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Quantocracy’s Daily Wrap for 11/01/2018

This is a summary of links featured on Quantocracy on Thursday, 11/01/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in October [Allocate Smartly]

    This is a summary of the recent performance of a wide range of excellent tactical asset allocation 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 you
  • Asset Diversification in a Flat World [Alpha Architect]

    Diversification is a fundamental principle of prudent investing due to its ability to mitigate/minimize risks. In fact, it has been called the only free lunch in investing because, done properly, it can reduce risk without reducing expected returns. This led to the conclusion that investors should diversify by including international equities, including emerging markets, in their portfolios,
  • This Incredibly Bullish Seasonal Period Has Just Begun [Quantifiable Edges]

    With the calendar moving from October to November, it has now entered its Best 6 Months. The Best 6 Months tendency was first published by Yale Hirsch, founder of the Stock Traders Almanac, in 1986. The concept behind the Best 6 Months is simple. Seasonality suggests that over the last several decades the market has made a massive portion of its gains between November and
  • The Existence Of A Bubble vs. The Timing Of Its Crash [Alex Chinco]

    Journalists love to talk about bubbles. The Wall Street Journal has hinted at bubbles in both the Chinese stock market and the market for Bitcoin during the past month alone. But, financial economists are much more reluctant to call something a bubble. Theres debate about whether bubbles even exist. And, much of this debate revolves around whether its possible to predict the timing of the
  • Synthetic prices and burgers [Quant Dare]

    If all finance developers around the world were asked to choose the main nightmare they have to face on daily basis, I bet most of them would choose overfitting. Furthermore, imagine you have to develop an algorithm which has only one ingredient to be modelled, only one time-series representing the historical information Yes, in that case, youll need the Synthetic Financial Time

Quantocracy’s Daily Wrap for 10/29/2018

This is a summary of links featured on Quantocracy on Monday, 10/29/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • When Simplicity Met Fragility [Flirting with Models]

    Research suggests that simple heuristics are often far more robust than more complicated, theoretically optimal solutions. Taken too far, we believe simplicity can actually introduce significant fragility into an investment process. Using trend equity as an example, we demonstrate how using only a single signal to drive portfolio allocations can make a portfolio highly sensitive to the impact of
  • The Dark Side of Low-Volatility Stocks [Factor Research]

    This research note was originally published by the CFA Institutes Enterprising Investor blog. Here is the link. SUMMARY Low-volatility stocks have outperformed the market over the last 25 years The strategy has reduced equity drawdowns in the US, Europe, and Japan significantly However, low-volatility stocks have partially been bond-proxies, which poses risk when rates rise MARKETING INGENUITY

Quantocracy’s Daily Wrap for 10/28/2018

This is a summary of links featured on Quantocracy on Sunday, 10/28/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Missing the best or worst market days [Alvarez Quant Trading]

    This morning I saw the chart on Ritholz.com of what happens when you miss the best X days of the market. I see a variation of this chart often and is used to argue why someone should not try and time the market. One concept I like to do is to invert. Meaning try the opposite idea and see what you get. What I rarely see is the chart if you missed the worst X days. Given it is a rainy Sunday morning

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