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Quantocracy’s Daily Wrap for 10/27/2018

This is a summary of links featured on Quantocracy on Saturday, 10/27/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Variance term premia [SR SV]

    Variance term premia are surcharges on traded volatility that compensate for bearing volatility risk in respect to underlying asset prices over different forward horizons. The premia tend to increase in financial market distress and decrease in market expansions. Variance term premia have historically helped predicting returns on various equity volatility derivatives. The premia themselves can be
  • Alpha Architect Weekly Recap: Tracking Error and the Mix Versus Integrate Debate [Alpha Architect]

    You can watch the video via the link below: This week Ryan and Jack discuss several important topics. First, they discuss the tracking error associated with trend-following strategies. Second, they chat about a paper by researchers from Goldman Sachs, Constructing Long-Only Multifactor Strategies: Portfolio Blending vs. Signal Blending.

Quantocracy’s Daily Wrap for 10/26/2018

This is a summary of links featured on Quantocracy on Friday, 10/26/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Explaining The Robot ETF s Bull Run With Factor Analysis [Capital Spectator]

    Bloomberg last week published an intriguing story about a new exchange traded fund (ETF) that uses artificial intelligence (AI) to outperform market indexes and active managers alike. The implication: a new era of AI-driven investing has dawned, putting the standard applications of indexing at a disadvantage. Yet a closer look at the so-called Robot ETFs results via a factor-analysis lens tells

Quantocracy’s Daily Wrap for 10/25/2018

This is a summary of links featured on Quantocracy on Thursday, 10/25/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How large is the tracking error created by trend following? [Alpha Architect]

    A question Ive received in the past is the following: If you could go back in time five years ago and tell yourself something about investing, what would it be? My response is the following: Tracking error. First, what is tracking error?(1) Tracking error is a measure of how much a strategy deviates from a benchmark. If you are a U.S. equity investor, a standard benchmark is the SP500.(2) One
  • Elevated CBI And New SPX Low Carry Bullish Implications [Quantifiable Edges]

    As we approached the close I noted on Twitter (@QuantEdges) that the Quantifiable Edges Capitulative Breadth Index (CBI) was starting to spike. And the closer we got to 4pm EST, the higher it got. At the end of the day, the CBI finished at 10, which is a level I have long considered bullish. The combination of a 10+ CBI and a 50-day closing low is something I have shown in the past to be bullish
  • Creating our own S&P 500 Momentum ETF [Quant Dare]

    Smart Beta ETFs are achieving an increasing popularity, seen as the perfect equilibrium between passive investment and active management. But, whats the difference between them and the traditional ones? Is it possible to create our own ETF with some previous experience and without assuming higher trading costs? Would it be worthwhile? Have you ever heard about Factor ETFs or Smart Beta

Quantocracy’s Daily Wrap for 10/23/2018

This is a summary of links featured on Quantocracy on Tuesday, 10/23/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Stocks, Significance Testing & p-Hacking: How volatile is volatile? [Patrick David]

    Over the past 32 years, October has been the most volatile month on average for the S&P500 and December the least, in this article we will use simulation to assess the statistical significance of this observation and to what extent this observation could occur by chance. All code included! Our goal: Demonstrate how to use Pandas to analyze Time Series Understand how to construct a hypothesis

Quantocracy’s Daily Wrap for 10/22/2018

This is a summary of links featured on Quantocracy on Monday, 10/22/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Attack of the Clone: Lessons from Replicating Long/Short Equity [Flirting with Models]

    In this commentary we attempt to identify the sources of performance in long/short equity strategies. Using Kalman Filtering, we attempt to replicate the Credit Suisse Long/Short Liquid Index with a set of common factors designed to capture equity beta, regional, and style tilts. We find that as a category, long/short equity managers make significant changes to their equity beta and regional tilts
  • Statistical Arbitrage in the US [Factor Research]

    Statistical arbitrage has attractive strategy characteristics However, the returns are highly dependent on transaction costs Best used as a tactical strategy when volatility is high INTRODUCTION Equity markets in 2018 can be characterized by divergence. There is the US, showing strong returns, versus most other developed and emerging markets, which are generating lower or negative returns. A
  • Math-TWS: Connecting Wolfram Mathematica to IB TWS [Jonathan Kinlay]

    At long last, its here! MATH-TWS is a new Mathematica package that connects Wolfram Mathematica to the Interactive Brokers TWS platform via the C++ API. It enables the user to retrieve information from TWS on accounts, portfolios and positions, as well as historical and real-time market data. MATH-TWS also enables the user to place and amend orders and obtain execution confirmations from
  • Constructing Long-Only Multifactor Strategies: Portfolio Blending vs. Signal Blending [Alpha Architect]

    The heightened interest in factor investing has been accompanied by a corresponding focus on the nuts and bolts of constructing multifactor portfolios. There are essentially two ways to go: In a one-step process, single factor signals are blended into a composite signal and one multifactor portfolio is created from the individual stock composites. Or in a two-step process, single factor portfolios

Quantocracy’s Daily Wrap for 10/20/2018

This is a summary of links featured on Quantocracy on Saturday, 10/20/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Bull Bear Background Plotting Function for Octave [Dekalog Blog]

    As part of my recent research I have found it convenient to write another custom plotting function for Octave, which plots a single line price plot against a conditionally coloured background, e.g. two separate colours for bull and bear market regimes. Being able to plot like this avoids the necessity to keep flipping between two separate charts to compare the plot of a potential input feature and
  • Weekly Recap: ETF Tax Efficiency, Profitability Factor, Trend Following [Alpha Architect]

    This week Ryan and I have a discussion on three topics. First, we discuss ETF tax efficiency based on the findings in a new paper by the RAFI team. Second, we discuss the profitability factor as Larry Swedroe highlights a new paper on international evidence. Third, we discuss my article on how one should benchmark a trend following strategy.

