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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

Quantocracy’s Daily Wrap for 04/08/2016

This is a summary of links featured on Quantocracy on Friday, 04/08/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Even bad strategies will perform well [Flirting with Models]

    Summary Following even the best practices in investing can go against us in the short run. Volatility in short-term performance is necessary for the long run outperformance opportunity to exist. However, the opposite also holds true: a strategy that will underperform over the long run should also go through fits of outperformance. Performance, therefore, can be a very misleading statistic when it
  • Bollinger Bands | Trading Strategy (Setup) [Oxford Capital]

    Developer: John Bollinger (Bollinger Bands). Concept: Trend-following trading strategy based on Bollinger Bands. Research Goal: Performance verification of the 3-phase model (long/short/neutral). Specification: Table 1. Results: Figure 1-2. Trade Setup: Long Trades: Close[i ? 1] > Upper_Band[i ? 1]. Short Trades: Close[i ? 1] II. Sensitivity Test All 3-D charts are followed by 2-D
  • Alpha or Assets [Investor’s Field Guide]

    More and more investors are buying factor based strategies which invest using measures like valuation and low volatility, but the most popular strategies are applying factors in the wrong way. Strategies should be built for alpha, not scalebut the asset management industry has gone in the opposite direction. Most factor-based strategiescommonly called Smart Betahave hundreds of
  • Chasing the Momentum-Burst Unicorn [Throwing Good Money]

    A reader of my blog, Matt B., commented recently on an old post Id written about momentum bursts. Like me, Matt was intrigued by the short 3 to 5-day momentum bursts he saw described time and again on Pradeep Bondes stockbee site. Those bursts look so pretty, so elegant, and more to the point: so profitable. My previous research felt a little like banging my head against the wall. Trading on

Quantocracy’s Daily Wrap for 04/07/2016

This is a summary of links featured on Quantocracy on Thursday, 04/07/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Myth of Scaling Out [Throwing Good Money]

    A common tactic for some traders is to scale out of successful positions. The logic is this: Ive already made some money, so I want to hold onto some of that. Ill cash out a portion of my trade now, and see how the trade continues, but with reduced risk. You see this behavior with day traders, as well as long-term investors. But its a fallacy. And its costing you money. Lets devise
  • Mean reversion, momentum, and volatility term structure [EP Chan]

    Everybody know that volatility depends on the measurement frequency: the standard deviation of 5-minute returns is different from that of daily returns. To be precise, if z is the log price, then volatility, sampled at intervals of ?, is volatility(?)=?(Var(z(t)-z(t-?))) where Var means taking the variance over many sample times. If the prices really follow a geometric random walk, then
  • Testing Asset Allocation Results With Random Market Selection [Capital Spectator]

    Skill is a slippery concept in finance, courtesy of the shady influence of chance in asset pricing. It's also an awkward topic in just about every corner of money management because discussing it in detail invariably raises serious doubts about our ability to engineer investment results that are satisfactory much less stellar. But ignored or not, randomness is a factor and perhaps a far more
  • Smart Beta: Data Mining, Arbitraged Away, Or Here To Stay? [Alpha Architect]

    Large institutional investors have had access to low-cost "smart beta" for many years. But for retail investors and their financial advisors, "smart beta" ETFs are a welcome innovation. Instead of trying to identify an expensive manager who can pick stocks, a retail investor can leverage relatively low-cost smart beta active management and capture better risk-adjusted returns.
  • March Madness Portfolio Challenge: All Hail Our Champion! [Skewu]

    With our inaugural March Madness Portfolio Challenge in the books, were going to cover three very important takeaways. Takeaway #1: I mean, it wasnt even close Yes, in this part we pay homage to our esteemed champion, who has earned the glory due unto him by leading more or less the entire way. His name is Dan Adams, and he pseudonymously submitted one of his entries under the
  • How to Select the Best Commodity CTAs [Quantpedia]

    This study documents persistent, net-of-fees, alpha-generating commodity trading advisor funds focused on commodity investment ("Commodity Funds"). The baseline for performance measurement is a new benchmark model that includes factors established in the literature. A nonparametric bootstrap test establishes the existence of alpha that cannot be explained by luck. Performance persists 12
  • ETF-Rebalancing Cascades [Alex Chinco]

    This post looks at the consequences of ETF rebalancing. These funds follow pre-announced rules that involve discrete thresholds. The well-known SPDR tracks the S&P 500, but there are over 1400 different ETFs tracking a wide variety of different underlying indexes. When any of these underlying indexes change, the corresponding ETFs have to change their holdings. These thresholding rules mean
  • Meet the inventor and author of dual momentum investing @GaryAntonacci [Quant Investing]

    As a passionate value investor it took me a long time (and a lot of research) to accept that momentum is a very important factor that you must incorporate in your investment strategy if you want high returns. Momentum simply works The simple reason is that it works. I summarised the most important points you should know about momentum here: 10 myths about momentum investing, squashed
  • State of Trend Following in March [Au Tra Sy]

    Following two strong months to start the year, the index was down in March, but still positive overall for 2016. Please check below for more details. Detailed Results The figures for the month are: March return: -5.90% YTD return: 5.16% Below is the chart displaying individual system results throughout March: StateTF March And in tabular format: System March Return YTD Return BBO-20 -3.38% 12.49%

