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Quantocracy’s Daily Wrap for 02/12/2016

This is a summary of links featured on Quantocracy on Friday, 02/12/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • It s All About Messages -I- [Algorythmn Trader]

    In my previous post I talked about a architectural concept I had defined for my trading platform. Looking backward from now, it was a good decision to go for a service oriented architecture. But how do all the communication is done in a distributed system? Lets talk about this, and try to find some answers. Messaging is one of the most fundamental topics in computer science and its so important. I
  • Speculation in a Path Dependent World [Largecap Trader]

    Im happy to publish my first paper on SSRN entitled, Speculation in a Path Dependent World I found many otherwise talented managers entering a multi-manager platform (assigned capital with strict risk limitations) having a difficult time transitioning. The paper is my simple attempt to suggest an alternative framework for new or potential managers dealing with drawdown risk. Any and all
  • An Examination of The Turn-of-the-Month-Effect [Quantpedia]

    The current study examines the turn of the month effect on stock returns in 20 countries. This will allow us to explore whether the seasonal patterns usually found in global data; America, Australia, Europe and Asia. Ordinary Least Squares (OLS) is problematic as it leads to unreliable estimations; because of the autocorrelation and Autoregressive Conditional Heteroskedasticity (ARCH) effects
  • More Small-Cap Quirks [Larry Swedroe]

    Given recent performance, the question of whether small-cap stocks really do outperform over time has made its way into the financial media. So far, weve sought to answer it by considering a multifactor approach and examining international evidence. Today well tackle a behavioral explanation. Behavioral Explanation The field of behavioral finance provides us with an explanation for the small
  • Using Heavy-Tailed Distributions with TASI: Pareto Distribution [Bayan Analytics]

    As established in a previous post, Tadawul All Shares Index (TASI) of the Saudi stock market has high excess kurtosis (9.903). The high kurtosis indicates that TASI has heavy tails. This means that the probability of extremely large negative returns is higher compared to a normal distribution. In this post, I use Pareto distribution to model TASIs left tail. Pareto distribution is used in

Quantocracy’s Daily Wrap for 02/11/2016

This is a summary of links featured on Quantocracy on Thursday, 02/11/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Dual Momentum on Individual Stocks. Wow. [Alpha Architect]

    Hot off the press and havent had time to reverse engineer and verify, but this is pretty interesting stuff at first glance. The Enduring Effect of Time-Series Momentum on Stock Returns Over Nearly 100-Years This study documents the significant profitability of time-series momentum strategies in individual stocks in the US markets from 1927 to 2014 and in international markets since 1975.
  • A Quant’s Approach to Building Trading Strategies: Part One [Quandl]

    Recently, Quandl interviewed a senior quantitative portfolio manager at a large hedge fund. We spoke about how she builds trading strategieshow she transitions from an abstract representation of the market to something concrete with genuine predictive powers. Can you tell us how you design new trading strategies? It all starts with a hypothesis. I conjecture that there ought to be a

Quantocracy’s Daily Wrap for 02/10/2016

This is a summary of links featured on Quantocracy on Wednesday, 02/10/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Avoiding Bear Markets to Improve Risk-Adjusted Returns [EconomPic]

    Ben Carlson of A Wealth of Common Sense has a recent post, When Global Stocks Go On Sale, outlining that it is typically a pretty good time to be buying when the MSCI World stock index is in a 20% or greater drawdown. His insightful takeaway and chart outlining the historical drawdowns and forward performance of the index is below: There were only two times out of the ten bear markets where stocks
  • How do stop-loss orders affect trading strategy performance? [Augmented Trader]

    A stop order is an order placed with a broker to sell a security when it reaches a certain price. A stop-loss order is designed to limit an investors loss on a position in a security investopedia. In this article we investigate how the addition of stop-loss orders affect a generic trading strategy. When investors enter a new position in a stock, they often simultaneously put in an
  • Get Shorty (again, research, not the movie ) [Throwing Good Money]

