Quantocracy

Quant Blog Mashup

Quantocracy’s Daily Wrap for 01/01/2018

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

  • Deep Learning for Trading: Part 1 [Robot Wealth]

    In the last few years, deep learning has gone from being an interesting but impractical academic pursuit to an ubiquitous technology that touches many aspects of our lives on a daily basis including in the world of trading. This meteoric rise has been fuelled by a perfect storm of: Frequent breakthroughs in deep learning research which regularly provide better tools for training deep neural

Quantocracy’s Daily Wrap for 12/29/2017

This is a summary of links featured on Quantocracy on Friday, 12/29/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean Reverting and Trending Properties of SPX and VIX [Relative Value Arbitrage]

    In the previous post, we looked at some statistical properties of the empirical distributions of spot SPX and VIX. In this post, we are going to investigate the mean reverting and trending properties of these indices. To do so, we are going to calculate their Hurst exponents. There exist a variety of techniques for calculating the Hurst exponent, see e.g. the Wikipedia page. We prefer the method
  • Best of Research Review 2017 [Capital Spectator]

    So many research papers, so little time. How do you separate the wheat from the chaff? You might start with the following five economic and financial papers that appeared in The Capital Spectators Research Review column in 2017. In a sea of newly minted studies over the past 12 months, these titles stand out as worthy of a second read. Time-Varying Risk Premiums and Economic Cycles Thomas
  • The Tax Efficiency of Long-Short Strategies [Alpha Architect]

    Conventional wisdom can be defined as ideas that are so accepted that they go unquestioned. Unfortunately, conventional wisdom is often wrong. Two great examples are that millions of people once believed the conventional wisdom that the Earth is flat, and millions also believed that the Earth is the center of the universe. Much of todays conventional wisdom about investing is also wrong. The
  • Persistance in Cryptocurrencies [Quantpedia]

    This paper examines persistence in the cryptocurrency market. Two different longmemory methods (R/S analysis and fractional integration) are used to analyse it in the case of the four main cryptocurrencies (BitCoin, LiteCoin, Ripple, Dash) over the sample period 2013-2017. The findings indicate that this market exhibits persistence (there is a positive correlation between its past and future

Quantocracy’s Daily Wrap for 12/27/2017

This is a summary of links featured on Quantocracy on Wednesday, 12/27/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Deep Learning Systems for Bitcoins Part 1 [Financial Hacker]

    Since December, bitcoins can not only be traded at more or less dubious exchanges, but also as futures at the CME and CBOE. And already several trading systems popped up for bitcoins and other cryptocurrencies. None of them can claim big success, with one exception. There is a strategy that easily surpasses all bitcoin systems and probably all other known historical trading systems. Its name:
  • Predicting Long Run Stock Returns? It’s All About the Payouts and the Real Economy [Alpha Architect]

    What are the research questions? Given the prevalence of buybacks as a form of corporate payouts, should they be explicitly included in supply-side models such as the dividend discount model (DDM) used to forecast of stock returns? Does the same superior performance extend to the prediction of short-term changes in expected returns? What are the Academic Insights? YES. Dividends, as a payout
  • A Not-so Merry VIX-mas Part 2 [Quantifiable Edges]

    Yesterday I decided to examine performance of XIV during the last few days of the year. The thought was that we are now in a time period that is generally regarded as seasonally bullish. Additionally, volume and volatility are often light this week with many traders on vacation. So I thought with low volatility and bullish seasonality, it could be a bullish time for XIV (the inverse-VIX etf). I

Quantocracy’s Daily Wrap for 12/26/2017

This is a summary of links featured on Quantocracy on Tuesday, 12/26/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Research Compendium 2017 [Factor Research]

    An investment in knowledge pays the best interest. (Benjamin Franklin) December 2017. Reading Time: Several hours. Author: FactorResearch. SUMMARY Contains 34 research papers that we published on FactorResearch.com in 2017 Focus on factor investing and quantitative strategies from an investors perspective They are kept brief, as simple as possible, and will hopefully stimulate debate Questions
  • Podcast: 2017 roundup: the year in review [Better System Trader]

    Well here we are, another year gone (and so fast too!). Im glad you could join me for this final episode for 2017, where well be reviewing all of the special guests we had on the show this year, the topics and insights theyve shared plus their top trading lessons. I think this is a great way to look back, to be reminded of some of the key points, and all of the amazing knowledge our
  • A Not-So Merry Vix-mas [Quantifiable Edges]

    During a time of year that is renowned for its low volatility and bullish seasonality, one might think XIV would have some strong historical returns. Well 2017-12-25 one would be wrong. Happy Holidays anyway!

Quantocracy’s Daily Wrap for 12/23/2017

This is a summary of links featured on Quantocracy on Saturday, 12/23/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Machine learning is for closers [Quantum Financier]

    Put that machine learning tutorial down. Machine learning is for closers only. As some of you that were around back in the early of this blog may know, I always held high hopes for the application of machine learning (ml) to generate trading edges. I think like many people first coming across machine learning the promises of being able to feed raw data in some algorithm you dont really
  • Hundreds of quant papers/libraries from #QuantLinkADay [Cuemacro]

    I tweet a lot, perhaps too much. The question is always what shall I tweet about? Sometimes its about burgers, other times itll be some puns or there might even be some vastly impressive observation in a tweet I make (well, perhaps not, but we can always hope!). Over the past 2 years, to give me a bit more discipline about trying to tweet quant finance, which is after all my chosen career,
  • The Art of War: How to beat a strategist in the futures market? [No Noise Only Alpha]

