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Quantocracy’s Daily Wrap for 08/22/2019

This is a summary of links featured on Quantocracy on Thursday, 08/22/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 08/20/2019

This is a summary of links featured on Quantocracy on Tuesday, 08/20/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quint Switching Filtered: Not as Simple as It Appears to Be [Allocate Smartly]

    This is a test of the Quint Switching Filtered strategy from Lewis Glenn. On the surface this is a run-of-the-mill tactical asset allocation strategy based on short-term momentum, not unlike several strategies that we track. But digging a little deeper, well highlight qualities that make this strategy unique both for the better and the worse. Results from 1970 net of transaction costs
  • A new way to sentiment-tag financial news [Vered Zimmerman]

    Over the past few years, financial-news sentiment analysis has taken off as a commercial natural language processing (NLP) application. Like any other type of sentiment analysis, there are two main approaches: one, more traditional, is by using sentiment-labelled word lists (which we will also refer to as dictionaries). The other, is using sentiment classifiers based on language models trained on
  • Crisis Proof Your Portfolio: part 1/2 [Alpha Architect]

    This is a unique article in that it directly assesses the feasibility and effectiveness of protecting equity portfolios using traditional passive means and more contemporary active strategies. It is jam-packed with information and analysis that is best consumed in two parts; however, a good summary of the article by Larry Swedroe can be found here. The focus in part 1 is the usefulness of

Quantocracy’s Daily Wrap for 08/19/2019

This is a summary of links featured on Quantocracy on Monday, 08/19/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Risk Parity Part I: Chasing Diversifiers [Two Centuries Investments]

    The rise and fall (?) of Risk Parity is a great case study of the frameworks I have been writing about so far. We start with the concept of Chasing Diversifiers. Chasing Diversifiers (link) Although Risk Parity is as close as you get to a pure risk diversification play, just like other diversifiers, the benefits of Risk Parity were sold and bought when both the forward looking consensus
  • Using PMI to Trade Cyclicals vs Defensives [Flirting with Models]

    After stumbling across a set of old research notes from 2009 and 2012, we attempt to implement a Cyclicals versus Defensives sector trade out-of-sample. Post-2012 returns prove unconvincing and we find little evidence supporting the notion that PMI changes can be used for constructing this trade. Using data from the Kenneth French website, we extend the study to 1948, and similarly find that
  • How Painful Can Factor Investing Get? [Factor Research]

    A classic long-short, multi-factor portfolio has lost close to 20% since 2018 The drawdown is within expectations, but the recovery period is abnormally long However, its difficult to argue for structural changes that make factor investing unattractive SEEKING DIVERSIFICATION THROUGH MULTI-FACTOR PRODUCTS Investors have flooded into multi-factor strategies over the last several years. The

Quantocracy’s Daily Wrap for 08/17/2019

This is a summary of links featured on Quantocracy on Saturday, 08/17/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Contract-Specific Trading Costs and Optimal Execution Strategy [Quant Fiction]

    There are as many strategies for extracting alpha from the markets as there are traders. Unfortunately, this article will be discussing none of them. If thats what youre looking for, I suggest you check out the very sophisticated techniques covered in this video. OK. If youre still reading, you probably take trading at least somewhat seriously. When setting up your trading business (and
  • The power of R for trading (part 2) [SR SV]

    The R environment makes statistical estimation and learning accessible to portfolio management beyond the traditional quant space. Overcoming technicalities and jargon, managers can operate powerful statistical tools by learning a few lines of code and gaining some basic intuition of statistical models. Thus, for example, R offers convenient functions for time series analysis (characterizing

Quantocracy’s Daily Wrap for 08/16/2019

This is a summary of links featured on Quantocracy on Friday, 08/16/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Variance Risk Premium is Pervasive [Alpha Architect]

    The variance risk premium (VRP) refers to the fact that, over time, the option-implied volatility has tended to exceed the realized volatility of the same underlying asset. This has created a profit opportunity for volatility sellersthose willing to write volatility insurance options, collect the premiums and bear the risk that realized volatility will increase by more than implied volatility.

