Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 04/01/2018

This is a summary of links featured on Quantocracy on Sunday, 04/01/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in March [Allocate Smartly]

    This is a summary of the recent performance of a wide range of excellent tactical asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. While we dont (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Learn more about what we do or let AllocateSmartly help you follow these strategies in
  • A Simple System For Hedging Long Portfolios [Relative Value Arbitrage]

    In this post, we are going to examine a trading system with the goal of using it as a hedge for long equity exposure. To this end, we test a simple, short-only momentum system. The rules are as follows, Short at the close when Close of today Cover at the close when Close of today > lowest Close of the last 10 days The Table below presents results for SPY from 1993 to the present. We performed

Quantocracy’s Daily Wrap for 03/30/2018

This is a summary of links featured on Quantocracy on Friday, 03/30/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Research Review | 30 March 2018 | Portfolio Analysis [Capital Spectator]

    Factor Momentum Robert D. Arnott (Research Affiliates), et al. January 31, 2018 Past industry returns predict the cross section of industry returns, and this predictability is at its strongest at the one-month horizon (Moskowitz and Grinblatt 1999). We show that the cross section of factor returns shares this property, and that industry momentum stems from factor momentum. Factor momentum is

Quantocracy’s Daily Wrap for 03/29/2018

This is a summary of links featured on Quantocracy on Thursday, 03/29/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trading rules that keep you trading [Alvarez Quant Trading]

    I have written the difficulty in trading and testing short strategies. I had stopped trading my short strategy because it was too hard to trade psychologically for me. About nine months ago, I revisited my short strategy to see how it had been doing since I stopped and of course it has been doing just fine even during these very bullish times. As strategy developers we often add rules to improve
  • Holy Bullish Thursday!! [Quantifiable Edges]

    Stock market performance leading up to and around many holidays has often been bullish. This is something I have written about several times over the years. Holy Thursday is one such day that has done quite well. I have shown Holy Thursday stats a few times in the past. The chart and statistics below are all updated through last year. 2018-03-28 Despite the last 2 years losing some ground, the
  • Newfound 2018 March Madness: Final Four Update [Flirting with Models]

    We outlined the full set of rules for the Newfound 2018 March Madness Brackets here. After two more rounds, we are down to the Final Four in our Newfound 2018 March Madness Bracket Challenge, and while the right hand side of the bracket looks predictable with two number 1 seeds facing off (Kansas and the current tournament favorite, Villanova) the left hand side has 11 seed Loyola facing off

Quantocracy’s Daily Wrap for 03/28/2018

This is a summary of links featured on Quantocracy on Wednesday, 03/28/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • R Quantifying Trend Days [Flare 9x]

    In this post I will be using R and data.table to extract all trend up / down days. The following method was used to quantify a trend day: 1. Trend up = Close price closes within 25% of the days high 2. Trend down = Close prices closes within 25% of the days low 3. Exclusive of gaps, if open is above yesterdays high or low exclude 4. Daily return must be over / below .75 / -.75% Other methods come
  • How Algo Trading Reacts to Market Stress [Quantpedia]

    A key issue raised by the rapid growth of computerised algorithmic trading is how it responds in extreme situations. Using data on foreign exchange orders and transactions that includes identification of algorithmic trading, we find that this type of trading contributed to the deterioration of market quality following the removal of the cap on the Swiss franc on 15 January 2015, which was an event

Quantocracy’s Daily Wrap for 03/27/2018

This is a summary of links featured on Quantocracy on Tuesday, 03/27/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Risk-Based Explanations for the Momentum Premium [Alpha Architect]

    Most of the literature on the momentum factor has focused on behavioral explanations, generally either investor underreaction or overreaction. For example, in his paper Explanations for the Momentum Premium, Yale University professor Tobias Moskowitz points out: Underreaction results from information traveling slowly into prices. That causes momentum. For example, there is ample evidence

Quantocracy’s Daily Wrap for 03/26/2018

This is a summary of links featured on Quantocracy on Monday, 03/26/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Protect & Participate: Managing Drawdowns with Trend Following [Flirting with Models]

    Trend following is an investment strategy that buys assets exhibiting strong absolute performance and sells assets exhibiting negative absolute performance. Despite its simplistic description, trend following has exhibited considerable empirical robustness as a strategy, having been found to work in equity indices, bonds, commodities, and currencies. A particularly interesting feature about trend
  • Factor Exposure Analysis: Dow Jones [Factor Research]

    Factor exposure should be considered a source of returns as well as of risk Factor biases can be measured top-down or bottom-up The results of the two approaches do not necessarily reconcile INTRODUCTION Factor investing has become immensely popular in recent years and assets in smart beta products surpassed $1 trillion in 2017. However, factor exposure should be regarded as much as a source of