    Strategy: core directional choices that best best moves you into your desired future Tactics: specific actions that will best implement your strategies Without a core strategy to anchor all tactics suggestions to see which best FIT (feasible, impactful, timely) the strategy, one could randomly suggest tactics that makes the discussion chaotic. Focus on one strategy first before moving on to the

Quantocracy’s Daily Wrap for 12/22/2017

This is a summary of links featured on Quantocracy on Friday, 12/22/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Stock Misplacement in Sized-Based Indices [Alpha Architect]

    There has been much discussion of the small-cap premium recently. Has its popularity cannibalized its utility? Are large firms preventing a level playing field? Will the small-cap premium exist going forward? This article does not address these concerns directly, but it does identify and analyze another potential issue that could diminish the utility of small cap exposures. In particular, I

Quantocracy’s Daily Wrap for 12/21/2017

This is a summary of links featured on Quantocracy on Thursday, 12/21/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • (Don t Get) Contangled Up In Noise [QuantStrat TradeR]

    This post will be about investigating the efficacy of contango as a volatility trading signal. For those that trade volatility (like me), a term you may see thats somewhat ubiquitous is the term contango. What does this term mean? Well, simple: it just means the ratio of the second month of VIX futures over the first. The idea being is that when the second month of futures is more than
  • The Returns to Value Strategies When Valuation Spreads Are Wide (Deep Value) [Alpha Architect]

    The academic research has generally found valuations, such as the earnings yield (E/P) (or the CAPE 10 earnings yield) and valuation spreads, have predictive value in terms of future returns. The higher the earnings yield, the higher the expected return, and the larger the spread in valuations between growth and value stocks, the larger the future value premium is likely to be in the future.(1)
  • Carry Trade Across Fixed and Floating Currency Regimes [Quantpedia]

    Carry trade returns vary across fixed and floating currency regimes. Over the last century, outsized carry returns occur exclusively in the floating regime, being zero in the fixed regime. The absence of skewness in floating carry returns rules out a skewness-based explanation for this result. Fixed-to-floating regime shifts deliver negative return shocks to the floating carry strategy, even when

Quantocracy’s Daily Wrap for 12/20/2017

This is a summary of links featured on Quantocracy on Wednesday, 12/20/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Forecasting S&P 500 using Machine Learning [Quant Dare]

    Is it possible to foresee the future movements of a stock? Lets use Machine Learning techniques to predict the direction of one of the most important stock indexes, the S&P 500. Pregaming The Standard & Poors 500 (S&P500) is a stock market index based on the capitalization of the 500 largest American companies. It is an index widely traded through index funds and ETFs, which
  • Book Review – Market Timing with Moving Averages [Alpha Architect]

    Trend-following is something Ive struggled with for years always felt like voodoo magic and data-mining. That said, I finally came around to appreciating the practice after a ton of research replication efforts, independent research. At the time I was investigating the topic there really werent any references or books that gave me the depth of understanding that I was looking for. What I
  • Cryptocurrencies vs. Other Asset Classes [CXO Advisory]

    Are cryptocurrencies potentially useful portfolio diversifiers? In their November 2017 paper entitled Exploring the Dynamic Relationships between Cryptocurrencies and Other Financial Assets, Shaen Corbet, Andrew Meegan, Charles Larkin, Brian Lucey and Larisa Yarovaya apply a battery of tests to analyze relationships: (1) among three cryptocurrencies; and, (2) between the cryptocurrencies and

Quantocracy’s Daily Wrap for 12/19/2017

This is a summary of links featured on Quantocracy on Tuesday, 12/19/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pairs Trading using Data-Driven Techniques: Simple Trading Strategies Part 3 [Auquan]

    Pairs trading is a nice example of a strategy based on mathematical analysis. Well demonstrate how to leverage data to create and automate a pairs trading strategy. Underlying Principle Lets say you have a pair of securities X and Y that have some underlying economic link, for example two companies that manufacture the same product like Pepsi and Coca Cola. You expect the ratio or difference
  • Industry Herding by Short Sellers Signals that Conditions are Changing [Alpha Architect]

    Does the industry concentration exhibited in short sellers holdings convey new material information about the industry? Are the excess returns obtained from industry shorting combined with firm-specific shorting strategies explained by risk? Is the industry shorting signal correlated with economic trends in the associated industry? What are the Academic Insights? YES. The results reported here

Quantocracy’s Daily Wrap for 12/18/2017

This is a summary of links featured on Quantocracy on Monday, 12/18/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Value 2.0 [Flirting with Models]

    Traditional value strategies simply sort the investment universe based on one or more valuation metrics (e.g. book-to-market, price-to-earnings, etc.) and purchase the securities that look the cheapest. However, this process is often prone to structural sector bets, which are uncompensated sources of risk within a strategy. By comparing the value of stocks within each sector along with the value
  • Factor Returns: Year-End Calendar Effects [Factor Research]

    Value & Size generate abnormally large positive returns in January, Momentum negative returns Abnormal returns are limited to the last week of December and first week of January Difficult to harvest these returns efficiently due to illiquidity of markets at these times INTRODUCTION At this time of the year investors tend to receive market outlooks for 2018 from a variety of service providers.
  • Machine Learning Classification Strategy In Python [Quant Insti]

    In this blog, we will step by step implement a machine learning classification algorithm on S&P500 using Support Vector Classifier (SVC). SVCs are supervised learning classification models. A set of training data is provided to the machine learning classification algorithm, each belonging to one of the categories. For instance, the categories can be to either buy or sell a stock. The