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Quantocracy’s Daily Wrap for 07/28/2018

This is a summary of links featured on Quantocracy on Saturday, 07/28/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Understanding Stock Price Range Forecasts [Jonathan Kinlay]

    Range forecasts are produced by estimating the parameters of a Geometric Brownian Motion process from historical data and using the model to project a large number of sample paths for the stock price over the coming month and year. For example, this is a range forecast for Netflix, Inc. (NFLX) as at 7/27/2018 when the price of the stock stood at $355.21: $NFLX As you can see, the great majority of
  • The dangerous disregard for fat tails in quantitative finance [SR SV]

    The statistical term fat tails refers to probability distributions with relatively high probability of extreme outcomes. Fat tails also imply strong influence of extreme observations on expected future risk. Alas, they are a plausible and common feature of financial markets. A summary article by Nassim Taleb reminds practitioners that fat tails typically invalidate methods and conventions

Quantocracy’s Daily Wrap for 07/27/2018

This is a summary of links featured on Quantocracy on Friday, 07/27/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Simple Hedging System With Time Exit [Relative Value Arbitrage]

    This post is a follow-up to the previous one on a simple system for hedging long exposure during a market downturn. It was inspired by H. Krishnans book The Second Leg Down, in which he referred to an interesting research paper [1] on the power-law behaviour of the equity indices. The paper states, We find that the distributions for t 4 days (1560 mins) are consistent with a power-law

Quantocracy’s Daily Wrap for 07/26/2018

This is a summary of links featured on Quantocracy on Thursday, 07/26/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Investing Insights You Won’t Hear from Fama and French [Alpha Architect]

    Factor investing research has a long storied past. Fama and Frenchs 1992 and 1993 papers arguably put factor investing on the map, but truth be told, factor investing is an old topic with roots grounded in the so-called arbitrage pricing theory. We have a longer piece on the history of factor investing here. There is a monster empirical research effort to determine which factors describe
  • A Look At Past NDX Leaders That Gapped Down Big (For FB Traders) [Quantifiable Edges]

    After the market close on Wednesday, Facebook (FB) released earnings, and the news and future outlook was not viewed well. After closing at an all-time high on Wednesday, it traded down in excess of 25% in the after-hours. So it seems certain it will be opening Thursday with a sizable gap lower. I decided to take a look back at other leading NDX stocks that suffered large gaps down. I first
  • NDX Leader Performance Over Several Weeks After Large Gaps Down (FB Follow-Up) [Quantifiable Edges]

    This is a follow-up from my FB post last night. Traders that looked to take advantage of a possible bounce from todays open have seen moderate gains so far today. So what are the chances FB continues to bounce over the next several days and weeks? I re-looked at the study from last night, and examined how the other 9 instances performed over the next month.

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