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Type	Master's Thesis
Scope	Discipline-based scholarship
Title	Smartphone Trading versus Traditional Online Trading
Organization Unit	Financial Economics (Thorsten Hens)
Authors	Michael Keck
Supervisors	Alexandre Ziegler
Language	English
Institution	University of Zurich
Faculty	Faculty of Business, Economics and Informatics
Number of Pages	67
Date	2021
Zusammenfassung	With the advent and the increased use of smartphones consumers have been empowered with new opportunities in modern technology. One of these new opportunities is the possibility to easily trade ﬁnancial products. This new technology oﬀers customers faster and easier trades directly from their smartphones. Trading apps for mobile devices compete for customers with ever lower fees. The acceptance on the customer side is also increasing. In 2018, more than every second investor in the United States of America made a trade via a smartphone (Lin et al. (2019)). But does this new possibility of trading via a smartphone change the investment behavior? The topic of whether an investor acts in a diﬀerent manner if he or she invests using a mobile device rather than a personal computer is an emerging topic in academia. In a recent study, Kalda et al. (2021) found that by switching to smartphone trading, customers of major German banks buy more lottery-type securities. In addition, customers increasingly follow short-term trends after switching to smartphone trading (Cen (2018)). A broader study cov-ering diﬀerent behavioral aspects on the topic of mobile and computer investing does not yet exist. This study is intended to provide an overview of diﬀerences in various behavioral eﬀects between mobile traders and traditional online traders. Eight diﬀerent behavioral eﬀects are studied to investigate the investors behavior. These are divided into categories according to their origin and are based on Hens, Kooij, and Ziegler (2020). The ﬁrst category is based on the overall trading activity of an investor. Here the turnover (percentage of a portfolio traded) of an investor is measured. Also, the investment temper-ament, which examines the dependence between market activity and investment behavior, and as an additional element, the activity level, which examines whether investors are more active in upward or downward trending markets are examined. The second category deals with the overall trading patterns of investors. Here the conviction is analyzed. It measures the size of a trade in relation to the portfolio of an investor. The third category focuses on the holdings’ historical returns. Here, the disposition eﬀect and the greediness vs. dou-bling down eﬀect are examined and it is investigated whether the historical performance of the securities in the portfolio has an inﬂuence on the sell or buy decision of an investor. Furthermore, the average share of complex products in a portfolio is used to investigate whether the types of assets held diﬀer between mobile traders and non-mobile traders. Last but not least, diversiﬁcation is considered by investors as part of the breakdown of risk eﬀects. In addition to the behavioral eﬀects studied, the performance between mobile traders and computer traders will be compared. Therefore, the portfolio change measure and the Sharpe ratio are analyzed. To investigate the above-mentioned eﬀects and performance, a dataset from a Swiss online bank is examined. Tens of thousands of clients and several million trades are examined. For each trade, the channel through which it was passed is identiﬁed. For those eﬀects that can be directly assigned to a channel, the eﬀect is calculated individually per channel and client. Afterwards, a regression model is constructed taking into account further personal data from the customers. In addition, an Anova test is performed, and if a signiﬁcant diﬀerence is found, a post hoc test is performed. In this way, the mean values between all channels can be compared. If the investigated eﬀect cannot be directly assigned to a channel, a regression model is set up with the proportion of mobile trades as an independent variable. The results suggest that the turnover, the conviction, the disposition eﬀect, the complex-ity as well as the diversiﬁcation for mobile traders are diﬀerent to computer traders. Other eﬀects, namely the investment temperament, the activity level, and the greediness vs. dou-bling down eﬀect do not seem to be inﬂuenced by the channel used to pass a trade. Furthermore, there is no diﬀerence for the portfolio change measure, but there seems to be one for the Sharpe ratio of the investors. The examined behavioral biases can help explain investors performance. A higher diversiﬁcation is positively correlated with the Sharpe ratio while the complexity, the turnover, and the conviction are negatively correlated. For the investment temperament, the activity level, the disposition eﬀect as well as the greediness vs. doubling down eﬀect, there were no diﬀerences observed.
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