This is a summary of links featured on Quantocracy on Thursday, 11/01/2018. To see our most recent links, visit the Quant Mashup. Read on readers!
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Tactical Asset Allocation in October [Allocate Smartly]This is a summary of the recent performance of a wide range of excellent tactical asset allocation strategies, net of transaction costs. These strategies are sourced from books, academic papers, and other publications. While we dont (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Learn more about what we do or let AllocateSmartly help you
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Asset Diversification in a Flat World [Alpha Architect]Diversification is a fundamental principle of prudent investing due to its ability to mitigate/minimize risks. In fact, it has been called the only free lunch in investing because, done properly, it can reduce risk without reducing expected returns. This led to the conclusion that investors should diversify by including international equities, including emerging markets, in their portfolios,
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This Incredibly Bullish Seasonal Period Has Just Begun [Quantifiable Edges]With the calendar moving from October to November, it has now entered its Best 6 Months. The Best 6 Months tendency was first published by Yale Hirsch, founder of the Stock Traders Almanac, in 1986. The concept behind the Best 6 Months is simple. Seasonality suggests that over the last several decades the market has made a massive portion of its gains between November and
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The Existence Of A Bubble vs. The Timing Of Its Crash [Alex Chinco]Journalists love to talk about bubbles. The Wall Street Journal has hinted at bubbles in both the Chinese stock market and the market for Bitcoin during the past month alone. But, financial economists are much more reluctant to call something a bubble. Theres debate about whether bubbles even exist. And, much of this debate revolves around whether its possible to predict the timing of the
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Synthetic prices and burgers [Quant Dare]If all finance developers around the world were asked to choose the main nightmare they have to face on daily basis, I bet most of them would choose overfitting. Furthermore, imagine you have to develop an algorithm which has only one ingredient to be modelled, only one time-series representing the historical information Yes, in that case, youll need the Synthetic Financial Time