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Quantocracy’s Daily Wrap for 10/16/2016

This is a summary of links featured on Quantocracy on Sunday, 10/16/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Podcast: Strategy Optimization with Robert Pardo [Better System Trader]

    Why is it that some traders can create trading strategies that perform well in real-time trading while other strategies fall apart? How do some traders keep their trading strategies fresh and adaptive to market conditions while other strategies just stop working altogether? Robert Pardo, president of Pardo Capital, author of the book The Evaluation and Optimization of Trading Strategies and

Quantocracy’s Daily Wrap for 10/15/2016

This is a summary of links featured on Quantocracy on Saturday, 10/15/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Measure Momentum? [Alpha Architect]

    Since weve released our new book, Quantitative Momentum, weve received a handful of basic questions related to momentumspecifically as it relates to stock selection. At this point, the so-called momentum effect has occupied academic researchers for several decades. Researchers have found that, on average, stocks with strong recent performance relative to other stocks in the cross
  • Client -III- [Algorythmn Trader]

    In my previous post, I started the implementation of WPF program entry point and the View ViewModel interaction basics. The goal of this Client chapter is to get a client application which connects to the basic server application I covered earlier. This post continues the basic infrastructure where the Views and ViewModels become integrated. So lets start with some auxiliary entities

Quantocracy’s Daily Wrap for 10/14/2016

This is a summary of links featured on Quantocracy on Friday, 10/14/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • So You Want to Build Your Own Algo Trading System? [Robot Wealth]

    Unlike any other business, algorithmic trading has the advantage of being independent of marketing, sales, customers and all those things that need the pretty people to make it run. Also, you get almost instant feedback on how good you are in your business. For anyone who is numerically inclined (and more often than not falls into a particular demographic in terms of their social
  • Zero-Crossing Variant of Pairs Trading Strategy [Quantpedia]

    Pairs trading is a venerable trading strategy. There is agreement that it worked fine in the far past. But it is less clear if it still profitable today. In this working paper the universe of eligible pairs is defined by the holdings of a given ETF. It is shown that the stocks must be from ETFs which select high-quality, low-volatility stocks. The usual closeness measure presented in the

Quantocracy’s Daily Wrap for 10/13/2016

This is a summary of links featured on Quantocracy on Thursday, 10/13/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Reverse Engineering AQR’s Risk Parity Strategy [Signal Plot]

    Im going to start this post by saying that it makes no sense for anyone to pay management fees to get a return stream that is highly correlated to any existing asset class. Unfortunately, many actively managed funds fall in this category. Theres two reasons for this. One, you can replicate this return stream by just investing in that asset class yourself, likely through low-cost ETFs. Two,
  • What Is The Best “Risk Off” Asset for Trend Followers? [Alpha Architect]

    So youre a trend-follower. Great. But here is a question: What do you invest in when your rules suggest risk off? Many investors suggest low duration cash or t-bills. Seems reasonable. But is it optimal? Perhaps we should invest in longer duration risk-off assets like 10-yr bonds? We investigate these questions and come to the conclusion that keeping it simple is probably the best
  • The illusion of choice in ETF’s [Factor Investor]

    A search for all equity ETF's available to U.S. investors in Bloomberg leads to a list of 969 candidates, a surprisingly large number of options for a relatively new investment vehicle. Given that most focus on large capitalization stocks here in the U.S. (not all, but most), this means that there has to be overlap in the underlying stock holdings…in some cases a lot of overlap. The
  • A Review of @AlphaArchitect Quantitative Momentum book [QuantStrat TradeR]

    This post will be an in-depth review of Alpha Architects Quantitative Momentum book. Overall, in my opinion, the book is terrific for those that are practitioners in fund management in the individual equity space, and still contains ideas worth thinking about outside of that space. However, the system detailed in the book benefits from nested ranking (rank along axis X, take the top decile,

Quantocracy’s Daily Wrap for 10/12/2016

This is a summary of links featured on Quantocracy on Wednesday, 10/12/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is Equal Weighting Beneficial For Asset Allocation? Part II [Capital Spectator]

