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Quantocracy’s Daily Wrap for 09/18/2019

This is a summary of links featured on Quantocracy on Wednesday, 09/18/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean-Reversion in Trend-Following Performance [CSS Analytics]

    In a recent post I showed that the momentum factor has been mean-reverting in the short-term, and that this effect can be used to trade both the factor and momentum strategies effectively. An obvious extension is to see whether trend-following as a factor is also mean-reverting. After all, time-series momentum and momentum have been shown to be related in the research. To represent the
  • Cognitive Trading System Model [Todo Trader]

    Yes, Artificial Intelligence (AI) is here to stay. Previously on this blog, I have written about the Basis of the Scientific Trading System as well as the Artificial Intelligence Trading Systems. Since then, I have designed a trading system model which I believe could satisfy all requirements of the present and future trading systems. In this post, I will describe the model following the
  • Factor Investing from Concept to Implementation [Alpha Architect]

    There is a substantial debate on the topic of factor investing and whether or not the backtested excess returns are actually achievable in practice. Much of the research on the topic suggests that practitioners in the field are unable to capture any of the so-called factor premiums. For example, the debate is covered here, here, and here. Turns out there is another piece of literature

Quantocracy’s Daily Wrap for 09/16/2019

This is a summary of links featured on Quantocracy on Monday, 09/16/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • No, the VIX is Not Broken [Six Figure Investing]

    Hardly a month goes by without some pundit trumpeting that the VIX is broken. But before you worry too much, consider some of the non-obvious characteristics of the Cboes Fear Gauge. First a Summary These charts list possible explanations for perceived VIX brokenness Complaint Market in Low Volatility State (21-day std dev close-to-close log returns VIX too high * VIX has an overhead
  • The Failure of Value Investing explained [Alpha Architect]

    Its no secret that value has had a bad bout of performance in recent memory. This underperformance has been thoroughly examined by multiple research teams and weve done some of our own work on the subject. Weve also done in-depth rebuttals to value investing is dead articles in the past (Alternative Facts about Formulaic Value Investing, The Re-Death of Value, or Dj Vu All
  • Factors and the Glide Path [Flirting with Models]

    Value and momentum equities exhibited significant performance last week raising short-term questions about factor crowding and long-term questions about appropriate factor diversification. We explore the idea of appropriate factor diversification through the lens of a retiring investor, asking the question, are all equity styles appropriate at all points in an investors lifecycle? Using a
  • Risk Parity Part II: The Long-Run View [Two Centuries Investments]

    In Part I Risk Parity, I discussed theChasing Diversifiers problem that harms investors performance. This week, I apply the Relevance of the Long-Run concept to Risk Parity. First, let me acknowledge upfront that deep historical data is messy and is not precise. Depending on the source, you might get different results. Having said that, I still prefer to supplement my analysis with
  • Will investors outlive their savings? [Mathematical Investor]

    As we explained in an earlier Mathematical Investor blog, target-date funds are currently the rage in the finance world. The term refers to a mutual fund that targets a given retirement date, and then steadily shifts the allocation of assets from, say, a 80%/20% mix of stocks and bonds at the start to, say, a 30%/70% or 20%/80% mix as the target date approaches. Vanguard Group, which manages
  • Is Low Vol the New Value? [Factor Research]

    The Low Volatility factor exhibited significant exposure to Value since 1989 The factors were highly correlated in the 1990s, but less after the financial crisis Quantitative easing was positive for Low Volatility, but negative for Value INTRODUCTION Riding the Ferris wheel in an amusement park is the equivalent of investing in a Low Volatility strategy in the stock market. It is all about
  • Reinforcement learning and its potential for trading systems [SR SV]

    In general, machine learning is a form of artificial intelligence that allows computers to improve the performance of a task through data, without being directly programmed. Reinforcing learning is a specialized application of (deep) machine learning that interacts with the environment and seeks to improve on the way it performs a task so as to maximize its reward. The computer employs trial and

