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Quantocracy’s Daily Wrap for 01/08/2018

This is a summary of links featured on Quantocracy on Monday, 01/08/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Levered ETFs for the Long Run? [Flirting with Models]

    We believe that capital efficiency should remain a paramount objective for investors. The prudent use of leverage can help investors employ more risk efficient portfolios without necessarily sacrificing potential returns. Many investors, however, do not have access to leverage (be it via borrowing or derivatives). They may, however, have access to leverage via Levered ETFs. Levered ETFs are often
  • Multi-Factor Models 101 [Factor Research]

    FactorResearch publishes a white paper on building multi-factor models. SUMMARY Three common approaches for creating multi-factor portfolios are the Combination, the Intersectional and the Sequential models The results from the Combination and Intersectional models are comparable in terms of trend Each model has its own advantages and disadvantages, the selection will depend on investor
  • Historical Results Following 4 Up Days To Begin A New Year [Quantifiable Edges]

    The simple fact that the SPX posted a gain on the first 4 days of the year is a pretty rare occurrence, with 2018 only being the 9th instance since 1961. While instances have been low, the intermediate-term performance following such strong starts to the year has been impressive. And looking at most timeframes from 50 days to 250 (or more) days, the returns have been strong. Below is the list of

Quantocracy’s Daily Wrap for 01/07/2018

This is a summary of links featured on Quantocracy on Sunday, 01/07/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Deep Learning for Trading Part 2: Configuring TensorFlow and Keras to run on GPU [Robot Wealth]

    This is the second in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques in trading systems, including a warning about attempting to extract meaningful signals from historical market data. If you
  • Academic Research Papers and Presentations Galore! [Alpha Architect]

    It is that time of year again, the American Finance Association Annual Meeting is underway. The conference is in Philadelphia, starting today (January 5) and running through Sunday (January 7). This 3-day conference has 73 sessions, 246 papers and 12 presentations with no papers (general discussions) a lot of information to cover! Here is the preliminary conference program, as well as the

Quantocracy’s Daily Wrap for 01/05/2018

This is a summary of links featured on Quantocracy on Friday, 01/05/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Beyond Excess Returns: How to Enhance Sentiment Strategies using MSCI Barra Risk Models [Raven Pack]

    We have just published a white paper showcasing the benefits of hedging a sentiment signal using risk factors from several MSCI Barra Risk Models. In this post, I provide some details on the methodology used for the strategy and on the achieved results. Excess returns: Ignores several risk factors For simplicity, researchers often use excess returns when evaluating the efficacy of a trading
  • Predicting Stock Returns Using Firm Characteristics [Alpha Architect]

    A few weeks ago, we did a deep dive into the factors versus characteristics debate. One of the reasons weve brought up this debate is due to the fact that factor loadings (from regressions) are arguably not as helpful as portfolio characteristics. In other words, knowing a portfolio P/E ratio is more informative for forecasting expected returns than knowing the HML factor loading is .6.

Quantocracy’s Daily Wrap for 01/04/2018

This is a summary of links featured on Quantocracy on Thursday, 01/04/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A novel capital booster: Sports Arbitrage [EP Chan]

    As traders, we of course need money to make money, but not everyone has 10-50k of capital lying around to start one's trading journey. Perhaps the starting capital is only 1k or less. This article describes how one can take a small amount of capital and multiply it as much as 10 fold in one year by taking advantage of large market inefficiencies (leading to arbitrage opportunities) in the
  • All About the Exits Revisited [Throwing Good Money]

    Back in June of 2016, I wrote this post about random entries and trailing exits. It turns out (on average) that you can beat buy-and-hold of the S&P 500 by simply buying members of the S&P 100 randomly, as long as you a) have a market-timing filter, and 2) have a trailing stop of 20%. Yes thats right, just pick them at random! Here are the details of that original system (its
  • Can the January effect be exploited in the market? [Mathematical Investor]

    The January effect, in common with the Halloween indicator and sell in May and go away, is a catchy, get-rich-quick investment idea adored by financial commentators because it is so easy to explain to unsophisticated readers. It rests on the claim that the U.S. stock market performs better in January, compared to the other months in the year. Unfortunately, financial reports
  • When A New Year Starts On A Positive Note [Quantifiable Edges]

    Last nights subscriber letter featured (an expanded version of) the following study, which looks at performance in the 1st couple of days following a positive 1st day of a new year. 2018-01-03 The stats and curve all suggest some immediate follow-through has been typical. There have now been 9 winners in a row, with the last loser occurring in 1998. Also notable is that 24 of the 26 instances
  • Deep Learning Insights for Factor Investing [Quantpedia]

    Deep learning is an active area of research in machine learning. I train deep feedforward neural networks (DFN) based on a set of 68 firm characteristics (FC) to predict the US cross-section of stock returns. After applying a network optimization strategy, I find that DFN long-short portfolios can generate attractive risk-adjusted returns compared to a linear benchmark. These findings underscore

Quantocracy’s Daily Wrap for 01/02/2018

This is a summary of links featured on Quantocracy on Tuesday, 01/02/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in December [Allocate Smartly]

    Blogging was light in December. We spent the month working on the launch of a new fintech project that many of our readers will be excited about. Well be sharing details in the coming month and getting back to our regular blogging and site development schedule. Allocate Smartly This is a summary of the recent performance of a wide range of excellent tactical asset allocation strategies.
  • A Null Hypothesis for the New Year [Flirting with Models]

    In statistics, the null hypothesis is the default statement that you test with data. From this test, you can either reject the null hypothesis in support of an alternative or assert that there is not enough evidence to believe anything other than the null hypothesis with a certain degree of confidence. In an industry driven by speculation and talking heads pushing the next hot investments, an
  • Factor Olympics 2017 [Factor Research]

    2017 was a positive year for most factors Quality, Growth and Momentum showed the strongest performance Value, Dividend Yield and Size generated negative returns INTRODUCTION We present the performance of seven well-known factors on an annual basis for the last 10 years and the full-year 2017. It is worth mentioning that not all factors have strong academic support, e.g. Growth lacks a long-term

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