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Quantocracy’s Daily Wrap for 12/23/2019

This is a summary of links featured on Quantocracy on Monday, 12/23/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Timing Trend Model Specification with Momentum [Flirting with Models]

    Over the last several years, we have written several research notes demonstrating the potential benefits of diversifying specification risk. Specification risk occurs when an investment strategy is overly sensitive to the outcome of a single investment process or parameter choice. Adopting an ensemble approach is akin to creating a virtual fund-of-funds of stylistically similar managers,
  • Gregory Zuckerman: The Man Who Solved the Man Who Solved the Market [Invest Resolve]

    For the quant community, it was arguably the most awaited book of 2019. Finally a peek behind the curtains into the most successful hedge fund manager in history. The +66% average (gross) returns that Jim Simons and his army of data scientists produced over the last 17 years in their Medallion fund captured the imagination of investors across the globe and their obsessive secrecy just added to the
  • Research Compendium 2019 [Factor Research]

    In 2019 we published more than 50 research notes on mostly factor investing and smart beta ETFs, but also on topics like ESG, activist investors, hedge fund replication, and artificial intelligence. The Research Compendium 2019 contains all of our research published this year. We would like to thank you for reading and always appreciate feedback, especially if critical. Download Research

Quantocracy’s Daily Wrap for 12/22/2019

This is a summary of links featured on Quantocracy on Sunday, 12/22/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quant Investing: Volatility Curve Model [Investing For A Living]

    This post introduces a quant trading model based on volatility. More specifically it uses the prices of volatility futures contracts based on the SP500 to make risk-on and risk-off decisions that can be used to trade various risk-assets. Why Volatility? There is a bunch of research that shows that historical volatility is predictive of future near-term volatility. See here for a good summary of
  • Why Did Trend-Following Underperform Last Decade? [Quantpedia]

    Trend-following funds and strategies were once extremely popular after the 2008/2009 crisis. They offered attractive performance, and diversification properties made them a nice addition to investors portfolios. Ten years later, trend-following strategy is not such a popular word. Strategies didnt blow-up, but their performance was far from spectacular. What are the main reasons for
  • Research Review | 20 December 2019 | Value Investing [Capital Spectator]

    Value Bubbles Messaoud Chibane and Samuel Ouzan (Neoma Business School) February 27, 2019 According to several extended behavioral theories, value profits should mirror momentum profits, and vary over time. We test these theories in the cross section of returns. Value returns depend on market states. From 1926 to 2018, following negative market return, the average so-called value premium is about

Quantocracy’s Daily Wrap for 12/20/2019

This is a summary of links featured on Quantocracy on Friday, 12/20/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How To Trade Distressed Stocks Using Free APIs [Harry Sauers]

    The Current Ratio is a vital metric for understanding a companys liquidity position as well as its ability to pay its obligations on time. It is defined simply as the companys total current assets divided by its current liabilities. Current assets are defined as assets that are cash or expected to become cash within one year, and current liabilities are liabilities that will be due in a year
  • Over a billion dollars in a single day with Rob Carver (@InvestingIdiocy) [System Trader Show]

    Robert Carver worked in the City of London for over a decade. For seven years he was a portfolio manager at AHL one of the worlds largest systematic hedge funds before, during and after the global financial meltdown of 2008. In this interview, we talk about many trading topics, mostly around the systematic approach and why its so important for most of us to base the decision-making

Quantocracy’s Daily Wrap for 12/18/2019

This is a summary of links featured on Quantocracy on Wednesday, 12/18/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Converting LOBSTER demo R code into Python [R Trader]

    It has been more than a year since my last post, Ive been super busy with consulting assignments working on algorithmic/electronic trading. The workload is still heavy but I managed to find a few hours to write this post as I came across a new great tool: LOBSTER (and before anyone asks Ive no link whatsoever with the company) LOBSTER stands for: Limit Order Book System The Efficient
  • Protecting the Downside of Trend When It Is Not Your Friend: Part 2/2 [Alpha Architect]

