism gives way to a rising Fear of Missing Out (FOMO) among asset managers and trading houses.
Big Data and AI Strategies – Machine Learning and Alternative Data Approaches to Investing, JP Morgan’s 280-page report on the future of machine learning in the finance industry, paints a picture of a future in which alpha is generated from data sources such as social media, satellite imagery, and machine-classified company filings and news releases.
Well that future is already here.
Amongst value managers, I saw scepticism become replaced with a sense of anxiety over being late to the party. The first question I was asked by nearly every value manager I met over the last year or so was: “what is everyone else doing with machine learning?”
This sense of FOMO is arising now because general knowledge of the potential of machine learning has reached a critical mass amongst the decision makers and management across the industry.
Despite the seclusion inherent in our industry, where ‘secret sauce’ is closely guarded, the fruits of the labour of the early adopters are gaining ever-increasing public exposure, shifting the perception of the technology from ‘potential’ to ‘proven’.
In short, finance is catching up to the many other industries where this technology is already in common use.
Shifting attitudes within the quant community
When my consulting company first started applying and recommending machine learning solutions to financial problems, we encountered mixed attitudes from the industry. While a few were enthusiastic adopters who could see the potential, the attitude that machine learning was less than useful – even dangerous – and dismissals of the technology as ‘voodoo science’ were incredibly common.
Surprisingly, these attitudes often came from other quant researchers.
Within the quant community, I’ve witnessed first-hand this attitude gradually giving way to one of recognition of machine learning as a useful tool. I’ve even noted some folks who decried the approach now calling themselves ‘machine learning experts’ on their business cards and LinkedIn profiles. Times really have changed, and they changed in an astonishingly short space of time.
More recently, I’ve seen an even more significant change, as participants increasingly recognise machine learning as the key to unlocking the next generation of alpha. Suddenly, it feels like the prevailing attitude towards machine learning and AI is one of excited and enthusiastic adoption, as opposed to reluctance and scepticism.
Amid the growing consensus that alpha is discoverable in alternative data, our own work and the work of others suggests that alpha from such sources may be uncorrelated with traditional factors like value and momentum. Perhaps, for the time being at least, they can coexist and even provide new dimensions of diversification.
Changing the way we look for alpha
Alpha generation has always been about information advantage – either having access to uncommon insights gained through ingenuity or common insights acted upon before everyone else.
Machine learning and artificial intelligence is simply the modern evolution of a repeating historical pattern in the context of today’s big data world. For example, interpreting satellite imagery of a retailer’s car park reveals insight about its sales figures before they are released to the market. Deriving sentiment from Twitter or Weibo and relating it to an asset’s returns provides an uncommon insight gained through ingenuity.
Artificial intelligence excels at tasks like these to the point that such AI is rapidly becoming a commodity.
As the pool of data (be it alternative, big, structured or unstructured) continues its exponential growth, machine learning and artificial intelligence tools will increasingly be adopted for processing and unravelling it – simply because they are the best tools for the job.
JP Morgan believes there will come a time when they are the only tools for the job.
My experience tells me that that time has already arrived – fund managers who are slow to the party would do well to get on board to not only build competitive advantage, but to maintain what they’ve already got.
Have you witnessed a shift in the way machine learning and artificial intelligence is viewed and used in the finance industry? I’d love to hear about other people’s experiences in the comments.
5 thoughts on “From Potential to Proven: Why AI is Taking Off in the Finance World”
Another great article! I’ve read many articles recently about how XYZ bank/hedge fund is embracing AI and revolutionising their workforce for the “new world”. I’m in two minds about it. On the one hand, I think machine learning and in particular deep learning represent the current best opportunities for untapped alpha available, but on the other I feel like we are riding a wave of hype around the space (and not just in finance!). FOMO seems to be driving a lot of the hiring decisions rather than a clear objective of what will be achieved with the technology. I feel like this will be a situation where the majority of the benefits accrue to a minority of players, those who are most willing to embrace research.
Thanks Jordan, glad you liked the article.