    Yesterdays post on equal weighting for asset allocation motivated a reader to point out that equal weightings tendency to outperform in equity portfolios is due to frequent rebalancing events. A passively managed market-cap-weighted portfolio, by contrast, is allowed to drift, with weights evolving based on Mr. Markets whims. But unrebalanced benchmarks were missing. Lets correct that

Quantocracy’s Daily Wrap for 10/10/2016

This is a summary of links featured on Quantocracy on Monday, 10/10/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is That Leverage in My Multi-Factor ETF? [Flirting with Models]

    The debate for the best way to build a multi-factor portfolio mixed or integrated rages on. FTSE Russell published a video supporting their choice of an integrated approach, arguing that by using the same dollar to target multiple factors at once, their portfolio makes more efficient use of capital than a mixed approach. We decompose the returns of several mixed and integrated multi-factor
  • Value Investing Got Crushed During the Internet Bubble – Here’s Why… [Alpha Architect]

    The dot-com bubble of the late 90s was a wild time in the stock market. Internet stocks were trading through the roof, tech IPOs were a practically daily experience, and people quit their jobs to make millions day trading. And why not? Even a day trading chimp could make money in a market that went up every day. The money flowed like water. In January 2000, just before the bubble popped, Superbowl
  • Presenting in Dallas and Austin, Texas [Alvarez Quant Trading]

    I will be in Texas next week giving presentations. Click the links below for more details. I hope to see some readers there. October 17, 2016 Austin Market Technicians Association For more information see https://www.mta.org/event-registration/austin-chapter-meeting-featuring-cesar-alvarez/ October 18, 2016 Dallas Association for Technical Analysis For more information see
  • More Reasons To Diversify Factors [Larry Swedroe]

    Since the publication in 1992 of Eugene Fama and Kenneth Frenchs paper The Cross-Section of Expected Stock Returns, the traditional way to think about diversification has been to view portfolios as a collection of asset classes. However, we now have a nontraditional way to think about diversification. Specifically, we can view portfolios as a collection of diversifying factors. Support

Quantocracy’s Daily Wrap for 10/09/2016

This is a summary of links featured on Quantocracy on Sunday, 10/09/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Diversification Key To Factor Investing [Larry Swedroe]

    As my co-author, Andrew Berkin, and I explain in our forthcoming book, Your Complete Guide to Factor-Based Investing, no matter how strong the evidence regarding the persistence and pervasiveness of an investment factors return premium, theres some chance that the factor will experience long periods of underperformance. You can see the evidence of this in the table below, which shows
  • The Walk-Forward Loop [Quintuitive]

    The previous post described the high level architecture of a walk-forward forecasting for time series data. As a hands-on implementation lets apply a simple QDA classifier on the series discussed previously. First things first, most of the relevant code is available on GitHub. Although in general I try to publish run-able code, this is not a self-contained executable script. There are

Quantocracy’s Daily Wrap for 10/07/2016

This is a summary of links featured on Quantocracy on Friday, 10/07/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Want to Learn Way Too Much About Stock Market Factors? Read This Paper [Alpha Architect]

    During the past few decades, newly discovered stock anomalies have been embarrassing existing factor models, such as the Fama-French 3-factor. As many readers know, each long or short leg of these popular long/short factor portfolios is generally constructed by ranking stocks on one specific characteristic (value, momentum, or volatility). For example, take the Fama French 3-factor model.
  • Cointegration and Pairs Trading in Stocks [Quantpedia]

    We examine a new method for identifying close economic substitutes in the context of relative value arbitrage. We show that close economic substitutes correspond to a special case of cointegration whereby individual prices have approximately the same exposure to a common nonstationary factor. A metric of closeness constructed from the cointegrating relation strongly predicts both convergence
  • Implementing Predictive Modeling in R for Algorithmic Trading [Quant Insti]

    Predictive modeling is a process used in predictive analytics to create a statistical model of future behavior. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends [1] The predictive modeling in trading is a modeling process wherein we predict the probability of an outcome using a set of predictor variables. Who should use it? Predictive models can

Quantocracy’s Daily Wrap for 10/05/2016

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!

  • 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.
  • 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
  • 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
  • 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.
  • 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