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Quantocracy’s Daily Wrap for 01/08/2016

This is a summary of links featured on Quantocracy on Friday, 01/08/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Interest Rates, Tax-Selling, and Stock Return Seasonality [Alpha Architect]

    We show that interest rates drive mispricing at the turn of a tax period as investors face the trade-off between selling a temporarily-depressed stock this period and selling next period at fundamental value, but with tax implications delayed accordingly. We confi rm these patterns in US returns, volume, and individual selling behavior as well as in UK data where tax and calendar years diff er. At
  • Employment Night Hot Streak Gone Cold [InvestiQuant]

    From August of 2012 until May of 2015 the night before the US Employment Report was a strong and consistent. Over that time period ES gapped up 76% of the time and the average employment night registered 5.00 ES points. I reported on the hot streak a number of times while it was in progress. But since then employment nights have cooled off dramatically. Below is a look at how employment nights

Quantocracy’s Daily Wrap for 01/07/2016

This is a summary of links featured on Quantocracy on Thursday, 01/07/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book from Meb Faber: Invest With The House: Hacking The Top Hedge Funds [Amazon]

    Picking stocks is hardand competitive. The most talented investors in the world play this game, and if you try to compete against them, its like playing against the house in a casino. Luck can be your friend for a while, but eventually the house wins. But what if you could lay down your bets with the house instead of against it? In the stock market, the most successful large
  • Augmented Dickey Fuller (ADF) Test for a Pairs Trading Strategy [Quant Insti]

    About two weeks ago I decided to attempt to write a blog series on Pairs trading and statistical arbitrage. What I found is that everyone tends to reference the ADF test but I really dont see a lot of posts that explain the test in full. As you read about building a pairs trading strategy there is talk of testing a pair for co-integration and then you learn that they use ADF to do this. However
  • Streaming OANDA with python and ZeroMQ [Shifting Sands]

    I have been looking at its REST API for OANDA, for potential use with an FX trading system I developed. The API has two streaming endpoints, one for prices and one for account events such as trades opening and stuff like that. Asynchronous IO is always a bit fiddly, and I wanted separate processes for incoming tick data and events. This enables them to be managed separately, and generally makes

Quantocracy’s Daily Wrap for 01/06/2016

This is a summary of links featured on Quantocracy on Wednesday, 01/06/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Technologies Screening I [Algorythmn Trader]

    This is the first destination from our Roadmap where what to post about my technologies screening. This was, and still is an exciting journey and I want to do it in several parts. Feel free to comment this blog or send me a mail with suggestions and I would come over it into the next part. The good thing about my project was that I has to solve a domain specific problem so I dont need to care
  • Genotick and the Dirty Sine (Machine Learning) [Throwing Good Money]

    I have been playing around with Genotick some more, the open-source genetic learning trading software by Lukasz Wojtow. One thing that has been puzzling me is that the software seems to do well on certain types of data, but not others. And Im having trouble identifying what sort of data its good at. At first I thought Genotick might have a long bias. It does smashingly well on
  • Using Stops: The Good, The Bad and The Ugly [Alvarez Quant Trading]

    I recently gave a presentation on Better System Trader about using stops on a breakout strategy. The research produced results I was not expecting and may be surprising to you. The stops tested are No stops Maximum Loss using ATR (Intraday and End of Day) Maximum Loss using percentage (Intraday) Trailing ATR (Intraday and End of Day) Profit target using ATR (Intraday and End of Day) The Strategy

Quantocracy’s Daily Wrap for 01/05/2016

This is a summary of links featured on Quantocracy on Tuesday, 01/05/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Daily Academic Alpha: Value and Momentum in Vietnam [Alpha Architect]

    An interesting out of sample test on return drivers in the Vietnamese market from 2006 to 2014. Not surprisingly, value and momentum show some mojo – Liquidity as wellsize, not so much. But size only matters if you control your junk, apparently. This is in line with our own research and the research of many others: Value: Never buy expensive stocks. Momentum: Ride Winners and Cut Losers. Size:
  • Are Size and Book-Value Factors Really Significant? [Quantpedia]

    The Fama and French (F&F) factors do not reliably estimate the size and book-to-market effects. Our paper shows that the former has been underestimated in the US market while the latter overestimated. We do so by replacing F&F's independent rankings by the conditional ones introduced by Lambert and Hubner (2013), over which we improve the sorting procedure. This new specification
  • [Academic Paper] Positive Skewness, Anti-leverage, Reverse Volatility Asymmetry, Short Sale Constraints: Chinese Markets [@Quantivity]

    There are some statistical anomalies in the Chinese stock market, i.e., positive return skewness, anti-leverage effect (positive returns induce higher volatility than negative returns); and reverse volatility asymmetry (contemporaneous return-volatility correlation is positive). In this paper, we first confirm the existence of these anomalies using daily firm-level stock return data on the raw

Best Links of the Week

The best quant mashup links for the week ending Saturday, 01/02 as voted by our readers.

