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Quantocracy’s Daily Wrap for 04/20/2020

This is a summary of links featured on Quantocracy on Monday, 04/20/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • One Factor World [Two Centuries Investments]

    For the past decade, asset managers have been educating clients about factor investing as it became the new norm. And yet after all these years, portfolios are still composed of one factor: Equity Beta. Among many questionable assertions and assumptions behind factor investing (our thoughts here, here and here), there is one that remains true: Equity Beta is the most significant Risk Factor in
  • Estimating Pandemic Economic Costs for “Face-to-Face” Businesses [Alpha Architect]

    To describe the impact of social distancing, a theory of communication is developed and described comprehensively in this article. The focus is on the relative importance of worker interactions, the cost of those interactions and their impact on the size of wage subsidies intended as compensation for the disruption due to social distancing. The authors develop a model of communication whereby the
  • Smart Beta Fixed Income ETFs [Factor Research]

    Factor investing in fixed income has been heralded as the next frontier in asset management Smart beta fixed income ETFs in the US manage only slightly more than $2 billion of assets Defensive strategies reduced drawdowns during the ongoing coronavirus crisis INTRODUCTION Investing is becoming more scientific over time as technology continues to advance, but it will never be a hard science like
  • Well, you No, you gotta do more than that. [Flirting with Models]

    Since 2009, any decision to de-risk in a trend equity portfolio has largely been the wrong decision. At the time of writing, we implement a 1-month tranching process in most of our trend mandates, which has the effect of dollar-cost averaging signal changes over a 1-month period. We adopted this approach for a number of reasons, including: (1) to align our rebalance frequency with what we believe
  • Dual Momentum & Rate of Change: Trading Strategy Review [Oxford Capital]

    Concept: Dual momentum trading strategy based on Rate of Change (ROC). Research Goal: Performance verification of dual momentum signals. Specification: Table 1. Results: Figure 1-2. Trade Filter: Long Filter: Slow Rate of Change (ROC1) is above zero. Short Filter: Slow Rate of Change (ROC1) is below zero. Trade Setup: Long Setup: Fast Rate of Change (ROC2) is above zero. Short Setup: Fast Rate of

Quantocracy’s Daily Wrap for 04/19/2020

This is a summary of links featured on Quantocracy on Sunday, 04/19/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Parameter Optimisation for Systematic Trading [Robot Wealth]

    Optimisation tools have a knack for seducing systematic traders. And whats not to love? Find me the unique set of parameters that delivered the greatest return in my ten-year backtest. And do it in under five seconds. Thats certainly attractive. But do you want to hear something controversial? When it comes to the parameters of a systematic trading strategy, in the majority of cases

Quantocracy’s Daily Wrap for 04/17/2020

This is a summary of links featured on Quantocracy on Friday, 04/17/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Petra on Programming: A Unique Trend Indicator [Financial Hacker]

    This months project is a new indicator by John Ehlers, first published in the S&C May 2020 issue. Ehlers had a unique idea for early detecting trend in a price curve. No smoothing, no moving average, but something entirely different. Lets see if this new indicator can rule them all. The basic idea of the Correlation Trend Indicator (CTI) is quite simple. The ideal trend curve is a straight
  • Dividends, Stock Prices, and Inflation [Alpha Architect]

    Building on the concepts presented in my Dividends Are Different article, here we present data and observations highlighting the relationship between inflation and 1) company fundamentals, 2) dividends, and 3) stock market movements. 1 We look at empirical data to investigate how inflation relates to market prices, earnings, and dividends. We measure results over 25-year time periods fairly
  • Attention Data Geeks: Our Factor Investing Data Library is Open [Alpha Architect]

    Are you doing independent factor research? Have you spent countless hours on Ken Frenchs website? Do you run factor regressions for fun? Congrats you are officially a finance geek and you will probably benefit from our new factor investing library. Our library has over 300 factors to choose from and has data available from 92 to the most recent period. The factors are built across the
  • Working with High-Frequency Tick Data – Cleaning the Data [Quantpedia]

    Tick data is the most granular high-frequency data available, and so is the most useful in market microstructure analysis. Unfortunately, tick data is also the most susceptible to data corruption and so must be cleaned and conditioned prior to being used for any type of analysis. This article, written by Ryan Maxwell, examines how to handle and identify corrupt tick data (for analysts unfamiliar

