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Quantocracy’s Daily Wrap for 07/10/2019

This is a summary of links featured on Quantocracy on Wednesday, 07/10/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Practical Pairs Trading [Robot Wealth]

    Some price series are mean reverting some of the time, but it is also possible to create portfolios which are specifically constructed to have mean-reverting properties. Series that can be combined to create stationary portfolios are called cointegrating, and there are a bunch of statistical tests for this property. Well return to these shortly. While you can, in theory, create mean reverting
  • Market Sell-off Analysis: Baseline Historical Facts [Alpha Architect]

    We often hear that the market is 5% off its highs or that it is down 5% from the high of the year. This alone does not tell us much. The questions I want to answer are as follows: How often does that 5% loss become a 10% loss? Or worse yet a 20% loss? In other words, what are the historical distribution of outcomes, given a loss of x%? I address this question in US markets and then
  • Day of Month and Market Timing [Alvarez Quant Trading]

    In my previous post, Market Timing with a Canary, Gold, Copper, LQD, IEF and much more, I tested several market timing methods. The signal was checked on the last day of the month. Now the question is what happens if we check on a different day? How different will the results be? The Test The backtest is from 1/1/2004 to 12/31/2018 on the SPY, dividends included. Buy Rule On N days before the end
  • Can You Minimize Regret By Analyzing Return Distributions? [Capital Spectator]

    In the grand scheme of investing, behavioral risk is second to none on the list of pitfalls that threaten to derail the best-laid plans for investing. The challenge is especially acute in the thankless task of trying to anticipate how youll react when a rough patch arrives. The mystery is all the deeper if your only experience with a fund or strategy is holding it during a bull market. There

Quantocracy’s Daily Wrap for 07/09/2019

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

  • Building a Risk Control Index with Drawdown Protection (Part 1) [CSS Analytics]

    Both trend-following and absolute momentum are well established methods for managing risk. Another method for managing risk is to use volatility targeting. The former are superior for reducing large drawdowns in bear markets while the latter tends to reduce kurtosis by normalizing the daily bet size. The combination of the two tends to increase the sharpe ratio while generally reducing both
  • Fact, Fiction, and the Size Effect [Alpha Architect]

    The size effect is the phenomenon in which small stocks (i.e., those with lower market capitalizations), on average, outperform large stocks (i.e., those with higher market caps) over time. The size effect was first documented by several academic papers in the early 1980s ( Banz, 1981). However, it remains one of the most debated market anomalies among scholars. See here, here, and here some

Quantocracy’s Daily Wrap for 07/08/2019

This is a summary of links featured on Quantocracy on Monday, 07/08/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Research Symposium: Big Data is the New Currency – New York City – September 10th [Raven Pack]

    Join top industry experts and practitioners as they debate the future of big data monetization in capital markets. Watch our previous event highlights video for what to expect in NYC! Register today. Industry Leaders For almost a decade, RavenPack Symposiums have consistently provided data-driven finance professionals with riveting forward-looking content, new research and insights, and practical
  • DeepTrading with TensorFlow VI [Todo Trader]

    Data corrupts. Absolute Data corrupts absolutely. This is my impression every time I am faced with the amount of data that is available to us in the current times. This is the moment of truth. Today you will learn how to make some predictions in the Forex market. This is probably the Far West of the financial markets. But you have nothing to fear as I am revealing step by step what could take
  • Decomposing the Credit Curve [Flirting with Models]

    In this research note, we continue our exploration of credit. Rather than test a quantitative signal, we explore credit changes through the lens of statistical decomposition. As with the Treasury yield curve, we find that changes in the credit spread curve can be largely explained by Level, Slope, and Curvature (so long as we adjust for relative volatility levels). We construct stylized portfolios
  • Thoughts on Factor Investing [Two Centuries]

    The question I get asked the most during the past twelve months is Why are factors not working? Here are my top 12 personal thoughts on the topicinformed by 15+ years of successfully factor investing. 1. There is no such thing as factor investing. There is only investing. 10 years ago, the term factor investing did not exist while the underlying ideas and approaches existed
  • Indexing: Out With Tradition? [Factor Research]

    Equal and fundamentally weighted equity indices outperformed market cap weighted in the US since 1990 The higher returns are explained by exposure to Value and Size factors The outperformance is not consistent across time given factor cyclicality THE RISE OF INDICES There are now more than 3.7 million indices, according to the Index Industry Association (IIA) 2018 survey. That represents an
  • The rise in risk spreads [SR SV]

    A risk spread is a premium for bearing economic risk of an investment, paid over and above the short-term real interest rate. Over the past 30 years, risk spreads in the U.S. have increased significantly and consistently: while real interest rates on safe bonds and deposits have collapsed, returns on private capital have remained roughly stable. Macroeconomic research suggests that this

Quantocracy’s Daily Wrap for 07/05/2019

This is a summary of links featured on Quantocracy on Friday, 07/05/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Absolute Multi-Factor Index [Quiet Quant]

    How do you make a factor investor more excited? Multi-factors! Terrible half-ass jokes aside, the multi-factor world has been the largest area of growth and discussion in the factor world over the last 5 years. Firms like AQR, MSCI, PIMCO via Research Affiliates, etc. all have multi-factor offerings. The underlying methodologies and stories are fantastic. Why just buy value stocks when you can buy

Quantocracy’s Daily Wrap for 07/04/2019

This is a summary of links featured on Quantocracy on Thursday, 07/04/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Flexible Returns Distribution- Part I (Generalized Lambda Distribution) [Asm Quant]

    It is commonly known that financial returns exhibit characteristics that are not captured by the widely applied normal and log-normal distributions. In a series of posts I want to present some flexible distributions that are well suited to model financial returns. We will work our way through quick modelling exercises in R that show how easy it is to use these alternative distributions. To begin,

