|
|
Analysis of the US stock market during the COVID-19 pandemic
Tůma, Adam ; Krištoufek, Ladislav (advisor) ; Fanta, Nicolas (referee)
This work investigates the effect of the COVID-19 pandemic on the S&P 500 stock index and its eleven sectors. Employing the ARMA and the T-GARCH model on a time series of daily returns from 2018 until March 2021, we examine the impact on volatility, returns, and day-of-the-week effect during the stock market crash caused by the pandemic and the period after. Our main findings imply that in the case of returns, the Monday effect was more negative than the Friday effect during the market crash and vice versa in the rising market after the crash. Concluding that the calendar time hypothesis holds for the observed periods. In terms of volatility, it drastically increased across the US stock market during and even after the crash. The increase was especially noticeable for the IT and Energy sectors. We also found the U-shaped daily volume pattern changed significantly with proportionately less volume of trades happening in the first half-hour of trading and more throughout the whole day.
|
|
|
Trading volume and expected stock returns: a meta-analysis
Bajzík, Josef ; Havránek, Tomáš (advisor) ; Červinka, Michal (referee)
I investigate the relationship between expected stock returns and trading volume. I collect together 522 estimates from 46 studies and conduct the first meta-analysis in this field. Use of Bayesian model averaging and Frequentist model averaging help me to discover the most influential factors that affect the return-volume relationship, since I control for more than 50 differences among primary articles such as midyear and type of data, length of the primary dataset, size of market, or model employed. In the end, I find out that the relation between expected stock returns and trading volume is rather negligible. On the other hand, the contemporaneous relation between returns and volume is positive. These two findings cut the mixed results from previously written studies. Moreover, the investigated relationship is influenced by the size of country of interest and the level of its development. Besides the primary studies that employ higher data frequency provide substantially larger estimates than the studies with data from longer time periods. On the contrary, there is no difference among different estimation methodologies used. Finally, I employ classical and modern techniques such as stem-based methodology for publication bias detection, and I find evidence for it in this field. 1
|
|
|
The Feltham-Ohlson Model: Goodwill and Price Volatility
Janský, Michael ; Novák, Jiří (advisor) ; Baruník, Jozef (referee)
This paper derives and tests the hypothesis that there exists a positive relationship between the amount of unrecognized goodwill a company has in relation to the book value of its equity, and the volatility of the price of its stock and the average trading volume of its shares, and that further this relationship is stronger when the source of that goodwill cannot be traced to items recognized in accounting. The hypothesis is derived from the theory of residual income valuation and the Feltham-Ohlson model of company valuation, and is tested on the accounting and market data of 92 companies listed on the New York Stock Exchange. While the results do not offer sufficient reason to reject any of the paper's hypotheses, they provide only partial support to them, and further research is required.
|
|
|
The Influence of Weather and Calendar Cycles on Trading Volumes at World Stock Exchanges
Kovaľová, Andrea ; Vozárová, Pavla (advisor) ; Slaný, Martin (referee)
Studies investigating stock-exchange anomalies -- mainly with respect to returns and volatility -- have been emerging in recent years and decades. This work explores whether weather conditions, days of the week, length of daylight, seasonal affective disorder, holidays, and lunar phase affect trading volume. Segmented into two parts, the work primarily analyses time-series cross-sectional data covering 12 major stock exchanges and spanning from January 2010 to March 2015. The other part of the work focuses on a detailed analysis of the New York Stock Exchange using only time-series data obtained for the time period from January 2001 to December 2009. Additionally, this period is further split to two time spans as the NYSE fundamentally changed its trading system during the period in question. We find strong evidence of the Monday effect -- manifested in low trading volume on Mondays -- recognizable in the time-series cross-sectional part of the analysis, as well as in the time-series part. Other aforementioned anomalies either do not affect the trading volume significantly or their effect is statistically significant only in one of the two parts of the analysis.
|
|
|
The identification and the significance of impulses on trading volume
Eichinger, Štěpán ; Musílek, Petr (advisor) ; Tuček, Miroslav (referee)
The diploma thesis deals with an influence of various impulses on increased trading activities of investors reflected in a number of stock titles contained in Dow Jones Industrial Average traded at the New York Stock Exchange and NASDAQ (Cisco, Intel and Microsoft) in 2010. The impulses had fundamentals characters, from macroeconomic data to company events. They were given out verbally in the various declarations of authorities or in a written form, but the contents and influence of which was spread immediately after publication by internet. The identification of impulses comprises determination of an important factor or specific news which might influence a number of stocks traded and its range in some trading day. The significance of an event will be expressed by a deviation upwards only from an average amount of stocks traded in each month and set at percentage.
|
guest ::
