Quantocracy

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Quantocracy’s Daily Wrap for 04/20/2016

This is a summary of links featured on Quantocracy on Wednesday, 04/20/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Kaufman’s Market Efficiency Model [Milton FMR]

    The trend following model by Kaufman says that trading by the direction of the trend is a conservative approach to the markets. Kaufmans Market Efficient Model states that longer trends are the most reliable but they respond rather slowly to changing market conditions. The main argument of the Market Efficiency Model is that an adaptive method must be applied to the markets for proper trend
  • CAPE 10 Ratio In Need Of Context [Larry Swedroe]

    The Shiller cyclically adjusted (for inflation) price-to-earnings ratioreferred to as the CAPE 10 because it averages the last 10 years earnings and adjusts them for inflationis a metric used by many to determine whether the market is undervalued, fairly valued or overvalued. Employing a 10-year average for earnings, instead of the most current 12-month earnings, was first suggested by
  • Information Content of Pre- and Post-Market Trading Sessions [Jonathan Kinlay]

    I apologize in advance for this rather "wonkish" post, which is aimed chiefly at the high frequency fraternity, or those at least who trade intra-day, in the equity markets. Such minutiae are the lot of those engaged in high frequency trading. I promise that my next post will be of more general application. Pre- and Post Market Sessions The pre-market session runs from 8:00 AM ET, while
  • What is the difference between Bagging and Boosting? [Quant Dare]

    Bagging and Boosting are both ensemble methods in Machine Learning, but what is the key behind them? Bagging and Boosting are similar as they are both ensemble techniques, where a set of weak learners are combined to create a strong learner that obtains better performance than a single one. So, lets start from the beginning: What is an ensemble method? Ensemble is a Machine Learning concept in
  • Analysis of US Dollar Carry Trades in the Era of ‘Cheap Money’ [Quantpedia]

    In this paper, we employ a unique dataset of actual US dollar (USD) forward positions against a number of currencies taken by so-called Commodity Trading Advisors (CTAs). We investigate to what extent these positions exhibit a pattern of USD carry trading or other patterns of currency trading over the recent period of the ultra-loose US monetary policy. Our analysis indeed shows that USD positions

Quantocracy’s Daily Wrap for 04/19/2016

This is a summary of links featured on Quantocracy on Tuesday, 04/19/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Machine Learning Section Added to Our Library with Robot Wealth [Quantocracy]

    Jacques Joubert of Quants Portal, curator extraordinaire of the books at Quantocracy, has collaborated with Robot Wealth to add the humble beginnings of a Machine Learning section to our library. Denizens of Quantocracy know Robot Wealth well. Within months of launching his blog, RW had already become one of the top rated quant bloggers here at Quantocracy (a feat only matched by Financial
  • A Better Way To Run Bootstrap Return Tests: Block Resampling [Capital Spectator]

    Developing confidence about a portfolio strategys track record (or throwing it onto the garbage heap), whether its your own design or a third partys model, is a tricky but essential chore. Theres no single solution, but a critical piece of the analysis for estimating return and risk, including the potential for drawdowns and fat tails, is generating synthetic performance histories with
  • A Closer Look At Growth and Value Indices [Flirting with Models]

    In a commentary a few weeks ago entitled Growth Is Not Not Value, we discussed a problem in the index construction industry in which growth and value are often treated as polar opposites. This treatment can lead to unexpected portfolio holdings in growth and value portfolios. Specifically, we may end up tilting more toward shrinking, expensive companies in both growth and value indices. 2D

Machine Learning Section Added to Our Library with Robot Wealth

Jacques Joubert of Quants Portal, curator extraordinaire of the books at Quantocracy, has collaborated with Robot Wealth to add the humble beginnings of a Machine Learning section to our library.

Denizens of Quantocracy know Robot Wealth well. Within months of launching his blog, RW had already become one of the top rated quant bloggers here at Quantocracy (a feat only matched by Financial Hacker). For a taste of the Robot Master’s brain, check out this brilliant article applying Machine Learning:

http://robotwealth.com/machine-learning-financial-prediction-david-aronson/

RW represents a prototypical example of the quality of workmanship we hope to foster here. RW can be found on his website at RobotWealth.com, or on Twitter at @Robot_Wealth.

