National Repository of Grey Literature 6 records found  Search took 0.01 seconds. 
Peer-to-peer lending as a substitute to conventional investment products
Vysušil, Tomáš ; Polák, Petr (advisor) ; Hronec, Martin (referee)
This thesis supplements the investor oriented research on Peer-to-peer(P2P) lending. In particular, P2P lending is examined as a substitute to conventional investment products. Due to the shortage of macrodata to analyse the topic, questionaired survey gathered microdata are used. The data are than used to estimate the effect of a change in annual rate of return on the distribution of portfolio invesntments across assets. The methods used in the analysis are the log-odds ratio transformation of the dependent variables and pooled OLS estimation with heteroskedasticity robust errors due to the results of Breusch-Pagan test. The results suggest that P2P loans are substitutes to Non-Fixed income investments rather than to Fixed income assets as it was expected. Further, it is found that investors in P2P loans undestand this form of investment to be safe as more risk averse investors are found to have higher fraction of their portfolio alocated in P2P loans. All the active P2P lending platforms on the Czech market are theoretically analyzed based on the developed method. 1
At the right time, in the right factor. Can factors be timed?
Nosek, Jiří ; Hronec, Martin (advisor) ; Červinka, Michal (referee)
This thesis examines the controversial prospect of Factor timing. We use Thompson Reuters data that allow us to construct international risk-factors and respective predictive signals and we test the capacity of these signals to time factors using the Kelly Criterion formula to determine the optimal fraction of capital to invest. Concerning the United States market, we showed that among all signals that we used only the Value Spread seems to contain some predictive power for all the factors in the study. All other timing signals were almost uniformly disappointing and were unable to time any of the factors. We further showed that timing strategies performed much better in the intentional setting, often outperforming the passive buy-and- hold approach. JEL Classification G12, G14, G17, G19 Keywords factors, factor timing, time-series, Kelly crite- rion, empirical analysis Title At the right time, in the right factor. Can factors be timed? Author's e-mail jirkaa.nosek@seznam.cz Supervisor's e-mail martin.hronec@fsv.cuni.cz 1
Pairs Trading in Cryptocurrency Markets
Fil, Miroslav ; Krištoufek, Ladislav (advisor) ; Hronec, Martin (referee)
Pairs trading is a trading strategy which tries to exploit mean-reversion among prices of certain securities. It is market-neutral and self-financing, and has been shown to produce high excess returns in historical backtests. We employ the most common distance and cointegration approaches on cryp- tocurrency data from an exchange called Binance spanning the year 2018. The strategy is mostly unprofitable under transaction costs, but certain combinations of hyperparameters can perform well. Overall, the distance method performs far better, being able to achieve 3% monthly profit even in our baseline real-life con- ditions while the cointegration method always achieves only a slight loss. We also found that increasing the sampling frequency of the data from daily to hourly brings mixed results. Moreover, since we have to reuse estimates of real-life considerations from equity markets, it is unclear if our results are truly representative of the cryp- tocurrency market. The strategy is found to be very sensitive to execution diffi- culties and transaction costs, making their determination crucially important. It is somewhat easy to get returns in excess of 5% monthly under ideal conditions, but whether this could be achieved in real trading conditions is still unclear. Keywords pairs trading,...
Portfolio selection in factor investing
Hronec, Martin ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
This thesis empirically examines the role of advanced portfolio selection methods in factor investing. These methods provide more efficient exposure to underlying risk sources in factor portfolios. Their performance is evaluated across number of prominent factors and compared with more naive equal- and value- weighting, typically used in asset pricing literature as well commercial investment vehicles. The most diversified portfolio consistently achieves the highest returns, while having only moderate volatility and one of the lowest tail risk exposure. On the other hand, the diversified risk parity portfolio suffers high volatility as well as the greatest tail risk exposure, while achieving only comparable average returns with other strategies. 1
Forecasting stock market returns and volatility in different time horizons using Neural Networks
Hronec, Martin ; Baruník, Jozef (advisor) ; Kraicová, Lucie (referee)
This thesis is focused on multiple-step-ahead forecasting of Nasdaq Composite index returns and daily range-based volatility. In order to capture the complex patterns potentially hidden to traditional linear models we use artificial neural networks as nonlinear, nonparametric and robust forecasting tool. We contribute to the ongoing discussion about stock market predictability with following empiri- cal results. In case of Nasdaq Composite returns, all four applied neural networks fail to outperform benchmark model in all time horizons, suggesting high unpre- dictability in accordance with Efficient market hypothesis. Also in case of Nasdaq Composite daily range-based volatility, 1 day and 1 month ahead predictions are not significantly more accurate than benchmark model. However, we find 1-week and 2-weeks-ahead forecasts to be significantly more accurate than benchmark model and able to capture the predictive patterns. Keywords predictability of stock returns, predictability of daily range-based volatility, multiple- step-ahead forecasting, neural networks, RPROP, BFGS learning algorithm

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