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Quantocracy’s Daily Wrap for 07/22/2019

This is a summary of links featured on Quantocracy on Monday, 07/22/2019. 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
  • TAA and Transaction Costs [Allocate Smartly]

    New to Tactical Asset Allocation? Learn more: What is TAA? There are two hard costs that investors must consider when comparing a tactical asset allocation strategy to conventional buy & hold: (1) increased tax liability (if trading in a taxable account), and (2) increased trading costs (transaction costs and slippage). Because we track so many published models (50 and counting) were in a
  • Stocks Don’t Do So Hot – Most equities don’t beat 1m Treasury bills (h/t @thodoha) [Mark Rzepczynski]

    Stocks are risky investments. Let's be very clear, stocks are risky with positive skew. Of course, everyone knows that but some data published about two years really drove that home. (See my earlier post "Most stocks are losers – Median and skew tell an important story" about the paper "Do stocks Outperform Treasury Bills" by Hendrik Bessembinder)That path-breaking work
  • Time Series Decomposition & Prediction in Python [Python For Finance]

    In this article I wanted to concentrate on some basic time series analysis, and on efforts to see if there is any simple way we can improve our prediction skills and abilities in order to produce more accurate results. When considering most financial asset price time series you would be forgiven for concluding that, at various time frames (some longer, some shorter) many, many of the data sets we
  • Ensemble Multi-Asset Momentum [Flirting with Models]

    We explore a representative multi-asset momentum model that is similar to many bank-based indexes behind structured products and market-linked CDs. With a monthly rebalance cycle, we find substantial timing luck risk. Using the same basic framework, we build a simple ensemble approach, diversifying both process and rebalance timing risk. We find that the virtual strategy-of-strategies is able to
  • The relation between value and momentum strategies [SR SV]

    Simple value and momentum strategies often end up with opposite market positions. One strategy succeeds when the other fails. There are two plausible reasons for this. First, value investors regularly bet against market trends that appear to have gone too far by standard valuation metrics. Second, value stocks carry particularly high market risk or bad beta and thus fare well when

Quantocracy’s Daily Wrap for 07/20/2019

This is a summary of links featured on Quantocracy on Saturday, 07/20/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Rebalancing Luck [Spring Valley]

    The date on which a portfolio is rebalanced can have a tremendous impact on realized performance. We demonstrate that a strategy rebalanced on different dates using the exact same investment process can exhibit return differentials of over 20% across short periods of time. These differences are entirely explained by path dependency, also known as rebalancing luck. In addition, we cannot

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