Without further ado, the humble beginnings of our Machine Learning library. Have a suggestion for a book to include here? Contact Jacques via LinkedIn, Twitter or email.

Machine Learning with R
Machine Learning with R
An Introduction to Statistical Learning with Applications in R
An Introduction to Statistical Learning with Applications in R
Applied Predictive Modeling
Applied Predictive Modeling
The Elements of Statistical Learning
The Elements of Statistical Learning
Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments
Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments

How Changing our Brand Supercharged Our Growth on Twitter

This is not quant related, but I found it interesting and thought it worth sharing for the benefit of our friends in the blogosphere.

Long-time readers remember that we rebranded this site from The Whole Street to Quantocracy at the start of April, 2015. Although the rebranding included changes to our website, nothing other than our name and profile pic changed on our Twitter feed. Both the subject of our tweets, and the way in which we formed our tweets, remained the same.

But interestingly, look at the impact of the rebranding on the number of new Twitter followers per day (dotted line marks the date of rebranding):

Followers per Day

In the seven months before the rebranding, we averaged 1.8 new followers per day or 648/year, but in the seven months after, 5.6 per day or 2038/year. That’s more than a 3-fold increase.

Note that we didn’t see even a remotely similar increase in site traffic, and we didn’t do anything radically different in terms of how we promoted the Twitter feed. In other words, the increase wasn’t the result of pushing more users to Twitter. Rather, it was a change in how the Twitter community perceived our Twitter account.

I realize that I just discovered what any first year marketing major could have told me in two seconds, but as a quant nerd, I tend not to think in these terms. The purist in me says that all that matters is the substance of what we do, but as the numbers clearly show, that’s naive.

If the goal of Quantocracy is to build and support this community of quants, then it’s also my responsibility to consider how I “market” this site. I get the heebeegeebees just using that word, but if a simple branding change means that the next amazing piece from Turing Finance, or Quant Start, or Financial Hacker, or Robot Wealth or any of the other 100+ kick ass quants included on the mashup gets more of the recognition that it deserves, then marketing becomes just as important as the substance of what we do. Lesson learned.

Mike @ Quantocracy

Quantocracy’s Daily Wrap for 04/16/2016

This is a summary of links featured on Quantocracy on Saturday, 04/16/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Taleb: Silent Risk, Section 1.3 [Blue Event Horizon]

    Towards the end of this section, Taleb inserts a sidebar as follows: Consider the right tail K^{+}\in \mathbb{R}^{+} and the left tail K^{-}\in \mathbb{R}^{-} . Without specifying the support of the distribution: Definition 1.3 (Probability swamps payoff (thin tails)). \lim_{K^{+ }\rightarrow\infty }E\left [ X\mid_{X> K^{+}} \right ]=K^{+}\textup{and}\lim_{K^{- }\rightarrow-\infty }E\left [

Quantocracy’s Daily Wrap for 04/15/2016

This is a summary of links featured on Quantocracy on Friday, 04/15/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • You can’t beat all the chimps [Following the Trend]

    It is a long established fact that a reasonably well behaved chimp throwing darts at a list of stocks can outperform most professional asset managers. While there would be obvious advantages with hiring chimps over hedge fund traders, such as lower salaries and better manners, there are also a few practical obstacles to such hiring practices. For those asset management firms unable to retain the
  • Benchmark Plus [Systematic Investor]

    To install Systematic Investor Toolbox (SIT) please visit About page. The overlay strategy is the market neutral strategy that can be applied to benchmark to improve benchmarks performance. The new strategy weights are equal to benchmark weights plus the overlay weights. Below I will present a very simple example. The Benchmark portfolio is a market cap weighted country portfolio. The Overlay
  • The 5 Mistakes Every Investor Makes [Meb Faber]