Quantocracy’s Daily Wrap for 03/23/2018

This is a summary of links featured on Quantocracy on Friday, 03/23/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tax Efficient Tactical Asset Allocation [Allocate Smartly]

    New to Tactical Asset Allocation? Learn more: What is TAA? In our previous post, we looked at the tax impact of TAA for investors trading in taxable accounts. Using our database of more than 40 published TAA models we concluded that, while TAA as a whole has been relatively tax efficient, the particular strategies that you choose mattersa lot. Individual strategies range from very to not
  • Two Centuries of Momentum [Flirting with Models]

    A momentum-based investing approach can be confusing to investors who are often told that chasing performance is a massive mistake and timing the market is impossible. Yet as a systematized strategy, momentum sits upon nearly a quarter century of positive academic evidence and a century of successful empirical results. Our firm, Newfound Research, was founded in August 2008 to offer
  • What s Cheap? A Factor Perspective [Factor Investor]

    There are a hundred ways to evaluate whether an investment is cheap–discounted cash flows, competitor multiples, mean reversion, multiple of projected earnings–the list goes on…and on. To each his own on what is the "best" valuation methodology, but suffice it to say that the persistent tug of our own behavioral biases suggests that objective measures (as opposed to subjective) are

Quantocracy’s Daily Wrap for 03/22/2018

This is a summary of links featured on Quantocracy on Thursday, 03/22/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Machine Learning Modelling in R Cheat Sheet [R Trader]

    I came across this excellent article lately Machine learning at central banks which I decided to use as a basis for a new cheat sheet called Machine Learning Modelling in R. The cheat sheet can be downloaded from RStudio cheat sheets repository. As the R ecosystem is now far too rich to present all available packages and functions, this cheat sheet is by no means exhaustive . Its rather a
  • Momentum Everywhere, Including in Factors [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 to use momentum, together with the three FamaFrench factors (market beta, size and value), to explain mutual fund
  • Liquidity Creation in a Short-Term Reversal Strategies and Volatility Risk [Quantpedia]

    We show, both theoretically and empirically, that liquidity creation induces negative exposure to volatility risk. Intuitively, liquidity creation involves taking positions that can be exploited by privately informed investors. These investors' ability to predict future price changes makes their payoff resemble a straddle (a combination of a call and a put). By taking the other side,
  • When NDX Has Closed At A Multi-Week Low On A Fed Day [Quantifiable Edges]

    As far as Fed Days go, Wednesday was a disappointment. Not only did it fail to rally, but it also left SPX and NDX at 10-day lows. With Fed Days typically bullish, finishing at a 10-day low is quite unusual. The results table below is part of a larger examination I did in last nights Subscriber Letter (click here for free trial). It looks at prior Fed Day instances of 10-day low closes for NDX

Quantocracy’s Daily Wrap for 03/21/2018

This is a summary of links featured on Quantocracy on Wednesday, 03/21/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book from Marcos Lopez de Prado: Advances in Financial Machine Learning [Amazon]

    Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms;
  • Review: Advances in Financial Machine Learning [Mathematical Investor]

    Two of the most talked-about topics in modern finance are machine learning and quantitative finance. Both of these are addressed in a new book, written by noted financial scholar Marcos Lopez de Prado, entitled Advances in Financial Machine Learning. In this book, Lopez de Prado strikes a well-aimed karate chop at the naive and often statistically overfit techniques that are so prevalent in the

Quantocracy’s Daily Wrap for 03/20/2018

This is a summary of links featured on Quantocracy on Tuesday, 03/20/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Newfound 2018 March Madness: Sweet 16 Update [Flirting with Models]

    We outlined the full set of rules for the Newfound 2018 March Madness Brackets here. We have two rounds in the bag in this years March Madness bracket competition, and what a wild ride its been: 16 seed UMBC made history by beating 1 seed Virginia, edging them out by a mere 20 points The highest seed left in the South is 5 seed Kentucky paving a (possibly) easy route to the Final Four.
  • Stock Buybacks are Bad? What About the Alternative [Alpha Architect]

    Recently there has been a fairly active discussion within the financial media on the topic of stock buybacks, or share repurchases. This is probably due to the recent tax reform, which has caused many to question where companies will spend their additional profits now that firms (on average) will be spending less on federal tax payments. Non-academic articles are generally against stock buybacks,
  • Fed Day Performance In Rising vs Falling Rate Environments [Quantifiable Edges]

    The Fed holds policy meetings 8 times per year. Many times since starting Quantifiable Edges in 2008, I have discussed the (primarily bullish) edge that exists on the final day of these meetings when the Fed releases its statement and announces any new policy changes. One question I often get about Fed Days is whether it matters if we are in a rising-rate environment, or a declining-rate