    Im running a high risk of running out of movies with short in the title. So this had better be the last blog post on the subject! In my previous post (here), I looked at a short-sale signal where a stock was shorted after it averaged 3% gains each day over five days (in any distribution). At the end of five days, it had to be up 15%. Yes, I could have just looked at it that way, but
  • Babel – Chapter 15 First Draft – JavaScript for Financial Analysts [John Orford]

    First draft of 'JavaScript for Financial Analysts' Chapter 15. ~ Like all superheroes JavaScript's biggest strength is also its main weakness. JavaScript can be distributed and run anywhere, easily. The problem is that each browser or platform supports a slightly different subset of the language. This book follows the current 2015 or EcmaScript 6 version of the language, which is OK
  • Predict returns using historical patterns [Quant Dare]

    Is it possible to predict the next returns sign by looking for historical patterns? Introduction One of the main problems when trying to develop investment algorithms is finding an estimator (with the intention of predict future returns) that minimizes the error between the estimation and the real return. As we can see in Vecinos cercanos en una serie temporal, there are many algorithms,
  • Double 7’s Strategy [Alvarez Quant Trading]

    In the book, Short Term Trading Strategies that Work, which Larry Connors and I published in early 2008, we wrote about a simple strategy called Double 7s Strategy. Through the years people often ask about this strategy. Does something that simple really work? How does it do in a portfolio? Does the concept work on stocks? Today, we will be answering these questions. The Original Rules
  • Relative Strength Sector Rotation Using ETFs [Backtest Wizard]

    In this article I will test a well-known relative strength trading model using ETFs. The test period will include the data between 01/01/2001 today. The starting hypothetical balance will be $100,000. The ETFs I will be testing are as follows: IYZ (Telecoms) XLB (Materials) XLE (Energy) XLF (Financial) XLI (Industrial) XLK (Technology) XLP (Consumer Staples) XLU (Utilities) XLV (Healthcare)

Quantocracy’s Daily Wrap for 02/08/2016

This is a summary of links featured on Quantocracy on Monday, 02/08/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Stock Market Prices Do Not Follow Random Walks [Turing Finance]

    Because volatility seems to cluster in real life as well as the markets, it has been a while since my last article. Sorry about that. Today we will be taking our first giant leap along A Non-Random Walk down Wall Street. The Non-Random Walk Series A Non-Random Walk Down Wall Street is the cheeky title of an academically challenging textbook written by Lo and MacKinlay in response to the
  • God, Buffett, and the Three Oenophiles [Flirting with Models]

    Our friends at Alpha Architect just wrote a great piece titled "Even God Would Get Fired as an Active Investor." In the study, they show that while an omnipotent investor with perfect foresight would have delivered great returns over the long run, he would be fired many times along the way due to short-term underperformance. Quoting from the post: "Our bottom line result is that
  • Does Academic Research Destroy Stock Return Predictability? (h/t @AbnormalReturns)

    We study the out?of?sample and post?publication return predictability of 97 variables shown to predict cross?sectional stock returns. Portfolio returns are 26% lower out?of?sample and 58% lower post?publication. The out?of?sample decline is an upper bound estimate of data mining effects. We estimate a 32% (58%-26%) lower return from publication?informed trading.

Quantocracy’s Daily Wrap for 02/07/2016

This is a summary of links featured on Quantocracy on Sunday, 02/07/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Architecture -II- [Algorythmn Trader]

    My previous post was about my thoughts concerning general architecture of a trading platform. During my way rethinking it from end to end it becomes clear that a client only approach would not fit with my needs. So I went back to my list of entities and started the puzzling again. To see all past posts and get a outlook about whats coming up, just have a look here: Content++. The obvious
  • Profit Margins – Are they Predicting a Crash? [Jonathan Kinlay]

    Is Jeremy Grantham, co-founder and CIO of GMO bullish or bearish these days? According to Myles Udland at Business Insider, hes both. He quotes Grantham: I think the global economy and the U.S. in particular will do better than the bears believe it will because they appear to underestimate the slow-burning but huge positive of much-reduced resource prices in the U.S. and the availability of
  • Quant Hunt: Ignore Tick-Box Companies [Quant at Risk]

