FOMO And The Adoption Of AI In Finance

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Perceptions around AI in finance are changing, as skepticism gives way to Fear of Missing Out among value managers. Here Kris Longmore of Quantify Partners Pty Ltd looks at how the robots are changing the way we look for alpha.

How times have changed! JP Morgan’s recently released 280-page report Big Data and AI Strategies – Machine Learning and Alternative Data Approaches to Investing paints a picture of a future in which alpha is generated from data sources like social media, satellite imagery and machine-classified company filings and news releases.

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But that future is already here.

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Amongst value managers, I have seen scepticism become replaced with a sense of anxiety over being late to the party. The first question I get from nearly every value manager I meet is “what is everyone else doing with machine learning?”

This sense of FOMO is arising now because the general knowledge of the potential of machine learning has reached critical mass amongst the decision makers and management across the industry. Despite the secrecy 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 in common use.

When we first started applying and recommending machine learning solutions to financial problems, we encountered very 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 firsthand this attitude gradually giving way to one of recognition of machine learning as a useful tool. More recently, I’ve seen a more significant change, as participants increasingly recognise machine learning as the key to unlocking the next generation of alpha. All of a sudden, 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.

Alpha generation has always been about information advantage: having access either 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 these tasks, 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.

It's not just about alpha generation

The promise of AI extends far beyond alpha generation to a whole realm of efficiencies and cost savings in the middle and back offices.

Business processes that have typically required human discretion and complex decision making are now being automated. Goldman Sachs, for example, developed the Deal Link system which automates the processing of legal and compliance paperwork related to IPOs, mergers and bond sales. JP Morgan’s Contract Intelligence System processes the paperwork for financial deals that previously took tens of thousands of human hours annually. Both of these systems are reported to deliver cost savings beyond automation by also eliminating human error.

Any middle or back office task that requires humans to move information around and process it according to some system or even their discretion can be automated by AI using existing technology.

We are also seeing the rise of the interactive bot: artificially intelligent agents that respond to IT requests, act as personal assistants, grant users access to internal software and systems, and even process customer or client interactions. These are already common in everyday life (think Apple’s Siri) but surprisingly lacking in finance.

The need to invigorate IT infrastructure

Financial services, banking in particular, is plagued by outdated, legacy systems that reside on on-premises hardware, often struggling to work together. Such a situation would seem outrageous in most other industries that have been touched by AI, machine learning and cloud computing.

The performance of an AI system is directly tied to the quality and quantity of the data it learns from. Therefore, the IT infrastructure of the financial services organisation of the future is data-centric and integrated to maximize the ability to leverage AI and automation.

Retiring old systems and moving to integration and data-centricity will require investment and some decent amount of vision, but it will result in future opportunities and cost savings: both from automation and from the ability of such systems to better take advantage of rapidly accelerating advancements in AI, which will require smart data collection, processing and management. Such effort and investment now will reap compounding future returns as the increasing benefits of AI are realised. The ongoing fintech revolution is throwing up more and more agile vendors, offering far more up-to-date and cheaper solutions than incumbent global financial technology houses. Firms with the smarts to identify the winners and partner early will be the winners in this race.

What does this mean for the future?

Automation and technology can be scary for people who imagine that their jobs were somehow secure or static. But in every major technological revolution, society has moved forward, adapted to the new normal and on the whole reaped tremendous benefit. There is little to suggest that this time will be any different.

On the alpha generation side, machines are increasingly replacing the function of human traders at high to medium trade frequencies. The growing ability of machines to analyse earnings reports, company documents, even videos of company meetings will almost certainly erode the need for fundamental managers. It is hard to imagine a world where there is no place for human oversight, but the humans who survive and thrive in the new world will be the ones who embrace modern tools and learn to leverage them for a competitive advantage – for instance, using machine learning to reveal previously unknown insights that inform an investment decision. There is a huge opportunity for such managers to adopt machine learning as a decision support tool to augment an already good investment business.

The JP Morgan report I mentioned above does note one particular area where the bank believes that human traders will retain an advantage: long term forecasting that assesses regime change or which involves interpreting complex economic, geopolitical and behavioural factors. JP Morgan’s machine learning team think that this area will be one of the last and most difficult frontiers to conquer. If you are trader who is not yet ready to join the robots, perhaps this is where you should carve out your niche.

Machine learning skills are very agreeable to people with existing quant skills – the idea that you need to be a ‘data scientist’ to apply these tools is completely bogus. Quant funds and quant traders can and should add machine learning skills to their arsenals, as these tools are the gatekeepers to the next wave of big- and alternative-data derived alpha.

I really dislike the buzzwords ‘disruption’ and ‘displacement’ and I don’t view the increasing role of artificially intelligent agents in these terms. Automation is really about outsourcing a task to the most sensible resource with the goal of freeing up the previously occupied to person to pursue higher, more important, and probably more fulfilling tasks. People will always have a role to play. Although we can automate document processing tasks, we aren’t close to encapsulating the human capacity for ingenuity and innovation in an AI system. For now, less people working on mundane, box-ticking paperwork translates to more people innovating. That is a huge win for firms that adopt the technology with the intent of elevating their people rather than replacing them, and a good example of the compounding returns of AI investment.

About the Author

As Founder and Head of Quantitative Research at Quantify Partners Pty Ltd, I consult to financial services and investment companies on artificial intelligence, machine learning and quantitative analytics. I am in a unique position to provide insight into the prevailing attitudes towards such technology within the traditionally secretive finance industry through my interactions with many and varied asset managers and financial services providers.

I also assist do-it-yourself investors interested in automated trading via, which I founded.

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