Quantocracy’s Daily Wrap for 04/16/2020

This is a summary of links featured on Quantocracy on Thursday, 04/16/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation: Mid-April Checkup [Allocate Smartly]

    Tactical Asset Allocation (TAA) weathered the storm in February and March, significantly paring down losses vs conventional buy & hold. So far it has trailed the bounce in April, but these are early days. We track 50+ TAA strategies sourced from books, papers, etc., allowing us to draw some broad conclusions about TAA as a style. In the table below we show the MTD and YTD returns of these 50+
  • A Review of Zorro for Systematic Trading [Robot Wealth]

    One of the keys to running a successful systematic trading business is a relentless focus on high return-on-investment activities. High ROI activities include: Implementing new trading strategies within a proven framework. An example might be to implement a portfolio of pairs trades in the equity market. Scaling existing strategies to new instruments or markets. For example, porting the pair
  • Is There a Tail Risk Premium in Stocks? [Alpha Architect]

    It has been well documented both that stock returns have much fatter tails than a normal distribution would generate, and that tail events occur much more frequently than a normal curve would predict. 1 For example, Benoit Mandelbroit and Richard Hudson examined the daily index movements of the Dow Jones Industrial Index from 1916 to 2003. They noted that if stock returns were normally

Quantocracy’s Daily Wrap for 04/15/2020

This is a summary of links featured on Quantocracy on Wednesday, 04/15/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Discounted expectations [OSM]

    After our little detour into GARCHery, were back to discuss capital market expectations. In Mean expectations, we examined using the historical average return to set return expectations when constructing a portfolio. We noted hurdles to this approach due to factors like non-normal distributions, serial correlation, and ultra-wide confidence intervals. While we highlighted these obstacles and
  • Generic Octave_Oanda_API Function [Dekalog Blog]

    My last two posts have shown Octave functions that use the Oanda API to access and download data. In the first of these posts I said that I would post more code for further functions as and when I write them. However, on further reflection this would be unnecessary as the generic form of any such function is: 1) create the required headers ## set up the headers query = [ 'curl -s –compressed
  • Curse of Dimensionality part 4: Distance Metrics [Eran Raviv]

    Many machine learning algorithms rely on distances between data points as their input, sometimes the only input, especially so for clustering and ranking algorithms. The celebrated k-nearest neighbors (KNN) algorithm is our example chief, but distances are also frequently used as an input in the natural language processing domain; You shall know a word by the company it keeps (Firth, J. R.
  • A primer on embedded currency risk [Quant Dare]

    In a previous post, we showed that unhedged currency exposure adds unrewarded risk to our investment, hurting risk-adjusted-performance. This risk should either be neutralized through passive hedging; or mitigated and turned into profit with an active overlay, the latter being what ETS has been doing for the last 20 years. Now, lets say we dont want to get involved in currency matters and we
  • Dual Momentum & Vortex Indicator: Trading Strategy Review [Oxford Capital]

    Developer: Etienne Botes and Douglas Siepman (Vortex Indicator). Concept: Dual momentum trading strategy based on Vortex Indicator. Research Goal: Performance verification of dual momentum signals. Specification: Table 1. Results: Figure 1-2. Trade Filter: Long Filter: Slow Positive Vortex Indicator (+VI1) is above Slow Negative Vortex Indicator (VI1). Short Filter: Slow Negative Vortex

Quantocracy’s Daily Wrap for 04/14/2020

This is a summary of links featured on Quantocracy on Tuesday, 04/14/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Inverting Differentiated Time-Series in pandas for Deep Learning Prediction Analysis [Quant at Risk]

    A differentiation of the time-series is a common transformation used when we want to get a stationary time-series given a non-stationary one. The latter usually displays time-dependent relationships like trends, seasonality, quasi-cyclic patterns, and their Fourier power spectrum is characterised by the colour noise. On the other hand, stationary time-series summary statistics are not dependent on
  • Trading and investing performance – year six [Investment Idiocy]

    Time for the annual review post, as my reviews follow the UK tax year which ended on the 5th April. And what a year it has been; well 10 months or so of fairly normal stuff, followed by several weeks of stomach churning market chaos. Previous updates can be found here, here, here, here and here. This post will follow the format of previous posts, but there will be some extra stuff related to the
  • Trend Following Reality: You Need Trends to Trend-Follow [Alpha Architect]

