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Quantocracy’s Daily Wrap for 01/26/2017

This is a summary of links featured on Quantocracy on Thursday, 01/26/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Algorithmic Options Trading, Part 1 [Financial Hacker]

    Despite the many interesting features of options, private traders rarely take advantage of them (Im talking here of serious options, not binary options). Maybe options are unpopular due to their reputation of being complex. Or due to their lack of support by most trading software tools. Or due to the price tags of the few tools that support them, and the historical data that you need. Whatever
  • Country ETF Rotation [Alvarez Quant Trading]

    My recent research has been focused on finding strategies that are not highly correlated with the S&P500 index. One of my most popular posts is ETF Sector Rotation. The idea for this post is to apply those concepts to a list of country ETFs. Would this produce decent returns that were not highly correlated to the S&P500 index? I would like to see the correlation under .50. What about
  • Density Confidence Interval [Eran Raviv]

    Density estimation belongs with the literature of non-parametric statistics. Using simple bootstrapping techniques we can obtain confidence intervals (CI) for the whole density curve. Here is a quick and easy way to obtain CIs for different risk measures (VaR, expected shortfall) and using what follows, you can answer all kind of relevant questions. Density Confidence Interval To get to the
  • Common Factor Structure in a Cross-Section of Stocks [Quantpedia]

    We seek to describe the broad cross-section of average stock returns. We follow the APT literature and estimate the common factor structure among a large cross-section containing 278 decile portfolios (associated with 28 market anomalies). Our statistical model contains seven common factors (with an economic meaning) and prices well both the original portfolio returns and an efficient combination

Quantocracy’s Daily Wrap for 01/25/2017

This is a summary of links featured on Quantocracy on Wednesday, 01/25/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Keuning & Keller’s Generalized Protective Momentum [Allocate Smartly]

    This is a test of the Generalized Protective Momentum (GPM) strategy from JW Keuning and Wouter Keller. The strategy builds off of the authors popular Protective Asset Allocation (PAA) model that we discussed last month. Results for the GPM strategy from 1989, net of transaction costs, follow. Read more about our backtests or let AllocateSmartly help you follow this strategy in near real-time.
  • Analyzing Portfolios With Risk-Factor Profiles [Capital Spectator]

    Most investment portfolios are a collection of risk factors, such as exposure to credit and equity risk. Monitoring and managing these factors is critical. The standard approach is reviewing portfolios through a plain-vanilla asset allocation lens 60% stocks, 30% bonds, 10% cash, for instance. But the standard methodology is a blunt instrument. For a clearer view of whats driving your

Quantocracy’s Daily Wrap for 01/22/2017

This is a summary of links featured on Quantocracy on Sunday, 01/22/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Sentiment Analysis Trading Strategy via Sentdex Data in QSTrader [Quant Start]

    In addition to the "usual" tricks of statistical arbitrage, trend-following and fundamental analysis, many quant shops (and retail quants!) engage in natural language processing (NLP) techniques to build systematic strategies. Such techniques fall under the banner of Sentiment Analysis. In this article a group of quantitative trading strategies will be developed that utilise a set of
  • Risk Management with @InvestingIdiocy [Better System Trader]

    Risk Management Its not as sexy as the latest hot indicator Or the undiscovered penny stock poised for an explosive move Or the trading guru who appeared out of nowhere and is now promising to share the secrets to making million dollar profits overnight But there are a whole host of risks that have the potential to destroy trading accounts in just seconds, so its an
  • PutWrite vs. BuyWrite Index Differences [Quantpedia]

    The CBOE PutWrite Index has outperformed the BuyWrite Index by approximately 1.1 percent per year between 1986 and 2015. That is pretty impressive. But troubling. Yes troubling because the theory of put-call parity tells us that such outperformance should be almost impossible via a compelling no-arbitrage restriction. This paper explains the mystery of this outperformance, which has
  • Machine Learning: An Introduction to Decision Trees [Quant Insti]

    A decision tree is one of the widely used algorithms for building classification or regression models in data mining and machine learning. A decision tree is so named because the output resulting from it is the form of a tree structure. Visualizing a sample dataset and decision tree structure Consider a sample stock dataset as shown in the table below. The dataset comprises of Open, High, Low,

Quantocracy’s Daily Wrap for 01/20/2017

This is a summary of links featured on Quantocracy on Friday, 01/20/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Cointegrated ETF Pairs Part II [Quantoisseur]

    Welcome back! This weeks post will backtest a basic mean reverting strategy on a cointegrated ETF pair time series constructed using the methods described in part I. Since the EWA (Australia) EWC (Canada) pair was found to be more naturally cointegrated, I decided to run the rolling linear regression model (EWA chosen as the dependent variable) with a lookback window of 21 days on this pair

Quantocracy’s Daily Wrap for 01/19/2017

This is a summary of links featured on Quantocracy on Thursday, 01/19/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Forecasting Returns with Shiller s CAPE and its 35-Year Moving Average [iMarketSignals]

    Shillers Cyclically Adjusted Price to Earnings Ratio (CAPE ratio) is at 27.8, which is 11.1 above its long-term mean of 16.7, signifying overvaluation of stocks and low forward returns. According to Jeremy Siegel it incorporates time-inconsistent data, and the failure to correct for changes in accounting methodology led to substantial under prediction of realized stock returns in recent
  • Why Bayesian Variable Selection Doesn t Scale [Alex Chinco]

    Traders are constantly looking for variables that predict returns. If x is the only candidate variable traders are considering, then its easy to use the Bayesian information criterion to check whether x predicts returns. Previously, I showed that using the univariate version of the Bayesian information criterion means solving () \begin{align*} \hat{\beta} &= \arg \min_{\beta} \big\{ \,

