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Quantocracy’s Daily Wrap for 06/14/2018

This is a summary of links featured on Quantocracy on Thursday, 06/14/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • False Promises: Going Passive is Not Momentum Investing [Factor Investor]

    There is some popular marketing spin going around that indexingconstructing portfolios based on market-cap weightsis effective because it allows an investor to own more of companies that have been successful and appreciated, while moving away from losers that have been unsuccessful and declined. This sounds logical, but it is empirically wrong. The strategy suggested above is tantamount to a
  • The 52 Week High and the Q-Factor Investment Model [Alpha Architect]

    In the past, we have examined the following two topics: (1) stock performance & the 52-week high and (2) the investment CAPM. When examining the performance of stocks relative to their respective 52-week high (highlighted by us here), the authors (George and Hwang) find the following: When coupled with a stocks current price, a readily available piece of informationthe 52-week high
  • A New Book For Portfolio Analysis Using R [Capital Spectator]

    Later this month Ill be publishing my third book: Quantitative Investment Portfolio Analytics In R: An Introduction To R For Modeling Portfolio Risk and Return. Although there are already many R books on the market, this one serves a particular niche: a short guide for recovering Excel addicts running relatively sophisticated analytics on investment portfolios and bumping up against the limits
  • State of Trend Following in May [Au Tra Sy]

    Positive May return for the State of Trend Following, with a YTD performance slightly negative. Please check below for more details. Detailed Results The figures for the month are: May return: 1.48% YTD return: -2.38% Below is the chart displaying individual system results throughout May: StateTF May And in tabular format: System May Return YTD Return BBO-20 4% 13.65% Donchian-20 5.88% 16.98%

Quantocracy’s Daily Wrap for 06/13/2018

This is a summary of links featured on Quantocracy on Wednesday, 06/13/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Help Fight MS and Receive Research & More From Quantifiable Edges [Quantifiable Edges]

    Last year Quantifiable Edges readers helped me raise over $4000 for Multiple Sclerosis (MS), and this year I am upping the incentive for people to donate! I am happy for any size donation, but I have created 2 donation levels this year so that people are incented to give more: Thank You Level: One bit of research that Quantifiable Edges has become known for are the many studies I have published on
  • RSI2 (Relative Strength Index) Analysis [Alvarez Quant Trading]

    From my time with working with Larry Connors, I have become known for using the 2-Period RSI (RSI2) (Relative Strength Index) in my trading. I have written lots of blog posts that use it and I often use it in my personal strategies. One thing I like to do with indicators that I use frequently is a thorough analysis of them. Often, I find characteristics that I did not expect. Do you know about the
  • Hedge Fund Return Predictability [Rayner Gobran]

    This is the sixth in my Hedge Fund Hacks series. In this post, I hammer home how little we know about the expected returns of a hedge fund or a managed futures strategy because we have so little data. You will learn just how large the uncertainty is and what you can do about it. Glimpses Through the Fog I remember hiking with my wife in North Wales in the Spring of 2003. Wales tends to be cloudy
  • New video explains the danger of selection bias in finance [Mathematical Investor]

    A new video has been produced by the Mathematicians Against Fraudulent Financial and Investment Advice (MAFFIA) group. It explains, in simple terms, how many of the financial strategies and funds available today are based on a statistically dubious foundation, typically rooted in selection bias effects, because the finance world, unlike other fields such as the pharmaceutical industry, has not yet

Quantocracy’s Daily Wrap for 06/12/2018

This is a summary of links featured on Quantocracy on Tuesday, 06/12/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Fun with the Cryptocompare API [Robot Wealth]

    Cryptocompare is a platform providing data and insights on pretty much everything in the crypto-sphere, from market data for cryptocurrencies to comparisons of the various crytpo-exchanges, to recommendations for where to spend your crypto assets. The user-experience is quite pleasant, as you can see from the screenshot of their real-time coin comparison table: cryptcurrency prices As nice as the
  • Labeling and Meta-Labeling Returns for ML Prediction [Black Arbs]

    This post focuses on Chapter 3 in the new book Advances in Financial Machine Learning by Marcos Lopez De Prado. In this chapter De Prado demonstrates a workflow for improved return labeling for the purposes of supervised classification models. He introduces multiple concepts but focuses on the Triple-Barrier Labeling method, which incorporates profit-taking, stop-loss, and holding period
  • Estimating the Hurst Exponent Using R/S Range [Flare 9x]

    In this post we will estimate the Hurst exponent using the R/S method. The Hurst exponent determines the long range memory of a time series (more below). If a series has no memory ie if each point in time is independent from previous points in time then its said to be more of a random process. Examples of random processes are markov processes, Brownian motion and white noise. A series which trends

