Quantocracy

Quant Blog Mashup

Quantocracy’s Daily Wrap for 02/20/2016

This is a summary of links featured on Quantocracy on Saturday, 02/20/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A simple statistical edge in SPY [Trading with Python]

    I've recently read a great post by the turinginance blog on how to be a quant. In short, it describes a scientific approach to developing trading strategies. For me personally, observing data, thinking with models and forming hypothesis is a second nature, as it should be for any good engineer. In this post I'm going to illustrate this approach by explicitly going through a number of
  • Technologies Screening -III- [Algorythmn Trader]

    In my previous post, I introduced the messaging topic. Now its time to talk about what I found on the message framework universe. To get a overview about past and upcoming topics, please have look here: Content++. There were several message frameworks I was come across and played around. The first was of course WCF comes as part of .Net and C#. This is a very convenient way to handle all the
  • Chasing Returns and Avoiding “Spaghetti against the Wall Fund Companies” [Alpha Architect]

    Psychology research suggests that when we make predictions, we suffer from representative bias, and mistakenly overweight observations that fit a particular narrative, and fail to consider base rate probabilities. For example, if we flip a coin 5 times and it shows up H, H, H, H, H, we may assume that Hs is more likely, even though the probability is still 50/50. Consider a more tangible
  • Using Heavy-Tailed Distributions with TASI: Student t Distribution [Bayan Analytics]

    In this post, I continue trying to fit the daily log returns of TASI index using heavy-tailed distributions. In the previous post, I used Pareto distribution to model TASI indexs left tail. In this post, I use Student t distribution. Recently, Student t distribution has been used widely by financial engineers as models for heavy-tailed distribution such as the distribution of financial

Quantocracy’s Daily Wrap for 02/18/2016

This is a summary of links featured on Quantocracy on Thursday, 02/18/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Low Volatility vs. High Volatility Days [Throwing Good Money]

    I read a blog post recently that began suppose you have a trading system that works well on low-volatility days and I thought, hmm. Is that a thing? Is there an edge to low-volatility days vs high volatility days? Lets turn this blog post into a speculators version of Dude, What Would Happen? The parameters: SPY, baby! From 2000 through 02/16/16. A low volatility day is
  • Make Volatility Your Friend (By Limiting Downside Volatility) [EconomPic]

    Josh Brown (i.e. The Reformed Broker) recently shared the aptly titled post How to Make Volatility Your Bitch highlighting how dollar cost averaging into a volatile market can lead to higher overall returns: Door number one you spend 15 years putting $1000 into an investment every month for 15 years, with the possibility of seeing that investment get cut in half twice. Door number two you
  • A Quant’s Approach to Building Trading Strategies: Part Two [Quandl]

    This is the second part of our interview with a senior quantitative portfolio manager at a large hedge fund. In the first part, we covered the theoretical phase of creating a quantitative trading strategy. In this part, we cover the transition into production. You can read the first part of the interview here. What does moving into production entail? For starters, I now have to worry about
  • Are Low-Volatility Stocks Expensive? [Quant Dare]

    The world of finance is no stranger to fashion and Low volatility equity investing has recently attracted serious interest from the investment community. Its popularity has led to doubts regarding the valuation level for this overcrowded arena. Just look at the current market caps of the most representative ETFs of the low volatility anomaly. If we highlighted the best known, in 2011 they began
  • What’s All The Fuss About [Systematic Relative Strength]

    If you own last years laggards you are probably wondering what all the fuss over the market is about. It has been tough sledding for the leaders so far this year as they have underperformed the laggards by quite a bit. In one of the models we track, the laggards moved in to positive territory with yesterdays price action! We track a model of the S&P 500 Sub-Industry Groups that is broken
  • New R/MATLAB Package Released: High Frequency Price Estimators & Models [Portfolio Effect]

    We are happy to announce PortfolioEffectEstim toolbox availability for both R & MATLAB. It is designed for high frequency market microstructure analysis and contains popular estimators for price variance, quarticity and noise. For R https://cran.r-project.org/web/packages/PortfolioEffectEstim/ Or via downloads section: https://www.portfolioeffect.com/docs/platform/quant/tools/r For MATLAB

Quantocracy’s Daily Wrap for 02/16/2016

This is a summary of links featured on Quantocracy on Tuesday, 02/16/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: Unholy Grails: A New Road to Wealth [Amazon]

    Whats the fastest way to lose money? Follow the herd. Nick Radge stopped following the herd many years ago. As a trader and stock broker, Nick learnt to recognise what the herd were doing and how they react to financial information. He also realised that it made no sense. Are you one of the herd? Heres a test: If a stocks price is falling do you think it represents good value, i.e. its
  • Intra-day data from Quandl and a new tick database in town party time! [Mintegration]

    Quandl will soon be offering intra-day data (1 min bars). Rock on ! I was kindly given some data to test out (see below). I cant say much more than this but keep an eye out for an official announcement soon 🙂 With both QuantGo and Quandl offering reasonably priced intra-day data, smaller trading shops have never had it so good. Ive been involved in the integration (i.e. messaging) of
  • Active strategies are an allocation, not a trade [Flirting with Models]

    Summary Active strategies are often defined by the factor tilts they take on. For factor tilts to continue to out-perform the market over the long-run, they must exhibit premium volatility that causes short-term under-performance. Since alpha is zero-sum, investors that fold during periods of under-performance are passing the relative performance to investors with the stomach to hold. To truly
  • Trading strategies: No need for the holy grail [Predictive Alpha]

    We demonstrate that weak trading signals, which do not offer high risk-adjusted returns on their own, can be combined into a powerful portfolio. In other words, no need for holy grails when researching signals. We start our experiment with some key assumptions. We have 20 signals with annualized log returns of 8% and annualized Sharpe Ratios of 0.6 not exactly stellar signals. The signals make
  • Top Python Libraries for Automated Trading [Quant Insti]

    In one of our recent articles weve talked about most popular backtesting platforms for quantitative trading. Here we are sharing most widely used Python libraries for quantitative trading. Python is a free open-source and cross-platform language which has a rich library for almost every task imaginable and specialized research environment. Python is an excellent choice for automated trading

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