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Porovnání množství vázaného uhlíku v nadzemní i podzemní biomase v různých typech využití půdy v okolí města Oxapampa, Peru
Chalupová, Karolína
The present work compares amount of sequestered carbon in different types of land use in the vicinity of Oxapampa in Peru. Research was conducted in the autumn of 2022 with focus on three main types of land use: coffee agroforestry plantations, montane tropical forests and silvopastoral systems. Data collection was carried out with Field-Map technology, where 47 study plots on 6 transects were compared. To estimate aboveground and belowground biomass allometric equations were used. The evaluation of soil carbon stocks was determined based on soil analysis using a Soli-TOC device (Elementar). The results showed that there was no statistically significant difference between the amount of carbon bound in the biomass of agroforestry coffee plantations and forest ecosystems. Soil carbon stocks were highest in forest stands in the upper soil layers. At lower depths (20–30 cm) the difference between the studied ecosystems was no longer registered. Average soil carbon values (%) for coffee agroforestry systems and particular depths were as follows: H (7,6 ± 3,8); 0–10 (5,1 ± 1,8); 10–20 (3,8 ± 0,8); 20–30 (3,4 ± 0,7). For study plots in forest ecosystems: H (34,2 ± 12,1); 0–10 (22,4 ± 14,7); 10–20 (9,5 ± 6,2); 20–30 (7,3 ± 5,8). Soil carbon stocks values for silvopasture systems were: H (12,1 ± 4,7); 0–10 (7,3 ± 3,3); 10–20 (5,3 ± 2,3); 20–30 (3,7 ± 2,1).
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Vliv deforestace krajiny na využívání původních druhů rostlin v okrese Sen Monorom, Kambodža
Chalupová, Karolína
The bachelor thesis focuses on the extent of landscape deforestation in Cambodia and its impact on the use of native plant species in the Sen Monorom district. To solve this problem a literature search and field research were used. Field research shows monitoring of the occurence and ways of using the native species of the district. Last but not least, a questionnaire survey was used, which focuses mainly on the objective and subjective perception of deforestation. The results of the work show that the deforestation in Cambodia is still increasing. Many species have succumbed to the onslaught of illegal logging and excessive demand. If there is no change in the system and forestry, it’s possible that Cambodia will lose another part of its biodiversity.
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Can Machines Explain Stock Returns?
Chalupová, Karolína ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
Can Machines Explain Stock Returns? Thesis Abstract Karolína Chalupová January 5, 2021 Recent research shows that neural networks predict stock returns better than any other model. The networks' mathematically complicated nature is both their advantage, enabling to uncover complex patterns, and their curse, making them less readily interpretable, which obscures their strengths and weaknesses and complicates their usage. This thesis is one of the first attempts at overcoming this curse in the domain of stock returns prediction. Using some of the recently developed machine learning interpretability methods, it explains the networks' superior return forecasts. This gives new answers to the long- standing question of which variables explain differences in stock returns and clarifies the unparalleled ability of networks to identify future winners and losers among the stocks in the market. Building on 50 years of asset pricing research, this thesis is likely the first to uncover whether neural networks support the economic mechanisms proposed by the literature. To a finance practitioner, the thesis offers the transparency of decomposing any prediction into its drivers, while maintaining a state-of-the-art profitability in terms of Sharpe ratio. Additionally, a novel metric is proposed that is particularly suited...
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Can Machines Explain Stock Returns?
Chalupová, Karolína ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
Can Machines Explain Stock Returns? Thesis Abstract Karolína Chalupová January 5, 2021 Recent research shows that neural networks predict stock returns better than any other model. The networks' mathematically complicated nature is both their advantage, enabling to uncover complex patterns, and their curse, making them less readily interpretable, which obscures their strengths and weaknesses and complicates their usage. This thesis is one of the first attempts at overcoming this curse in the domain of stock returns prediction. Using some of the recently developed machine learning interpretability methods, it explains the networks' superior return forecasts. This gives new answers to the long- standing question of which variables explain differences in stock returns and clarifies the unparalleled ability of networks to identify future winners and losers among the stocks in the market. Building on 50 years of asset pricing research, this thesis is likely the first to uncover whether neural networks support the economic mechanisms proposed by the literature. To a finance practitioner, the thesis offers the transparency of decomposing any prediction into its drivers, while maintaining a state-of-the-art profitability in terms of Sharpe ratio. Additionally, a novel metric is proposed that is particularly suited...
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The Impact of Just-in-Time Inventory Management on Business Cycle Severity
Chalupová, Karolína ; Novák, Jiří (advisor) ; Hanus, Luboš (referee)
This thesis examines the impact of the just-in-time management (JIT) on volatility of inventory and the magnitude of inventory recessionary cuts. Firms' inventory is an important macroeconomic variable - prior research shows that a decrease in inventory volatility is likely an important source of the Great Moderation and that inventory cuts are a crucial part of GDP decreases during recessions. My results show that JIT decreases volatility of inventory change and makes the recessionary inventory cuts milder. Combined with previous research, the results imply that likely, JIT is an important source of the Great Moderation and mitigates recessions. I test the hypotheses with quarterly 1975-2014 data on U.S. publically traded manufacturing firms, consisting of 116 JIT adopters and 116 matched control firms.
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