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Relationship between Stock Returns and Net Income: Evidence from U.S. Market
Kolář, Michal ; Kočenda, Evžen (advisor) ; Krištoufek, Ladislav (referee)
It is important to know if earnings variables influence stock returns. This is important not just for investors who want to know what drives stock returns, but also for the overall economy as stock returns and stock markets are also considered to be significant indicators of its performance. Many studies were conducted in the past but with inconclusive results. The aim of the thesis is to examine the relationship between net income and stock returns using two approaches, namely panel data model and multiple linear regression. We utilize a dataset of companies selected from the S&P500 Index. We also analyse possible heterogeneity in cross section and time. Moreover, we incorporate additional factors which have been proven to have significant explanation power for stock returns. Our findings from the panel data estimation suggest that there is no relationship between scaled net income and stock returns. We find there are random effects present between the companies and three structural breaks in time. Furthermore, we explore the significance of the consumer sentiment index and the percentage change in the book value per share variables in the panel estimation. We do not confirm the debt to equity ratio and the GDP growth news factors in the panel estimation as significant. Results concerning the...
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Predicting Field Experiment Results in a Lab
Chadimová, Kateřina ; Cingl, Lubomír (advisor) ; Krištoufek, Ladislav (referee)
This thesis is aimed at forecasting of experimental results in a lab environment, investigating often discussed external validity of laboratory experiments. We run a novel laboratory experiment in which the subject pool is asked to make predictions on results of a certain field experiment. The collected data is ana lyzed using different accuracy measures, arriving at several interesting results. First, the forecast among the 94 subjects is quite informative about the actual treatment effects although its accuracy substantially varies based on a type of accuracy measure and a particular treatment. Second, the average forecast is either more accurate or at least comparable to the mean individual forecast, proving the presence of "wisdom-of-crowds" effect.
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Can the stock markets predict changes in macroeconomic variables?
Vařeka, Marek ; Krištoufek, Ladislav (advisor) ; Hayat, Arshad (referee)
A bstract There is a consensus in the literature, that the stock market can predict the Gross domestic product on quarterly base or the industrial production, which is good proxy for GDP, on monthly basis and that the causal rela tionship between stock market and GDP should work both ways. However, using Vector autoregression model on US data since 1950, model shows that the stock market can not only predict the Industrial production on monthly basis, but also ISM non-manufacturing index, which is a good proxy for services in the economy. Furthermore I have managed to prove, that the unemployment can be predicted by past realizations of the stock market and managed to explain almost one third of all variations in change in un employment using S&P500 and oil prices during last 20 years. The Granger causality test concluded that stock market does cause the unemployment but not vice versa, at least during last 20 years.
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Visualization of changes in correlations of stock returns during and after financial crisis
Zbožínek, David ; Krištoufek, Ladislav (advisor) ; Hauzr, Marek (referee)
This thesis aims to describe structural changes in US stock markets during and after global financial crisis. We utilize correlation coefficients of logar- ithmic differences in daily closing prices to generate correlation networks. Minimal spanning tree and hierarchical tree are used to filter out less im- portant information from correlation network, and thus they enable us to obtain unique taxonomy of stocks. Daily closing prices from 8 June 2007 to 31 December 2010 for 73 constituents of market index S&P 100 are di- vided into nine 100 trading-days-long time intervals. The effect of market shock after the fall of Lehman Brothers on 15 September 2008 is investigated. Minimal spanning tree significantly shrinks in the period from 15 September 2008 to 7 January 2009 and afterwards, it gradually reverts back to its pre- crisis state. We also describe clustering patterns of stocks and their changes during the crisis. Clusters of companies from financial, energy, and utilit- ies sectors are recognized in most time windows with only slight variations. In the time window after 15 September 2008, several topological shifts are identified. Additionally, companies from industrials sector are found to form significantly larger clusters in time windows following 8 January 2009.
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Short-term electricity price forecasting - evaluation of selected hybrid models
Svoboda, Štěpán ; Krištoufek, Ladislav (advisor) ; Jonášová, Júlia (referee)
In this thesis a thorough study of the previous literature and the division and special aspects of EPF was carried out. Then the evaluation and comparison of several models was done - the ARIMA, SVR, SVRARIMA and PSF model. This comparison was done on the intra-day Nord Pool market, which is quite unique as almost all short-term EPF is carried out on the day-ahead market. Our results are robust as the modeling was done on 100 test periods and we have tested the difference in predictive accuracy using the modified DM test. Our conclusion is the PSF model is inadequate in our intra-day set- ting and the overall ARIMA model seems to outperform the SVR and SVRARIMA model somewhat. The dominance of ARIMA is not very strong and a further investigation of the causes of these results can better illuminate the strengths and weaknesses of these models.
