Abstract
This work in progress aims to explore ethical dilemmas connected to the use of Artificial Intelligence (AI) in financial portfolio management, and their managerial implications. In old school quantitative investing, portfolio allocation decisions are typically based on a well-defined investment strategy. Financial portfolio managers devise and apply investment strategies to maximize expected returns for customers’ portfolios. The introduction of AI-enhanced algorithms enables smart machines to automatically revise and update investment strategies, learning from the past. AI itself might produce significant effects on the gains and losses of the portfolio management strategies, raising ethical dilemmas connected with human versus machine responsibility, accountability, and risk. From the managerial point of view, a new dimension of performance measuring, competence evaluation and incentive allocation is required for managing AI software developers in this area. To explore such dilemmas, empirical evidence is drawn here from MDOTM, an innovative and successful young enterprise developing AI-driven investment strategies for financial markets.
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Notes
- 1.
These studies develop quantitative trading strategies based on statistical models designed to predict the sign of subsequent excess returns from accounting ratios.
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Acknowledgements
We wish to thank Nien-hê Hsieh for his contribution in the initial phase of the interviews. We also acknowledge the helpful comments by reviewer and participants at ITAIS and MCIS conference (Naples 2019), and at AEDBF Conference (Milan 2019). This research was supported by the Italian Ministry of Education (MIUR): “Dipartimenti di Eccellenza” Program (2018–2022)—Department of Economics and Business—University of Sassari.
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Beccalli, E., Elliot, V., Virili, F. (2020). Artificial Intelligence and Ethics in Portfolio Management. In: Agrifoglio, R., Lamboglia, R., Mancini, D., Ricciardi, F. (eds) Digital Business Transformation. Lecture Notes in Information Systems and Organisation, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-030-47355-6_2
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