Quantocracy’s Daily Wrap for 08/14/2019

This is a summary of links featured on Quantocracy on Wednesday, 08/14/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Synthetic ETF Data Generation (Part-2) – Gaussian Mixture Models [Black Arbs]

    This post is a summary of a more detailed Jupyter (IPython) notebook where I demonstrate a method of using Python, Scikit-Learn and Gaussian Mixture Models to generate realistic looking return series. In this post we will compare real ETF returns versus synthetic realizations. To evaluate the similarity of the real and synthetic returns we will compare the following: visual inspection histogram

Quantocracy’s Daily Wrap for 08/13/2019

This is a summary of links featured on Quantocracy on Tuesday, 08/13/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Movement Capital’s Composite Strategy: Balancing Strategy and Asset Risk [Allocate Smartly]

    This is a test of Movement Capitals Composite Strategy. It combines tactical asset allocation with passive buy & hold. This balance between strategy risk and asset risk may be psychologically easier to trade, encouraging investors to stick with a smart investment plan when either style finds itself out of favor. Results from 1970 net of transaction costs follow. Read about our backtests or
  • Do Most Individual Stocks Outperform Cash? No. [Alpha Architect]

    Id argue that a typical investor believes the followingIn the past and over the long run, stocks outperformed bonds.(1) However, as highlighted here, an academic paper last year shows that the majority of individual U.S. stocks actually lost compared to Treasury Bills (i.e. the return to cash)! For many investors, that is a stunning finding. (note: the stats for stocks with value and

Quantocracy’s Daily Wrap for 08/12/2019

This is a summary of links featured on Quantocracy on Monday, 08/12/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Value and Momentum in a Cone [Two Centuries Investments]

    One of the most effective performance reporting formats I know is a Cone Chart, popularized by Bridgewater Associates. Here are some reasons why a Cone Chart is so effective: It clearly establishes ex-ante expectations of both return and volatility. When actual outcomes deviate within expectations, its just volatility, not risk (see Volatility vs Risk) It effectively captures both the drawdown
  • Your Style-age May Vary [Flirting with Models]

    New research from Axioma suggests that tilting less through lower target tracking error can actually create more academically pure factor implementation in long-only portfolios. This research highlights an important question: how should long-only investors think about factor exposure in their portfolios?Is measuring against an academically-constructed long/short portfolio really
  • Quant Strategies: Theory vs Reality [Factor Research]

    The live performance of quant strategies is significantly worse than in backtesting Factor investing returns from research are frequently challenged as being overstated However, the performance of smart beta and long-short multi-factor funds match theoretical returns INTRODUCTION When pitching an investment product with a backtested history the frequent response from potential investors is that
  • A Historical Look at Opex Week in August [Quantifiable Edges]

    It is options expiration week this week. Options expiration weeks often have a bullish tendency. You can see it broken down by month in this post from March. But the summer months of June, July, & August have not seen that same bullish tendency. Augusts performance has actually been net negative. June is the only other negative month. Below is a look at the profit curve for August.

Quantocracy’s Daily Wrap for 08/10/2019

This is a summary of links featured on Quantocracy on Saturday, 08/10/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How and why I got 75Gb of free foreign exchange Tick data (h/t @PyQuantNews) [Detlev Kerkovius]

    Towards the end of completing my masters in data science, I started picturing myself doing clever things with machine learning and automated trading. If like me, you have run into the how do I get historical free tick data connundrum, then this post is for you. I have structured my post in three sections: Some background for context. Storytime How to fail and then succeed. Putting it all
  • Does Meta-Labeling Add to Signal Efficacy? [Hudson and Thames]

    Successful and long-lasting quantitative research programs require a solid foundation that includes procurement and curation of data, creation of building blocks for feature engineering, state of the art methodologies, and backtesting. In this project we create a open-source python package (mlfinlab) that is based on the work of Dr. Marcos Lopez de Prado in his book Advances in Financial
  • The power of R for trading (part 1) [SR SV]

    R is an object-oriented programming language and work environment for statistical analysis. It is not just for programmers, but for everyone conducting data analysis, including portfolio managers and traders. Even with limited coding skills R outclasses Excel spreadsheets and boosts information efficiency. First, like Excel, the R environment is built around data structures, albeit far more

Quantocracy’s Daily Wrap for 08/09/2019

This is a summary of links featured on Quantocracy on Friday, 08/09/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Probability of Normality [Philipp Kahler]

    As an option seller you want the market to stay within the range prognosticated by implied volatility. But what is the historic probability that markets behave as expected? And what other analysis could be done to enhance your chances and find the periods when it is wise to sell an at the money straddle? This article will try to give some answers to this question. The normal distribution cone