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Quantocracy’s Daily Wrap for 07/19/2019

This is a summary of links featured on Quantocracy on Friday, 07/19/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trading Evolved Taking it to the Next Level [Following the Trend]

    A year in the making, my third book is now complete. Trading Evolved is quite different from my previous books, and substantially more information packed. This book is a practical, in-depth guide on how to backtest and analyze strategies using powerful Python techniques. To my knowledge, no such book exist at the moment. Trading Evolved is a trading book. Not a programming book. This book will
  • Compound Your Knowledge Ep 18: Size, Mom, Sell-Offs, & R Code [Alpha Architect]

    In this weeks post, we discuss four articles. The size, written by the folks at AQR, is titled Fact, Fiction, and the Size Effect and is a deep dive into the Size effectI highly recommend everyone read the underlying paper as well. The second article examines the baseline historical facts of market sell-offs, both within the U.S. and other markets. The third paper, summarized by Larry

Quantocracy’s Daily Wrap for 07/18/2019

This is a summary of links featured on Quantocracy on Thursday, 07/18/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Value Investing & Concentration [Alpha Architect]

    As many investors have experienced, Value investing has underperformed for some time now. For the period following the Global Financial Crisis, Value investing (in general) has underperformed (1) the market and (2) Growth stocks. So while the past decade has been rough for Value investors, it can be a good time to examine the process and importantly determine how one forms their Value portfolio,
  • An intuition behind currency risk [Quant Dare]

    Although we find currency risk particularly interesting, it is not often the case with many investors for whom it is no more than a necessary inconvenience. As such, they tend to neglect it, accepting undesirable non-remunerated risks and missing potential opportunities. To prevent this, in this post we will try to provide an understanding of the mechanics of currency risk. For the sake of

Quantocracy’s Daily Wrap for 07/16/2019

This is a summary of links featured on Quantocracy on Tuesday, 07/16/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Philosophical Economics Growth-Trend Timing (Redux) [Allocate Smartly]

    This is a test of two variations of the Growth-Trend Timing (GTT) strategy from the always thought-provoking Philosophical Economics. GTT combines trends in both price and key economic indicators to switch between US equities and cash. Like most trend-following strategies, the strength of GTT hasnt been in generating outsized returns; it has been in maintaining returns while managing losses.
  • Strategies to Reduce Crash Risk in Stocks [Alpha Architect]

    Because equities are much riskier than high-quality bonds, the vast majority of the risk of a conventional 60 percent equity/40 percent bond portfolio is equity risk. Heres the simple math demonstrating the point. Well-diversified equity portfolios have volatility of about 20 percent, and high-quality intermediate bond portfolios have volatility of about 5 percent. Thus, in terms of risk
  • The $VIX / $SPX Action Is Suggesting A Brief Pullback [Quantifiable Edges]

    While the SPX closed up the VIX also rose. Most often they trade opposite each other, so this kind of action is somewhat unusual. But VIX has a tendency to decline going into the weekend (Friday afternoons), and then rise when it returns from the weekend. So to see this action on the first trading day of the week is less unusual than at any other time. Still, combined with the SPX 50-day high, it

Quantocracy’s Daily Wrap for 07/15/2019

This is a summary of links featured on Quantocracy on Monday, 07/15/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Follow up to last week’s Factors Don’t Exist [Two Centuries Investments]

    In last weeks post, I made a strong assertion that has caused some great feedback and comments. When I first heard Mark Kritzman make a similar point at a UBS conference a few years ago, I had a similar reaction: Hey, Im a quant and I love my factors. They are definitely real!. I still believe that using the ideas that are captured by what we call factors can be done
  • The Fed s Driving With A Foot On Each Pedal [Quantifiable Edges]

    Part of the reason the market has rallied over the past few days is an indication that a rate cut is likely coming as soon as the next Fed meeting. It is interesting timing for the Fed to begin cutting rates, since their QT program still remains in place (though it is winding down). By reducing the SOMA at the same time they are cutting rates, the Fed is basically going to be driving with one foot
  • Pathetic Protection via Protective Puts [Alpha Architect]

    Investors would like to maximize upside participation while mitigating losses. This preference is at the base of the growth of the liquid insurance market in the form of equity index options. The author investigates the following research question: Are protective put options an effective tail hedge? What are the Academic Insights? By testing ( via a real world implementable strategy* and via Monte
  • Quantile Regression [Asm Quant]

    In this post, I would like to quickly introduce what I believe to be an underutilized modelling technique that belongs in most analysts toolkit: the quantile regression model. As I am discussing some of the main points, I will be working with Rs quantreg package that is maintained by the inventor of quantile regression. See link here for more details. To highlight the benefits of building
  • Dynamic Spending in Retirement Monte Carlo [Flirting with Models]

    Many retirement planning analyses rely on Monte Carlo simulations with static assumptions for withdrawals. Incorporating dynamic spending rules can more closely align the simulations with how investors would likely behave during times when the plan looked like it was on a path to failure. Even a modest reduction in withdrawals (e.g. 10%) can have a meaningful impact on reducing failure rates,
  • ESG: What is Under the Hood? [Factor Research]

    The ESG factor generated positive returns since 2011 Strong sector biases (long tech & short discretionary) explain the performance Residual returns from ESG investing are essentially zero INTRODUCTION Investing is complicated as it is simple and complex at the same time. Common advice for new investors is to pursue a buy-and-hold approach for long-term wealth creation. Although this strategy

Quantocracy’s Daily Wrap for 07/13/2019

This is a summary of links featured on Quantocracy on Saturday, 07/13/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to build a Bitcoin Sentiment Analysis Strategy [Augmento]

    TL;DR: We built a profitable Bitcoin sentiment strategy yielding 2400% returns over 24 months. Adding trading fees made the strategy more realistic while finding optimal sentiment combinations and window sizes increased returns dramatically. In the previous article, we described how to build a strategy based on Augmento Bullish and Bearish Bitcoin sentiment, and backtested it on Bitmex XBTUSD. The
  • The mighty long-long trade [SR SV]

    One of the most successful investment strategies since the turn of the century has been the risk-parity long-long of combined equity, credit and duration derivatives. In a simple form this trade takes continuous joint equal mark-to-market exposure in equity or credit and duration risk. A simple passive portfolio in the G3 would have outmatched most macro hedge funds since 2000, with a Sharpe
  • Enhancing the Performance of Momentum Strategies [Alpha Architect]

    In Your Complete Guide to Factor-Based Investing, Andy Berkin and I presented the evidence demonstrating that momentum, both cross-sectional (or relative) momentum and time-series (or absolute, trend following) momentum, not only increases the explanatory power of asset pricing models while providing (historically) a premium, but that the premium has been persistent across time and economic

Quantocracy’s Daily Wrap for 07/12/2019

This is a summary of links featured on Quantocracy on Friday, 07/12/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Momentum, Quality, and R Code [Alpha Architect]

    Welcome to the first installment of Reproducible Finance by way of Alpha Architect. For the uninitiated, this series is a bit different than the other stuff on AA well focus on writing clean, reproducible code, mostly R (but some python too), applied to different ideas from the world of investing. We wont delve deep into those ideas because the goal is to get familiar with the code.
  • Research Review | 12 July 2019 | Yield Curve Analysis [Capital Spectator]

    Yield Curve and Financial Uncertainty: Evidence Based on Us Data Efrem Castelnuovo (University of Melbourne) June 2019 How do short and long term interest rates respond to a jump in financial uncertainty? We address this question by conducting a local projections analysis with US monthly data, period: 1962-2018. The state-of-the-art financial uncertainty measure proposed by Ludvigson, Ma, and Ng

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