by Dr Mehrshad Motahari, Research Associate, Cambridge Centre for Finance and Cambridge Endowment for Research in Finance
Artificial intelligence (AI) has become a major trend and has disrupted most industries in recent years. The financial services sector has not been an exception to this development. With the advent of fintech, which has had an emphasis on the use of AI, the sector has experienced a revolution in some of its core practices. Asset management is probably the most affected practice and is expected to suffer the highest number of job cuts in the foreseeable future. A sizeable proportion of asset management companies are now using AI instead of humans to develop statistical models and run trading and investment platforms.
In a recent article entitled “Artificial intelligence in asset management”, CERF Research Associate Mehrshad Motahari and co-authors Söhnke M. Bartram and Jürgen Branke (Warwick Business School, University of Warwick) provide a systematic overview of the wide range of existing and emerging AI applications in asset management and set out some of the key debates. The study focusses on three major areas of asset management in which AI can play a role: portfolio management, trading, and portfolio risk management.
Portfolio management involves making decisions on the allocation of assets to build a portfolio with specific risk and return characteristics. AI techniques improve this process by facilitating fundamental analysis to process quantitative or textual data and generate novel investment strategies. Essentially, AI helps produce better asset return and risk estimates and solve portfolio optimisation problems under complex constraints. All these result in AI achieving portfolios with better out-of-sample performance compared to traditional approaches.
Another popular area for AI applications is trading. Today, the speed and complexity of trades nowadays have made AI techniques an essential part of trading practice. Algorithms can be trained to automatically execute trades on the basis of trading signals, which have given rise to a whole new industry of algorithmic (or algo) trading. In addition, AI techniques can help minimise transaction costs. Many traders have started using algorithms that automatically analyse the market and subsequently identify the best time and amount for trade at any point in time.
Since the 2008 financial crisis, risk management (and compliance) have been at the forefront of asset management practices. With the increasing complexity of financial assets and global markets, traditional risk models may no longer be sufficient. Here, AI techniques that learn and evolve through the use of data can improve the tools required for monitoring risk. Specifically, AI approaches can extract information from various sources of structured or unstructured data more efficiently and produce more accurate forecasts of bankruptcy and credit risk, market volatility, macroeconomic trends, financial crises, etc. than traditional techniques. AI also assists risk managers in the validation and back-testing of risk models.
AI techniques have also started gaining popularity in new practices, such as robo-advising. This area has gained significant public interest in recent years. Robo-advisers are computer programs that provide investment advice tailored to the needs and preferences of investors. The popularity of robo-advisers stems from their success in democratising investment advisory services by making them less expensive and more accessible to unsophisticated individual investors. It is a particularly attractive tool for young (millennial) and tech-savvy investors. AI can be considered the backbone of robo-advising algorithms, relying heavily on the applications of AI in asset management discussed above.
With all the above advantages, there are also costs associated with the use of AI approaches. These models are often opaque and complex, making them difficult, if not impossible, for managers to scrutinise. AI models are also highly sensitive to data. They may be improperly trained as a result of using poor quality or inadequate data. Insufficient human supervision can result in systematic crashes, the inability to identify inference errors, and a lack of understanding of investment practices and attribution of performance by investors. Last but not least, asset managers need to ask whether the benefits associated with AI can justify their considerable development and implementation costs.
AI is still in its early days in finance and has a long way to go before it can replace humans in all aspects of asset management. What AI does today is limited to automating specific tasks within asset management, often with some form of human intervention at the implementation stage. In fact, there is not much new about the AI techniques used in finance, and they have been around as part of statistics for a long time. Instead, what has led to the recent hype is the availability of vast new data sources and the computing power to extract information from them. AI’s ability to capture complex and nonlinear relationships from the ever-growing volumes of data, including textual ones that are relatively time-consuming for humans to analyse, has proven to be highly beneficial. One can imagine that AI’s footprint will only increase as asset managers compete for more information at higher speeds. Hype or not, AI is here to stay, and its heyday is yet to come.
Bartram, S.M., Branke, J. and Motahari, M. (2020) “Artificial intelligence in asset management.” Cambridge Judge Business School Working Paper No.01/2020.