Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 05/07/2016

This is a summary of links featured on Quantocracy on Saturday, 05/07/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Relative Strength Index (RSI) Model | Trading Strategy (Entry) [Oxford Capital]

    I. Trading Strategy Developer: Larry Connors (The 2-Period RSI Trading Strategy), Welles Wilder (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

Quantocracy’s Daily Wrap for 05/05/2016

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

  • How to Learn Advanced Mathematics Without Heading to University – Part 2 [Quant Start]

    In the last article in the series we looked at the foundational courses that are often taken in a four-year undergraduate mathematics course. We saw that the major courses were Linear Algebra, Ordinary Differential Equations, Real Analysis and Probability. In the "second year" of our self-study mathematics degree we'll be digging deeper into analysis and algebra, with discussions on
  • The Academic Finance Papers That Changed My Mind [Alpha Architect]

    What does it mean to be the best research? For me, this means the most influential in changing my view on the world. So the below list of best research represents the research that 1) changed my view of the world 2) helped sharpen my thinking. For context, Ive been reading source journal finance research for over 15 years. In the early days it would take me a week to grasp a paper,

Quantocracy’s Daily Wrap for 05/03/2016

This is a summary of links featured on Quantocracy on Tuesday, 05/03/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Backtesting Strategies with R (h/t algotrading Reddit) [Tim Trice]

    This book is designed to not only produce statistics on many of the most common technical patterns in the stock market, but to show actual trades in such scenarios. Test a strategy; reject if results are not promising Apply a range of parameters to strategies for optimization Attempt to kill any strategy that looks promising. Let me explain that last one a bit. Just because you may find a strategy
  • Get Rich Slowly [Financial Hacker]

    Most trading systems are of the get-rich-quick type. They exploit temporary market inefficiencies and aim for annual returns in the 100% area. They require maintenance, supervision, and regular adaption to market conditions. Their expiration is often accompanied by large losses. But what if youve nevertheless collected some handsome gains, and now want to park them in a more safe haven? Put the
  • Forecast averaging example [Eran Raviv]

    Especially in economics/econometrics, modellers do not believe their models reflect reality as it is. No, the yield curve does NOT follow a three factor Nelson-Siegel model, the relation between a stock and its underlying factors is NOT linear, and volatility does NOT follow a Garch(1,1) process, nor Garch(?,?) for that matter. We simply look at the world, and try to find an apt description of
  • Further dip in April for Trend Following [Wisdom Trading]

    Another down month for the index, with a slight loss. Two mild down months after two strong up months keep the index in positive territory for 2016. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart for April: WSTF-201604-Index And the 12-month chart: WSTF-201604-Index-12months Below are the summary stats:

Quantocracy’s Daily Wrap for 05/01/2016

This is a summary of links featured on Quantocracy on Sunday, 05/01/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: Elements of Statistical Learning (h/t @Robot_Wealth) [Amazon]

    During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics.
  • Index Investing Makes Markets and Economies More Efficient [Philosophical Economics]

    U.S. equity index funds have grown dramatically in recent decades, from a negligible $500MM in assets in the early 1980s to a staggering $4T today. The consensus view in the investment community is that this growth is unsustainable. Indexing, after all, is a form of free-riding, and a market can only support so many free-riders. Someone has to do the fundamental work of studying securities in

Quantocracy’s Daily Wrap for 04/28/2016

This is a summary of links featured on Quantocracy on Thursday, 04/28/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What You Should Remember About the Markets [Dual Momentum]

    Because I have been an investment professional for more than 40 years, I sometimes get asked my opinion about the markets. These questions usually come from those who invest without a systematic approach toward investing. Here are some typical questions and answers: Question: How much do you think the stock market can drop? Response: 89% Question: What?!! Response: Well, that is the most it has
  • How Different Are These Things From One Another? [Blue Event Horizon]

    In an earlier post I was looking at distance measures for clustering. In a still earlier post I had referred to analyzing hedge fund regulatory data using clustering to try to put the funds into groups by inferred strategy. I had to solve a problem with clustering that has being bothering me for a while: how do you measure distances between observations when the data is sparse? In my case the
  • Facts, Fiction, and Merger Arbitrage [Alpha Architect]

    Investors love to chase after the next big thing, as investment strategies and styles come in and go out of vogue. The latest object of investor infatuation may be a revitalized interest in merger arbitrage. Consider this bloomberg headline: Hedge Fund Investors Have Fallen in Love with Merger Arb (Again). It would appear that merger arbitrage is a hot strategy once again. But while it
  • K-Means never fails , they said [Quant Dare]

    It is known that data mining algorithms are not perfect and they can fail under certain conditions. K-Means is an example of that triviality but there is a good alternative, K-Medoids. In a previous post, Machine Learning: A Brief Breakdown we already mentioned that K-Means is the cluster analysis algorithm par excellence and it is one of the most important data mining and machine learning
  • Optimum Asset Allocation using Correlation [Milton FMR]

    The concept of diversification is based on the concept that a trader can reduce his risk exposure by entering several positions at the same time. The success of a traders portfolio is therefore based on reducing risk rather than maximizing returns. A trader should be able to withstand a string of losses no matter how low the probability might be for a large string of losses. When testing our

Quantocracy’s Daily Wrap for 04/27/2016

This is a summary of links featured on Quantocracy on Wednesday, 04/27/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: Applied Predictive Modeling [Amazon]

    Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns

Quantocracy’s Daily Wrap for 04/26/2016

This is a summary of links featured on Quantocracy on Tuesday, 04/26/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Block Bootstrapped Monte Carlo in R [Open Source Quant]

    A few weeks back i wrote a post including the source code for a Monte Carlo simulation function in R. The idea was to randomly sample daily returns produced by a backtest and build a confidence interval distribution of the middle 50% and 90% of returns. Since then Brian Peterson got in touch with me asking if i would work with him in getting some form of Monte Carlo simulation functionality
  • A New Analysis of Commodity Momentum Strategy [Quantpedia]

    Conventional momentum strategies rely on 12 months of past returns for portfolio formation. Novy-Marx (2012) shows that the intermediate return momentum strategy formed using only twelve to seven months of returns prior to portfolio formation significantly outperforms the recent return momentum formed using six to two month returns prior. This paper proposes a more granular strategy termed