Quantocracy’s Daily Wrap for 04/06/2016

This is a summary of links featured on Quantocracy on Wednesday, 04/06/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Update on the Valuation Metric Horserace: 2011-2015 [Alpha Architect]

    Jack and I published, Analyzing Valuation Measures: A Performance Horse-Race Over the Past 40 Years, in the 2012 Journal of Portfolio Management. horse race Here is a summary of the research paper on our own blog. The paper asked a simple question: Which valuation metric has historically performed the best? Here were the participants in this horse-race: Earnings to Market
  • Outliers: Looking For A Needle In A Haystack [Quant Dare]

    Outliers are annoying. The analysis would be easier if they did not exit. Then, why not to remove them? As libesa told us in her last post titled Machine Learning: A Brief Breakdown, world is going crazy with Machine Learning and now we use it in all domains. In this post, we will see another application of Machine Learning. In Data Science we work with a great deal of data, but not all of

Quantocracy’s Daily Wrap for 04/05/2016

This is a summary of links featured on Quantocracy on Tuesday, 04/05/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Detecting Human Fear in Electronic Trading: Emotional Quantum Entanglement [Quant at Risk]

    This post presents an appealing proof for the progressing domination of algorithmic trading over human trading. By analysing the US stock market between 1960 and 1990, we estimate a human engagement (human factor) in live trading decisions taken after 2000. We find a clear distinction between 2000-2002 dot-era trading behaviour and 2007-2008 crisis. By the use of peculiar data samples, we
  • Taleb: “Problems and Inverse Problems” Follow-Up [Blue Event Horizon]

    In my previous post I published a bunch of R Scripts that will enable a reader of Taleb's "Silent Risk", Chapter 3, Section 3.2 "Problems and Inverse Problems" to play with the ideas he presents. I thought I should discuss one of the results those scripts produce that does not jive with Taleb's. I know from writing blog posts that it is incredibly difficult to be
  • Bounceback portfolio 2016 [UK Stock Market Almanac]

    The Bounceback Portfolio invests in the 10 worst performing FTSE 350 stocks of the previous year and holds them for the 3-month period, January-March. Performance in 2016 The following table lists the ten worst performing FTSE 350 stocks in 2015. These ten stocks form the 2016 Bounceback Portfolio. Company TIDM 2015 2016 (Jan-Mar) Anglo American AAL -75.1 84.4 Glencore GLEN -69.7 73.9 KAZ Minerals

Quantocracy’s Daily Wrap for 04/04/2016

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

  • Strategy Development with Perry Kaufman [Better System Trader]

    Im sure we all want to create trading strategies that perform better and last for longer but there are a number of issues we need to look out for when developing robust trading strategies, some are well-known and some perhaps arent. In this episode well be talking with Perry Kaufman about strategy development and more specifically some of the issues that can catch us out when creating
  • The case for Regime-Switching GARCH [Eran Raviv]

    GARCH models are very responsive in the sense that they allow the fit of the model to adjust rather quickly with incoming observations. However, this adjustment depends on the parameters of the model, and those may not be constant. Parameters estimation of a GARCH process is not as quick as those of say, simple regression, especially for a multivariate case. Because of that, I think, the
  • Snake Oil and Low Volatility Investing [Factor Investor]

    It is estimated that 180,000 Chinese immigrated to the United States in the latter half of the 19th century; many of them worked on the Transcontinental Railroad. Deeply routed in Chinese culture, the immigrants brought with them various medicinal remedies for common ailments. It was believed that the oil of Chinese water snakes was effective in treating inflammation and arthritis. Given the harsh
  • Taleb: “Silent Risk”, Chapter 3, Section 3.2 “Problems and Inverse Problems” [Blue Event Horizon]

    ection 3.2 in Chapter 3 of "Silent Risk", a draft of a book by Nassim Nicholas Taleb defines the "inverse problem" as follows: Definition 3.4 (The inverse problem). There are many more degrees of freedom (hence probability of making a mistake) when one goes from a model to the real world than when one goes from the real world to the model. He thens brings the problem into
  • Yahoo Data and Momentum Rotation – Analysis of 2015 Data [DTR Trading]

    I've taken a bit of a break from posting options strategy research, but before I dive back in I'm going to revisit some material I posted on Momentum Rotation systems last year. If you're new to my blog you may have missed my posts related to rotation system results and data. For the last several years, I have been trading monthly Momentum Rotation strategies across six accounts.
  • Trend Following Down in March [Wisdom Trading]

    March 2016 Trend Following: DOWN -2.84% / YTD: +6.86% The index gave back some of its gains from the beginning of the year, last month. The performance is still positive Year-To-Date and over the last 12 months. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart for March: Wisdom State of Trend Following – March 2016 And the 12-month chart:

Quantocracy’s Daily Wrap for 04/02/2016

This is a summary of links featured on Quantocracy on Saturday, 04/02/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Evolving Neural Networks through Augmenting Topologies Part 2 of 4 [Gekko Quant]

    This part of the tutorial on using NEAT algorithm explains how genomes are crossed over in a meaningful way maintaining their topological information and how speciation (group genomes into species) can be used to protect weak genomes with new topological information from prematurely being eradicated from the gene pool before their weight space can be optimised. The first part of this tutorial can