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Quantocracy’s Daily Wrap for 08/19/2016

This is a summary of links featured on Quantocracy on Friday, 08/19/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trading strategy: Making the most of the out of sample data [R Trader]

    When testing trading strategies a common approach is to divide the initial data set into in sample data: the part of the data designed to calibrate the model and out of sample data: the part of the data used to validate the calibration and ensure that the performance created in sample will be reflected in the real world. As a rule of thumb around 70% of the initial data can be used for calibration
  • Taleb: Silent Risk, Section 1.4.4 Mean Deviation vs Standard Deviation [Blue Event Horizon]

    We are going to play around with a mixture distribution made up of a large proportion of ~N(0, 1) and a small proportion of ~N(0, 1+a). The wider distribution is "polluting" the standard normal distribution. We are going to see that mean absolute deviation is a more efficient estimator of the distribution's dispersion than standard deviation. We are also going to see some unexpected
  • Dealing with Delistings: A Critical Aspect for Stock-Selection Research [Alpha Architect]

    Eric Crittenden was recently on Meb Fabers podcast and he tells a compelling story about the perils of survivor bias in backtesting. Erics story begins when he is an undergraduate working on a project for a quantitative finance course. The professor asked that the students develop a systematic investment program and get their hands dirty with backtesting. Eric decided to backtest a portfolio
  • Dividend income investing this is what really works [Quant Investing]

    Is your high dividend investment strategy based on buying companies with a high dividend yield and high dividend cover? Saving_chalkIf so you can do a lot better. In this article I summarise an interesting research paper that found the normal way most investors look at dividend income investing is all wrong. I also show you how to find ideas that fit with what the researchers found that really

Quantocracy’s Daily Wrap for 08/17/2016

This is a summary of links featured on Quantocracy on Wednesday, 08/17/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Risk Parity isn’t the Problem, it’s the Solution [GestaltU]

    Bank of America Merrill Lynch recently released a research note suggesting that Risk Parity investment strategies currently represent a substantial source of systematic risk in global markets. The note was picked up breathlessly by several media outlets and posted under sensationalist headlines employing eye-catching terms like "spectre," and "mayhem." The introduction to the
  • Surprise! Size, Value and Momentum Anomalies Survive After Trading Costs [Alpha Architect]

    Anyone who has spent time reading this blog has become familiar with research involving asset pricing anomalies that generate excess returns. In particular, the academic literature has addressed the following: size, or a portfolio of small minus big stocks (SMB) (see here for background) value, or a portfolio of high minus low book-to-market stocks (HML) (tons of research on value

Quantocracy’s Daily Wrap for 08/16/2016

This is a summary of links featured on Quantocracy on Tuesday, 08/16/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Podcast: Market behavior with Adam Grimes [Better System Trader]

    Today's guest is a trader that has been requested quite a few times actually, I've had a lot of requests to have this person as a guest on the show, and the guest is Adam Grimes. Adam has two decades of experience in the industry as a trader, analyst and system developer and is currently Chief Investment Officer of Waverly Advisors. He's previously held positions at Level Partners,

Quantocracy’s Daily Wrap for 08/11/2016

This is a summary of links featured on Quantocracy on Thursday, 08/11/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Shorting at High: Algo Trading Strategy in R [Quant Insti]

    Milind began his career in Gridstone Research, building earnings models and writing earnings notes for NYSE listed companies, covering Technology and REITs sectors. Milind has also worked at CRISIL and Deutsche Bank, where he was involved in modeling of Structured Finance deals covering Asset Backed Securities (ABS), and Collateralized Debt Obligations (CDOs) for the US and EMEA region. Milind
  • Low Vol Benefits Fading [Larry Swedroe]

    Low-volatility strategies have quickly become the darling of many investors, thanks largely to trauma caused by the bear market that arose from the 2008-2009 financial crisis combined with academic research showing that the low-volatility anomaly exists in equity markets around the globe. Earlier this week, we took a detailed look at a 2016 study from David Blitz, The Value of Low

Quantocracy’s Daily Wrap for 08/10/2016

This is a summary of links featured on Quantocracy on Wednesday, 08/10/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Taming the Momentum Investing Roller Coaster: Fact or Fiction? [Alpha Architect]

    Intermediate-Term Price momentum, originally researched by Jegadeesh and Titman in 1993, documented a how recent stock returns tended to continue in the future. Stocks that were past winners (on average) continue to do well, while stocks that were past losers (on average) continue to perform poorly. A natural inclination is to create a long-short portfolio to take advantage of this buy the