Quantocracy’s Daily Wrap for 09/13/2019

This is a summary of links featured on Quantocracy on Friday, 09/13/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pattern Recognition with the Frechet Distance [Robot Wealth]

    Chart patterns have long been a favourite of the technical analysis community. Triangles, flags, pennants, cups, heads and shoulders. Name a shape, someone somewhere is using it to predict market behaviour. But, is there a grain of truth or reliability in these patterns? Can it really give you a hint as to the future direction of the market? ah, I see a blue star pattern on my chart a good
  • Mo Data: Using Mean-Reversion in the Momentum Factor to Time Momentum [CSS Analytics]

    In the last post we used the data available for the momentum factor using an ETF (ticker: MOM) which seeks to replicate The Dow Jones Thematic Market Neutral Momentum Index to time when to be in or out of high momentum stocks. Alpha Architect recently did some interesting analysis of the distribution of returns for the same momentum index in this post. One of the challenges was the lack of data

Quantocracy’s Daily Wrap for 09/12/2019

This is a summary of links featured on Quantocracy on Thursday, 09/12/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • When Should You Buy Momentum? Mean-Reversion in The Momentum Factor [CSS Analytics]

    new concepts in quantitative research Home CSSA Investor IQ When Should You Buy Momentum? Mean-Reversion in The Momentum Factor September 12, 2019 by david varadi Recently there was a good post by Bespoke Research highlighting the Momentum Massacre that we recently witnessed in the market. High- flying momentum stocks were decimated and the low momentum/losing stocks made a roaring comeback.
  • Value: Don’t Call it a Comeback, it’s Been Here for Years [Alpha Architect]

    Value and Momentum each had back to back extreme returns (five sigma) days on Monday, September 9th and Tuesday, September 10th. The Dow Jones Thematic Market Neutral Value Index (Value) started the week up 3.45%, its best day since inception on December 31st, 2001. The Value Index followed this up on Tuesday, September 10th with a 2.56% return, its 7th best day since inception. The Dow
  • Exploring Simplicity In Tactically Managed ETF Portfolios [Capital Spectator]

    Risk management has become a high priority for many investors over the past decade. The worst financial crisis and recession since the Great Depression in 2008-2009 clearly has the power to focus minds. Research shops have moved heaven and earth to search for solutions that attempt to limit risk without sacrificing return. The question is whether simplicity is competitive in this quest? As a

Quantocracy’s Daily Wrap for 09/11/2019

This is a summary of links featured on Quantocracy on Wednesday, 09/11/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • K-Means Clustering Algorithm For Pair Selection In Python [Quant Insti]

    From showing related articles at the end of the article you have browsed through to creating a personalised recommendation based on your viewing habits, you would be surprised of the number of times you have been interacting with the K-means algorithm without even realising it. The above examples were simply in the realm of everyday life. When you turn your eye towards the colossal industries
  • Encoding financial texts into dense representations [Quant Dare]

    The market is driven by two emotions: greed and fear. Have you ever heard that quote? It is quite popular in financial circles and there may just be some truth behind it. After all, when people, with short-term investments, think are going to lose a lot of money, many of them sell as fast as they can. When they think they can make money the same happens, they tend to buy as fast as they can.

Quantocracy’s Daily Wrap for 09/10/2019

This is a summary of links featured on Quantocracy on Tuesday, 09/10/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Can you apply factors to trade performance? [Robot Wealth]

    When tinkering with trading ideas, have you ever wondered whether a certain variable might be correlated with the success of the trade? For instance, maybe you wonder if your strategy tends to do better when volatility is high? In this case, you can get very binary feedback by, say, running backtests with and without a volatility filter. But this can mask interesting insights that might surface if

Quantocracy’s Daily Wrap for 09/09/2019

This is a summary of links featured on Quantocracy on Monday, 09/09/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bagging in Financial Machine Learning: Sequential Bootstrapping [Hudson and Thames]

