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Quantocracy’s Daily Wrap for 07/25/2018

This is a summary of links featured on Quantocracy on Wednesday, 07/25/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • We Are All FX Traders Now [Alpha Scientist]

    It's easy to forget that virtually all asset classes we hold are priced in US dollar terms. Portfolio valuations are as impacted by the denominator (US dollars) as by the numerator (asset value) Commodity assets like oil and gold are highly (negatively) correlated to US dollar strength. Certain equity asset classes, especially emerging equities, are extremely negatively correlated to USD
  • Conviction, evidence, and accepting ignorance [Factor Investor]

    Countless studies have demonstrated that incorporating feedback loops into life is beneficial. Want to improve at work; seek a mentor. Want to nix that slice; get a swing coach. Want to get in shape; find a trainer. Want to become a better surgeon; get a coach. When left to our own designs, discipline falls away and we fail to learn and grow. Investing is no different. Yet, because the topic of

Quantocracy’s Daily Wrap for 07/23/2018

This is a summary of links featured on Quantocracy on Monday, 07/23/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Machine Learning, Subset Resampling, and Portfolio Optimization [Flirting with Models]

    Portfolio optimization research can be challenging due to the plethora of factors that can influence results, making it hard to generalize results outside of the specific cases tested. That being said, building a robust portfolio optimization engine requires a diligent focus on estimation risk. Estimation risk is the risk that the inputs to the portfolio optimization process (i.e. expected
  • 2D Asset Allocation Using PCA (Part 1) [CSS Analytics]

    Asset allocation is a complex problem that can be solved using endless variations of different approaches that range from theoretical like Mean-Variance to heuristic like Minimum Correlation or even tactical strategies. Another challenge is defining an appropriate asset class universe which can lead to insidious biases that even experienced practitioners can fail to grasp or appreciate.
  • ETFs, Smart Beta and Factor Exposure [Factor Research]

    Factor exposure analysis can be used to derive factor themes Smart beta ETFs offer relatively low factor exposure It is all about how factors are defined INTRODUCTION The Austrian energy drinks company Red Bull advertised for almost two decades that Red Bull gives you wings and improves a consumers concentration and reaction speed. Consumers in the US sued the company successfully in 2014

Quantocracy’s Daily Wrap for 07/22/2018

This is a summary of links featured on Quantocracy on Sunday, 07/22/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Practical Statistics for Algo Traders [Robot Wealth]

    How do you feel when you see the word statistics? Maybe you sense that its something you should be really good at, but arent. Maybe the word gives you a sense of dread, since youve started exploring its murky depths, but thrown your hands up in despair and given up perhaps more than once. If you read lots of intelligent-sounding quant blogs, you might even feel like your lack of

Quantocracy’s Daily Wrap for 07/21/2018

This is a summary of links featured on Quantocracy on Saturday, 07/21/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The importance of volatility of volatility [SR SV]

    Options-implied volatility of U.S. equity prices is measured by the volatility index, VIX. Options-implied volatility of volatility is measured by the volatility-of-volatility index, VVIX. Importantly, these two are conceptually and empirically different sources of risk. Hence, there should also be two types of risk premia: one for the uncertainty of volatility and for the uncertainty of variation

Quantocracy’s Daily Wrap for 07/20/2018

This is a summary of links featured on Quantocracy on Friday, 07/20/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Complex Backtesting in Python Part II Zipline Data Bundles [Following the Trend]

    In the last article on Python backtesting, we looked at how to install the Zipline library and get a basic simulation going. But what we did not touch upon was how to get your own data hooked up. If you are reading this, there is a good chance that you take your backtesting and trading simulation quite seriously. And in that case, you probably have your own preferred data source and your own local
  • Which Investment Factors Drive Corporate Bond Returns [Alpha Architect]

    What are the research questions The presence of historical prices impacting future returns, i.e., momentum, has been well researched in the equity market, which weve covered here. Weve also closely looked at momentum in bond markets here, here, and here. What the Bali, Subrahmanyam, & Wen are exploring is whether momentum shows up in the corporate bond market, and if so where? Does the

