This is a summary of links featured on Quantocracy on Monday, 11/04/2019. To see our most recent links, visit the Quant Mashup. Read on readers!
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16 Articles on Quantamental Investing [Two Centuries Investments]As the book about the most successful quant, Jim Simons, comes out tomorrow (The Man Who Solved the Market), I felt inspired to review the recent press on quant investing, but with a focus on the quantamental theme – where quantitative and qualitative ideas can collaborate well to produce alpha. Sorted by date: 1. Human Insight, Computer Power: What is Quantamental Investing?
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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 (TAA) 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
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Global Growth-Trend Timing [Flirting with Models]While trend following may help investors avoid prolonged drawdowns, it is susceptible to whipsaw where false signals cause investors to either buy high and sell low (realizing losses) or sell low and buy high (a missed opportunity). Empirical evidence suggests that using economic data in the United States as a filter of when to employ trend-following a growth-trend timing model has
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Factor Investing in Emerging Markets [Factor Research]The trends in factor performance are similar in emerging and developed markets Factor returns were higher in emerging than in developed markets However, higher transaction costs need to be considered carefully INTRODUCTION Capital markets of developed countries like the US are highly efficient and mutual fund managers have struggled to generate any alpha, at least after fees. Theoretically, fund
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A method for de-trending asset prices [SR SV]Financial market prices and return indices are non-stationary time series, even in logarithmic form. This means not only that they are drifting, but also that their distribution changes overtime. The main purpose of de-trending is to mitigate the effects of non-stationarity on estimated price or return distribution. De-trending can also support the design of trading strategies. The simplest basis
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Preliminary Results from Weight Agnostic Training [Dekalog Blog]Following on from my last post, below is a selection of the typical resultant output from the Bayesopt Library minimisation 3 3 2 2 2 8 99 22 30 1 3 3 2 3 2 39 9 25 25 1 2 2 3 2 2 60 43 83 54 3 2 1 2 2 2 2 0 90 96 43 3 2 3 2 2 2 2 43 33 1 2 3 2 3 2 2 0 62 98 21 2 2 2 2 2 18 43 49 2 2 2 3 2 4 1 2 0 23 0 0 2 2 1 2 3 2 0 24 63 65 3 2 2 2 3 5 92 49 1 0 2 3 2 1 1 7 84 22 17 1 3 2 4 1 1 46 1 0 99 7 2 2