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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

Quantocracy’s Daily Wrap for 02/01/2016

This is a summary of links featured on Quantocracy on Monday, 02/01/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The three oenophiles [Flirting with Models]

    Summary Most investment strategies can be broken down into the risk premia they wish to harvest, whether it is vanilla like the equity risk premium or more exotic, like the value premium. Different risk premia mature at different rates. Value can take years to mature while momentum can take only a few weeks. Aligning your approach to rebalancing with how long you expect the premium to take to

Best Links of the Last Two Weeks

The best quant mashup links for the two weeks ending Saturday, 01/30 as voted by our readers:

We also welcome two blogs making their first ever appearance on the mashup:

* * *

My fellow traders, ask not what Quantocracy can do for you, ask what you can do for Quantocracy. Vote for your favorite links on our quant mashup to encourage bloggers to write quality content. We do our part by providing this site without annoying advertising. All we ask is that you take a moment to participate in the process.

If you haven’t done so already, register to vote. Once registered, you can choose to remain logged in indefinitely, making voting as simple and painless as possible.

Read on Readers!
Mike @ Quantocracy

Quantocracy’s Daily Wrap for 01/29/2016

This is a summary of links featured on Quantocracy on Friday, 01/29/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Correlations, Weights, Multipliers…. (pysystemtrade) [Investment Idiocy]

    This post serves three main purposes: Firstly, I'm going to explain the main features I've just added to my python back-testing package pysystemtrade; namely the ability to estimate parameters that were fixed before: forecast and instrument weights; plus forecast and instrument diversification multipliers. (See here for a full list of what's in version 0.2.1) Secondly I'll be
  • Computers vs Humans – considering the median [Investment Idiocy]

    Or why you aren't, and will never be, John Paulson The systematic versus discretionary trading argument is alive and well; or if you prefer, computers versus humans*. In this post I pose the question – who is better the average systematic trader, or the average discretionary human? * Though even fully discretionary traders will be relying on a computer at some point; fully manual trading and
  • The most concise explanation of behavioral finance I’ve ever seen [Alpha Architect]

    One of the most overused and misunderstood terms Ive seen used by finance practitioners is behavioral finance. Many professionals consider themselves to be behavioral finance experts because they identify irrational investors.1 Newsflash: Identifying irrational investors is not behavioral finance. But here is a great summary from a Baker, Bradley, and Wurgler paper weve
  • Peak Crashes: Are They a Shortable Opportunity? [Throwing Good Money]

    In hindsight, its fun to look at stocks that have had a huge surge, only to collapse violently after they peak. And by fun I mean sitting on the sidelines watching, as opposed to pulling ones hair out when youre long that particular trade. Above you can see Apple (AAPL) in 2008, where it hit its 52-week high, only to collapse a few days later, for a very significant loss. Are these
  • Latest Twist In The Stock Market s Wild 2016 Ride [Dana Lyons]

    The stock markets wild ride to begin the year continues, with the latest twist reminiscent of a roller coaster. Over the past 4 days, the Dow Jones Industrial Average (DJIA) has moved at least 1%, with each day alternating up and down. Since 1900, this is the 68th such streak and just the 17th in the past 70 years (actually, today narrowly missed making it 5 days in a row which would have been

Quantocracy’s Daily Wrap for 01/27/2016

This is a summary of links featured on Quantocracy on Wednesday, 01/27/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Case For High Volatility Strategies [EconomPic]

    Which investment would you prefer to invest in to diversify your existing stock allocation? Asset A with an expected: 3% annualized return 3.5% annualized standard deviation 0.00 correlation with your existing investment Asset B with an expected: -5% annualized return > 50% annualized standard deviation 0.00 correlation with your existing investment Easy question right? Perhaps not. Asset B may
  • Technologies Screening II [Algorythmn Trader]

    In my last post I introduced how I identified possible technologies and learned the basics about different languages, operating systems and IDEs. I also mentioned that my choice was to build on top of .Nets C# and Visual Studio, as my base for developing. In this post I want to dig a little deeper into the overall tool chain for my trading system development. When I was starting to work with
  • How Does Analyst Optimism Affect Momentum Strategies? [Alpha Architect]

    We examine the effect of security analyst recommendations on stock price momentum. Results show that momentum profits are directly linked to analyst optimism. Specifically, we find that a 1-unit change in recommendation quintile translates to about a 50 basis point change in subsequent 3-month momentum profits. We also examine uncovered stocks by using parallel projection methods to project
  • The Internal Bar Strength Indicator [Backtest Wizard]

    The internal bar strength or (IBS) is an oscillating indicator which measures the relative position of the close price with respect to the low to high range for the same period. The calculation for Internal Bar Strength is as follows IBS = (Close Low) / (High Low) * 100; For example, on 13/01/2016 the QQQ etf had a high price of $106.23, a low price of $101.74 and a close price of
  • 7 Best Backtesting Platforms for Quantitative Trading [Quant Insti]