Quantocracy’s Daily Wrap for 07/19/2018

This is a summary of links featured on Quantocracy on Thursday, 07/19/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Strategy Added: Vigilant Asset Allocation Balanced [Allocate Smartly]

    Vigilant Asset Allocation from Dr. Keller and JW Keuning is one of the most popular tactical asset allocation strategies that we track (click for the full list). The authors original paper includes multiple variations of the strategy, based on the number of assets held at any given time and how aggressively the strategy moves to defensive assets during periods of market stress. Up to this point
  • A Very Influential Paper About Tether-Bitcoin Relationship (Manipulation?) [Quantpedia]

    This paper investigates whether Tether, a digital currency pegged to U.S. dollars, influences Bitcoin and other cryptocurrency prices during the recent boom. Using algorithms to analyze the blockchain data, we find that purchases with Tether are timed following market downturns and result in sizable increases in Bitcoin prices. Less than 1% of hours with such heavy Tether transactions are

Quantocracy’s Daily Wrap for 07/18/2018

This is a summary of links featured on Quantocracy on Wednesday, 07/18/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Stock Prediction with ML: Walk-forward Modeling [Alpha Scientist]

    Key Takeaways: Traditional methods of validation and cross-validation are problematic for time series prediction problems The solution is to use a "walk-forward" approach which incorporates new information as it becomes available. This approach gives us a more realistic view of how effective our model would truly have been in the past, and helps to avoid the overfitting trap. It's
  • Our Conversation with Tobias Carlisle (@Greenbackd) [Flirting with Models]

    This post covers our conversation with Tobias Carlisle, which you can listen to here. 2:09 – Toby starts at the beginning: with school classes that included sheering sheep in Australia. Corey Hoffstein ("CH"): I was so taken aback by this introduction that I was totally caught off-guard. I knew Toby had grown up in a fairly remote town in Australia (he likes to joke he's the only
  • 10 Reasons for loving Nearest Neighbors algorithm [Quant Dare]

    I fell in love with k-Nearest Neighbors algorithm at first sight, but it isnt blind love. I have plenty of reasons to be mad about it. 1. Its pretty intuitive and simple Given that all you need to do is to compare samples, the Nearest Neighbors (k-NN) algorithm is a perfect first step to introduce Machine Learning. Its very simple to understand, easy to explain and perfect to demonstrate

Quantocracy’s Daily Wrap for 07/16/2018

This is a summary of links featured on Quantocracy on Monday, 07/16/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Momentum’s Magic Number [Flirting with Models]

    In HIMCOs May 2018 Quantitative Insight, they publish a figure that suggests the optimal holding length of a momentum strategy is a function of the formation period. Specifically, the result suggests that the optimal holding period is one selected such that the formation period plus the holding period is equal to 14-to-18 months: a somewhat magic result that makes little intuitive,
  • A look at SOMA changes influence on SPX since Quantitative Tightening began [Quantifiable Edges]

    The chart below is from this weekends QE subscriber letter. It is one I have updated frequently the last few months. It looks at compound performance of two opposing strategies. The blue line represents a strategy that is invested in the market during weeks that the Feds SOMA account value rises. During weeks where the SOMA declines, the blue line is sidelined (earning no interest). The red
  • Portfolio Craftsmanship is Just as Important as Choosing an Investment Style [Alpha Architect]

    This is an important article for practitioners because it brings specific investing decisions that are often treated as afterthoughts, to the forefront in style-based investing. The authors propose that decisions made beyond the initial decision to invest in a style, such as value or momentum, are alpha-generating. The authors label this, Craftsmanship Alpha. Although the same style labels
  • Stock Portfolio Optimization [Factor Research]

    Portfolios frequently contain stocks representing duplicate factor risks or insignificant weights An optimisation process focused on factor exposure can increase the portfolio efficiency Increasing or decreasing factor exposure requires a view on expected factor performance and risks INTRODUCTION Gardens tend to lose their curated design quickly, if not cared for constantly, as grass, bushes and
  • Sell in May and Go Away? [Alpha Scientist]

