Better computing and artificial intelligence are driving improvements in many diverse areas, with the advancements set to take robotics and automation to the next level. This opens up new and exciting investment possibilities. Meanwhile, some investment strategies are themselves employing algorithmic and deep learning techniques. Their models have been described as a 'black box’, owing to the lack of transparency with machine learning and its recondite inner workings. For active managers chasing returns, these models are not without risks. Yet, for investors to understand the benefits of the latest investment techniques, we need to delve deeper inside the box.

How Computing Power Has Supercharged Investing
timeline desktoptimeline desktop

Chapter 1: The rise of computer-aided investment strategies

In the 1990s, the digitalisation of both stock market trading and financial data, combined with an increase in computing power, made programmable investment strategies an attractive and explorative area of innovation. What started within the proprietary trading desks at investment banks soon made its way into the broader financial community...

Read the complete chapter

Chapter 2: Black Swans and Black Boxes

In August 2007, a three-day period of dramatic losses occurred when fund managers had to sell similar positions in their quant books to cover margin calls from other portfolio losses. In a period commonly referred to as the ‘quant quake’, simultaneous selling caused losses at other firms – and further selling...

Read the complete chapter

Chapter 3: Searching for the edge in a world of data

Since the Global Financial Crisis, the financial industry has seen a resurgence in interest and innovation when it comes to technology-driven investing. Pioneering firms now use AI and machine learning techniques to trawl through vast amounts of data to identify patterns, leading to a flourishing new market for alternative datasets...

Read the complete chapter

Chapter 4: From programmable to self-teaching

Improved computing power and broader access to financial and non-financial information has helped investment managers refine and improve their models and techniques. Where strategies used to employ teams of human quants using machines to build large statistical models, new techniques automatically recognise changes in the market and adapt...

Read the complete chapter

Chapter 5: A new theme emerges

Cutting-edge techniques developed in the 1990s and 2000s by Yoshua Bengio, Geoffrey Hinton and Yann LeCun underpin the current proliferation of AI technologies, from self-driving cars to automated medical diagnoses. These ‘godfathers of AI’ won the 2018 Turing Award for their work developing the AI subfield of ‘deep learning’...

Read the complete chapter

Chapter 6: AI in a time of Covid-19

A throwaway remark in a Bloomberg interview in March 2020 posited ‘robots don’t catch coronavirus’. It got people thinking that the Covid-19 pandemic crisis might add impetus to the trend of automation in industries that are less reliant on physical human exchanges with their customers, such as investment management.

Read the complete chapter

Download Full Whitepaper

Affiliate Articles

Our affiliated investment managers employ a broad range of AI, algorithmic and machine or computer-based investment techniques, while others offer opportunities for investors to access many of the latest innovations as part of an investment portfolio. Read more about the managers behind the strategies from across our affiliate range:

All investing involves risk, including risk of loss of capital.