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From Biased to Balanced: Overcoming Behavioral Biases with Robo-Advisors in Sweden

Roth, Disa LU and Ricklander, Sara (2024) NEKH03 20232
Department of Economics
Abstract
In recent years, the emergence of artificial intelligence and automated algorithms in financial advisory services have democratized financial advice and have the potential to revolutionize investment strategy. This shift is of importance for retail investors, as they deal with behavioral biases affecting their investment decisions. This thesis investigates robo-advisors' ability to mitigate these biases and outperform portfolios of Swedish retail investors. Based on real data of stock prices, a robo-advisor portfolio as well as portfolios influenced by different biases are calibrated using MatLab to answer our research question. Our findings reveal that robo-advisors, by offering more objective, data-driven advice, can significantly... (More)
In recent years, the emergence of artificial intelligence and automated algorithms in financial advisory services have democratized financial advice and have the potential to revolutionize investment strategy. This shift is of importance for retail investors, as they deal with behavioral biases affecting their investment decisions. This thesis investigates robo-advisors' ability to mitigate these biases and outperform portfolios of Swedish retail investors. Based on real data of stock prices, a robo-advisor portfolio as well as portfolios influenced by different biases are calibrated using MatLab to answer our research question. Our findings reveal that robo-advisors, by offering more objective, data-driven advice, can significantly enhance portfolio performance, particularly for less experienced investors. (Less)
Please use this url to cite or link to this publication:
author
Roth, Disa LU and Ricklander, Sara
supervisor
organization
course
NEKH03 20232
year
type
M2 - Bachelor Degree
subject
keywords
Robo-advisors, Behavioral Finance, Retail Investors, Markowitz Optimization, Portfolio Performance
language
English
id
9147181
date added to LUP
2024-04-16 09:30:11
date last changed
2024-04-16 09:30:11
@misc{9147181,
  abstract     = {{In recent years, the emergence of artificial intelligence and automated algorithms in financial advisory services have democratized financial advice and have the potential to revolutionize investment strategy. This shift is of importance for retail investors, as they deal with behavioral biases affecting their investment decisions. This thesis investigates robo-advisors' ability to mitigate these biases and outperform portfolios of Swedish retail investors. Based on real data of stock prices, a robo-advisor portfolio as well as portfolios influenced by different biases are calibrated using MatLab to answer our research question. Our findings reveal that robo-advisors, by offering more objective, data-driven advice, can significantly enhance portfolio performance, particularly for less experienced investors.}},
  author       = {{Roth, Disa and Ricklander, Sara}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{From Biased to Balanced: Overcoming Behavioral Biases with Robo-Advisors in Sweden}},
  year         = {{2024}},
}