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Quantocracy’s Daily Wrap for 04/16/2018

This is a summary of links featured on Quantocracy on Monday, 04/16/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why My 1994 Low-Vol Dissertation Didn’t Make Impact [Falkenblog]

    Pim van Vliet posted a link to my 1994 dissertation, noting it was an early documentation of the low-vol effect. One may wonder, why did this early evidence fall flat? Clearly, lots of things, but I'll try to highlight the keys. Here's my lead paragraph, which makes clear I saw the low vol effect before most everyone: This paper documents two new facts. First, over the past 30 years
  • Benchmarking, Behavioral Biases, and the March Madness Tournament Challenge Recap [Flirting with Models]

    Benchmarks can be a very difficult subject to pin down. Choosing different ones can create drastically different backdrops to frame both short and long-term results. This was even true in our 2018 March Madness Bracket Challenge, with the value-weighted benchmark taking the top place. As investors, we must constantly battle behavioral biases such as hindsight, anchoring, and confirmation. Managing
  • ESG and Factor Investing [Alpha Architect]

    A growing number of investors are seeking to construct portfolios that simultaneously capture the 1) long-term factor premia ( value, momentum, size etc.) and 2) have attractive ESG profiles. The main research questions of the paper are as follows: Is the relationship between ESG and factor stable over time? How should blended ESG-factor portfolios be constructed? In particular, should ESG be
  • Low Volatility Factor: Interest Rate Sensitivity and Sector-Neutrality [Factor Research]

    The interest rate-sensitivity of the Low Volatility factor has increased in recent years Mainly due to the sectoral biases from the long portfolio Sector-neutrality reduces the interest rate-sensitivity, albeit at the cost of performance INTRODUCTION Low Volatility strategies have become popular over recent years and become a staple in factor portfolios. From a classic financial theory perspective
  • Trading performance – year four [Investment Idiocy]

    Time flies, and it's time for another annual update on the performance of my own investment and trading. Previous updates can be found here, here and here. These updates follow the UK tax year; from 6th April to 5th April, as I have to do my taxes anyway it makes sense to analyse everything at the same time. Following the mind numbing detail of the performance analysis there are some
  • The Day of the Week Effect in the Crypto Currency Market [Quantpedia]

    This paper examines the day of the week effect in the crypto currency market using a variety of statistical techniques (average analysis, Student's t-test, ANOVA, the Kruskal-Wallis test, and regression analysis with dummy variables) as well as a trading simulation approach. Most crypto currencies (LiteCoin, Ripple, Dash) are found not to exhibit this anomaly. The only exception is BitCoin,
  • April Opex Week s Bullish Tendency [Quantifiable Edges]

    Last month I shared a table that showed performance of opex weeks by month. April was one of the most bullish. The study below looks specifically at April opex week. I last showed it on the blog in 2016. Results are all updated. 2018-04-15 The numbers are impressive, and suggest a bullish edge. Traders may want to keep this in mind the first few days of this week.

Quantocracy’s Daily Wrap for 04/11/2018

This is a summary of links featured on Quantocracy on Wednesday, 04/11/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Julia Download Free Data Using Alphavantage API [Flare 9x]

    For those of you wanting to get started/familiar with the Julia language. I wrote a small script to import .csv data using the Alphavantage API. In this example we demonstrate how to download a single .csv file as well as batch download multiple .csv files and export as .csv. First lets download a single .csv. AAPL 1 minute data using the alphavantage API. You will need to insert your API key.

