This is a summary of links featured on Quantocracy on Saturday, 03/11/2023. To see our most recent links, visit the Quant Mashup. Read on readers!
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SetFit: Fine-tuning a LLM in 10 lines of code and little labeled data [Gautier Marti]This blog is a follow-up to the series of posts Snorkel Credit Sentiment – Part 1 (May 2019) May the Fourth: VADER for Credit Sentiment? (May 2019) Experimenting with LIME – A tool for model-agnostic explanations of Machine Learning models (May 2019) Using LIME to explain Snorkel Labeler (August 2019) which share a common dataset of portfolio managers comments focused on the CDS market.
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Algorithmic Trading in Python with Machine Learning: Walkforward Analysis [Ed West]Implementing a successful trading strategy with code can be a challenging task. While some traders prefer to use basic trading rules and indicators, a more advanced approach involving predictive modeling may be necessary. In this tutorial, I will guide you through the process of training and backtesting machine learning models in PyBroker, an open-source Python framework that I developed for
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Research Review | 10 March 2023 | ETFs [Capital Spectator]ETF Dividend Cycles Pekka Honkanen (University of Georgia), et al. February 2023 Exchange-traded funds (ETFs) collect approximately 7% of all U.S. corporate dividends, which they are required to redistribute to investors. How do the funds manage these dividend flows, and does such management have spillover effects on other financial markets? In this paper, we document a new stylized fact of the