This is a summary of links featured on Quantocracy on Thursday, 06/29/2023. To see our most recent links, visit the Quant Mashup. Read on readers!
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Analyzing the Profitability Factor with Alphalens [Quant Rocket]How does a company's profitability affect its stock returns? In this post, I use Alphalens, a Python library for analyzing alpha factors, to investigate the relationship between operating margin, a profitability ratio, and future returns. This is the second post in the fundamental factors series, which explores techniques for researching fundamental factors using Pipeline, Alphalens, and
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Calculating Realised Volatility with Polygon Forex data [Quant Start]In the previous article we wrote a Python function which utilised the Polygon API to extract a month of minutely data for both a major (EURUSD) and exotic (MZXZAR) FX pair. We plotted the returns series and looked at some of the issues that can occur when working with this type of data. This article is part of series where we will be creating a machine learning model which uses realised volatility
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BloombergGPT: Where Large Language Models and Finance Meet [Alpha Architect]Developments in the use of Large Language Models (LLM) have successfully demonstrated a set of applications across a number of domains, most of which deal with a very wide range of topics. While the experimentation has elicited lively participation from the public, the applications have been limited to broad capabilities and general-purpose skills. Only recently have we seen a focus on
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Can AI Explain Company Performance? [Finominal]The rapid evolution of language models has the potential to revolutionise financial analysis GPT outperformed when analyzing earnings calls, followed by Word2Vec and BERT However, overall models should be selected carefully as each has its pros and cons ABSTRACT This paper aims to evaluate the quality of word vectors produced by different word embedding models on two text similarity related