    The 5 Mistakes Every Investor Makes is a recent book I read by Peter Mallouk, the #1 Investment Advisor in America. (Im not poking fun, thats just what it says on the cover.) In general it is an easy to read book that it quite reasonable it its advice, and you can get a free copy from their website. The five mistakes are: Market Timing Active Trading Misunderstanding Performance and

Quantocracy’s Daily Wrap for 04/14/2016

This is a summary of links featured on Quantocracy on Thursday, 04/14/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Upcoming Panel Appearances [Flirting with Models]

    Justin and I will be speaking on panels in New York City in May. May 3rd 3:10pm Princeton Club in New York I will be sitting on a panel titled Advancements in Asset Allocation at WealthManagement.com's BUILD conference. Here is a quick description of the panel: The level of sophistication deployed in the investment solutions made available to investors continues to evolve. Many of the
  • My experience dealing with Zorro s support team [Robot Wealth]

    Disclaimer: I am not posting this at the behest of the developers of Zorro, nor do I receive any form of payment or commission for this post. I felt that I should relay this experience because it was an example of customer service that went way above and beyond the call of duty in terms of its promptness and professionalism. Credit where credit is due. I have been using the Zorro platform for

Quantocracy’s Daily Wrap for 04/13/2016

This is a summary of links featured on Quantocracy on Wednesday, 04/13/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Changing Generations of Financial Data [Quandl]

    As quants, were all aware that every model has a shelf-life. Sooner or later, the ideas and techniques behind every proprietary analytical technique diffuse into the broader world, at which point that technique is no longer the source of a competitive edge or alpha. Whats less well appreciated is that a similar pattern applies to the world of data. Rare, unique and proprietary data
  • The SPY RSI No Lie Swing Trade System [Throwing Good Money]

    Heres a free system for you. I call it the SPY RSI No Lie system. Its called that because I like stupid titles, and internal rhymes are an added plus. I read a post on Jeff Swansons System Trader Success recently about using a short-period RSI value to trigger trades with the S&P 500. Jeffs post was more from a theoretical standpoint, as it used the SPX index (rather than a
  • Relative Strength Index (RSI) Analysis [Alvarez Quant Trading]

    Recently I have been researching longer term hold strategies. I wondered which indicators by themselves would show an edge 3 to 6 months out. I am not looking to create a strategy from the indicator alone but want to know is there a statistical edge with it. Naturally, I started with my favorite Relative Strength Index, RSI. Rules Test period is from 1/1/2006 to 12/31/2015. Rules in parenthesis

Quantocracy’s Daily Wrap for 04/12/2016

This is a summary of links featured on Quantocracy on Tuesday, 04/12/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Market Timing Factor Premiums: Exploiting Behavioral Biases for Fun and Profit [Flirting with Models]

    Justin and I submitted a paper for the NAAIM Wagner 2016 competition. Unfortunately, it didn't place. The good news is that we can share it with everyone that much earlier! The paper is about trying to time factor premiums using the same behavioral biases that we believe cause them. Here is the abstract: When outperformance fixation leads to large inflow temptation: premiums erode, investors
  • Volatility is a value factor [Factor Investor]

    In my previous post, I looked at the historical performance of investing in low volatility stocks and identified that outperformance from the factor tends not to be very consistent over time, but is instead clustered. That raised some questions on whether volatility is a true investment factor, or if it's positive benefits are the product of other, more robust, investment factors. Below again
  • What You Pay Matters Less than What You’re Paying For [EconomPic]

    Patrick OShaughnessy has a great post, The More Unique Your Portfolio, The Greater Its Potential, outlining how active share is what drives the level of potential before fee excess return for an active manager. If you allocate to active managers… go through it twice. As Patrick notes: If there is a lot of overlap between your portfolio and the market, there is only so much alpha you can earn.
  • Equity Supply/Demand Indicator [Largecap Trader]

    I read a very interesting post from AlephBlog which led me to another blog called Philosophical Economics. Its a long and in depth article I had to read a few times to understand but the basic gist of it is that when investors are under allocated to equities, future returns are better than when they are over allocated. It utilizes the Fed Flow of Funds report to develop a ratio of the value of
  • Can Twitter Predict the Market’s Reaction to Fed FOMC Decisions? [Alpha Architect]

