Quantocracy

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Quantocracy’s Daily Wrap for 08/25/2016

This is a summary of links featured on Quantocracy on Thursday, 08/25/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation Software [Meb Faber]

    I used to update an old post on free data sources and stock screeners for investors. I thought Id summarize a handful of websites that focus on tactical asset allocation software, tools, and backtesters. For a long time I was going to build this on tacticalassetallocation.com, but there are now lots of resources here so we just make our Excel sheet available on The Idea Farm. In no particular
  • Cesar’s Ask Me Anything Webinars [Alvarez Quant Trading]

    To those on my new blog notification list, I sent out the opportunity to join me in a one hour webinar where people could ask me anything about trading. I had a ton of fun answering lots of great questions. See the bottom of the post for links to download the mp3 files of the webinars. Some questions, I answered are: What types of strategies are you trading? How long a period of underperformance
  • Managed Futures: Understanding a Misunderstood Diversification Tool [Alpha Architect]

    In my two previous blog posts (here and here), I analyze the performance of bonds during really bad months for US stocks (Crisis Alpha months), and I analyze the performance of US stocks during really bad months for US bonds. A quick summary of the results from those prior studies: Bonds have historically provided some diversification benefit during bad months for stocks. Bonds have

Quantocracy’s Daily Wrap for 08/24/2016

This is a summary of links featured on Quantocracy on Wednesday, 08/24/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation Performance in July [Allocate Smartly]

    This is a summary of the recent performance of a number of excellent asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. They range from simple, static portfolio allocations, to complex and dynamic portfolio optimization. AllocateSmartly is still its early days, so expect to see this list grow in the months and years ahead. Read more about

Quantocracy’s Daily Wrap for 08/23/2016

This is a summary of links featured on Quantocracy on Tuesday, 08/23/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The folly of panic selling [Mathematical Investor]

    Mark Hulbert has compiled an interesting list of recent market panics: August 2015: Concerns about the Chinese economy and stock market led to panic selling, with the Shanghai index plunging 8.5% in one day. Soon after in the U.S., on August 24, 2015, the DJIA plunged over 1,000 points in just a few minutes, its most precipitous drop ever, ending the day down 588 points, its worst one-day loss in
  • Client -1- Intro [Algorythmn Trader]

    After I covered some basics about WCF Services and setup a server, we need to connect a client. In this post I want explain a little more the overall design philosophy. Than in followup posts we come closer to coding and bring it all up. First, lets talk about some basic design stuff. There are many choices and crossroads when it comes to front end design. The first and most important choice is
  • Equity Anomalies Persist in International Markets [Quantpedia]

    Motivated by McLean and Pontiff (2016), we study the pre- and post-publication return predictability of 138 anomalies in 39 stock markets. Based on more than a million anomaly country-months, we find that the United States is the only country with a statistically significant and economically meaningful post-publication decline in long/short returns. The surprisingly large differences between the

Quantocracy’s Daily Wrap for 08/22/2016

This is a summary of links featured on Quantocracy on Monday, 08/22/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Are Stocks Actually Undervalued? [Flirting with Models]

    Summary We have noticed the market reaching a broad consensus that equities are overvalued, implying a drag on forward expected returns as valuation multiples contract. While there is often great wisdom in the crowd, there can also be great madness. We believe it is prudent to consider how the crowd might be wrong. In this commentary, we explore why valuations matter in the first place and how if
  • Metal Logic [Jonathan Kinlay]

    Precious metals have been in free-fall for several years, as a consequence of the Feds actions to stimulate the economy that have also had the effect of goosing the equity and fixed income markets. All that changed towards the end of 2015, as the Fed moved to a tightening posture. So far, 2016 has been a banner year for metal, with spot prices for platinum, gold and silver up 26%, 28% and 44%
  • Optimizing Mean Variance Optimization [Alpha Architect]

    In the 1950s, Harry Markowitz proposed a method to identify the optimal trade-off between risk and return for a portfolio. The theory is broadly termed, Mean-Variance Optimization (MVO). Sam Wittig, a Drexel graduate I advised and who did some research for Alpha Architect, shared with us his undergraduate thesis project regarding Markowitzs analysis. Here is a link to Sams work:
  • Beginner’s Guide to Decision Trees for Supervised Machine Learning [Quant Start]

    In this article we are going to consider a stastical machine learning method known as a Decision Tree. Decision Trees (DTs) are a supervised learning technique that predict values of responses by learning decision rules derived from features. They can be used in both a regression and a classification context. For this reason they are sometimes also referred to as Classification And Regression
  • PyFolio Performance Reporting in Python [Largecap Trader]

