Research Review | 6 April 2023 | Artificial Intelligence and Finance

Multi-(Horizon) Factor Investing with AI
Ruslan Goyenko and Chengyu Zhang (McGill University)
April 2023
Can the backbone technology behind ChatGPT create and manage portfolios? We apply this tech-engine, adapted for finance applications, to multi-factor investing by a long-horizon investor who uses bigger that traditionally used data and takes into consideration long-term versus short-term volatility, liquidity and trading costs trade offs while maximizing expected portfolio returns. The answer is yes, as we are able to actively time factors’ premium realizations while dynamically re-balancing and diversifying between factors. Moreover, the long horizon perspective is critical, as it allows for more patient trading and re-balancing needs, more strategic factor timing, and a different set of fundamental signals to rely on.

GPTQuant’s Conversational AI: Simplifying Investment Research for All
Thomas Yue and Chi Chung Au (MechaniX Limited)
March 2023
This paper presents GPTQuant, a conversational AI chatbot for developing and evaluating investment strategies. GPTQuant leverages prompt templates and LangChain’s integration to activate few-shot learning capabilities of GPT3 for generating Python code instantly, which can be executed using the Python interpreter. Our case studies demonstrate GPTQuant’s efficacy in investment research, highlighting its potential to reduce the workload of human agents and democratize investment research. GPTQuant’s contributions to fintech, including its ability to generate Python code via natural language commands and its intuitive interface for evaluating investment strategies, make it a valuable tool for investors. Our case studies demonstrate the practical applications of the chatbot and compare its performance with Open AI’s ChatGPT. We highlight the chatbot’s potential to overcome limitations in GPT variants and to open new doors in domain-specific applications in finance.

Automation and Stock Prices: The Case of ChatGPT
Magnus Blomkvist (EDHEC Business School), et al.
March 2023
This study examines the impact of ChatGPT’s introduction on stock prices. Following the introduction, firms operating in industries with workforces more substitutable to AI techniques are associated with significantly negative stock returns. We attribute the negative share price reaction to the increased competition from the new technology.

SAFE Artificial Intelligence in Finance
Paolo Giudici and Emanuela Raffinetti (University of Pavia)
February 2023
Financial technologies, boosted by the availability of machine learning models, are expanding in all areas of finance: from payments (peer to peer lending) to asset management (robot advisors) to payments (blockchain coins). Machine learning models typically achieve a high accuracy at the expense of an insufficient explainability. Moreover, according to the proposed regulations, high-risk AI applications based on machine learning must be “trustworthy”, and comply with a set of mandatory requirements, such as Sustainability and Fairness. To date there are no standardised metrics that can ensure an overall assessment of the trustworthiness of AI applications in finance.

The Adoption of Artificial Intelligence by Venture Capitalists
Maxime Bonelli (HEC Paris)
November 2022
I study how the adoption of artificial intelligence (AI) by venture capitalists (VCs) to screen startups affects the funding of early-stage companies. Using global data on VC investments, I show that after adopting AI, VCs tilt their portfolios towards startups whose business is similar to those already tested by past startups. Within this pool of startups, AI- empowered VCs become better at picking those that survive and receive follow-on funding. At the same time, these VCs’ investments become 18% less likely to result in breakthrough success. I exploit plausibly exogenous variation in VCs’ incentives to automate screening from the introduction of Amazon’s Web Services to establish causality between AI adoption and the above effects. Overall, my results are consistent with AI exploiting past data that are not informative about breakthrough companies. AI adoption by investors may therefore reduce the capital directed towards innovation.

Algorithmic Black Swans
Noam Kolt (University of Toronto)
February 2023
From biased lending algorithms to chatbots that spew violent hate speech, AI systems already pose many risks to society. While policymakers have a responsibility to tackle pressing issues of algorithmic fairness, privacy, and accountability, they also have a responsibility to consider broader, longer-term risks from AI technologies. In public health, climate science, and financial markets, anticipating and addressing societal-scale risks is crucial. As the COVID-19 pandemic demonstrates, overlooking catastrophic tail events — or “black swans” — is costly. The prospect of automated systems manipulating our information environment, distorting societal values, and destabilizing political institutions is increasingly palpable. At present, it appears unlikely that market forces will address this class of risks. Organizations building AI systems do not bear the costs of diffuse societal harms and have limited incentive to install adequate safeguards. Meanwhile, regulatory proposals such as the White House AI Bill of Rights and the European Union AI Act primarily target the immediate risks from AI, rather than broader, longer-term risks. To fill this governance gap, this Article offers a roadmap for “algorithmic preparedness” — a set of five forward-looking principles to guide the development of regulations that confront the prospect of algorithmic black swans and mitigate the harms they pose to society.

Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return

By James Picerno