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Quantocracy’s Daily Wrap for 02/23/2016

This is a summary of links featured on Quantocracy on Tuesday, 02/23/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 02/22/2016

This is a summary of links featured on Quantocracy on Monday, 02/22/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book from GestaltU: Adaptive Asset Allocation: Dynamic Global Portfolios to Profit in Good Times – and Bad [Amazon]

    Build an agile, responsive portfolio with a new approach to global asset allocation Adaptive Asset Allocation is a no-nonsense how-to guide for dynamic portfolio management. Written by the team behind Gestaltu.com, this book walks you through a uniquely objective and unbiased investment philosophy and provides clear guidelines for execution. From foundational concepts and timing to forecasting and
  • Volatility Futures and S&P500 Performance [Blue Sky AM]

    Do Volatility Futures Provide Useful Information for Future S&P500 Performance? Volatility or VIX Futures are based on the S&P500 index and are calculated from the implied volatility of different option strike prices across different expiration periods. In contrast to the VIX index, VIX Futures represent forward expectations for volatility as well as the demand for insurance against tail
  • Alpha is not a risk management technique [Flirting with Models]

    Investors often focus their analysis on benefits. In the finance industry, weve distilled this down to a single metric: alpha. Benefits and risk require separate analysis. Increasing benefits does not necessarily reduce risk or even leave it unchanged. In practice, increasing alpha can actually increase risk in many portfolios. We would like to tip our hats to N.N. Taleb, whose recent writings
  • Advanced Trading Infrastructure – Portfolio Class [Quant Start]

    In the previous article in the Advanced Trading Infrastructure series I discussed and presented both the code and initial unit tests for the Position class that stores positional information about a trade. In this article we will consider the Portfolio class, used to store a list of Position classes, as well as a cash balance. In the last month I've made a lot of progress on QSTrader, the
  • Choosing your risk [Quants Portal]

    Risk is not a simple entity, it comes in many flavours, and requires respect and consideration even when you least expect. If one was to try totally avoid risk in their market endeavours they would most likely just be holding cash where are the returns in that? The truth is that in almost all circumstances one must take at least some form of risk to have the chance of potentially

Quantocracy’s Daily Wrap for 02/21/2016

This is a summary of links featured on Quantocracy on Sunday, 02/21/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Best Links While I Was Away [Quantocracy]

    Ive been off the beaten path for the last few weeks in East Africa, so Im long overdue one of these best of posts. Below are the best quant mashup links while I was away, as voted by our readers: Stock Market Prices Do Not Follow Random Walks [Turing Finance] Fitting time series models to the forex market: are ARIMA/GARCH predictions profitable? [Robot Wealth] A Quants Approach to
  • Genotick and UPRO [Throwing Good Money]

    That graph looks like a bunch of spaghetti, I realize. But Ill explain! Im back testing Genotick. Its an open-source machine-learning java script. Probably to the developers dismay, Im always throwing things at it to make it break. Much like a small child throwing a temper tantrum, but with stocks. The previous version of the software had an added feature, which allowed for
  • In Search of the Perfect Recession Indicator [Philosophical Economics]

    Given the downturn in the energy sector and the persistent economic weakness abroad, investors have become increasingly focused on the possibility of a U.S. recession. In this piece, Im going to examine a historically powerful indicator that would seem to rule out that possibility, at least for now. The following chart (source: FRED) shows the seasinally-adjusted civilian unemployment rate in

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