This is a summary of links featured on Quantocracy on Wednesday, 10/05/2016. To see our most recent links, visit the Quant Mashup. Read on readers!
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The Problem With Depmix For Online Regime Prediction [QuantStrat TradeR]This post will be about attempting to use the Depmix package for online state prediction. While the depmix package performs admirably when it comes to describing the states of the past, when used for one-step-ahead prediction, under the assumption that tomorrow's state will be identical to today's, the hidden markov model process found within the package does not perform to expectations.
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Quantitative Momentum: A Guide to Momentum-Based Stock Selection [Alpha Architect]The long wait is over. Our newest bookQuantitative Momentumis finally here. After 2 years of research review, results replication, reverse engineering, internal idea generation, writing, editing, and final publication, we have a final product. We think the book will help fulfill our firm mission to empower investors through education. Others agreed: To include Cliff Asness of AQR and
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Ask Me Anything Video for October 5, 2016 [Alvarez Quant Trading]In this short five minute video I will answer the following questions: Have you used HV10/HV100 ratio? Have you found any value in it? When trading multiple strategies, how do you decide what percentage to allocate to each. What do you think about asset allocation ETF strategies, like Ray Dalios All Season portfolio? List of strategies: https://allocatesmartly.com/list-of-strategies/ Do you
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Lower Volatility Smart Beta Funds – A Safe Haven in Turbulent Times? [Markov Processes]Smart Beta funds are hot. According to ETF.com, more than half of the 150 funds launched in 2016 implemented smart beta strategies. For the year to June 30, 2016, ETFGIs most recent data show that assets in smart beta funds have a five-year annual compound growth rate of 31.3 percent. And, low volatility funds, up $15.1 billion in the first seven months of the year are the most popular.
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Market Timing Using Performance of Hi-Beta and Lo-Beta Stocks [iMarketSignals]This market timing model compares the performance of two different types of stock groups over time and provides signals when to invest or not to invest in the stock market. When the performance of the Hi-Beta stocks becomes lower than, or equal to Lo-Beta stocks the model exits the stock market and enters the bond market. It re-enters the market when the performance of the Hi-Beta stocks becomes