Where is Machine Learning driving the future of the finance industry?

Historically, investing has primarily rested on bedrock of numbers, such as stock prices and profits, revenues and research spending.

Stock prices and profits, revenues and research spending … these have always been the foundational numbers of investing.

But that’s changing. The input of artificial intelligence (AI) and machine learning (ML) is now allowing traders to harness machines to amass tradable signals from unstructured data and to analyse it at rapid-fire speeds.

The most advanced quantitative investors now leaning on algorithms to systematically trade, rather than traditional human fund managers, are already reaping the rewards.

How? Primarily through these main performance boost applications:

Reinforcement learning (RL): Bots powered with RL can ‘learn’ from the trading and stock market environment through their interactions within the landscape. They use trial and error to augment their learning strategy based on the characteristics of each and every stock listed in the stock market. In other words, RL automatically learns a stock trading strategy by maximizing investment return and avoiding risk, rivalling professional traders’ strong assumptions and removing bias.

Natural language processing (NLP): Bots powered with NLP involves teaching machines how to read and understand the intricacies of human language and to analyse, for example, financial documents such as 10-k forms to forecast stock movements. Again it has the advantage of bias elimination. Through sentiment analysis, a subfield of natural language processing, investors can quickly glean if the tone of a report is positive, negative, or litigious etc.

GPT-3 – the latest NLP breakthrough?

But ahead is what looks set to be NLP’s biggest game changer. It’s GPT-3 or to give it its full name, Generative Pre-trained Transformer 3, an autoregressive language model, created by OpenAI, a San Francisco-based artificial intelligence research laboratory.

Arguably the world’s most impressive AI, it’s still in its infancy but still a case of watch this space but people are saying it's like "watching god wake up".

ML’s multiple applications in trading What ML means in practice is a structural shift of the trading floor. High-speed computer programmes are fast replacing the men in suits.

And the actual applications? Here are some examples from both the buy side and sell side:

• Asset management firms are using machine learning to test investment combinations (credit/trading) • Banks are putting machine-learning algorithms to work that recommend the best rate swaps for a firm’s balance sheet (rates/trading)
• FX is bringing to play supervised ML algorithms to find discover correlations among asset prices and other data to predict currency prices a few minutes or hours into the future
• Buy side traders are making use of computers to trawl through historical data to identify potential stock, bond, commodity, and currency trades, using ML to model how they would perform under various economic scenarios.

AI and the future of hedge fund machine learning revolution

Across the board, managers are ramping up their use of AI and machine learning tools, and improving efficiency as data consumption grows and skillsets evolve.

This hybrid of data technology, proprietary secret sauce and grey matter looks set to drive the evolution within hedge fund businesses over the next decade.

Large quant funds using computer-driven models to uncover new trading strategies are fast becoming household names in the hedge fund industry such as Man AHL, Two Sigma, Citadel, Bridgewater and D.E. Shaw. Many of these now hire more technologists than traditional portfolio managers.

And then there is Numerai, a recognized AI hedge fund, now uncovering investment strategies by hosting competitions among external AI experts, mathematicians and data scientists.

Where it will end remains to be seen. But one thing’s for sure. The relationship between humans and machines is getting ever closer.

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