    Trend Following, as an investing strategy has delivered strong performance during market chaos (e.g., Global Financial Crisis of 20072009), but the strategy has gone through a significant drawdown (save the last few months where things are perking up!). We have seen dismal returns in the recent decade relative to the historical record (this article refers to the period from 2010-2018). In some

Quantocracy’s Daily Wrap for 04/13/2020

This is a summary of links featured on Quantocracy on Monday, 04/13/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Low Vol-Momentum vs Value-Momentum Portfolios [Factor Research]

    Low Vol-Momentum & Value-Momentum portfolios outperformed stock markets since 1989 Low factor correlations contributed to the attractive risk-return profiles Excess returns have been lower in the most recent than in previous decades INTRODUCTION If an investor would state today that in ten or twenty years most portfolios would include an allocation to cryptocurrencies, he would likely be
  • Macro trading and macroeconomic trend indicators [SR SV]

    Macroeconomic trends are powerful asset return factors because they affect risk aversion and risk-neutral valuations of securities at the same time. The influence of macroeconomics appears to be strongest over longer horizons. A macro trend indicator can be defined as an updatable time series that represents a meaningful economic trend and that can be mapped to the performance of tradable assets

Quantocracy’s Daily Wrap for 04/09/2020

This is a summary of links featured on Quantocracy on Thursday, 04/09/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Fermi’s Intuition on Models [Falkenblog]

    In this video snippet, Freeman Dyson talks about an experience he had with Enrico Fermi in 1951. Dyson was originally a mathematician who had just shown how two different formulations of quantum electrodynamics (QED), Feynman diagrams and Schwinger-Tomonoga's operator method, were equivalent. Fermi was a great experimental and theoretical physicist who built the first nuclear reactor and
  • How Do Investment Strategies Perform After Publication? [Quantpedia]

    In many academic fields like physics, chemistry or natural sciences in general, laws do not change. While economics and theory of investing try to find rules that would be true and always applicable, it is not that simple, there is a complication human. Psychology of humans is very complex. In the one hand, it creates anomalies in the market, that academics study and practitioners use.

Quantocracy’s Daily Wrap for 04/08/2020

This is a summary of links featured on Quantocracy on Wednesday, 04/08/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The other way around: from correlations to returns [Quant Dare]

    In one way or another, most quantitative models somehow seek to find and exploit relationships between two or more series of returns. Therefore, the usual pipeline has a time-series go through mathematical procedures which condensate in a couple of figures meaningful information: the expected mean, volatility, drawdowns, runups, correlations, among others. That is, the space of returns, large and
  • Daily vs. Monthly Trend-Following Rules…Plus Some DIY Tools! [Alpha Architect]

    Trend-following strategies are a lot like stock-picking strategies there are endless approaches and varying levels of complexity. In this short piece, we explore the decision related to implementing basic trend-following strategies on either a daily or a monthly basis. Many traders intuitively believe that daily data is better than monthly data. Is this belief justified? Like most things in

Quantocracy’s Daily Wrap for 04/06/2020

This is a summary of links featured on Quantocracy on Monday, 04/06/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Volatility, Risk Management, and Market Chaos: Research that Might Help [Alpha Architect]

    Given the recent market decline, we thought it would be helpful to review some of our blog posts from the past that may be relevant to the current crisis atmosphere. These posts focus on research that explores investment strategies that are believed to help investors manage risk and diversify their portfolios. Short Selling Bans Generally Dont Work! Most regulators around the world reacted to
  • Factor Olympics Q1 2020 [Factor Research]

    We present the performance of five well-known factors on an annual basis for the last 10 years. We only present factors where academic research highlights positive excess returns across market cycles and asset classes. Other strategies like Growth might be widely-followed investment styles, but lack academic support and are therefore excluded. METHODOLOGY The factors are created by constructing
  • A L-U-V-Wy Recovery [Flirting with Models]

    There has been considerable speculation as to the shape of the markets recovery. Practitioners have taken to using letters as short hand for the recovery they forecast. Whether the market makes a fast V-shaped recovery, a slower U-based formation, a W-style double-bottom, or an L-shaped reset is heavily debated. As a path dependent strategy, trend following will behave differently in each of
  • First Octave Function using Oanda API [Dekalog Blog]

    As part of my on-going code revision I have written my first Octave function to use the Oanda API. This is just a simple "proof of concept" function which downloads an account summary. ## Copyright (C) 2020 dekalog ## ## This program is free software: you can redistribute it and/or modify it ## under the terms of the GNU General Public License as published by ## the Free Software