Abstract
Robo-advisor (RA) platforms have become fundamental in the areas of finance and technology, as they have revolutionized the way investments are carried out and managed. Nevertheless, investment strategies using traditional financial management are currently being maintained. The objective of this research is to compare an investment portfolio that utilizes similar strategies to those of a RA against an investment portfolio that makes decisions through a consensus of valuation analysts. Fuzzy Jensen’s Alpha is used to compare both portfolios. To create a new portfolio strategy proposal for RA platform as well as a value investing portfolio, we selected Latin American listed companies in Mexico, Colombia, Peru, Chile, and Brazil for the sample. Additionally, we used ETFs to replicate those countries. The timespan considered was from January 15, 2015 to June 28, 2019. The results report that both strategies succeeded in surpassing the benchmark; however, the analysts’ portfolio has accelerated its growth since 2018, increasing its positive gap against the RA portfolio. In this sense, the portfolio developed by the analysts gives an average return higher than the average return of the RA. Nonetheless, the RA portfolio has a higher possibility of obtaining abnormal or unexpected returns than the analyst's value investing portfolio, given the systematic risk involved.
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References
P Gomber JA Koch M Siering 2017 Digital finance and FinTech: current research and future research directions J. Bus. Econ. 87 5 537 580
Fein, M.L.: Robo-Advisors: A closers look. SSRN Working Paper. https://ssrn.com/abstract=2658701 (2015)
ICI: Investment Company Fact Book, 58th edn. Investment Company Institute, Washington, DC (2018)
M Beketov K Lehmann M Wittke 2018 Robo advisors: quantitative methods inside the robots J. Asset Manag. 19 6 363 370
Melone, C.: Investors Attitudes Towards Robo-advisors. My Private Banking Research, Kreuzlingen, Switzerland (2016)
L Bjerknes A Vukovic 2017 Automated advice: A Portfolio Management Perspective on Robo-advisors Norwegian University of Science and Technology, Trondheim Master’s thesis
MW Uhl P Rohner 2018 The compensation portfolio Financ. Res. Lett. 27 60 64
A Ling J Sun M Wang 2019 Robust multi-period portfolio selection based on downside risk with asymmetrically distributed uncertainty set Eur. J. Oper. Res. 285 1 81 95
R Bird J Whitaker 2004 The performance of value and momentum investment portfolios: Recent experience in the major European markets part 2 J. Asset Manag. 5 3 157 175
F D’Acunto N Prabhala AG Rossi 2019 The promises and pitfalls of robo-advising Oxford Univ. Press behalf of The Society for Financial Studies 32 5 1983 2020
B Graham DL Dodd 2009 Security Analysis 6 McGraw Hill New York
EF Fama KR French 1993 Common risk factors in the returns on stocks and bonds J. Financ. Econ. 33 3 56
A Damodaran 2005 Value and risk: beyond betas Financ. Analyst J. 61 2 38 43
C Asness A Frazzini R Israel T Moskowitz 2015 Fact, fiction, and value investing J. Portfolio Manage. 42 1 34 52
SD Campbell SA Sharpe 2009 Anchoring bias in consensus forecasts and its effect on market prices J. Financ. Quant. Anal. 44 2 369 390
E Otuteye M Siddiquee 2015 Overcoming cognitive biases: heuristic for making value investing decisions J. Behav. Financ. 16 2 140 149
H Markowitz 1952 Porfolio selection J. Financ. 7 1 7 91
T Bodnar T Zabolotskyy 2016 How risky is the optimal portfolio which maximizes the Sharpe ratio? Adv. Stat. Anal. 101 1 1 28 https://doi.org/10.1007/s10182-016-0270-3
WF Sharpe 1966 Mutual fund performance J. Bus. 39 1 119 138
Sironi, P.: Fintech innovation: from robo-advisors to goal based investing and gamification, 1st edn. Wiley Finances Series, Hoboken (2016)
K Phoon F Koh 2018 Robo-advisors and wealth management Alternat. Invest. 20 3 79 94
O Ivanov O Snihovyi V Kobets 2018 Implementation of robo-advisors tools for different risk attitude investment decisions CEUR Workshop Proceedings 2104 161 195 206
K Cortez M Rodríguez B Mendez 2013 An Assessment of abnormal returns and risk in socially responsible firms using fuzzy alpha Jensen and fuzzy beta Fuzzy Econ. Rev. 18 1 37 59
Tanaka, H., Ishibuchi, H.: Possibilistic regression analysis based on linear programming. In: Kacprzyk, J., Fedrizzi M. (eds.) Fuzzy Regression Analysis, pp. 47–60. Omnitech Press, Warsaw and Physica-Verlag, Heidelberg (1992)
J Grefenstette 1986 Optimization of control parameters for genetic algorithms IEEE Trans. Syst. Man Cybern. 16 1 122 128
JE Grable S Chatterjee 2014 Reducing wealth volatility: the value of financial advice measured by zeta J. Financ. Plan. 27 8 45 51
CM Jensen 1968 The performance of mutual funds in the period 1945–1964 J. Financ. 23 2 389 416
R Jarrow P Protter 2013 Positive alphas, abnormal performance, and illusory arbitrage Math. Financ. 23 1 39 56
S Flaherty J Li 2004 Composite performance measures Chin. Econ. 37 3 39 66
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Caballero-Fernández, R., Cortez, K., Ceballos-Hornero, D. (2022). Robo Advisors vs. Value Investing Strategies: A Fuzzy Jensen’s Alpha Assessment. In: Rodríguez García, M.d.P., Cortez Alejandro, K.A., Merigó, J.M., Terceño-Gómez, A., Sorrosal Forradellas, M.T., Kacprzyk, J. (eds) Digital Era and Fuzzy Applications in Management and Economy. XX SIGEF 2021. Lecture Notes in Networks and Systems, vol 384. Springer, Cham. https://doi.org/10.1007/978-3-030-94485-8_13
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DOI: https://doi.org/10.1007/978-3-030-94485-8_13
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