    In Part 1 of this article, we reviewed the performance of using a more complex form of simple trend following (i.e. adding a channel breakout rule alongside a simple time-series momentum rule). The simple trend signal (S) used was based on the sign of the trailing 12-month return of the asset. The universe tested was robust. Assets included at least 60 liquid futures/forward contracts across
  • An Analysis of Testing Benjamin Graham s Net Current Asset Value Strategy in London [Alpha Architect]

    This is our third post in our series on net-nets having previously analyzed Benjamin Grahams Net Current Asset Values: A Performance Update by Henry R. Oppenheimer and Grahams Net-Nets: Outdated or Outstanding? by James Montier. The focus of this post is the research paper Testing Benjamin Grahams Net Current Asset Value Strategy in London which was originally

Quantocracy’s Daily Wrap for 12/16/2019

This is a summary of links featured on Quantocracy on Monday, 12/16/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Re-specifying the Fama French 3-Factor Model [Flirting with Models]

    The Fama French three-factor model provides a powerful tool for assessing exposures to equity risk premia in investment strategies. In this note, we explore alternative specifications of the value (HML) and size (SMB) factors using price-to-earnings, price-to-cash flow, and dividend yield. Running factor regressions using these alternate specifications on a suite of value ETFs and Newfounds
  • Global Pension Funds: The Coming Storm [Factor Research]

    The outlook for US equity and bond returns is low based on historical data The return assumptions of US public pension funds are difficult to achieve Only an extreme allocation to alternatives would meet the expected rate of return THE GLOBAL PENSION FUND CRISIS Tens of thousands of Dutch workers took to the streets in the spring of 2019 to protest a proposal to raise the retirement age. Then, in
  • The Most Wonderful Week of the Year Until Last Year [Quantifiable Edges]

    I have written many times over the years about the bullish tendency of the market during opex week in December. Ive even referred to it as The Most Wonderful Week of the Year. And it wasup until last year. So below is an updated look at the stats and profit curve for owning SPX from the close of the Friday before December opex to the close on December opex. 2019-12-16 That is a 7% loss

Quantocracy’s Daily Wrap for 12/15/2019

This is a summary of links featured on Quantocracy on Sunday, 12/15/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Tale of an Edgy Panda and some Python Reviews [QuantStrat TradeR]

    This post will be a quickie detailing a rather annoyingfinding about the pandas package in Python. For those not in the know, Ive been taking some Python courses, trying to port my R finance skills into Python, because R seems to have fallen out of favor in the world of finance. (If you know of an opportunity, heres my resume.) So, Im trying to get my Python skills going, hopefully
  • Rebalancing and market price distortions [SR SV]

    Price distortions are an important source of short-term trading profits, particularly in turbulent markets. Here price distortions mean apparent price-value gaps that arise from large inefficient flows. An inefficient flow is a transaction that is not motivated by rational risk-return optimization. One source of such inefficient flows is rebalancing, large-scale institutional transactions

Quantocracy’s Daily Wrap for 12/13/2019

This is a summary of links featured on Quantocracy on Friday, 12/13/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quant’s Look on ESG Investing Strategies [Quantpedia]

    ESG Investing (sometimes called Socially Responsible Investing) is becoming a current trend, and its proponents characterize it as a modern, sustainable, and responsible way of investing. Some people love it, others see it as just another fad that will soon be forgotten. We at Quantpedia have decided to immerse in academic research related to this trend to understand it better. How are ESG scores
  • Trend-Following Plus Momentum in ETFs [Alvarez Quant Trading]

    In a previous post, Trend-following vs. Momentum in ETFs, I compared trend-following and momentum to see which produced better results on a basket of ETFs. In the post, I mentioned combining trend-following and momentum into one strategy to see if combined they can beat buy and hold more often. Testing Info Dates: 1/1/2006 to 10/31/2019 The basket of EYFs is AGG, DBX, EEM, EFA, GLD, HYG, IWM, LQD,
  • Improving the Performance of Deep Value Strategies [Alpha Architect]