We also welcome one blog making its first ever appearance on the mashup this week:

* * *

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 01/03/2016

This is a summary of links featured on Quantocracy on Sunday, 01/03/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Exploring mean reversion and cointegration: part 2 [Robot Wealth]

    In the first post in this series, I explored mean reversion of individual financial time series using techniques such as the Augmented Dickey-Fuller test, the Hurst exponent and the Ornstein-Uhlenbeck equation for a mean reverting stochastic process. I also presented a simple linear mean reversion strategy as a proof of concept. In this post, Ill explore artificial stationary time series and

Quantocracy’s Daily Wrap for 01/02/2016

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

  • VIX Trading Strategies in November & December [Volatility Made Simple]

    Weve tested 24 simple strategies for trading VIX ETPs on this blog (separate and unrelated to our own strategy). And while I cant speak for all traders, based on all of my readings both academic and in the blogosphere, the strategies weve tested are broadly representative of how the vast majority of traders are timing these products. Below Ive shown the November/December and full year
  • Trend Following In Financial Markets: A Comprehensive Backtest [Philosophical Economics]

    My metric for everything I look at is the 200-day moving average of closing prices. Ive seen too many things go to zero, stocks and commodities. The whole trick in investing is: How do I keep from losing everything? If you use the 200-day moving average rule, then you get out. You play defense, and you get out. Paul Tudor Jones, as interviewed by Tony Robbins in Money: Master
  • Ivy Portfolio January Update [Scott’s Investments]

    The Ivy Portfolio spreadsheet track the 10 month moving average signals for two portfolios listed in Mebane Fabers book The Ivy Portfolio: How to Invest Like the Top Endowments and Avoid Bear Markets. Faber discusses 5, 10, and 20 security portfolios that have trading signals based on long-term moving averages. The Ivy Portfolio spreadsheet tracks both the 5 and 10 ETF Portfolios listed in

Quantocracy’s Daily Wrap for 12/30/2015

This is a summary of links featured on Quantocracy on Wednesday, 12/30/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Towards a better equity benchmark: random portfolios [Predictive Alpha]

    Random portfolios deliver alpha relative to a buy-and-hold position in the S&P 500 index even after allowing for trading costs. Random portfolios will serve as our benchmark for our future quantitative equity models. The evaluation of quantitative equity portfolios typically involves a comparison with a relevant benchmark, routinely a broad index such as the S&P 500 index. This is an
  • Strong Rally Days Between Christmas & New Year s [Quantifiable Edges]

    The week between Christmas and New Years is often a quiet one that is not prone to large-move days. So strong rallies like we saw on Tuesday are a bit unusual this time of year. I looked back to 1970 to see what has followed other times when SPX rose over 1% on a day between Christmas and New Years. Results are below. 2015-12-30 image1 The stats here all point to a bullish edge. Most of the

Quantocracy’s Daily Wrap for 12/29/2015

This is a summary of links featured on Quantocracy on Tuesday, 12/29/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Three Value Investors Meet in a Bar [Investor’s Field Guide]

    Bill, Ernie and Samthree lifelong value investorsmet in a bar on November 30th, 2015. Bill was despondent. Hed underperformed the market by -47% over the past 10-years and was questioning his very belief in value. Ernie was happier. Hed done poorly in 2015, but over the last ten years hed outperformed the market by +19%. Sam offered to buy the next round. Hed outperformed by +52%
  • Portfolio Analysis in R: Part V | Risk Analysis Via Factors [Capital Spectator]

    In the previous installment in this series of analyzing a globally diversified portfolio we reviewed the results after adding a momentum-based risk-management system. The test suggested that a tactical overlay can be productive maybe, depending on the details. Lets continue to investigate our sample portfolio by taking a closer look at the underlying factors that are driving risk and return.
  • