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Quantocracy’s Daily Wrap for 06/07/2016

This is a summary of links featured on Quantocracy on Tuesday, 06/07/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Will Bonds Deliver Crisis Alpha in the Next Crisis? [Alpha Architect]

    Bonds are often viewed as being great diversifiers due to the perception that they perform well during tough times for stocks. Historically this has been a true statement. But will it continue? Our answer: unclear. Most investors use correlation to measure the diversification benefit an investment might provide an existing portfolio. However, this article uses a slightly different approach to

Quantocracy’s Daily Wrap for 06/06/2016

This is a summary of links featured on Quantocracy on Monday, 06/06/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: Data Science from Scratch with Python [Amazon]

    Data science libraries, frameworks, modules, and toolkits are great for doing data science, but theyre also a good way to dive into the discipline without actually understanding data science. In this book, youll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills,
  • Need for Speed: High Frequency Economic News Trading [Justinas Brazys]

    Markets are efficient if new information is incorporate instantly and essentially without any trading, i.e. price jumps to the correct level that represents all available information at the time. If you believe markets are indeed perfectly efficient, there seems to be no point in using news as a source of alpha in trading. However it is unlikely that information can be incorporated instantaneously
  • Tactical Trend-Following: Core or Alternative? [Flirting with Models]

    Answering whether a strategy should be a core holding or an alternative holding often has less to do with the investment strategy itself and more to do with an investors understanding of how that strategy will perform. Asset classes and strategies that investors are comfortable with, and have a strong understanding of why and when they will perform a certain way, are strong contenders for core
  • Summer, Winter and the Volatility Premium [Factor Wave]

    A member of our slack channel recently asked if there was an equivalent of "sell in May" for volatility trading. Does the volatility premium, the difference between implied volatility and the subsequent realized volatility, differ during summer and winter months? To test this idea for the S&P 500, I calculated the difference between the VIX and the realized volatility over the next

Quantocracy’s Daily Wrap for 06/05/2016

This is a summary of links featured on Quantocracy on Sunday, 06/05/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mini-Meucci : Applying The Checklist – Step 2 [Return and Risk]

    "Guessing before proving! Need I remind you that it is so that all important discoveries have been made? Henri Poincar, French mathematician (1854-1912) In this second leg of The Checklist tour, Estimation, we are going to make some educated guesses about the true unknown distribution of the invariants. But first… Recap and a bit more on Quest for Invariance The quest for invariance is
  • Computation of the Loss Distribution not only for Operational Risk Managers [Quant at Risk]

    In the Operational Risk Management, given a number/type of risks or/and business line combinations, the quest is all about providing the risk management board with an estimation of the losses the bank (or any other financial institution, hedge-fund, etc.) can suffer from. If you think for a second, the spectrum of things that might go wrong is wide, e.g. the failure of a computer system, an

Quantocracy’s Daily Wrap for 06/03/2016

This is a summary of links featured on Quantocracy on Friday, 06/03/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: Python Machine Learning [Amazon]

    Leverage Pythons most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask and answer tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python
  • Relative Strength Index (RSI) Model [Oxford Capital]

    I. Trading Strategy Developer: Larry Connors (The 2-Period RSI Trading Strategy), Welles Wilder (The RSI Momentum Oscillator). Source: (i) Connors, L., Alvarez, C. (2009). Short Term Trading Strategies That Work. Jersey City, NJ: Trading Markets; (ii) Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research. Concept: The long equity trading system based on the
  • Factor Attribution of Jim Cramer’s Mad Money Charitable Trust [Quantpedia]

    This study analyzes the complete historical performance of Jim Cramers Action Alerts PLUS portfolio from 2001 to 2016 which includes many of the stock recommendations made on Cramers TV show Mad Money. Both since inception of the portfolio and since the start of Mad Money in 2005 (when it was converted into a charitable trust), Cramers portfolio has underperformed the S&P
  • Do European stocks follow the US on a daily basis? [UK Stock Market Almanac]

    Do European stocks follow the lead of the US market from the previous day? In other words if, say, the US market is down one day are European stocks more likely to fall in their trading session the following day? To test this the following chart plots the daily returns of the S&P 500 Index against the corresponding daily return of the EuroSTOXX 50 Index for the following day. Europe v US
  • Bubble Investing: Learning from History [Alpha Architect]

    We just wrote a piece for Forbes on financial bubbles in the lab. Punchline: investors initially underreact to fundamentals, then they overreact, and eventually prices correct. But how common are crashes? Ben has some interesting thoughts, but the results are limited to the US market. Now, one of my favorite academic authors Prof. Bill Goetzmann has a new paper that speaks to understand
  • Webinar: Feature Selection with Machine Learning [Quant Insti]

