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Quantocracy’s Daily Wrap for 04/22/2017

This is a summary of links featured on Quantocracy on Saturday, 04/22/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • “Alternative” Facts about Formulaic Value Investing [Alpha Architect]

    A new paper, Facts about Formulaic Value Investing, is making the rounds and professes to plunge a dagger directly into the heart of systematic value investors. Half of my inbox is filled with questions regarding this paper, since we are considered by some rightly or wrongly to be experts on systematic value investing. The implication from the research piece is that systematic

Quantocracy’s Daily Wrap for 04/21/2017

This is a summary of links featured on Quantocracy on Friday, 04/21/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Abnormal Returns Memberships (shout out to the inspiration for Quantocracy) [Abnormal Returns]

    I am excited to announce that Abnormal Returns now supports memberships. The most common compliment I get about Abnormal Returns is that it saves you time. We all well know that time is money. A great way to help keep Abnormal Returns an ongoing, independent entity is to contribute to its upkeep at the $25 or $75 level. Please note this is annual, recurring annual membership.* Monetizing Abnormal
  • K-Means in investment solutions: fact or fiction [Quant Dare]

    Weve spoken previously about different clustering methods many times: K-Means, Hierarchical Clustering, and so on. However, this field does not end here. In this post, I will try to find how K-Means clustering works in an investment solution. K-Means Clustering The K-Means algorithm partitions the points in a data set into clusters. This partition minimises the sum, across the clusters, of the
  • Analysis of Commodity Futures Returns Over the Last Decade [Quantpedia]

    Long-only commodity futures returns have been very disappointing over the last decade, leading some to wonder if it was a mistake to invest in commodities. The poor performance is the result of poor income returns and not of falling commodity prices. This observation may be surprising for many commodity investors who were not aware, who misperceived, they were making a bet on income returns,
  • Using the BayesOpt Library to Optimise my Planned Neural Net [Dekalog Blog]

    Following on from my last post, I have recently been using the BayesOpt library to optimise my planned neural net, and this post is a brief outline, with code, of what I have been doing. My intent was to design a Nonlinear autoregressive exogenous model using my currency strength indicator as the main exogenous input, along with other features derived from the use of Savitzky-Golay filter

Quantocracy’s Daily Wrap for 04/19/2017

This is a summary of links featured on Quantocracy on Wednesday, 04/19/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Modelling Treasury ETF Performance in an Era of Rising Rates [Allocate Smartly]

    US Treasuries and other interest rate sensitive instruments form the backbone of many asset allocation strategies. Investors are justifiably concerned about a future of rising interest rates and the potential impact on those instruments. In this post we model that impact on constant maturity Treasury assets like the ETF TLT, which tracks long-term (20+ year) US Treasuries. For brevity, Im going

Quantocracy’s Daily Wrap for 04/18/2017

This is a summary of links featured on Quantocracy on Tuesday, 04/18/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Value and Momentum Investing in Frontier Stock Markets [Alpha Architect]

    Value and Momentum investing have been studied across many different markets and asset classes (Asness et al 2013) and have shown to be effective factors. A working paper, Frontier Stock Markets: Local vs Global Factors by Douglas W. Blackburn and Nusret Cakici examines Value and Momentum investing in Frontier Markets from 2005-2016. This paper is unique because prior research has focused on

Quantocracy’s Daily Wrap for 04/17/2017

This is a summary of links featured on Quantocracy on Monday, 04/17/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Simulation-Based Rebuttal to Research Affiliates (@RA_Insights) [Flirting with Models]

    Research Affiliates published a new piece of research exploring mutual fund returns over the last 25 years and the implied ability for managers to capture popular factor premiums published by the academic community. They argue that several factors accepted in academia may not be implementable after real life frictions (e.g. transaction costs, cost of shorting, missed trades, et cetera). Their
  • Swedroe Spotlight: Does Market Sentiment Help Explain Momentum? [Alpha Architect]

    Momentum is the tendency for assets that have performed well (poorly) in the recent past to continue to perform well (poorly) in the future, at least for a short period of time. In 1997, Mark Carhart, in his study On Persistence in Mutual Fund Performance, was the first academic to use momentum, together with the three Fama-French factors (market beta, size and value), to explain mutual fund

Quantocracy’s Daily Wrap for 04/15/2017

This is a summary of links featured on Quantocracy on Saturday, 04/15/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Parallelized Random Portfolio Generation [Quant Bros]

    Generate an efficient frontier of random portfolios with custom constraints in R. Accelerate your project with the power of cloud computing using Techila Technologies solution in combination with Rs plyr functions. Source code included. The embedded source code below is available due to the generous sponsorship of Techila Technologies. ############################# # Set Environment
  • Demystifying Bollinger Bands [Milton FMR]

    How do we know if a price is right and when do we enter the trade ? Is the S&P 500 Index trading at 2355 for that day to high or to low. Should I buy or sell ? Proponents of the Bollinger Band say that this indicator can greatly improve your odds in being on the right side of the market. The purpose of the bollinger band is to help you decide when to make your move by illustrating the momentum

Quantocracy’s Daily Wrap for 04/14/2017

This is a summary of links featured on Quantocracy on Friday, 04/14/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Convolutional Neural Network for Time Series [Quintuitive]

    Neural networks have been around for a while, but its fair to say that many successful practical applications use at least one convolutional layer. Naturally, convolutions make sense for time series, so I went and added a few to the Walk-Forward Analysis. To make the code easier to use, I ended up creating a self-contained GitHub repository. CNTKs code to create the network layers is
  • Upcoming Webinar: How to use Mixture Models to Predict Market Bottoms w/ @BlackArbsCEO [Quant Insti]