Quantocracy’s Daily Wrap for 08/09/2016

This is a summary of links featured on Quantocracy on Tuesday, 08/09/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Optimal Data Windows for Training a Machine Learning Model for Financial Prediction [Robot Wealth]

    It would be great if machine learning were as simple as just feeding data to an out-of-the box implementation of some learning algorithm, then standing back and admiring the predictive utility of the output. As anyone who has dabbled in this area will confirm, it is never that simple. We have features to engineer and transform (no trivial task see here and here for an exploration with
  • What if Factors Rarely Matter? [EconomPic]

    Back in December I wrote that It's Generally Smart to Avoid Credit Risk outlining that more than 100% of credit's excess performance over time has come when the level of credit spread was extreme. What if the same were true for well known investment factors? Taking a Look at the Small Cap Premium The chart below takes the average market cap of the 30% largest companies within Fama French
  • Can Investors Replicate the Dorsey Wright Focus 5 ETF Strategy? [Alpha Architect]

    A long-time reader asked that we examine the performance and process associated with the Dorsey Wright Focus Five ETF (ticker: FV). For those who are unfamiliar with the product, FV is a $3B+ sector rotation fund. The fund is designed to provide targeted exposure to five sector- and industry-based ETFs that Dorsey, Wright & Associates (DWA) believes offer the greatest potential to outperform
  • Low Vol Advantage Not What You d Expect [Larry Swedroe]

    One of the problems for the first formal asset pricing model developed by financial economists, the Capital Asset Pricing Model (CAPM), was that it predicted a positive relationship between risk and return. However, empirical studies have found the actual relationship to be flat, or even negative. Over the last 50 years, the most defensive stocks have delivered higher returns than the most

Quantocracy’s Daily Wrap for 08/08/2016

This is a summary of links featured on Quantocracy on Monday, 08/08/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Finding 7.5% Returns [Flirting with Models]

    This blog post is available as a PDF here. Summary Over the last year, weve written about how low interest rates and high equity valuations point to a low return rates for traditionally allocated portfolios. In a State Street survey of over 400 institutional investors, the expected return rate for stocks and bonds was 10.0% and 5.5% respectively: significantly higher than we would expect. The
  • When is a “Value” Company not a Value? (h/t Abnormal Returns) [Investing Research]

    Value has broadly been accepted as an investing style, and historically portfolios formed on cheap valuations outperformed expensive portfolios. But value comes in many flavors, and the factors(s) you choose to measure cheapness can determine your long-term success. In particular, several operating metrics of value, like Earnings and EBITDA, have outperformed the more traditional Price-to-Book
  • Backtests for VelocityShares’ BSWN, LSVX, and XIVH [Six Figure Investing]

    I have generated simulated end-of-day close indicative share values (4:15 PM ET) for VelocityShares' BSWN, LSVX, and XIVH Exchange Traded Notes (ETNs) from March 31st, 2004 through July 14th, 2016. BSWN VelocityShares VIX Tail Risk ETN LSVX VelocityShares VIX Variable Long/Short ETN XIVH VelocityShares VIX Short Volatility Hedged ETN These simulated ETN histories are useful if you want to
  • Machine Learning Trading Systems [Jonathan Kinlay]

    The SPDR S&P 500 ETF (SPY) is one of the widely traded ETF products on the market, with around $200Bn in assets and average turnover of just under 200M shares daily. So the likelihood of being able to develop a money-making trading system using publicly available information might appear to be slim-to-none. So, to give ourselves a fighting chance, we will focus on an attempt to predict the
  • Using Fundamentals to Improve Pairs Trading Strategy [Quantpedia]

    Pairs trading strategys return depends on the divergence/convergence movements of a selected pair of stocks prices. However, if the stable long term relationship of the stocks changes, price will not converge and the trade opened after divergence will close with losses. We propose a new model that, including companies fundamental variables that measure idiosyncratic factors, anticipates

Quantocracy’s Daily Wrap for 08/07/2016

This is a summary of links featured on Quantocracy on Sunday, 08/07/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Maximum Likelihood Estimation for Linear Regression [Quant Start]

    The purpose of this article series is to introduce a very familiar technique, Linear Regression, in a more rigourous mathematical setting under a probabilistic, supervised learning interpretation. This will allow us to understand the probability framework that will subsequently be used for more complex supervised learning models, in a more straightforward setting.