This is a summary of links featured on Quantocracy on Monday, 08/22/2016. To see our most recent links, visit the Quant Mashup. Read on readers!
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Are Stocks Actually Undervalued? [Flirting with Models]Summary We have noticed the market reaching a broad consensus that equities are overvalued, implying a drag on forward expected returns as valuation multiples contract. While there is often great wisdom in the crowd, there can also be great madness. We believe it is prudent to consider how the crowd might be wrong. In this commentary, we explore why valuations matter in the first place and how if
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Metal Logic [Jonathan Kinlay]Precious metals have been in free-fall for several years, as a consequence of the Feds actions to stimulate the economy that have also had the effect of goosing the equity and fixed income markets. All that changed towards the end of 2015, as the Fed moved to a tightening posture. So far, 2016 has been a banner year for metal, with spot prices for platinum, gold and silver up 26%, 28% and 44%
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Optimizing Mean Variance Optimization [Alpha Architect]In the 1950s, Harry Markowitz proposed a method to identify the optimal trade-off between risk and return for a portfolio. The theory is broadly termed, Mean-Variance Optimization (MVO). Sam Wittig, a Drexel graduate I advised and who did some research for Alpha Architect, shared with us his undergraduate thesis project regarding Markowitzs analysis. Here is a link to Sams work:
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Beginner’s Guide to Decision Trees for Supervised Machine Learning [Quant Start]In this article we are going to consider a stastical machine learning method known as a Decision Tree. Decision Trees (DTs) are a supervised learning technique that predict values of responses by learning decision rules derived from features. They can be used in both a regression and a classification context. For this reason they are sometimes also referred to as Classification And Regression
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PyFolio Performance Reporting in Python [Largecap Trader]Pyfolio is a Python library that takes a return series of an asset, hedge fund, trading strategy, anything with daily returns and automatically generates some really cool statistics and charts. There is a LOT of cool stuff to explore in the library, have fun! Performance statistics Backtest annual_return 0.98 annual_volatility 0.62 sharpe_ratio 1.41 calmar_ratio 1.71 stability_of_timeseries 0.91
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Importing CSV Data in Zipline for Backtesting [Quant Insti]In our previous article on Introduction to Zipline package in Python, we created an algorithm for moving crossover strategy. Recall, Zipline is a Python library for trading applications and to create an event-driven system that can support both backtesting and live-trading. In the previous article, we learnt how to implement Moving Average Crossover strategy on Zipline. The strategy code in
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Use Caution With Low Vol Strategies [Larry Swedroe]As we have discussed before, one of the major problems for the first formal asset pricing model developed by financial economists, the capital asset pricing model (CAPM), was that it predicts a positive relation between risk and return. But empirical studies have found the actual relation to be flat, or even negative. Over the past five decades, the most defensive stocks have furnished