Quantocracy

Quant Blog Mashup

ST

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

Quantocracy’s Daily Wrap for 01/10/2017

This is a summary of links featured on Quantocracy on Tuesday, 01/10/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Go Skew Yourself with Managed Futures [Alpha Architect]

    Skewness is a statistical measure of how returns behave in the tails of a probability distribution. Wikipedia has a more robust definition of skewness with some good visuals here. If an investment (e.g., stocks) has negative skewness this means that the extreme returns are more likely to be negative than positive (it has a tendency to crash). However, if its return has a positive skewness (e.g.,

Quantocracy’s Daily Wrap for 01/09/2017

This is a summary of links featured on Quantocracy on Monday, 01/09/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Asymmetry of Reaching for Yield at Low Spreads [EconomPic]

    Bloomberg Gadfly's Lisa Abramowicz (follow her on twitter here) outlined in a recent piece The Credit Boom that Just Won't Die the insatiable demand for investment grade credit. Last month, bankers and investors told Bloomberg's Claire Boston that they expected U.S. investment-grade bond sales to finally slow after six consecutive years of unprecedented issuance. But the exact
  • Webinar: Alpha Generation 01/10/2017 [Portfolio Effect]

    Asset returns based on low frequency prices (e.g. end-of-day quotes) are still dominating modern portfolio analysis. To make portfolio metrics more relevant intraday and improve the precision of estimates, new data frequency needs to be explored. In this presentation we demonstrate how using high frequency market data for portfolio risk management and optimization could improve the classic
  • The Laguerre RSI vs Classic RSI [System Trader Success]

    John Ehlers is a name youll run across when you start your journey into testing various indicators and filters to be used in your trading models. I remember reading about the Laguerre Filter and Laguerre RSI many years ago when they first appeared on the scene. At the time I was not nearly into quantitative trading as I am today. So lets take a closer look at the Laguerre RSI and answer a

Quantocracy’s Daily Wrap for 01/08/2017

This is a summary of links featured on Quantocracy on Sunday, 01/08/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quantitative Momentum with Jack Vogel (@jvogs02) [Better System Trader]

    The guest for this episode is Jack Vogel from Alpha Architect, a quantitative asset management and consulting firm. Jack has published a number of papers on SSRN and also co-authored a couple of books including Quantitative Momentum: a practitioners guide to building a momentum-based stock selection system. In our chat with Jack you will hear:
  • Seasonalities in Stock Returns [Quantpedia]

    Existing research has documented cross-sectional seasonality of stock returns the periodic outperformance of certain stocks relative to others during the same calendar month, weekday, or pre-holiday periods. A model based on the differential sensitivity of stocks to investor mood explains these effects and implies a new set of seasonal patterns. We find that relative performance across stocks

Quantocracy’s Daily Wrap for 01/07/2017

This is a summary of links featured on Quantocracy on Saturday, 01/07/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Advanced Time Series Plots in Python [Black Arbs]

    POST OUTLINE Motivation Get Data Default Plot with Recession Shading Add Chart Titles, Axis Labels, Fancy Legend, Horizontal Line Format X and Y Axis Tick Labels Change Font and Add Data Markers Add Annotations Add Logo/Watermarks MOTIVATION Since I started this blog a few years ago, one of my obsessions is creating good looking, informative plots/charts. I've spent an inordinate amount of

Quantocracy’s Daily Wrap for 01/06/2017

This is a summary of links featured on Quantocracy on Friday, 01/06/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Beat the Market with Meucci and Markowitz [Propfolio Management]

    I am very excited to finally share some of my research exploring Meuccis (Meucci (2005)) portfolio optimization methods, and how the resulting portfolios compare to the use of historical data. For those unfamiliar with Attilio Meucci, he runs an annual Advanced Risk and Portfolio Managment Bootcamp in New York City every summer. The bootcamp attracts academics and professionals within the
  • Writing Puts, Or Just Pretending To [Throwing Good Money]

    Which color do you like better? Green or brown? Im partial to the green curve myself. That green curve comes from writing putssort of. Writing puts can be a lower volatility play that makes you money in choppy or flat markets, falls more softly in down markets, and seriously under-performs when the market goes on a tear upward. Whats a put and where do you put them? If youre an