    Twitter seems to be a favorite dataset for financial researchers. Researchers keep trying to map tweets to profits. For example, we covered an idea related to this almost 5 years ago: Is trading with twitter only for twits? We had another post that was released about a year after our original highlight that discusses the death of a hedge fund dedicated to the idea of using tweets for profit: The

Quantocracy’s Daily Wrap for 04/11/2016

This is a summary of links featured on Quantocracy on Monday, 04/11/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Best Links of the Last Two Weeks and a Shout-Out to Quant News [Quantocracy]

    The best quant mashup links for the two weeks ending Saturday, 04/09 as voted by our readers: Build Better Strategies! Part 4: Machine Learning [Financial Hacker] Momentum for Buy-and-Hold Investors [Dual Momentum] A Monte Carlo Simulation function for your back-test results in R [Open Source Quant] Even bad strategies will perform
  • Are 3-year track records meaningful? [Flirting with Models]

    Many asset management decisions are based on the three-year track record. Three-years is suspiciously close to a common rule-of-thumb for calculating statistics, but in this case, it is a misapplication. With many strategies, short-term luck swamps long-term skill. Combining strategies can reduce the risk of making investment decisions based on results driven by luck. This makes it easier to
  • High Frequency Trading: Equities vs. Futures [Jonathan Kinlay]

    Pretty obviously, he had been making creative use of the "money management" techniques so beloved by futures systems designers. I invited him to consider how it would feel to be trading a 1,000-lot E-mini position when the market took a 20 point dive. A $100,000 intra-day drawdown might make the strategy look a little less appealing. On the other hand, if you had already made millions of
  • 10 Tips to Help Discretionary Traders Compete with Quants [Greg Harris]

    I've never been a discretionary trader, but I have spent the last 10 years doing quant work: modeling, information extraction, and automation. I know the areas where quantitative methods are weakest. It seems sensible for discretionary traders to focus on these areas instead of struggling in areas where quantitative methods are well-suited: 1. Don't Use Price Data Don't focus on
  • Machine Learning and Its Application in Forex Markets – Part 2 [Quant Insti]

    In our previous post on Machine learning we derived rules for a forex strategy using the SVM algorithm in R. In this post we take a step further, and demonstrate how to backtest our findings. To recap the last post, we used Parabolic SAR and MACD histogram as our indicators for machine learning. Parabolic SAR indicator trails price as the trend extends over time. SAR is below prices when prices
  • Registration for R/Finance 2016 is open! [FOSS Trading]

    You can find registration information and agenda details on the conference website. Or you can go directly to the Cvent registration page. Note that registration fees will increase by 50% at the end of early registration on May 6, 2016. The conference will take place on May 20 and 21, at UIC in Chicago. Building on the success of the previous conferences in 2009-2015, we expect more than 250
  • Testing Different Momentum Rules [Backtest Wizard]

    In this article I will test a variety of different momentum indicators which can be used to build a long only equity portfolio which has historically outperformed the market. To begin with, we need a baseline momentum strategy Baseline Momentum Strategy Rank stocks in the S&P500 by order of 1 Year Rate of Change %. Buy the top 20 strongest stocks. Exit a position if the stock falls out of
  • Momentum Rotation 60 Day ROC System Results [DTR Trading]

    In my last post, Yahoo Data and Momentum Rotation – Analysis of 2015 Data, the big take away was the importance of performing a full download / update of historical data before generating your signals. This is particularly important when using dividend adjusted data, which is typical for most equities and ETFs. The dividend adjustments need to be reflected in the entire series for a particular
  • Connors 2-Period RSI Update For 2015 [System Trader Success]

    Here we are four months into 2016 and Ive not updated some of the more interesting articles. One of those is Connors 2-period RSI strategy. This is a very popular trading method by Larry Connors and Cesar Alvarez. We all know there are no magic indicators but there is an indicator that certainly acted like magic over several decades. What indicator is it? Our reliable RSI indicator. The