This is a summary of links featured on Quantocracy on Monday, 01/30/2017. To see our most recent links, visit the Quant Mashup. Read on readers!
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Market Timing with Value [Flirting with Models]Cliff Asness, Antti Ilmanen, and Thomas Maloney of AQR are out with a new paper about market timing with value, titled: Market Timing: Sin a Little. Specifically, the paper explores whether the Shiller PE (also known as the cyclically adjusted P/E, or CAPE) can be effectively used to directionally time equity market exposure. Hot off the presses, we wanted to provide our take on the results. The
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Should We Be Holding More Cash? [Flirting with Models]Modern portfolio theory provides a way for investors to identify the efficient frontier: the set of portfolios that maximize return per unit of risk. Taken to its logical conclusion, modern portfolio theory states that all investors should invest in the same global market portfolio and increase or decrease risk through the use of leverage or cash, respectively. In practice, investors appear to
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Deep Learning for the Walk-Forward Loop [Quintuitive]In the previous posts in these series (here, here and here) I used conventional machine learning to forecast the trading opportunities. Lately however I have been trying to move more and more towards deep learning. My first attempt was to extend the walk-forward loop to support neural networks, the building blocks of deep learning. To experiment with a neural network, I could have simply used the
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The Definitive Guide to Shorting Leveraged ETFs [Signal Plot]This post documents some of my research in creating a trading strategy centered around shorting leveraged exchange-traded funds (ETFs). I present the following thought experiment to motivate readers: Suppose an underlying instrument increases by 25% on day 1 and decreases by 20% on day 2. The return of the underlying instrument is (1 + 0.25) * (1 0.20) 1 = 0%. Now suppose I construct a