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Reaction of retail investors to financial market movements and sentiment changes
Hromčík, Jakub ; Schwarz, Jiří (advisor) ; Petrásek, Lukáš (referee)
This bachelor thesis investigates two areas. First, we study the impact of sociodemographic attributes on retail ivnestors following robo-advice in the choices of ready-made portfolios of passive ETFs with unique risk levels by employing a logistic regression model. Second, we investigate the impact of sociodemographic attributes on retail investors' trading volume adjustments in periods of high expected market volatility as proxied by the VIX index, for which we employ panel data regression methods over 18 consecutive months during a relatively stable period from January 1st 2021 to the end of 2022. We find, in agreement with reasearch on human financial advice, that women are more likely than men to follow risk level recommended by a robo-advisor, while being a man is associated with assuming more risk than recommended. Due to model assumption issues, our results are rather inconclusive in whether men tend to react differently to periods of high expected market volatility. JEL Classification D90, D91, G40, G41, J16 Keywords ETFs, VIX, Robo-advisor, Ready-made portfo- lio Title Reaction of retail investors to financial market movements and sentiment changes Author's e-mail kubahromcik@gmail.com Supervisor's e-mail jiri.schwarz@fsv.cuni.cz
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Vliv sentimentu na vývoj ceny Bitcoinu
Bohuslav, Tomáš
The bachelor's thesis is about the influence between the price of Bitcoin and market sentiment. This connection is looked at during recurring cycles for Bitcoin, which are started in July 2010. Clarification of the influence of sentiment is also carried out during periods of significant economic events (the period of the covid-19 pandemic and the Russian invasion of Ukraine). The thesis discusses the specifics of the bitcoin market, basic information for understanding its functioning, and the psychology of investors. The relationship between sentiment and Bitcoin price is then tested using correlation analysis. A recommendation for including Bitcoin in the investment portfolio is also formulated. Based on the results of this work a price increase is expected in the medium-term investment horizon.
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Extending volatility models with market sentiment indicators
Röhryová, Lenka ; Krištoufek, Ladislav (advisor) ; Jakubík, Petr (referee)
In this thesis, we aim to improve forecast accuracy of a heterogenous au- toregressive model (HAR) by including market sentiment indicators based on Google search volume and Twitter sentiment. We have analysed 30 com- panies of the Dow Jones index for a period of 15 months. We have performed out-of-sample forecast and compiled a ranking of the extended models based on their relative performance. We have identified three relevant variables: daily negative tweets, daily Google search volume and weekly Google search volume. These variables improve forecast accuracy of the HAR model se- parately or in a Twitter-Google combination. Some specifications improve forecast accuracy by up to 22% for particular stocks, others impair forecast accuracy by up to 24%. The combination of daily negative tweets and weekly search volume is a superior model to the basic HAR for 17 stocks according to RMSE and for 16 stocks according to MAE and MASE. The daily nega- tive tweets specification outperforms the basic HAR for 17 and 19 stocks, respectively. And, the combination of daily negative tweets and daily search volume outpaces the basic HAR for 15 and 18 stocks, respectively. Based on the average MASE improvement, the combination of daily negative tweets and weekly search volume is a clear winner as it lowers the...
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Extending volatility models with market sentiment indicators
Röhryová, Lenka ; Krištoufek, Ladislav (advisor) ; Jakubík, Petr (referee)
In this thesis, we aim to improve forecast accuracy of a heterogenous au- toregressive model (HAR) by including market sentiment indicators based on Google search volume and Twitter sentiment. We have analysed 30 com- panies of the Dow Jones index for a period of 15 months. We have performed out-of-sample forecast and compiled a ranking of the extended models based on their relative performance. We have identified three relevant variables: daily negative tweets, daily Google search volume and weekly Google search volume. These variables improve forecast accuracy of the HAR model se- parately or in a Twitter-Google combination. Some specifications improve forecast accuracy by up to 22% for particular stocks, others impair forecast accuracy by up to 24%. The combination of daily negative tweets and weekly search volume is a superior model to the basic HAR for 17 stocks according to RMSE and for 16 stocks according to MAE and MASE. The daily nega- tive tweets specification outperforms the basic HAR for 17 and 19 stocks, respectively. And, the combination of daily negative tweets and daily search volume outpaces the basic HAR for 15 and 18 stocks, respectively. Based on the average MASE improvement, the combination of daily negative tweets and weekly search volume is a clear winner as it lowers the...
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Risk appetite estimation on financial markets
Fidler, Vojtěch ; Geršl, Adam (advisor) ; Babin, Adrian (referee)
The thesis studies role of risk appetite on financial markets. In theoretical part, author describes a notion of this concept, refers to known methods and describes the role of behavioral economics in treatment of this concept. In practical part, models are constructed to explain influence of selected indices on CDS which proxy for sovereign risk of individual developed and emerging markets. Across the globe, there is found strong common component which can be explained by selected indices. It is also observed that GRAI indicator can play role in case of emerging markets. In case of developed markets, however, this property is missing. Granger causality does not prove relationship of GRAI explanation power in direction to sovereign risk.
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Behavioural Breaks in the Heterogeneous Agent Model
Kukačka, Jiří ; Baruník, Jozef (advisor) ; Víšek, Jan Ámos (referee)
This thesis merges the fields of Heterogeneous Agent Models (HAMs) and Be- havioural Finance in order to bridge the main deficiencies of both approaches and to examine whether they can complement one another. Our approach suggests an alternative tool for examining HAM price dynamics and brings an original way of dealing with problematic empirical validation. First, we present the original model and discuss various extensions and attempts at empirical estimation. Next, we develop a unique benchmark dataset, covering five par- ticularly turbulent U.S. stock market periods, and reveal an interesting pattern in this data. The main body applies a numerical analysis of the HAM extended with the selected Behavioural Finance findings: herding, overconfidence, and market sentiment. Using Wolfram Mathematica we perform Monte Carlo simu- lations of a developed algorithm. We show that the selected findings can be well modelled via the HAM and that they extend the original HAM considera- bly. Various HAM modifications lead to significantly different results and HAM is also able to partially replicate price behaviour during turbulent stock market periods. Bibliographic Record Kukačka, J. (2012): Behavioural Breaks in the Heterogeneous Agent Model. Rigorous thesis, Charles University in Prague, Faculty of Social...
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Behavioural Breaks in the Heterogeneous Agent Model
Kukačka, Jiří ; Baruník, Jozef (advisor) ; Víšek, Jan Ámos (referee)
This thesis merges the fields of Heterogeneous Agent Models (HAMs) and Be- havioural Finance in order to bridge the main deficiencies of both approaches and to examine whether they can complement one another. Our approach suggests an alternative tool for examining HAM price dynamics and brings an original way of dealing with problematic empirical validation. First, we present the original model and discuss various extensions and attempts at empirical estimation. Next, we develop a unique benchmark dataset, covering five par- ticularly turbulent U.S. stock market periods, and reveal an interesting pattern in this data. The main body applies a numerical analysis of the HAM extended with the selected Behavioural Finance findings: herding, overconfidence, and market sentiment. Using Wolfram Mathematica we perform Monte Carlo sim- ulations of a developed algorithm. We show that the selected findings can be well modelled via the HAM and that they extend the original HAM consider- ably. Various HAM modifications lead to significantly different results and HAM is also able to partially replicate price behaviour during turbulent stock market periods. Bibliographic Record Kukačka, J. (2011): Behavioural Breaks in the Heterogeneous Agent Model. Master thesis, Charles University in Prague, Faculty of Social Sciences,...
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