Quantocracy’s Daily Wrap for 10/18/2018

This is a summary of links featured on Quantocracy on Thursday, 10/18/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Scaling/ normalisation/ standardisation: a pervasive question [Quant Dare]

    One of the most asked questions when dealing with several features is how you can summarise or transform them to similar scales. As you probably know, many Machine Learning algorithms demand the input features being in similar scales. But, what if they arent? Can we just work with raw data in the hope that our analysis will be right? Well, in some cases, the answer is yes. When you use
  • The Profitability Factor: International Evidence [Alpha Architect]

    Robert Novy-Marxs 2013 paper The Other Side of Value: The Gross Profitability Premium not only provided investors with new insights into the cross-section of stock returns, but also helped further explain some of Warren Buffetts superior performance. (Wes Gray summarized that paper here.) His study built upon the 2006 paper Profitability, Investment and Average Returns by Eugene

Quantocracy’s Daily Wrap for 10/17/2018

This is a summary of links featured on Quantocracy on Wednesday, 10/17/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Backtesting a Dividend Strategy [Alvarez Quant Trading]

    I was recently at a NWTTA presentation about the S&P 500 Dividend Aristocrats and how to trade these stocks. The strategy was part quantitative and part discretionary. It was popular talk with lots of good questions. People always seem interested in dividend stocks but for me they are just another stock with another reason to go up or down. I dont like to dismiss ideas without

Quantocracy’s Daily Wrap for 10/16/2018

This is a summary of links featured on Quantocracy on Tuesday, 10/16/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Generation AI – The New Data-Driven Investor: Event takeaways, slides & videos [Raven Pack]

    Close to 1,000 finance professionals registered to attend the event, an increase of nearly 50% from last years event. Surely, artificial intelligence and big data continues to grab the attention of the investment industry. The event took place on September 12, 2018 at the Convene Center by Times Square in Midtown, New York. In case you weren't able to attend and listen to the valuable
  • What is the correct benchmark for trend following? [Alpha Architect]

    What is the correct benchmark for trend following? This is a difficult question, and there really is no perfect answer. As many of our readers know, we are fans of trend following and trend-followed portfolios. For those unfamiliar with trend following, the idea is rather simpleinvest in an asset class if the price/return to that asset class is trending up. If not, go to cash or hedge
  • Extended Backtest of Global Equities Momentum [Dual Momentum]

    In 2013, I created my Global Equities Momentum (GEM) model that applied dual momentum to stock and bond indices. We hold U.S. or non-U.S. stock indices when stocks are strong. Bonds are a safe harbor when stocks are weak. When my book was published in 2014, I had Barclays bond index data back to 1973. Since one year of data is needed to initialize the model, GEM results were from 1974 through

Quantocracy’s Daily Wrap for 10/15/2018

This is a summary of links featured on Quantocracy on Monday, 10/15/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Carry-Trend-Hedge Approach to Duration Timing [Flirting with Models]

    In this paper we discuss simple rules for timing exposure to 10-year U.S. Treasuries. We explore signals based upon the slope of the yield curve (carry), prior returns (trend), and prior equity returns (hedge). We implement long/short implementations of each strategy covering the time period of 1962-2018. We find that all three methods improve both total and risk-adjusted returns
  • Improving the Odds of Value [Factor Research]

    Value investors earn a premium for holding undesirable stocks Market skewness may identify periods where the premium is more attractive The returns from the Value factor since 1926 were zero when market skewness was negative INTRODUCTION Although buying cheap stocks is intuitively appealing, holding them is highly unappealing for most investors. Value stocks tend to be companies that lack growth,
  • Price Movement Prediction [Eran Raviv]

    Just finished reading the paper Stock Markets Price Movement Prediction With LSTM Neural Networks. The abstract attractively reads: The results that were obtained are promising, getting up to an average of 55.9% of accuracy when predicting if the price of a particular stock is going to go up or not in the near future., I took the bait. You shouldnt. In short, the authors use over 170
  • Reversal Patterns: Part 2 | Trading Strategy (Exits) [Oxford Capital]

    Developer: Richard Wyckoff; Toby Crabel; Gerald Appel. Source: Crabel, T. (1990). Day Trading with Short Term Price Patterns and Opening Range Breakout. Greenville: Traders Press, Inc.; Appel, G. (2005). Technical Analysis. NJ: Pearson Education, Inc. Concept: Trading strategy based on reversal patterns confirmed by a price-momentum divergence. Research Goal: Performance verification of reversal
  • A Look At How Fridays Create The Most Reliable Bounces [Quantifiable Edges]

    Friday is generally not terribly reliable in being a day where the market bounces from a low. It is one of the least popular days for this to occur (along with Wednesday). But a potential positive about a Friday bounce is that when they do occur, they tend to be the most reliable moving forward. The below tables look at performance following a bounce from a 50-day low. The 1st table looks at