Quantocracy’s Daily Wrap for 06/11/2018

This is a summary of links featured on Quantocracy on Monday, 06/11/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Commodities for the Long Run [Alpha Architect]

    The paper investigates this issue by answering the following research questions: Are commodities returns positive on average? How do they vary in different economic states (backwardation/contango; expansion/recession periods; unexpected inflation) ? How have they contributed to a broad portfolio? What are the Academic Insights? By studying a novel dataset consisting of daily futures prices going
  • Better Returns From Seasonal Investing In The S&P 500 (1950-2018) [iMarketSignals]

    From 1950 to 2018 the S&P 500 performed best from November to April, and significantly worse from May to October during most years. From 1950-2018 the real annualized return for the S&P 500 was 6.71%. Had one only invested from November to April each year the return would have been 6.60%, almost the same. Investing in a money-market fund from May to October each year and the remaining time
  • The Siren of Statistics [Highly Evolved Vol]

    A siren was a mythological being who lured sailors with their enchanting music to shipwreck on the rocky coasts of their island. Their songs were almost impossible to resist. But more generally a siren is a bad thing that we are attracted to, either physically or psychologically. For investors, an example of a sirens song is simplicity. Many investors are prone to looking for just a few
  • Factor Fimbulwinter [Flirting with Models]

    Value investing continues to experience a trough of sorrow. In particular, the traditional price-to-book factor has failed to establish new highs since December 2006 and sits in a 25% drawdown. While price-to-book has been the academic measure of choice for 25+ years, many practitioners have begun to question its value (pun intended). We have also witnessed the turning of the tides against the
  • Skewness as a Factor [Factor Research]

    Skewness is a feature of stocks with high firm-risks Stocks with positive or negative skewness outperform the market Can partially be explained by the Size factor INTRODUCTION Many investors started their investment career at an early age, typically buying a stock that showed an enticing performance chart, was featured somewhere or recommended by a family member. Moving to a professional

Quantocracy’s Daily Wrap for 06/09/2018

This is a summary of links featured on Quantocracy on Saturday, 06/09/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Future for Factor Investing May Be Different Than its Backtested Past [Alpha Architect]

    We believe there are cause and effect relationships in the world and in investing that hold true over time. Many are common sense and easily observable like fire creates smoke while others are harder to see and understand. With factor investing, true relationships can be hard to see because of randomness and noise in data, and theres a risk we convince ourselves certain
  • FX carry strategies (part 1) [SR SV]

    FX forward-implied carry is a valid basis for investment strategies because it is related to policy subsidies and risk premia. However, it also contains misdirection such as rational expectations of currency depreciation. To increase the signal-noise ratio FX carry should at the very least be adjusted for expected inflation differentials and external deficits. Even with such plausible

Quantocracy’s Daily Wrap for 06/08/2018

This is a summary of links featured on Quantocracy on Friday, 06/08/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Kelly versus Classical portfolio theory, and the two kinds of uncertainty premium [Investment Idiocy]

    Since I was a young lad there has been an ongoing fight in Financial Academia 'n' Industry between two opposing camps: In the red corner are the Utilitarians. The people of classical finance, of efficient frontiers, of optimising for maximum return at some level of maximum risk. In the blue corner are the Kellyites. Worshipping at the feet of John Kelly and Ed Thorpe they have only one
  • Update on Improved Currency Strength Indicator [Dekalog Blog]

    Following on from my previous post I have now slightly changed the logic and coding behind the idea, which can be seen in the code snippet below Essentially the change simultaneously optimises, using Octave's fminunc function, for both the gold_x and all currency_x geometric multipliers together rather than just optimising for gold and then analytically deriving the currency multipliers. The

Quantocracy’s Daily Wrap for 06/06/2018

This is a summary of links featured on Quantocracy on Wednesday, 06/06/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Machine Learning for Financial Market Prediction With Sklearn and Keras [Alpha Architect]

    Recently, Wes pointed me to this interesting paper by David Rapach, Jack Strauss, Jun Tu and Guofu Zhou: Dynamic Return Dependencies Across Industries: A Machine Learning Approach. The paper presents a strategy that forecasts industry returns and shows excellent historical returns. In this piece, we explore the strategy and then try to improve the results with more sophisticated machine

Quantocracy’s Daily Wrap for 06/05/2018

This is a summary of links featured on Quantocracy on Tuesday, 06/05/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in May [Allocate Smartly]

    This is a summary of the recent performance of a wide range of excellent tactical asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. While we dont (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Learn more about what we do or let AllocateSmartly help you follow these strategies in
  • The Strength Of Two Unfilled Up Gaps & A 50-Day High [Quantifiable Edges]