    Pyfolio is a Python library that takes a return series of an asset, hedge fund, trading strategy, anything with daily returns and automatically generates some really cool statistics and charts. There is a LOT of cool stuff to explore in the library, have fun! Performance statistics Backtest annual_return 0.98 annual_volatility 0.62 sharpe_ratio 1.41 calmar_ratio 1.71 stability_of_timeseries 0.91
  • Importing CSV Data in Zipline for Backtesting [Quant Insti]

    In our previous article on Introduction to Zipline package in Python, we created an algorithm for moving crossover strategy. Recall, Zipline is a Python library for trading applications and to create an event-driven system that can support both backtesting and live-trading. In the previous article, we learnt how to implement Moving Average Crossover strategy on Zipline. The strategy code in
  • Use Caution With Low Vol Strategies [Larry Swedroe]

    As we have discussed before, one of the major problems for the first formal asset pricing model developed by financial economists, the capital asset pricing model (CAPM), was that it predicts a positive relation between risk and return. But empirical studies have found the actual relation to be flat, or even negative. Over the past five decades, the most defensive stocks have furnished

Quantocracy’s Daily Wrap for 08/21/2016

This is a summary of links featured on Quantocracy on Sunday, 08/21/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Finding Alpha pdf [Falkenblog]

    My book The Missing Risk Premium is a steal at only $15, but my first book, Finding Alpha, is a $65, which is a bit much for anyone not expensing their books. Finding Alpha goes over why the current asset pricing model fails, with lots of evidence, explains why economists still like it, and then in chapters 10-13 shows concrete examples of how investors have actually found alpha. The risk begets

Quantocracy’s Daily Wrap for 08/19/2016

This is a summary of links featured on Quantocracy on Friday, 08/19/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trading strategy: Making the most of the out of sample data [R Trader]

    When testing trading strategies a common approach is to divide the initial data set into in sample data: the part of the data designed to calibrate the model and out of sample data: the part of the data used to validate the calibration and ensure that the performance created in sample will be reflected in the real world. As a rule of thumb around 70% of the initial data can be used for calibration
  • Taleb: Silent Risk, Section 1.4.4 Mean Deviation vs Standard Deviation [Blue Event Horizon]

    We are going to play around with a mixture distribution made up of a large proportion of ~N(0, 1) and a small proportion of ~N(0, 1+a). The wider distribution is "polluting" the standard normal distribution. We are going to see that mean absolute deviation is a more efficient estimator of the distribution's dispersion than standard deviation. We are also going to see some unexpected
  • Dealing with Delistings: A Critical Aspect for Stock-Selection Research [Alpha Architect]

    Eric Crittenden was recently on Meb Fabers podcast and he tells a compelling story about the perils of survivor bias in backtesting. Erics story begins when he is an undergraduate working on a project for a quantitative finance course. The professor asked that the students develop a systematic investment program and get their hands dirty with backtesting. Eric decided to backtest a portfolio
  • Dividend income investing this is what really works [Quant Investing]

    Is your high dividend investment strategy based on buying companies with a high dividend yield and high dividend cover? Saving_chalkIf so you can do a lot better. In this article I summarise an interesting research paper that found the normal way most investors look at dividend income investing is all wrong. I also show you how to find ideas that fit with what the researchers found that really

Quantocracy’s Daily Wrap for 08/17/2016

This is a summary of links featured on Quantocracy on Wednesday, 08/17/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Risk Parity isn’t the Problem, it’s the Solution [GestaltU]

    Bank of America Merrill Lynch recently released a research note suggesting that Risk Parity investment strategies currently represent a substantial source of systematic risk in global markets. The note was picked up breathlessly by several media outlets and posted under sensationalist headlines employing eye-catching terms like "spectre," and "mayhem." The introduction to the
  • Surprise! Size, Value and Momentum Anomalies Survive After Trading Costs [Alpha Architect]

    Anyone who has spent time reading this blog has become familiar with research involving asset pricing anomalies that generate excess returns. In particular, the academic literature has addressed the following: size, or a portfolio of small minus big stocks (SMB) (see here for background) value, or a portfolio of high minus low book-to-market stocks (HML) (tons of research on value

Quantocracy’s Daily Wrap for 08/16/2016

This is a summary of links featured on Quantocracy on Tuesday, 08/16/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Podcast: Market behavior with Adam Grimes [Better System Trader]

    Today's guest is a trader that has been requested quite a few times actually, I've had a lot of requests to have this person as a guest on the show, and the guest is Adam Grimes. Adam has two decades of experience in the industry as a trader, analyst and system developer and is currently Chief Investment Officer of Waverly Advisors. He's previously held positions at Level Partners,