Quantocracy’s Daily Wrap for 04/10/2018

This is a summary of links featured on Quantocracy on Tuesday, 04/10/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Portfolio Construction with R [Eran Raviv]

    Constructing a portfolio means allocating your money between few chosen assets. The simplest thing you can do is evenly split your money between few chosen assets. Simple as it is, good research shows it is just fine, and even better than other more sophisticated methods (for example Optimal Versus Naive Diversification: How Inefficient is the 1/N). However, there is also good research that
  • Problems with a Long Horizon Predictability [Quantpedia]

    Long-horizon return regressions have effectively small sample sizes. Using overlapping long-horizon returns provides only marginal benefit. Adjustments for overlapping observations have greatly overstated t-statistics. The evidence from regressions at multiple horizons is often misinterpreted. As a result, there is much less statistical evidence of long-horizon return predictability than implied
  • State of Trend Following in March [Au Tra Sy]

    A fairly neutral month for the index after the up-and-down start of the year, leaving the index in slight negative mode for the year. Please check below for more details. Detailed Results The figures for the month are: March return: 0.9% YTD return: -1.1% Below is the chart displaying individual system results throughout March:

Quantocracy’s Daily Wrap for 04/09/2018

This is a summary of links featured on Quantocracy on Monday, 04/09/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Systematic Investment Strategies are Hot. But What Happens Next? [Alpha Architect]

    How will systematic (coordinated) investing affect prices? What is the risk of increasingly coordinated holdings (crowding)? How does coordinated investing affect market microstructure and optimal execution? Is factor timing possible? What are the issues in the design of factor-based strategies? What is the role of data science and machine learning? What are the Academic Insights? The author
  • Common Misconceptions About Momentum [Dual Momentum]

    Momentum is one of the most researched topic in financial market literature. A search of the SSRN database on momentum will turn up around 1000 papers written over the past three years and 3000 papers in total. With so much information available, it should not be surprising that many analysts have missed seeing some of the research. Based on the way momentum is used by practitioners, it is clear
  • Smart Beta or Smart Marketing? [Factor Research]

    Smart beta ETF investors seem to ignore empirical evidence Excess returns from smart beta are substantially different from factor returns Smart beta ETFs offer little diversification for an equity-centric portfolio INTRODUCTION Assets under management in smart beta products surpassed $1 trillion in 2017, according to Morningstar. That was three years earlier than predicted by BlackRock, the single
  • Failing Slow, Failing Fast, and Failing Very Fast [Flirting with Models]

    For most investors, long-term failure means not meeting ones financial objectives. In the portfolio management context, failure comes in two flavors. Slow failure results from taking too little risk, while fast failure results from taking too much risk. In his book, Red Blooded Risk, Aaron Brown summed up this idea nicely: Taking less risk than is optimal is not safer; it

Quantocracy’s Daily Wrap for 04/08/2018

This is a summary of links featured on Quantocracy on Sunday, 04/08/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Speed Check! Juilia Vs R Back Test Script [Flare 9x]

    In a quest for speed enhancements over R. I opted to look at the Julia language. It is a high level programming language touting similar speed to C. I find the syntax not at all that different from Python and R. If you have knowledge of the how to solve many problems using those languages, the same logic applies to using Julia only having to learn a slightly new but similar syntax. I created

Quantocracy’s Daily Wrap for 04/05/2018

This is a summary of links featured on Quantocracy on Thursday, 04/05/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Capital Efficiency Trumps Fees in the Search for Portfolio Diversifiers [Invest Resolve]

    Returns to the simplest domestic capitalization weighted indexes have dominated virtually all active strategies over the nine years since the Global Financial Crisis. Its not hard to understand why many investors have opted to eschew active strategies altogether, and instead have migrated en masse to the lowest cost index products. And for most investors, when considering traditional active
  • Tail Risk Hedging: An Alternative Approach to Risk Management [Alpha Architect]

    This article proposes tail risk hedging (TRH) as an alternative model for managing risk in investment portfolios. The standard risk management approach involves a significant allocation to hiqh-quality bonds. However, this approach has historically reduced expected returns over the long term (see article here and PDF available here). Accordingly, it could be sensible to pursue an alternative

Quantocracy’s Daily Wrap for 04/04/2018

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

  • When Breaking up is Easy To Do [Factor Investor]