    A large body of evidence demonstrates that investment strategies focused on buying stocks that are cheap relative to measures of fundamental value have achieved higher long-term returns than the broad market. Motivated by such legendary investors as Benjamin Graham, David Dodd, and Walter Schloss, deep value investors look to build portfolios of stocks with the cheapest valuations relative to

Quantocracy’s Daily Wrap for 12/11/2019

This is a summary of links featured on Quantocracy on Wednesday, 12/11/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Dimensionality reduction method through autoencoders [Quant Dare]

    Weve already talked about dimensionality reduction long and hard in this blog, usually focusing on PCA. Besides, in my latest post I introduced another way to reduce dimensions based on autoencoders. However, in that time I focused on how to use autoencoders as predictor, while now Id like to consider them as a dimensionality reduction technique. Just a reminder about how autoencoders work.
  • Historical Fed Day Performance By Chairperson [Quantifiable Edges]

    I have written about Fed Day edges for years. Much of the research can be found in the Fed Study category blog posts. Today I decided to share a chart showing historical performance on Fed Days over the course of the last 5 Fed chairpeople. 2019-12-10 Have a happy Fed Day tomorrow!

Quantocracy’s Daily Wrap for 12/10/2019

This is a summary of links featured on Quantocracy on Tuesday, 12/10/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New and Improved Sharpe Ratio adjustment in the handcrafting method [Investment Idiocy]

    In my recent posts on skew and kurtosis I've put together a large number of ideas for possible trading strategies. The next step will be to create and test these ideas out. However I already know from my initial analysis that many of these ideas will probably have poor performance. This leaves me in something of a conundrum. In fact this is just one example of the conundrum that strategy
  • Monte Carlo Simulation: Definition, Example, Code [Quant Insti]

    Years ago, I had made it to the nal round in an interview for a Senior Delta One/Quantitative Futures position at an HFT rm (unnamed for privacy). Things were going well, I had answered two out of three of those ridiculous questions that are only applicable in Subsaharan Africa or Finance interviews (Like how to get 5 gallons from a 6 and 4-gallon jug); I was feeling good. They asked me

Quantocracy’s Daily Wrap for 12/09/2019

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

  • US nonfarm employment prediction using RIWI Corp alternative data [EP Chan]

    The monthly US nonfarm payroll (NFP) announcement by the United States Bureau of Labor Statistics (BLS) is one of the most closely watched economic indicators, for economists and investors alike. (When I was teaching a class at a well-known proprietary trading firm, the traders suddenly ran out of the classroom to their desks on a Friday morning just before 8:30am EST.) Naturally, there were many
  • How to Evaluate Smart Beta ETFs [Factor Research]

    Smart beta ETFs can be compared via a factor score, which relates fees to the factor exposure Value-focused ETFs in the US show a wide range of factor scores Large firms offer more attractive factor scores, but largely due to lower fees INTRODUCTION Beta is like ice cream and comes in many flavors. Broadly we can categorize it into the following four types: Plain beta: Market
  • A Conversation on Rebalance Timing Luck [Flirting with Models]

    My guest today is me. But rather than interview myself, my co-portfolio manager Nathan Faber joins the podcast to take the reigns. In this episode, we talk all things rebalance timing luck. Its been an obsession of mine for years and something we believe to be a dramatically misunderstood and outright ignored source of risk in portfolios. We discuss how we first came across the topic, some
  • Protecting the Downside of Trend When It Is Not Your Friend : Part 1 [Alpha Architect]

    Weve done a poor job hiding our interest in Trend Following (see Trend, Trend, Trend, is your friend. And swing over to Corey Hoffsteins site for even more!). So this paper hits on a subject we know and love. The authors of this study (part 1) have one basic objective: determine if the downside performance of a simple trend-following strategy can be improved by either adding complexity to