    We have a large number of vendor-developed backtesting platforms available in market which can be very efficient in backtesting automated strategies; but to decide which once will suit your requirements, needs some research. Ideally custom development of a backtesting environment within a first-class programming language provides the most flexibility and third party platforms might make a number
  • ‘Tis the Season for Bold Prediction [GestaltU]

    It is with a giddy sense of schadenfreude that every year around this time, we get to read bold prediction rubbish like this: A best-selling personal finance guru, a behavioral economics columnist at MarketWatch, a Harvard-educated economist and other notable financial experts all warn the stock market is going to hell in 2016. Wall Streets major powerhouses, on the other hand, beg to differ.

Quantocracy’s Daily Wrap for 01/26/2016

This is a summary of links featured on Quantocracy on Tuesday, 01/26/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Random portfolios: correlation clustering [Predictive Alpha]

    We investigate whether two clustering techniques, k-means clustering and hierarchical clustering, can improve the risk-adjusted return of a random equity portfolio. We find that both techniques yield significantly higher Sharpe ratios compared to random portfolio with hierarchical clustering coming out on top. Our debut blog post Towards a better benchmark: random portfolios resulted in a lot of
  • Linear regression assumes nothing about your data [Eran Raviv]

    We often see statements like linear regression makes the assumption that the data is normally distributed, Data has no or little multicollinearity, or other such blunders (you know who you are..). Lets set the whole thing straight. Linear regression assumes nothing about your data It has to be said. Linear regression does not even assume linearity for that matter, I argue. It is
  • Breadth Diffusion Predicts a Bounce? [Throwing Good Money]

    Recently I posted a number of articles on various breadth diffusion indicators and their relative effectiveness in predicting the health of the S&P 500. The big winner was the system that compared the number of stocks in the historical constituents of the Russell 3000 that were up 30% or more over the last quarter (60 trading days) vs those were 30% or down over the same period. You can read
  • RUT Straddle – Normalized Return Charts [DTR Trading]

    In the last two articles (here and here), we reviewed the backtest results of 28,840 short options straddles on the Russell 2000 Index (RUT). If you haven't read the last two articles, you may want to first read the introductory article for this series Option Straddle Series – P&L Exits. In this post, I am going to show the P&L results in line-chart form rather than the heat map
  • Are Stocks Cheap? Checking in on Current Valuations [EconomPic]

    I'll leave it to others to chime in whether forward P/E's are useful or not given the fact they typically overstate earnings and I'll ignore that earnings may be at a cyclical peak (more on the latter here). As an aside, technicals in the market are filthy, as most short-term signals I look at are providing caution (example here). BUT, based purely on current forward P/E's

Quantocracy’s Daily Wrap for 01/25/2016

This is a summary of links featured on Quantocracy on Monday, 01/25/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Advanced Trading Infrastructure – Position Class [Quant Start]

    At the end of last year I announced that I would be working on a series of articles regarding the development of an Advanced Trading Infrastructure. Since the initial announcement I haven't mentioned the project to any great extent. However, in this article I want to discuss the progress I've made to date. At the outset I decided that I wanted to re-use and improve upon as much of the
  • Dissecting a trend following strategy in 2015 [Flirting with Models]

    Summary We run a U.S. sector-based, long-or-flat trend following strategy. With largely sideways market action, 2015 was a tough year for trend following, especially for long-only trend following, since the large negative trend in the Energy sector could not be monetized. We believe portfolio construction can be broken down into the signals generated and the rules used to transform these signals
  • Following Opportunistic insiders Earns over 1% Monthly Alpha [Alpha Architect]

    We show that opportunistic insider traders can be identified through the profitability of their trades prior to quarterly earnings announcements (QEAs), and that opportunistic trading is associated with various other kinds of managerial and firm misconduct. The subsequent buys and sells of opportunistic insiders (insiders with high past pre-QEA profits) are substantially more profitable than those

Quantocracy’s Daily Wrap for 01/23/2016

This is a summary of links featured on Quantocracy on Saturday, 01/23/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • [Academic Paper] Using Financial Reports to Predict Stock Market Trends With Machine Learning Techniques

    Stock markets as a fundamental component of financial markets play an important role in the countries economies. The factors that a ? ect the price of stocks include the political situations, company performance, economics activities, and some other unpredicted events. The traditional prediction approach is based on historical nu- merical data such as the previous trend, trading volume,
  • The Few Rule the Many Power Laws in Market Returns [Investor’s Field Guide]

    As index investing has grown in popularity, investors focus more and more on the markets overall return and less on the return of its component parts (individual stocks). But underneath the hood of each market index we find many inequalities. The top 20% of stocks represent 85% of the overall markets size in 2015. Similarly, the top 20% of the stocks account for 85% of the markets total