The wealth management industry is facing a wave of digital disruption. Developments in finance, science and technology have led to a new generation of financial technology start-ups. Most “fin-techs” focus on automated investment services, or so-called ‘robo-advisers’.
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Betterment, the pioneer of automated investing, recently surpassed $5 billion in assets under management, only 8 years after it was founded. What makes robo-advisory revolutionary? And what can we expect from the automated advisers of the future?
Financial advice for the masses
Wealth management is the science of enhancing your financial situation. The idea behind automated advisory is to make financial advice available for everyone. In contrast to expensive human advisers, you can start using robo-advisory services with only 1 euro deposit. After setting up your account, the service requires minimal intervention.
Interest rates are historically low. This reduces the incentive to save and pushes customers to search for alternatives. Robo-advisers open the door to the financial markets and give you the possibility to invest in stocks, bonds and other securities.
Next to automation, robo-advisers keep their costs low by trading Exchange-Traded Funds. ETF’s are baskets of securities. Besides lower costs, the risk of individual securities is mitigated due to diversification. If one security in the basket underperforms, the effect on the portfolio’s performance is relatively small.
Low-cost automated investment services bring us high quality financial advice in a time that we need it the most. But new research and technologies are promising and can lead to a new wave of robo-advisory services.
Robo-advisors use algorithms based on mean-variance optimization, a mathematical framework to create a portfolio of assets such that the expected return is maximized for a given level of risk. Financial market data is used to estimate expected return, standard deviation and correlation for every asset class.
If you open an account, robo-advisers ask simple questions about your age, income, savings and willingness to take risk. This data is collected to estimate your risk tolerance and fit their model to your current situation and preferences.
Gomes, Kotlikoff and Viceira⁴ developed a lifecycle model in 2008 which includes flexible consumption, investment and labor supply. They optimize a utility function using simulation, recursive Bellman equations and backward induction to derive optimal lifecycle behaviour. Parameter values are estimated using empirical data.
Their results highlight an indisputable relation between investing, consumption and labour supply. Flexible consumption and labour supply materially alters investment decisions, suggesting that financial advisers should not only focus on investing but also on the clients consuming and working behaviour.
New technologies can significantly improve the input for these models and increase the quality of the advice. By replacing a questionnaire by more sophisticated data sources and machine learning models, a significant increase in accuracy of risk tolerance, income and consumption estimates can be expected.
Consumption patterns and life expectancy can be predicted by using more data points about the user’s expenditures. Personal financial data merged with job market data provide insights into future income and corresponding risks.
More complex is to estimate personal preferences. Since one cannot directly measure emotions, approximating utility and risk tolerance requires creative approaches. Expectations are high for artificial intelligence applications for decision making.
Using powerful machine intelligence, merging more data sources and leveraging scientific results to better understand the client’s situation would lead to a next generation of automated advisers. Besides high quality investment advice, these services help with complex financial decision making and improve life-quality accordingly.