Quantocracy

Quant Blog Mashup

ST

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

Quantocracy’s Daily Wrap for 10/04/2016

This is a summary of links featured on Quantocracy on Tuesday, 10/04/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Case for Put Writing in an Expensive Market [EconomPic]

    Pensions and Investments wrote about the interest pension plans have shown in put writing (seemingly one of the more misunderstood investment strategies out there) in a recent article Funds Go Exotic with Put-write Options to Stem Volatility. I thought the article did a nice job of outlining the strategic case for the strategy as a risk reducing equity alternative. In this post I'll outline
  • Prospecting Dual Momentum With GEM [TrendXplorer]

    Gary Antonacci popularized dual momentum with an effective and simple approach for dynamic asset allocation: Global Equities Momentum (GEM). Using simulated ETF data series, GEMs performance over past market conditions can be approximated. For longer investment horizons GEMs implementation with ETFs obtained positive returns with high consistency. After winning first place in 2012 in the

Quantocracy’s Daily Wrap for 10/03/2016

This is a summary of links featured on Quantocracy on Monday, 10/03/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Hidden Markov Models for Regime Detection using R [Quant Start]

    In the previous article in the series Hidden Markov Models were introduced. They were discussed in the context of the broader class of Markov Models. They were motivated by the need for quantitative traders to have the ability to detect market regimes in order to adjust how their quant strategies are managed. In particular it was mentioned that "various regimes lead to adjustments of asset
  • Index Front Running: What Happens When a Stock is Added to an Index? [Signal Plot]

    This post documents some of my research on index front running. This trading strategy is simply buying stocks before they are added to indexes that passively managed funds are designed to track. I initially came across this idea through a Bloomberg article, The Hugely Profitable, Wholly Legal Way to Game the Stock Market. The article made it seem like this is easy money, so I decided to do some
  • The Perils of Backtesting with Unrealistic Data [Allocate Smartly]

    As readers hear us repeat often, our results tend to be less optimistic than youll find elsewhere. We do our best to show backtested results that are as realistic as possible (even though showing results that are as good as possible would probably be better for business). Thats partially a result of simple things, like accounting for transaction costs of 0.1% per trade (or roughly $10 on a
  • A shock to the covariance system [Flirting with Models]

    Mean-variance optimization assumes that you can fully describe the risks and returns of assets in a few simple numbers. Extreme market events often cause volatilities and correlations to spike dramatically, but stress testing on an individual asset basis can allow our own biases and oversights to creep into the process. By decomposing the risk structure into independent sources of risks and
  • Implementing Python in Interactive Brokers C++ API [Quant Insti]

    In the previous article on IBPy Tutorial to implement Python in Interactive Brokers API, I talked about Interactive Brokers, its API and implementing Python codes using IBPy. In this article, I will be talking about implementing python in IBs C++ API using a wrapper, written by Dr. Hui Liu. About Dr. Hui Liu Dr. Hui Liu is the founder of Running River Investment LLC, which is a private hedge
  • Better To Buy Strength or Weakness? [System Trader Success]

    Emotionally its a lot easier to buy on strength than to buy on weakness. Buying into a falling market feels unnatural. Your instincts warn that price may continue to fall resulting in lost capital. On the other hand buying when the market makes new highs feels more natural. Price is moving in your direction and the sky is the limit! However, with so many other aspects of trading what feels

Quantocracy’s Daily Wrap for 10/02/2016

This is a summary of links featured on Quantocracy on Sunday, 10/02/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in September [Allocate Smartly]

    This is a summary of the recent performance of a number of excellent tactical asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. While we don't (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Read more about our backtests or let AllocateSmartly help you follow these strategies in
  • Podcast: Reducing Drawdown with Scott Phillips [Better System Trader]

    Who wants a steadily rising equity curve with little or no drawdown? I'm sure most traders do, but unfortunately it doesn't usually end up that way. Drawdown is a big part of trading and can be one of the the biggest challenges traders face, so what techniques can we use to potentially help reduce drawdowns? Our guest for this episode, Scott Phillips, is going to share techniques he uses

Quantocracy’s Daily Wrap for 09/30/2016

This is a summary of links featured on Quantocracy on Friday, 09/30/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Really, Beware of Low Frequency Data [EP Chan]

    I wrote in a previous article about why we should backtest even end-of-day (daily) strategies with intraday quote data. Otherwise, the performance of such strategies can be inflated. Here is another brilliant example that I came across recently. Consider the oil futures ETF USO and its evil twin, the inverse oil futures ETF DNO*. In theory, if USO has a daily return of x%, DNO will have a daily
  • Buyback Bulls and Bears [Investing Research]

    A lot of attention has been paid to share repurchases recently, which makes sense given the amount of money involved. As of June 30th, 2016, there had been almost $450 billion net transferred from companies to shareholders over the trailing twelve months through repurchase programs, very close to the all-time high in March of 2008. Transferring that much wealth between stakeholders will garner
  • Research Review | 30 Sep 2016 | Managing Volatility Risk [Capital Spectator]

    Managed portfolios that take less risk when volatility is high produce large alphas, substantially increase factor Sharpe ratios, and produce large utility gains for mean-variance investors. We document this for the market, value, momentum, profitability, return on equity, and investment factors in equities, as well as the currency carry trade. Volatility timing increases Sharpe ratios because
  • Option Pricing Methods in the Late 19th Century [Quantpedia]

    This paper examines option pricing methods used by investors in the late 19th century. Based on the book called PUT-AND-CALL written by Leonard R. Higgins in 1896 and published in 1906 it is shown that investors in that period used routinely the put-call parity for option conversion and static replication of option positions, and had developed no-arbitrage pricing formulas for determining
  • Sell Rosh Hashanah, Buy Yom Kippur [UK Stock Market Almanac]

    In 1935, the Pennsylvania Mirror referred to a Wall Street adage, Sell before Rosh Hashanah; buy before Yom Kippur. Recently an academic paper quoted this article and set out to establish if the adage was true and still valid today. The theory is that the market is weak during the approximately seven trading-days gap between the Jewish New Year (Rosh Hashanah ) and the Day of Atonement (Yom