This is a summary of links featured on Quantocracy on Wednesday, 05/13/2020. To see our most recent links, visit the Quant Mashup. Read on readers!
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Machine Learning and Investing: Forecasting Fundamentals w/ Ensembles [Alpha Architect]Quantitative factor portfolios generally use historical company fundamental data in portfolio construction. The key assumption behind this approach is that past fundamentals proxy for elements of risk and/or systematic mispricing. However, what if we could forecast fundamentals, with a small margin of error, and compare that with market expectations? Intuitively, this seems like a more promising
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Get Rich Quick Trading Strategies (and why they don’t work) [Robot Wealth]Every aspiring millionaire who comes to the markets armed with some programming ability has implemented a systematic Get Rich Quick (GRQ) trading strategy. Of course, they dont work. Deep down even the greenest of newbies knows this. Yet, still, we are compelled to give them a try, just once, just for fun (or so we tell ourselves). In this series, well explore three of the Get Rich Quick
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Designing an energy arbitrage strategy (h/t @PyQuantNews) [Steve Klosterman]The price of energy changes hourly, which opens up the possibility of temporal arbitrage: buying energy at a low price, storing it, and selling it later at a higher price. To successfully execute any temporal arbitrage strategy, some amount of confidence in future prices is required, to be able to expect to make a profit. In the case of energy arbitrage, the constraints of the energy storage