Quantocracy’s Daily Wrap for 07/03/2019

This is a summary of links featured on Quantocracy on Wednesday, 07/03/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Deep Trading with TensorFlow V [Todo Trader]

    o you want to know how to build a multi-layered neural network? As deep as you want? In the next post, we will use real market data. In this one, we will still use non-trading data, because we are looking for a well-established knowledge of the basic concepts of Tensorflow. But we will use data used in other very real and current problems. OK, remember to keep in mind our other posts that make up
  • Graph Theory in portfolio analysis. Part I [Quant Dare]

    Have you ever thought about the bias of your portfolio to specific countries or asset types? Do you know that high concentration in one region implies a riskier path for your portfolio? If you want to know how to improve your portfolio using Graph Theory, first youll need to understand the basics. We discussed this topic in previous posts: Graph theory: connections in the market. To understand

Quantocracy’s Daily Wrap for 07/02/2019

This is a summary of links featured on Quantocracy on Tuesday, 07/02/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in June [Allocate Smartly]

    This is a summary of the recent performance of a wide range of excellent Tactical Asset Allocation (TAA) strategies, net of transaction costs. 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
  • Investment Portfolio Optimisation with Python Revisited [Python For Finance]

    In this post I am going to be looking at portfolio optimisation methods, touching on both the use of Monte Carlo, brute force style optimisation and then the use of Scipys optimize function for minimizing (or maximizing) objective functions, possibly subject to constraints, as it states in the official docs (https://docs.scipy.org/doc/scipy/reference/optimize.html). I have to

Quantocracy’s Daily Wrap for 07/01/2019

This is a summary of links featured on Quantocracy on Monday, 07/01/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The average is better than average [Spring Valley]

    Researchers often devote a significant amount of time trying to determine the optimal, or best performing, configuration of a trading model. With the proliferation of data and advances in high-performance computing, it is trivial to optimize millions, even billions, of trading models and parameter sets. While these developments are undoubtedly powerful, researchers are virtually guaranteed to
  • Factor Olympics 1H 2019 [Factor Research]

    Most factors generated positive returns in 1H 2019 Low Volatility produced the best and Value the worst performance Factor performance is comparable in the US & Europe, but markedly different in Japan INTRODUCTION 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
  • Debunking myths about stock buybacks [Alpha Architect]

    What are the research questions? The authors present 4 MYTHs regarding stock buybacks popular in the financial press. MYTH 1: Companies are self-liquidating using share repurchases at a historically high rate. MYTH 2: Share repurchases have come at the expense of profitable investment. MYTH 3: The recent run-up in prices is the result of share repurchases. MYTH 4: Companies that repurchase shares
  • Value and the Credit Spread [Flirting with Models]

    We continue our exploration of quantitative signals in fixed income. We use a measure of credit curve steepness as a valuation signal for timing exposure between corporate bonds and U.S. Treasuries. The value signal generates a 0.84% annualized return from 1950 to 2019 but is highly regime dependent with meaningful drawdowns. Introducing a nave momentum strategy significantly improves the
  • 12 Reasons Why Traditional Asset Allocation Doesn t Work [Two Centuries Investments]

    1. Crashes and Low Returns (link) Static asset allocation locks in the Two Risks that Ruin Investing – crashes and low returns. If you accept a static asset allocation strategy, you accept its history repeating in the future. For example, a 60/40 strategy drawdown of -63% in the 1930s. 2. Low Conviction (link) Data shows most people cannot stick with their static asset allocation
  • Bitcoin Swing Trading [Philipp Kahler]

    I published a bitcoin swing trading strategy in 2015 over here (German only). Time to review the methodology of swing trading and have a look on the performance. Can a rational strategy get an edge in an irrational market? Have a look and be surprised! Swing Point Trading Technique Swing trading is a short term, trend following trading technique which focuses on the local highs and lows of the
  • State of Trend Following in June [Au Tra Sy]

    Positive month for the Wizards which lifts the YTD performance further up in the positive territory at the halfway mark. Please check below for more details. Detailed Results The figures for the month are: June return: 1.46% YTD return: 4.52% Below is the chart displaying individual system results throughout June: StateTF June And in tabular format: System June Return YTD Return BBO-20 1.07% 7.59%

Quantocracy’s Daily Wrap for 06/29/2019

This is a summary of links featured on Quantocracy on Saturday, 06/29/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Models, Little Green Men, And Machine Learning [Alex Chinco]

    Economists use machine learning (ML) to study asset prices in two different ways. Approach #1: use these techniques to predict the cross-section of expected returnsi.e., to predict which stocks are most likely to have high or low future returns. e.g., see here, here, or here. Approach #2: use them to try to uncover the true asset-pricing modela.k.a., the set of priced risk
  • Bad and good beta in FX strategies [SR SV]

    Bad beta means market exposure that is expensive to hedge. Good beta is market exposure that is cheap to hedge. Distinguishing between these is crucial for FX trading strategies. The market sensitivity of FX positions can be decomposed into a risk premium beta (bad beta) and a real rate beta (good beta). FX positions with risk premium betas are associated with a positive price of risk

Quantocracy’s Daily Wrap for 06/27/2019

This is a summary of links featured on Quantocracy on Thursday, 06/27/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Ichimoku Trading Strategy With Python Part 2 [Python For Finance]

    This is part 2 of the Ichimoku Strategy creation and backtest with part 1 having dealt with the calculation and creation of the individual Ichimoku elements (which can be found here), we now move onto creating the actual trading strategy logic and subsequent backtest. The Ichimoku approach concerns itself with two major elements firstly the signals and insights produced by the cloud