    I was really surprised by a huge popularity of the past section of QuantAtRisk entitled Motivation for Quants. My readers made me thinking. Again. If there is a need for posts that expose and discuss the naked truth about quant job space, lets make it, again! This time bigger, better, and with big big balls! Therefore, this is the very first post in a new series of Quant Hunt. This is where we
  • Data Mining vs Out of Sample Data [Throwing Good Money]

    So in this last post, I data-mined the hell out of the S&P500 index (well ok SPY) and found an anomaly: every time SPY drops more than 1% from the previous close to the current close, you wait (thats Day 0). You then buy at the close 13 days later, and sell at the close of Day 14. This showed significantly better return than if you did the same thing but owned all the Day 16s instead.
  • Interview with Murray Ruggiero [Better System Trader]

    Murray Ruggiero is the chief systems designer and market analyst at Tuttle Tactical Management with around 200 million dollars under management. He is one of the worlds foremost experts on the use of intermarket and trend analysis in locating and confirming developing price moves in the markets. He is also a speaker, author and has been a contributing editor to Futures magazine since 1994,
  • Managing Risk in Retirement: Part II [Blue Sky AM]

    The Challenge of Being a Passive Investor Investors face the prospect of poor expected long-term returns making buying and holding less desirable for both equity and bond holders Given that bond yields are so low, investors are being forced to hold risky assets such as equities to earn sufficient returns. This forces passive investors to have to tolerate substantial volatility. Passive investing
  • C# Historical Dividend retrieval [Smile of Thales]

    Today in SmileOfThales we will provide you some brief but useful C# code (the whole code is available at the end of the article) to retrieve historical cash dividend data in Excel. The topic covers Excel-Dna, data caching, Html parsing with HtmlAgilityPack thats it and its already pretty 🙂 At the very beginning I needed to retrieve dividend history to experiment implicit dividend
  • Stocks That Triple In One Year [Investor’s Field Guide]

    There have been 1,700 individual U.S. stocks (with starting market caps of at least $200MM, inflation adjusted) which have tripled in a 12-month period since 1962. Many of these individual stocks tripled in more than one 12-month period, so we have 7,500 or so separate observations of a stock tripling in a 12-month period. Tripling your money quickly is pretty good. So what do these three-baggers

Quantocracy’s Daily Wrap for 02/05/2016

This is a summary of links featured on Quantocracy on Friday, 02/05/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Autocorrelation of SPY, and the Redneck Correlogram [Throwing Good Money]

    Ive been reading books by Michael Halls-Moore and my head hurts. Not having any formal training in statistics, I only understand about half of the material. None the less, I found his discussion of correlograms interesting. I even installed R on my computer (even though I havent fully grasped Python yet!) and was able to make some correlograms with R. However not knowing anything about
  • Loosening Short Sale Constraints Makes Markets More Efficient [Alpha Architect]

    We examine the causal effect of limits to arbitrage on ten well-known asset pricing anomalies using Regulation SHO, which reduced the cost of short selling for a random set of pilot stocks, as a natural experiment. We find that the anomalies become substantially weaker on portfolios constructed with pilot stocks during the pilot period. Regulation SHO reduces the anomaly long-short portfolio
  • When Risk Doesn t Lead To Return [Larry Swedroe]

    Among the more notable anomalies in modern finance is the finding that the lowest-beta stocks have produced higher returns than the highest-beta stocks. Another anomaly is that idiosyncratic (diversifiable) volatility negatively predicts equity returns. In other words, stocks with the lowest idiosyncratic volatility outperform stocks with the highest idiosyncratic volatility. These findings have
  • Replicating Private Equity [Quantpedia]

