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Quant Models for Robo-Advisors

In: Robo-Advisory

Author

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  • Thorsten Ruehl

    (CSR Beratungsgesellschaft mbH)

Abstract

Robo-advice uses modern computational capabilities. So do quant models in portfolio management. Since both concepts profit from the same source of technology, it is tempting to merge them into one holistic approach. In this chapter, we show which quantitative models are a particularly good fit with a robo-advisor platform and which quantitative techniques will become available for a broader segment of retail investors thanks to modern technological advances. Our focus is set on risk-based strategies. Excess Return Forecasts can help to improve performance but introduce another source of potential pitfalls. The Black & Litterman approach can help to handle these. Apart from that private investors often come with unrealistic return to risk expectations. Therefore, this relationship should be made as clear as possible before any investment is made to avoid disappointment. In this context, portfolio insurance strategies to meet investors’ risk budgets are considered at the end.

Suggested Citation

  • Thorsten Ruehl, 2021. "Quant Models for Robo-Advisors," Palgrave Studies in Financial Services Technology, in: Peter Scholz (ed.), Robo-Advisory, chapter 0, pages 71-92, Palgrave Macmillan.
    • Handle: RePEc:pal:psincp:978-3-030-40818-3_5
    DOI: 10.1007/978-3-030-40818-3_5
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