Quantocracy’s Daily Wrap for 01/18/2017

This is a summary of links featured on Quantocracy on Wednesday, 01/18/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A New Study Quantifies The Impact Of Time Horizon On Risk [Capital Spectator]

    Can you distinguish alpha from beta? Childs play, right? Measure an investment portfolio against a relevant benchmark and, voila, all is clear. But as a new paper reminds, analyzing risk and return based on time horizon changes a black-and-white world of equity factors into 50 shades of gray. Different risk factors are priced over different horizons, write the authors of Short-horizon
  • A Tactical Asset Allocation Researcher You Should Know [Alpha Architect]

    Im a huge fan of hard-core academics that produce incredible research, and yet, very few are familiar with their research. I call these folks, undiscovered gems. One might ask why undiscovered gems exist. On one hand, if a researcher produces incredible research, they should be widely recognized. However, this logical construct relies on an assumption: good researchers are good at sharing
  • Playing with Docker – some initial results (pysystemtrade) [Investment Idiocy]

    This post is about using Docker – a containerisation tool – to run automated trading strategies. I'll show you a simple example of how to use Docker with my python back testing library pysystemtrade to run a backtest in a container, and get the results out. However this post should hopefully be comprehensible to non pysystemtrade and non python speaking people as well. PS: Apologies for the
  • Essential Books on Algorithmic Trading [Quant Insti]

    These are some of the questions that popular forums get inundated with from aspiring novice algorithmic traders around the world. A good starting point for a wannabe trader would be to pick up a good book, immerse oneself, and absorb all that the book has to offer. This post details down the core areas in which aspiring quants need to focus on, and covers some of the good reads in each of these
  • Asset Allocation is Not for the Faint of Heart (Long Live Diversification) [GestaltU]

    Im starting to feel like a rancourous curmudgeon, but I am frustrated by some of the misguided commentary on asset allocation and how diversification is a myth. We have posted a lot of research on fairly complex asset allocation topics, but I think many readers would be surprised to learn that I am actually highly skeptical about historical market statistics. I dont think that we can draw

Quantocracy’s Daily Wrap for 01/15/2017

This is a summary of links featured on Quantocracy on Sunday, 01/15/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Purifying Factor Premiums in Equity Markets [Quantpedia]

    In this paper we consider the question of how to improve the efficacy of strategies designed to capture factor premiums in equity markets and, in particular, from the value, quality, low risk and momentum factors. We consider a number of portfolio construction approaches designed to capture factor premiums with the appropriate levels of risk controls aiming at increasing information ratios. We

Quantocracy’s Daily Wrap for 01/13/2017

This is a summary of links featured on Quantocracy on Friday, 01/13/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Caveat On Backtesting Caveats [Capital Spectator]

    Ben Carlson at Ritholtz Asset Management reminds us that backtesting offers no shortcuts to investment nirvana. As he correctly points out, there are numerous shortcomings in the art/science of reconstructing the historical results of an investment strategy. But its also true that backtesting, if used wisely, can be a powerful tool for sensibly managing expectations with regards to return and
  • A Decade of Trend Following [Wisdom Trading]

    Last year was not a good year for trend following, with many commenting that the performance for the strategy has been declining over the last few years. We decided to look at the performance of the Wisdom State of Trend Following index on a long timeframe, to let the results speak over the long term, rather than focusing on recent performance. Here are the results over the last decade:
  • It’s 2017: Do You Know Where Your Risk Is? [Flirting with Models]

    Last weeks commentary highlighted why we believe traditionally built portfolios may face return headwinds going forward. Traditionally built stock/bond allocations also exhibit extremely high risk concentrations. Non-traditional exposures, now available as low-cost ETFs, can help introduce non-standard risk exposures. By combining non-traditional exposures, we can build a portfolio that has a

Quantocracy’s Daily Wrap for 01/12/2017

This is a summary of links featured on Quantocracy on Thursday, 01/12/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Aluminum Smelting Cointegration Strategy in QSTrader [Quant Start]

    In previous articles the concept of cointegration was considered. It was shown how cointegrated pairs of equities or ETFs could lead to profitable mean-reverting trading opportunities. Two specific tests were outlinedthe Cointegrated Augmented Dickey-Fuller (CADF) test and the Johansen testthat helped statistically identify cointegrated portfolios. In this article QSTrader will be used to

Quantocracy’s Daily Wrap for 01/11/2017

This is a summary of links featured on Quantocracy on Wednesday, 01/11/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The January Effect: An Evidence-Based Perspective [Alpha Architect]

    January is here again and market commentators are already telling stories about the so-called January Effect. Some articles (examples here and here) are saying the effect is an illusion, while others are claiming the effect can help you make some profits (examples here and here). Before we dig into the academic research on the subject, lets first understand the January Effect. Put simply, the
  • Cointegrated ETF Pairs Part I [Quantoisseur]

    The next two blog posts will explore the basics of the statistical arbitrage strategies outlined in Ernest Chans book, Algorithmic Trading: Winning Strategies and Their Rationale. In the first post we will construct mean reverting time series data from cointegrated ETF pairs. The two pairs we will analyze are EWA (Australia) EWC (Canada) and IGE (NA Natural Resources) EWZ (Brazil). 1
  • When Noise Overwhelms Signal Sorting out Sorts Review [Alphaism]

    In his 1998 paper, Jonathan Berk illustrated that by sorting stocks based on a variable (e.g. B/E ratio) correlated to a known variable (e.g. beta), the power of the known variable to predict expected return within each group diminishes when tested with cross-sectional regression. This is very likely why Fama and French found the explanatory power of beta disappeared (1992) and Daniel and Titman