    Private equity funds tend to select relatively small firms with low EBITDA multiples. Publicly traded equities with these characteristics have high risk-adjusted returns after controlling for common factors typically associated with value stocks. Hold-to-maturity accounting of portfolio net asset value eliminates the majority of measured risk. A passive portfolio of small, low EBITDA multiple

Quantocracy’s Daily Wrap for 02/04/2016

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

  • Fitting time series models to the forex market: are ARIMA/GARCH predictions profitable? [Robot Wealth]

    Recently, I wrote about fitting mean-reversion time series models to financial data and using the models predictions as the basis of a trading strategy. Continuing my exploration of time series modelling, I decided to research the autoregressive and conditionally heteroskedastic family of time series models. In particular, I wanted to understand the autogressive integrated moving average
  • Navigating Active Asset Allocation When Diversification Fails [GestaltU]

    Exactly one month ago clients of ReSolve Asset Management received our 2015 annual letter, entitled Navigating Active Asset Allocation When Diversification Fails. People who signed up for our email distribution list received it aa few days later. If you would like to receive premium content in a timely manner, we invite you to sign up and download the full report here. CHECK YOUR NARRATIVE
  • Does my Tail Look Fat in This? Part 2 [Cantab Capital]

    Investors and managers are concerned with fat tails. In the second part of this post, we look at kurtosis in more detail. An apology and a warning This piece is more technical and longer than I had expected. The problem we're looking at here is subtle and not easy to distill down to a short, punchy and maths-free post. Sometimes the world isn't simple. Introduction In Part 1 of
  • MythBusters: Oil Driving Stocks More Than Ever? [Flirting with Models]

    As the news cycle spins faster and faster, we are seeing more and more market observations based on gut feelings. One such observation that I have heard recently is that oil and energy are driving stocks more than ever before. I thought we would look to the hard data in our own version of MythBusters. So what does the data say? Below we plot three sets of rolling 1-year correlations using data
  • State of Trend Following in January [Au Tra Sy]

    Strong start of the year for the State of Trend Following index, nearly closing the month with double-digit gains. Please check below for more details. Detailed Results The figures for the month are: January return: 8.28% YTD return: 8.28% Below is the chart displaying individual system results throughout January: StateTF January And in tabular format: System January Return YTD Return BBO-20
  • The Strong Historical Tendency for the Feb Employment Report [InvestiQuant]

    I have discussed the employment report a number of times here on the blog. Over the years the release of the report has generated a high amount of volatility for overnight trades. While the direction of those volatile moves has undergone some big hot and cold streaks, it has not provided a consistent long-term directional edge except around Groundhog Day. Below are results of going long the

Quantocracy’s Daily Wrap for 02/03/2016

This is a summary of links featured on Quantocracy on Wednesday, 02/03/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Fama French Multifactor Model in Python [Largecap Trader]

    Factor modelling is everywhere these days. I wrote about smart beta here. It is good to quantify performance drivers but the usual caveats apply to quantitative studies utilizing backward looking data, past performance does not guarantee future results. I wanted to share a little exercise I did in Python comparing a fund, stock, or anything with a ticker available on Yahoo Finance with the
  • Dream team: Combining classifiers [Quant Dare]

    Can a set of weak systems turn into a single strong system? When you are in front of a complex classification problem, often the case with financial markets, different approaches may appear while searching for a solution. These systems can estimate the classification and sometimes none of them is better than the rest. In this case, a reasonable choice is to keep them all and then create a final
  • Trend Following: Good Start to 2016 [Wisdom Trading]

    Similarly to last year, trend following starts the year on strong footing. January returned over 5% for our trend following index after flirting with the double-digit territory to establish new all-time highs. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart for January:
  • SPX Straddle – Normalized Return Charts [DTR Trading]

    The last article on RUT straddles (here) was very popular, so I thought I'd write a similar post on SPX straddles. Recall that from September, 2015 through November, 2015 I reviewed the backtest results form 28,840 short options straddles on the S&P 500 Index (SPX). You can read the summary articles from that SPX series here and here, and the introductory article for the straddle series