This is a summary of links featured on Quantocracy on Monday, 11/07/2022. To see our most recent links, visit the Quant Mashup. Read on readers!
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Matching data between data sources with Python [Wrighters.io]Data is often messy and rarely in perfect shape. This is especially true if the data comes from many different sources and the specifications are loosely defined. If you have access to data that is in great shape, its probably because someone else did the dirty work of validating it, cleaning it up, and normalizing it for you. One particular type of data problem is matching data between data
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Skewness: the fallacy of the expected return [Artifact Research]In this post we will take a closer look at the expected return that is often stated for investments like stocks and other financial assets, or for certain outcomes in gambling. The point we want to convey is that the expected return is only valid for one period or a single iteration (say, one year, or one round of a game such as Blackjack), but that the expected return can be highly
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The Cross Section of Stock Returns Pre CRSP data [Alpha Architect]What are the Research Questions? Several studies reveal variables that predict cross-sectional differences in stock returns but mainly rely on a sample of U.S. stocks, mostly covering the post-1963 period. These studies are often criticized for potential data mining issues since the database never changes, but new findings crop up all the time. This paper studies the cross-section of U.S.
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Top 10 blogs on Machine Learning in 2022 [Quant Insti]Algorithmic Trading is seeing a rapid expansion of the application of artificial intelligence (AI) and machine learning (ML). These technological developments have completely transformed Algo trading. Making informed decisions requires carefully analyzing both current and historical market data. In order to analyze data and make effective forecasts for effective trading decisions, artificial
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Sector & Factor Performance During Wartime [Finominal]The S&P 500 increased during two of the three largest wars of the United States Value, size, and momentum factors had positive returns during WW II The top and worst-performing industries during WW II were diverse INTRODUCTION Before 2020, the threat of a global pandemic shutting down the world economy was not a top-of-mind concern for most investors. Pandemics were nothing new, of course, but
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Market Risk and Speculative Factors [Alpha Architect]There are basically two types of investors, those that are risk averse and, thus, both demand risk premiums for taking risk and diversify their holdings, and those who are risk seekers who have a preference for positively skewed (lottery-like) returns which leads them to speculate and concentrate risks. The psychological preferences of risk seekers drives up the valuations of the lottery-like