Catherine D'Hondt, Rudy De Winne, Eric Ghysels, Steve Raymond
arXiv 8 Jul 2019 · Finance — Portfolio Management · publishedJournal of Empirical Finance (2020) · 56 citations (OpenAlex)
arXiv:1907.03370 · PDF · DOI · OpenAlex · Extracted main text
Artificial intelligence, or AI, enhancements are increasingly shaping our daily lives. Financial decision-making is no exception to this. We introduce the notion of AI Alter Egos, which are shadow robo-investors, and use a unique data set covering brokerage accounts for a large cross-section of investors over a sample from January 2003 to March 2012, which includes the 2008 financial crisis, to assess the benefits of robo-investing. We have detailed investor characteristics and records of all trades. Our data set consists of investors typically targeted for robo-advising. We explore robo-investing strategies commonly used in the industry, including some involving advanced machine learning methods. The man versus machine comparison allows us to shed light on potential benefits the emerging robo-advising industry may provide to certain segments of the population, such as low income and/or high risk averse investors.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | D'Hondt, De Winne, Ghysels, and Raymond (2019) Artificial Intelligence Alter Egos: Who benefits from robo-investing? - Online Appendix | 1.000 | 8 | 4 | 100% |
| 2 | Welch and Goyal (2007) A comprehensive look at the empirical performance of equity premium prediction | 0.843 | 3 | 3 | 100% |
| 3 | DeMiguel, Garlappi, and Uppal (2007) Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? | 0.737 | 3 | 2 | 100% |
| 4 | Engle, Ledoit, and Wolf (2019) Large dynamic covariance matrices | 0.737 | 3 | 2 | 100% |
| 5 | Markowitz (1952) Portfolio selection | 0.644 | 2 | 2 | 100% |
| 6 | Odean (1998) Are investors reluctant to realize their losses? | 0.644 | 2 | 2 | 100% |
| 7 | Breiman (2001) Random forests | 0.511 | 2 | 1 | 100% |
| 8 | Friedman, Hastie, and Tibshirani (2016) The elements of statistical learning - Second Ed | 0.511 | 2 | 1 | 100% |
| 9 | Ledoit and Wolf (2004) Honey, I shrunk the sample covariance matrix | 0.511 | 2 | 1 | 100% |
| 10 | Shestopaloff and Shestopaloff (2007) A hierarchy of methods for calculating rates of return | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 13 scored citations.