National Repository of Grey Literature 3 records found  Search took 0.01 seconds. 
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...
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...
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.

See also: similar author names
9 CHALUPOVÁ, Kateřina
9 Chalupová, Kateřina
5 Chalupová, Kristýna
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