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Executive and professional education

 

by Dr Mehrshad Motahari, Research Associate, Cambridge Centre for Finance and Cambridge Endowment for Research in Finance

Human brain in the form of artificial intelligence.

Mehrshad Motahari.
Dr Mehrshad Motahari

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.

References

Bartram, S.M., Branke, J. and Motahari, M. (2020) “Artificial intelligence in asset management.” Cambridge Judge Business School Working Paper No.01/2020.