    To understand the Sequential Bootstrapping algorithm and why it is so crucial in financial machine learning, first we need to recall what bagging and bootstrapping is and how ensemble machine learning models (Random Forest, ExtraTrees, GradientBoosted Trees) work. It all starts from a Decision Tree algorithm. As we all know Decision Tree is an extremely useful machine learning algorithm which
  • CTAs in Perspective [Spring Valley]

    CTAs, mostly trend followers, have historically delivered meaningful diversification to both traditional and alternative asset classes. However, CTAs have struggled over the last ten years. There have been various explanations such as low volatility, increased correlations, and suppressed interest rates. By understanding the drivers behind trend following, we isolate the impact each variable has
  • Build Your Own Long/Short [Flirting with Models]

    We exploit the idea that long-only strategies are long/short portfolios all the way down, we demonstrate how to isolate the active bets of portfolio managers. Using the example of a momentum / low-volatility barbell portfolio, we construct a simple long/short portfolio using ETFs and S&P 500 futures. Recognizing that not all investors will have access to S&P 500 futures, we argue

Quantocracy’s Daily Wrap for 09/07/2019

This is a summary of links featured on Quantocracy on Saturday, 09/07/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Stock Market Trends (h/t @PyQuantNews) [Frank Ceballos]

    Purpose: The purpose of this article is to introduce the reader to some of the tools used to spot stock market trends. Materials and Methods: We will utilize a data set consisting of five years of daily stock market data for Analog Devices. The time period we consider starts on January 1, 2013 and ends on December 31, 2017. We will start analyzing the data using line plots, then introduce
  • The low-risk effect: evidence and reason [SR SV]

    The low-risk effect refers to the empirical finding that within an asset classes higher-beta securities fail to outperform lower-beta securities. As a result, betting against beta, i.e. leveraged portfolios of longs in low-risk securities versus shorts in high-risk securities, have been profitable in the past. The empirical evidence for the low-risk effect indeed is reported as strong and

Quantocracy’s Daily Wrap for 09/06/2019

This is a summary of links featured on Quantocracy on Friday, 09/06/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Interview with Marcos Lopez de Prado [Mathematical Investor]

    Marcos Lopez de Prado, who was named Quant of the Year for 2019 by the Journal of Portfolio Management, and who has recently formed his own investment firm True Positive Technologies, was recently interviewed by KNect365, an organization that sponsors numerous conferences and other exchanges between professionals in various fields, including finance, life sciences, technology, law,

Quantocracy’s Daily Wrap for 09/05/2019

This is a summary of links featured on Quantocracy on Thursday, 09/05/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • March for the Fallen 2019: Detailed Logistics Outline and What to Expect [Alpha Architect]

    Action Item: Please let us know your trip details so we can support you as much as possible. We are a little over 3 weeks away from March for the Fallen (#MFTF). NOTE: There is a monster training event occurring simultaneously to MFTF this year so be prepared to dodge humvees and watch out for stray artillery shells. Im joking. But seriously, we are anticipating potential logistical issues so
  • Neural Network In Python: Introduction, Structure and Trading Strategies [Quant Insti]

    You are probably wondering how a technical topic like Neural Network Tutorial is hosted on an algorithmic trading website. Neural network studies were started in an effort to map the human brain and understand how humans take decisions but algorithm tries to remove human emotions altogether from the trading aspect. What we sometimes fail to realise is that the human brain is quite possibly the
  • Preliminary Test Results of Time Series Embedding [Dekalog Blog]

    Following on from my post yesterday, this post presents some preliminary results from the test I was running while writing yesterday's post. However, before I get to these results I would like to talk a bit about the hypothesis being tested. I had an inkling that the dominant cycle period might having some bearing on tau, the time delay for the time series embedding implied by Taken's
  • An Analysis of Benjamin Graham s Net Current Asset Values: A Performance Update [Alpha Architect]

    The study examined the performance of securities that were trading at no more than two-thirds of its Net Current Asset Value (NAV) during the 1970-82 period in the US Net nets, on a gross basis, more than tripled the returns of the market (as measured by the S&P 500 TR) Net nets, on a net basis (i.e. after commissions and potential taxes) more than doubled the returns of the market (as