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Entropy as a Measure of Predictability in Financial Time Series
Nahodil, Vladimír ; Krištoufek, Ladislav (advisor) ; Wang, Yao (referee)
This work studies stock markets efficiency and predictability using the information-theoretic concepts of approximate entropy (ApEn) and sample entropy (SampEn) and compares them to the estimates of the Hurst exponent. This is assessed together with the property of distinguishing between developing and developed markets. Moreover, an investment strategy based on the value of the sample entropy is tested. ApEn shows very weak relationship with other measures and performs poorly as a measure of efficiency. SampEn and the Hurst exponent clearly confirm lower overall efficiency of developing markets. The sample entropy also forms quite strong downward linear relationship with hit-rates of forecasting models. ARMA shows highest hit-rates in periods with SampEn values around 1.6 - 1.7. This could be considered as an investment strategy with lower risk; however, also as one with potentially lower accumulated returns due to smaller investing windows.
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Event Study on Financial Announcements: New Evidence of Stock Sensitivity and Post-Earnings-Announcement Drift
Čonka, Matěj ; Krištoufek, Ladislav (advisor) ; Habiňák, Ladislav (referee)
This thesis investigates the presence of abnormal returns after the companies announce their earnings (earnings-price anomaly) on 23 companies listed on STOXX 50 Europe index. Weuse the event studies framework and we summarize main models for abnormal returns' estimation with closer look on the Market Model and CAPM. We do not find considerable value added when using more complex CAPM compared to the Market Model. The results show significant abnormal returns for good news and bad news earnings surprises with bigger market reaction on good news earnings surprises. The findings also provide the evidence of market inefficiency and the possibility of pre-announcement leakage of information. We find post-earnings-announcement drift for good news earnings surprisesandthepresenceofcontrarianreturns.
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Strategies for Spread Trading using Futures Contracts
Gottlieb, Oskar ; Krištoufek, Ladislav (advisor) ; Čech, František (referee)
The focus of this thesis are futures spreads, more specifically trading strategies based on two approaches - cointegration tested on inter-commodity spreads and seasonality observed amongst calendar spreads. Commodity pairs which we identify to be cointegrated are tested for four mean reversion strategies, three of them being based on fair value approach, the fourth on the relative value approach. Similarly calendar spreads exhibiting seasonality are optimized for naive buy and hold trading strategies. Both approaches are tested on in-sample and out-of-sample data. Amongst seasonal strategies we have not found a pattern yielding sufficiently profitable signals in both in-sample and out-of-sample periods. Inter-commodity spreads on the other returned profitable strategies on cointegrated spreads which were also similar in physical nature. The exception to that rule were spreads known well in the industry, which failed to deliver positive results in the out-of-sample period.
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Scale of Market Movements for US stock market
Kašpárek, Radim ; Krištoufek, Ladislav (advisor) ; Smutná, Šarlota (referee)
Currently, there is no singular, codified, and widely accepted approach to wards measuring the depth of financial crises. One of the approaches ap plied towards this problematic has been to build on the observed similarity between financial markets and dynamic systems in physics and to create analogous systems. The Scale of Market Shocks originally proposed for foreign exchange markets has been adapted for the US stock market in or der to provide US policy makers with a tool to assess the severity of such crises. Using methodology adapted from relevant research and literature we used volatilities calculated with different sampling resolution as the basis for our scale as we believe that these capture the behavior of different market agents. The resultant scale correctly identifies sharp movements and assign them a numerical value that denotes the importance of a crash. This scale is applicable for US policy makers to assess outcomes of proposed policies, however, the use of Principal Component Analysis to ease the computational complexity proved to not yield required results.
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Using the log-periodic power-law model to detect bubbles in stock market
Kožuch, Samuel Maroš ; Krištoufek, Ladislav (advisor) ; Nevrla, Matěj (referee)
Stock market crashes were considered as an chaotic even for a long time. However, more than a decade ago a specific behavior was observed, which accompanied most of the crashes: an accelerating growth of price and log-periodic oscillations. The log-periodic power law was found to have an ability to capture the behavior prior to crash and even predict the most probable time of the crash. The log-periodic power law requires a complicated fitting method to find the estimated values of its seven parameters. In the thesis, an alternative simpler fitting method is proposed, which is equally likely to find the true estimates of parameters, thus generating an equally good fit of log-periodic power law. Furthermore, four stock indices are fitted to log-periodic power law and examined for possible log-periodic oscillations in different time periods, including a very recent period of 2017. In all of the analyzed indices, a log-periodic oscillations could be observed. One index, analyzed in past period, was fitted to log-periodic power law, which was able to capture the oscillations and predict the critical time of crash. In the rest of the selected stocks, which were analyzed in a recent period, the critical time was estimated with varying results.
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