    Most investors have heard the adage "Sell in May and go away" which reflects the common wisdom that markets perform less well during the summer months than during the winter. This anomaly is well described here. Many widely held beliefs go away, precisely because they're widely held and get priced into the market. I'd like to test the "sell in May" myth to see how

Quantocracy’s Daily Wrap for 07/12/2018

This is a summary of links featured on Quantocracy on Thursday, 07/12/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Stock Prediction with ML: Feature Selection [Alpha Scientist]

    This is the third post in my series on transforming data into alpha. If you haven't yet see the data management and guide to feature engineering, please take a minute to read those first… This post is going to delve into the mechanics of feature selection to help choose between the many variations of features created in the feature engineering stage. By design, many of the features
  • Announcing Defensive Asset Allocation (DAA) [TrendXplorer]

    Defensive Asset Allocation (DAA) builds on the framework designed for Vigilant Asset Allocation (VAA) For DAA the need for crash protection is quantified using a separate canary universe instead of the full investment universe as with VAA DAA leads to lower out-of-market allocations and hence improves the tracking error due to higher in-the-market-rates In our brand new SSRN-paper Breadth
  • Deconstructing the Low Volatility/Low Beta Anomaly [Alpha Architect]

    One of the big problems for the first formal asset pricing model developed by financial economists, the Capital Asset Pricing Model (CAPM), was that it predicts a positive relationship between risk and return. However, the historical evidence demonstrates that, while the slope of the security market line is generally positive (higher-beta stocks provide higher returns than low-beta stocks), it is

Quantocracy’s Daily Wrap for 07/11/2018

This is a summary of links featured on Quantocracy on Wednesday, 07/11/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Excerpt, Part II: Quantitative Investment Portfolio Analytics In R [Capital Spectator]

    A couple of weeks back I published the first part of a full-chapter excerpt from my new book, Quantitative Investment Portfolio Analytics In R: An Introduction To R For Modeling Portfolio Risk and Return. Heres the second half of this two-part excerpt of Chapter 5, which reviews the basics for factor analysis via R code. The chapter sample below focuses on additional analytics, including a
  • Our Conversation with Adam Butler [Flirting with Models]

    This post is the first of a series where we will be providing some of our own thoughts and commentary the conversations we had in the first season of our new podcast. This post covers our conversation with Adam Butler, which you can listen to here. 1:57 – Corey introduces Adam via a blog post Adam wrote about his experience with the emerging market and commodity super cycle theory of the early
  • Multiple Managers vs A Single Manager: Return Predictability [Rayner Gobran]

    This is the seventh in my Hedge Fund Hacks series. It is a natural follow-up to my sixth hack on Hedge Fund Return Predictability in which I identified the following conundrum: You need a track record of 8+ years of monthly data to have reasonable confidence in a managers expected returns. The longer the track record you demand, the fewer managers you will have to choose from. A long track
  • Hierarchical Risk Parity [Quant Dare]

    Building profitable portfolios has been giving investment managers headaches for decades. Many approaches have been used up until now, some of the most well-known being Markowitzs Efficient Frontier and Risk Parity. Today, we are presenting a brand new approach to this recurrent problem developed by Dr. Marcos Lpez de Prado applying Modern Graph Theory and Machine Learning techniques. Lpez
  • Impact of Single Stocks On Factor Returns [Factor Research]

    Factor portfolios are typically created by equal weighting stocks The impact of single stocks is therefore reduced compared to market-cap weighted indices The FAANG stocks impacted factors differently INTRODUCTION The famous FAANG quintet of Facebook, Amazon, Apple, Netflix, and Google has driven much of the performance of the Nasdaq 100 in 2018 and currently accounts for approximately 35% of the
  • Double Gaps and Hens Teeth [Throwing Good Money]

    I looked at the chart for SPY just now, and thought, Huhtwo days in a row that have gapped up. Wonder if thats significant in any way? By gap, I mean that todays low was higher than yesterdays high. When this happens two days in a row, does it mean we should use quintuple leverage to buy everything we can? Sell at the opening bell and hide under a rock? Something else?