Quantocracy

Quant Blog Mashup

ST

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

Quantocracy’s Daily Wrap for 04/25/2016

This is a summary of links featured on Quantocracy on Monday, 04/25/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is tactical broken? [Flirting with Models]

    Summary Many tactically risk-managed strategies use trend following to manage the risk of severe drawdowns, but in sideways markets, like those experienced in 2011 and 2015, trend following ends up lagging the market by buying high and selling low. As with insurance policies or static allocations to bonds, this underperformance is an implicit cost of managing risk. Underperformance in periods
  • How the day of the week affects stock market anomalies [Alpha Architect]

    This paper documents a new empirical fact. Long-short anomaly returns are strongly related to the day of the week. Anomalies for which the speculative leg is the short (long) leg experience the highest (lowest) strategy returns on Monday. The exact opposite pattern is observed on Fridays. The effects are large; Monday (Friday) alone accounts for over 100% of monthly returns for all anomalies
  • Measurement error bias [Eran Raviv]

    What is measurement error bias? Errors-in-variables, or measurement error situation happens when your right hand side variable(s); your x in a y_t = \alpha + \beta x_t + \varepsilon_t model is measured with error. If x represents the price of a liquid stock, then it is accurately measured because the trading is so frequent. But if x is a volatility, well, it is not accurately measured. We simply

Quantocracy’s Daily Wrap for 04/24/2016

This is a summary of links featured on Quantocracy on Sunday, 04/24/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Best Links of the Last Two Weeks [Quantocracy]

    The best quant mashup links for the two weeks ending Saturday, 04/23 as voted by our readers: Lossless Compression Algorithms and Market Efficiency? [Turing Finance] You cant beat all the chimps [Following the Trend] My Year-Long Experience as the Fastest Form-4 Trader [Greg Harris] Are 3-year track records meaningful? [Flirting with Models] The Changing Generations of Financial Data [Quandl]
  • New Book Added: Intro to Statistical Learning with Applications in R [Amazon]

    An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant

Best Links of the Last Two Weeks

The best quant mashup links for the two weeks ending Saturday, 04/23 as voted by our readers:

We also welcome one blog making its first ever appearance on the mashup:

And finally, the latest from Quantocracy:

* * *

Votes by Clickthroughs

[click graph to enlarge]

Your votes matter to the quant community.

The graph to the right shows the average number of clickthroughs a link receives from our website (excluding RSS, Twitter and Stocktwits), broken out by the number of votes cast by our readers.

A core goal of Quantocracy is to have a positive impact on our corner of the financial world by rewarding the best work, and encouraging the best minds to keep writing.

As the graph makes clear, the citizens of Quantocracy are doing just that (way to go guys). Links with 11 or more votes receive nearly 6-times as many clickthroughs as a link with no votes (wow).

If you haven’t done so already, we invite you to register to vote and be a part of the effort. Your votes matter to the quant community.

Read on Readers!
Mike @ Quantocracy

Quantocracy’s Daily Wrap for 04/22/2016

This is a summary of links featured on Quantocracy on Friday, 04/22/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: Machine Learning with R [Amazon]

    Machine learning, at its core, is concerned with transforming data into actionable knowledge. This makes machine learning well suited to the present-day era of big data. Given the growing prominence of a cross-platform, zero-cost statistical programming environment there has never been a better time to start applying machine learning to your data. Whether you are new to data analytics or a
  • PDF: Combining Value and Momentum [Gerstein Fisher]

    This paper considers several popular portfolio implementation techniques that maximize exposure to value and/or momentum stocks while taking into account transaction costs. Our analysis of long-only strategies illustrates how a strategy that simultaneously incor- porates both value and momentum outperforms a strategy that combines pure-play value and momentum portfolios that are formed
  • 50% Returns Coming for Commodities and Emerging Markets? [Meb Faber]

    If history is any guide, were standing at the edge of 40%96% returns over the next two years. This isnt wishful thinking or wild speculation. Im not selling anything. Rather, Im just reporting historical gains from a market set-up thats repeating itself right now. So whats going on? Well, imagine a rubber band. If you stretch it only slightly then let it go, its not going
  • Minimum volatility: what’s in a name? [Factor Investor]

    Mad Men watchers may recognize the name Bernbach from the quote above. Bernbach is referred to in the second season as the innovative competitor firm that challenges Sterling Cooper's orthodoxy. Bill Bernbach was the brain behind several successful campaigns, including Avis' We Try Harder and Volkswagen's Think Small. The quote couldn't be more applicable to many of the Smart
  • The Moving Average Research King: Valeriy Zakamulin [Alpha Architect]

    Some weekend reading for trend-followers who want to question their beliefs. Valeriy Zakamulin is an animal when it comes to generating research on moving averages. Weve done a lot of the same work, but were too lazy to tabulate the results in an academic paper format. king of ma The king of moving average research. Check these papers out: Revisiting the Profitability of Market Timing with
  • Research Review | 22 Apr 2016 | Risk Analysis [Capital Spectator]

    The Market Portfolio is NOT Efficient: Evidences, Consequences and Easy to Avoid Errors Pablo Fernandez (University of Navarra), et al. March 16, 2016 The Market Portfolio is not an efficient portfolio. There are many evidences that tell us that: the equal weighted indexes have beaten their market-value weighted indexes for many years, many easy-to-build portfolios (some smart-beta,

Quantocracy’s Daily Wrap for 04/21/2016

This is a summary of links featured on Quantocracy on Thursday, 04/21/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Introducing fidlr: FInancial Data LoadeR [R Trader]

    fidlr is an RSutio addin designed to simplify the financial data downloading process from various providers. This initial version is a wrapper around the getSymbols function in the quantmod package and only Yahoo, Google, FRED and Oanda are supported. I will probably add functionalities over time. As usual with those things just a kind reminder: THE SOFTWARE IS PROVIDED AS IS, WITHOUT
  • Get ready for R/Finance 2016 [Revolutions]

    R/Finance 2016 is less than a month away and, as always, I am very much looking forward to it. In past years, I have elaborated on what puts it among my favorite conferences even though I am not a finance guy. R/Finance is small, single track and intense with almost no fluff. And scattered among the esoterica of finance and trading there has, so far, always been a rich mix of mathematics, time
  • Sentiment Analysis in Trading Explained Using R [Quant Insti]

    In this post we discuss sentiment analysis in brief and then present a basic sentiment analysis model in R. Sentiment analysis is the analysis of the feelings (i.e. attitudes, emotions and opinions) which are expressed in the news reports/blog posts/twitter messages etc., using natural language processing tools. Natural language processing (NLP) in simple terms refers to the use of computers to