    The webinar will explain Mixture Models and explore its application to predict an assets return distribution and identify outlier returns that are likely to mean revert. The webinar will cover Why bother? Motivating experimentation with Mixture Models How do Mixture Models work? (An intuitive explanation) Designing the Research Experiment (How do we answer the original question?) Define the

Quantocracy’s Daily Wrap for 04/13/2017

This is a summary of links featured on Quantocracy on Thursday, 04/13/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Buy When There’s Blood in the Streets? Market Timing with Volatility Triggers [EconomPic]

    An 18th century British nobleman, Baron Rothschild, was rumored to have made his fortune buying during the panic that followed the Battle of Waterloo against Napoleon. He is behind the often quoted saying "Buy when there's blood in the streets, which he continued even if the blood is your own." This post will share a framework that may identify regimes that benefit from buying
  • Nuts and Bolts of Quantstrat, Part V [QuantStrat TradeR]

    This post will be about pre-processing custom indicators in quantstratthat is, how to add values to your market data that do not arise from the market data itself. The first four parts of my nuts and bolts of quantstrat were well received. They are even available as a datacamp course. For those that want to catch up to todays post, I highly recommend the datacamp course. To motivate this
  • Open Up! [Throwing Good Money]

    This is kind of a weird one. I was mulling over the question of what happens when the market opens up, i.e. above its previous close. Is the day likely to be an up day? A down day? I got out my data and started poking around. I looked at all open-up days with an open at least 0.25% above the previous days close. I looked at only days that opened up after a previous close-to-close down
  • Introduction to a Basic “Quant” Match Making Service [Alpha Architect]

    We were recently asked by Aaron Brask, one of our guest bloggers, why we dont provide a job board on our site. Aaron works for several large family offices and he says it is incredibly difficult to find intellectually honest talent. Aaron also pointed out that we have a unique community of savvy, curious, and genuinely engaged readers both on the job seeker side and on the employer side.
  • Holy Bullish Thursday! [Quantifiable Edges]

    Below is a quick look at how the SPX has performed in the past on Holy Thursday. Like the last day before many long weekends, it has shown a bullish propensity over the years. 2017-04-13 The numbers are compelling, and it is especially impressive to see how much the winners have outsized the losers

Quantocracy’s Daily Wrap for 04/12/2017

This is a summary of links featured on Quantocracy on Wednesday, 04/12/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Back to Basics Part 3: Backtesting in Algorithmic Trading [Robot Wealth]

    Nearly all research related to algorithmic trading is empirical in nature. That is, it is based on observations and experience. Contrast this with theoretical research which is based on assumptions, logic and a mathematical framework. Often, we start with a theoretical approach (for example, a time-series model that we assume describes the process generating the market data we are interested in)
  • How are ETFs Affecting the Stock Market: Is There a Dark Side to ETFs? [Alpha Architect]

    The Dark Side of ETFs? Sounds interesting, and in my humble opinion, an image of Darth Vader on page 1 would be a great addition to the paper. The paper, Is there a dark side to exchange traded funds? An information perspective, written by Doron Israeli, Charles M. C. Lee, and Suhas A. Sridharan, digs into the details on some implications of higher ETF ownership on individual stocks. The
  • ConnorsRSI Strategy: Optimization Selection [Alvarez Quant Trading]

    In the previous post, Simple ConnorsRSI Strategy on S&P500 Stocks, I showed a simple strategy which I optimized which gave 1,300 variations. Today, I will cover various methods to choose a strategy to potentially trade. Goals The goals were to find a variation with over 20% CAR and Max Drawdown under 25%. Know ahead of time what metrics you care about and what values you want is important.
  • Podcast: A crash course in long-term investing – for short-term traders w/ @MebFaber [Chat With Traders]

    Mebane Faber is the founder and CIO at Cambria Investment Management, where he manages Cambrias ETFs, separate accounts and private investment funds. Hes also authored numerous white papers and five books now, on various investing subjects. Mebs a budding podcaster too, his podcast; The Meb Faber Show. The main reason why I asked Meb to join me for this episode, was to share some simple
  • The Intrinsic Value of Gold [Quantpedia]

    In this paper, we propose a gold price index that enables market participants to separate the change in the intrinsic value of gold from changes in global exchange rates. The index is a geometrically weighted average of the price of gold denominated in different currencies, with weights that are proportional to the market power of each country in the global gold market, where market power is

Quantocracy’s Daily Wrap for 04/11/2017

This is a summary of links featured on Quantocracy on Tuesday, 04/11/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pop or Drop part 2: Big Moves Upward [Throwing Good Money]

    In the last post (here), I examined what happened after a stock moved down a significant multiple of its previous days Average True Range (ATR20 in this case). Stocks tended to have up days on day 1 and days 3-5, with a down day on day 2 as an average. What about bursts upward? Are they the opposite? Would they make a good shorting opportunity? Turns out, the answer is no! The Pop. Big pops
  • State of Trend Following in March [Au Tra Sy]

    Another negative month for this (delayed) State of Trend Following report. To say that the last 12 months have been far from the best performing period for the index seems an understatement. I also include in this months report (further below) the 12-month chart from the post I write for the Wisdom State of Trend Following for a longer-horizon picture. Please check below for more details.
  • The Shape of Supply and Demand Curves in Rapidly Clearing Markets [Mechanical Markets]

    A central challenge in economics is understanding how price affects the quantity of supply and demand, a relationship often assumed to be approximately linear. But there are markets where this notion of linearity, sometimes called elasticity, may not hold. In a paper that deserves more attention, Donier and Bouchaud show that supply/demand curves of rapidly clearing markets (with a Brownian