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This paper proposes a data-based measure of model performance to discriminate among competing asset pricing models of return predictability. I form a set of variance bounds on pricing kernels based on different systems for predicting asset returns. For a given asset pricing model, I define the...
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We extract contextualized representations of news text to predict returns using the state-of-the-art large language models in natural language processing. Unlike the traditional bag-of-words approach, the contextualized representation captures both the syntax and semantics of text, thus...
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the importance of domain knowledge and financial theory when designing deep learning models. I also show return prediction …
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the different channels postulated by theory. The results indicate that asset price misalignments are not robust …
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This paper studies subsampling hypothesis tests for panel data that may be nonstationary, cross-sectionally correlated … the tests without estimating nuisance parameters. The tests include panel unit root and cointegration tests as special … shown that subsampling provides asymptotic distributions that are equivalent to the asymptotic distributions of the panel …
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