    One interesting study that I discussed in last nights subscriber letter considered the fact that SPY left an unfilled upside gap for the 2nd day in a row while closing at a 50-day high. The results table I shared can be found below. 2018-06-05 The size of the follow-through isnt terribly large, but it has been quite consistent that some follow through was achieved in the next few days. The
  • Currency Management with FX Style Factors [Quantpedia]

    Currency hedging is often approached with an all-or-nothing mentality: either full hedging of all foreign exchange (FX) positions or no hedging at all. As a more nuanced alternative, we suggest systematically harvesting the benefits of the FX style factors carry, value and momentum. In particular, we demonstrate how these factors can expand the opportunity set of traditional asset allocation when

Quantocracy’s Daily Wrap for 06/04/2018

This is a summary of links featured on Quantocracy on Monday, 06/04/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • RavenPack Research Symposition – Generation AI: The New Data-Driven Investor [Raven Pack]

    The RavenPack Research Symposium returns to New York on September 12th, register to receive updates on the agenda. RavenPack Research Symposium: Generation AI: The New Data-Driven Investor RavenPacks events have become global, with attendance exceeding 250 buy-side professionals at the London Big Data and Machine Learning Revolution in April 2018. RavenPack Research Symposium returns to New
  • ETF Rotation Strategies in Zorro [Robot Wealth]

    At Robot Wealth we get more questions than even the most sleep-deprived trader can handle. So whilst we develop the algo equivalent of Siri and brag about how we managed to get 6 hours downtime last night, we thought wed start a new format of blog posts answering your most burning questions. Lately our Class to Quant members have been looking to implement rotation-style ETF and equities
  • A Season for Sectors [Flirting with Models]

    Seasonality is an effect that shows up in data but is difficult to justify from a theoretical perspective using behavioral, risk-based, or structural reasoning. However, diving deeper into the effect within the U.S. sectors, we find that seasonality has been economically significant and surprisingly robust to specification methods over the past 75 years. Furthermore, it is not explained away when
  • The Inconsistent Performance of Value and Size Factors in the Chinese A-Share Market [Alpha Architect]

    What are the research questions? Does the Fama-French size factor explain the cross-section of stock returns in the Chinese A-share market? Does the Fama-French value factor explain the cross-section of stock returns in the Chinese A-share market? What are the Academic Insights? YES. The authors report a strong negative relationship between the size factor and returns. Out of 10
  • Factors from Scratch with Philosophical Economics (@Jesse_Livermore)

    With a million dollars to invest today, would you rather buy a portfolio of New York City taxi medallions or shares in Uber stock? When we ask investing audiences this question, the answer is almost always Uber. People tend to think in terms of fundamental growth without also thinking about price. In terms of growth and excitement, Uber has the clear edge. But in this paper we're going to
  • Market Timing with Multiples, Momentum and Volatility [Factor Research]

    Equity multiples have been elevated in recent years Using valuation multiples for allocation decisions is a challenging strategy Momentum and volatility-based strategies are more attractive INTRODUCTION In recent years the stock market in the US has been expensive on a variety of valuation multiples, which is often cited for why investing has become more difficult. However, in 2009 valuation
  • Kalman Filter Techniques And Statistical Arbitrage In China’s Futures Market In Python [Quant Insti]

    This article is the final project submitted by the author as a part of his coursework in Executive Programme in Algorithmic Trading (EPAT) at QuantInsti. Do check our Projects page and have a look at what our students are building. About the Author Xing Tao is a Bachelor in Computer Science (LZU), Masters in Information System and Management Science (PKU), and has passed CFA level 1-3 exams.

Quantocracy’s Daily Wrap for 06/03/2018

This is a summary of links featured on Quantocracy on Sunday, 06/03/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Sharpe Ratio > 1 : Careful What You Wish For [Quantum Financier]

    I had the pleasure to hang out with the good folks over at Resolve Asset Management recently. Part of our discussion centered around the differences between our two worlds. As you can imagine, prop trading is fairly different from institutional asset management. A topic of particular interest to both of us however was how to manage expectations. On their end one can imagine that in order to be
  • Cleaning data for trading [Cuemacro]

    Nobody likes doing the boring stuff. We all want to go on holiday, but packing our bags is not the fun bit. We all want to have a nice burger (well, I do), but queuing for ages at an ever popular burger joint is not what we want. Traders all want to have positive P&L, but well negative P&L happens, sometimes. When it comes to data science, in whichever industry, cleaning data is going to
  • Shapley Value Allocation of Operational Risk Capital Charges using Airport Problem Solution [Quant At Risk]

    In Financial Risk Management the most challenging part for quantitative modeling is, beyond any doubt, the Operational Risk (Ops Risk). It is defined as the risk of loss resulting from inadequate or failed internal processes, people and systems or from external events. This definition includes legal risk, but excludes strategic and reputation risk. According to Basel Committee, (not only American)