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  • PAPER SUMMARY: PORTFOLIO ALLOCATION MODELS’ QUESTIONNAIRE: INFERRING THE QUALITY OF POTENTIAL INVESTMENT PERFORMANCE THROUGH MODELS’ INPUTS ASSESSMENT

    08 Aug 2018
    2004
    41

    Introduction
    Over the past nine years of low-interest rates and slow economic growth, retail investors have become increasingly sensitive to traditional wealth managers, who charge high fees for their financial advice. They are now turning to a new breed of advisors – robo-advisor: automated portfolio construction software that is fully distributed online. Robo-advisors usually rely on exchange-traded funds (ETFs) to construct clients’ portfolios but can sometimes incorporate more sophisticated asset classes such as smart beta funds, long equity funds, bond funds, long/short hedge funds, mutual funds, and leveraged funds. Since the first robo-advisors were introduced to retail investors, these automated solutions’ assets under management (AUM) have been steadily growing year-on-year. There are now more than 200 of them worldwide (Investopedia, 2018).

    With the advent of simplified coding, math, algorithms processing, and cloud computing, this number is now growing every day, showcasing retail investors’ unabated demand for the value for money robo solutions deliver and robos’ digital service convenience, compared to traditional wealth managers. However, AUM managed by robo-advisors around the world and in Asia has not taken off as much as expected. Both Hong Kong and Singapore robo-advisors have, so far, failed to reach the staggering growth rates projected at the start of 2015 (Collins, 2016) by digital wealth space observers. This is despite governments of both cities recognizing the trend and actively supporting fintech start-ups.
     
    Need for further research
    Few possible explanations according to Okhonko E., 2017 can be the areas of clients’ concerns that robo-advisors must still address: lack of trust and understanding of algorithms, preference for human touch and somebody to call, and uncertainty and lack of transparency of robo investment. The need for further research and information transparency in two related aspects of robo-advisory offering, investment models’ algorithms and investment performance results generated by them, was explicitly highlighted.
    The main issue in addressing this need lies in the fact that most robo-advisory solutions use an extremely scattered variety of investment algorithms and methodologies to construct clients’ portfolios. Nonetheless, few authors attempted to address this need by directly investing with several robo-advisors and then, reporting their cumulative performance. Some of the articles and reports along with their results can be found in the Internet (Lou, 2018), (Value Penguin Inc., 2018), (Scholz, 2017). These “studies” are limited to the available historical performance data, which, for the newly launched robos, have not crossed the financial industry standard of a 5-year horizon, which is required to calculate meaningful Sharpe and Sortino ratios. Besides, the majority of the “studies” only disclose returns data, leaving the readers guessing about the risk that was taken to produce it (YEO, 2017). Additionally, performance track record cannot be used as a criterion to evaluate B2B robo-advisory players, as they do not serve end clients directly.
     
    New methodology and paper scope
    This summary article aims to highlight all the key aspects of the white paper “Portfolio allocation models’ questionnaire: inferring the quality of potential investment performance through models’ inputs assessment”, which was written in order to present a new method that looks at robos’ investment performance without the need to invest in them directly. This method also provides a workaround that allows not to rely on historical returns and risks data, but at the same time, gives rich insights into the potential investment performance. Through analysing and evaluating the different types of portfolio asset allocation models, this paper presents the Portfolio Allocation Models’ Questionnaire that has been designed to distinguish major robo-advisory portfolio construction models and segment them according to the level of complexity and sophistication.
     
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