    This post is a bit of an experiment. My good friend Steven Wood and I started discussing some collaborations a few months ago. We hope that it ups the quality of our research and also brings some new insights into each of our philosophies. Below is his recent post, for which I helped provide some research on the performance of spin-offs, which was popularized by Joel Greenblatt in the mid
  • Stitching data for a more ‘balanced’ backtest [Better System Trader]

    When traders set in-sample and out-of-sample periods for their backtests, its common just to pick some dates that split the data into a pre-defined percentage or range. The trouble with this approach is that it often doesnt take into account the type of market environments that exist in those periods. For example, your in-sample period may be very bearish, and if your out-of-sample period is
  • Isolation forest: the art of cutting off from the world [Quant Dare]

    We have talked about outliers several times in this blog. Examples include how to detect them or how to transform the data to remove them. Here we have another technique to detect outliers in our big data set: the isolation forest algorithm. The idea behind the isolation forest method The name of this technique is based on its main idea. The algorithm isolates each point in the data and splits
  • On The Diversification Dangers of DIY Tactical Asset Allocation [Allocate Smartly]

    We wanted to take a moment to highlight two must read posts from Newfound Research. Newfound is a thought leader in the TAA space and we highly recommend following them now. The Diversification Dangers of DIY Tactical and Diversifying the What, How, and When of Trend Following Newfound outlines three ways in which TAA investors often fail to diversify: WHAT they trade: failing to diversify
  • What is Bitcoin’s Fair Value? [Quantpedia]

    We develop a strong diagnostic for bubbles and crashes in bitcoin, by analyzing the coincidence (and its absence) of fundamental and technical indicators. Using a generalized Metcalfes law based on network properties, a fundamental value is quantified and shown to be heavily exceeded, on at least four occasions, by bubbles that grow and burst. In these bubbles, we detect a universal

Quantocracy’s Daily Wrap for 04/03/2018

This is a summary of links featured on Quantocracy on Tuesday, 04/03/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Timing Country Exposure with Value: A Valuation Measure Horserace [Alpha Architect]

    Buy cheap. This is a motto many live by, not only in their daily lives but also in their investment philosophy. Historically, buying cheap stocks was a good idea (i.e., the so-called value premium). But how might valuation matter when it comes to country allocations? In other words, given valuations, how much money should I allocate to U.S. equity and how much should I allocate to
  • How to Perform Investment Sentiment Analysis on Twitter [Alpha Architect]

    What are the research questions? By studying tweets on thirty companies in the Dow Jones Index, the authors ask the following research question: Is it possible to develop a system for the detection and discovery of the popularity of special events on Twitter that may influence the financial markets? What are the Academic Insights? The system devised by the authors is composed of five steps:

Quantocracy’s Daily Wrap for 04/02/2018

This is a summary of links featured on Quantocracy on Monday, 04/02/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The most important plot in finance [Mathematical Investor]

    In this post we look at the one plot that proves that technical analysis (TA) is useless. TECHNICAL ANALYSIS AND HOROSCOPES As volatility has made a come back in recent months, investors have sought advice to asset managers. In many instances, such advice consists in technical indicators. Charles Schwab represents TA as an indispensable tool for active traders (examples: here and here). Merrill
  • Diversifying the What, How, and When of Trend Following [Flirting with Models]

    Nave and simple long/flat trend following approaches have demonstrated considerable consistency and success in U.S. equities. While there are many benefits to simplicity, an overly simplistic implementation can leave investors naked to unintended risks in the short run. We explore how investors can think about introducing greater diversification across the three axes of what, how, and when in
  • Factor Olympics Q1 2018 [Factor Research]

    2018 started negative for the majority of factors Momentum, Quality and Growth showed the strongest performance Low Volatility, Dividend Yield and Value generated negative returns INTRODUCTION We present the performance of seven well-known factors on an annual basis for the last 10 years and the first quarter 2018. It is worth mentioning that not all factors have strong academic support, e.g.

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