This is a summary of links featured on Quantocracy on Tuesday, 12/13/2022. To see our most recent links, visit the Quant Mashup. Read on readers!
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Experimental Design and Common Pitfalls of Machine Learning in Finance [Hudson and Thames]The first lecture from the Experimental Design and Common Pitfalls of Machine Learning in Finance series addresses the four horsemen that present a barrier to adopting the scientific approach to machine learning in finance. The second lecture focuses on a protocol for backtesting and how to avoid the seven sins of backtesting. By implementing the research protocol outlined in these articles, an
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CoinGecko API Python Tutorial [Analyzing Alpha]This article will show you how to access the CoinGecko API endpoints in Python to retrieve live cryptocurrency information. You will use the pycoingecko and the Python requests library to fetch data from CoinGecko API. The official CoinGecko API and pycoingecko libraries documentations lack concrete examples and explanations. Despite having over a decade of Python programming experience, It
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Beware of Spurious Factors [Eran Raviv]The word spurious refers to outwardly similar or corresponding to something without having its genuine qualities. Fake. While the meanings of spurious correlation and spurious regression are common knowledge nowadays, much less is understood about spurious factors. This post draws your attention to recent, top-shelf, research flagging the risks around spurious factor analysis. While formal
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Myth Busting: Alts’ Uncorrelated Returns Diversify Portfolios [Finominal]Alternatives with lower correlations to equities & bonds did not lead to greater diversification benefits Correlations often break when markets crash Better metrics are required to measure the diversification potential of alternatives INTRODUCTION Alternative investments accounted for $13 trillion in assets under management (AUM) in 2021, nearly twice what it was 2015. By 2026, that figure is
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Volatility scaling: is it useful for factor timing? [Alpha Architect]The research summarized here is built upon a documented risk management strategy applied to factor investing (Barroso and Santa-Clara, 2015; Moreira and Muir, 2017). The idea was to overlay a scaled volatility measure designed to change risk exposures and hopefully produce higher Sharpe ratios. That basic research is tweaked in this article by analyzing the effect of scaling on portfolios