    Feature Selection is the automatic selection of attributes in your data (such as columns in tabular data) that are most relevant to the predictive modeling problem you are working on. Feature selection methods aid you in your mission to create an accurate predictive model. They help you by choosing features that will give you as good or better accuracy whilst requiring less data. The methods can
  • The Internal Bar Strength Indicator [Jonathan Kinlay]

    Internal Bar Strength (IBS) is an idea that has been around for some time. IBS is based on the position of the days close in relation to the days range: it takes a value of 0 if the closing price is the lowest price of the day, and 1 if the closing price is the highest price of the day. More formally: IBS = (Close Low) / (High Low) The IBS effect may be related to intraday

Quantocracy’s Daily Wrap for 06/02/2016

This is a summary of links featured on Quantocracy on Thursday, 06/02/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Cointegrated Time Series Analysis for Mean Reversion Trading with R [Quant Start]

    A while back we considered a trading model based on the application of the ARIMA and GARCH time series models to daily S&P500 data. We mentioned in that article as well as other previous time series analysis articles that we would eventually be considering mean reverting trading strategies and how to construct them. In this article I want to discuss a topic called cointegration, which is a
  • A Factor Investor s Perspective of the Economic Cycle [Factor Investor]

    Debates abound on the relative importance of the economic cycle to investment success. Peter Lynch famously said, "If you spend more than 13 minutes analyzing economic and market forecasts, you've wasted 10 minutes. On the flip side, macro investment houses have constructed intricate frameworks to understand the economic machine. The challenge with economic data is that it is

Quantocracy’s Daily Wrap for 05/31/2016

This is a summary of links featured on Quantocracy on Tuesday, 05/31/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Diversification Will Always Disappoint [Flirting with Models]

    Summary For its ability to reduce risk without necessarily sacrificing potential reward, diversification is known as the only free lunch on Wall Street. Diversification provides investors with the important ability to invest in the face of uncertainty. When viewed for its pieces instead of as a whole, a well-diversified portfolio will likely always contain an element that disappoints. This has
  • mini-Meucci : Applying The Checklist – Step 1 [Return and Risk]

    Introduction In this mini-Meucci series of posts we'll put the 10 steps of The Checklist into practice by constructing a low volatility portfolio in Python. This toy/basic example will be a short tourist trip, highlighting key attractions that you can then explore further… Of course, these posts should be read in conjunction with the latest slides (I'll be referring to the slides
  • A Few Little Links [Factor Wave]

    I'm currently working on three things: a VIX option trading strategy, a piece about how factors relate to earnings announcements and a Kelly criterion type thing for options. But none is particularly close to being done. So I thought i would post a few links to articles that I found interesting. I'm not (just) doing this to put up a blog post. I think both of these are good reads. First,

Quantocracy’s Daily Wrap for 05/30/2016

This is a summary of links featured on Quantocracy on Monday, 05/30/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Survey of Deep Learning Techniques Applied to Trading [Greg Harris]

    Deep learning has been getting a lot of attention lately with breakthroughs in image classification and speech recognition. However, its application to finance doesn't yet seem to be commonplace. This survey covers what I've found so far that is relevant to systematic trading. Please tell me if you know of some research I've missed. Acronyms: DBN = Deep Belief Network LSTM = Long
  • Build Technical Indicators in Python [Quant Insti]

    Technical Indicator is essentially a mathematical representation based on data sets such as price (high, low, open, close, etc.) or volume of a security to forecast price trends. There are several kinds of technical indicators that are used to analyse and detect the direction of movement of the price. Traders use them to study the short-term price movement, since they do not prove very useful for
  • A Long Term Look At Memorial Week Seasonality [Quantifiable Edges]

    The week of Memorial Day has shown some interesting seasonal tendencies over the years. And for a long time it exhibited consistent bullishness. But it has faltered greatly the last several years. The chart below examines SPX performance from the Friday before Memorial Day to the Friday after it. 2016-05-30 image1 There was no substantial edge apparent throughout the 70s, but starting in 1983

Quantocracy’s Daily Wrap for 05/28/2016

This is a summary of links featured on Quantocracy on Saturday, 05/28/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 05/27/2016

This is a summary of links featured on Quantocracy on Friday, 05/27/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Exploring Extreme Asset Returns [Quant Dare]

    Tail or extreme assets returns have been extensively studied. In his amazing paper: Empirical properties of assets returns: stylized facts and statistical issues, Rama Cont provides a framework on statistical analysis of price variations in various types of financial markets. He presents Heavy tails in asset returns as a stylized fact, i.e., statistical properties common across a wide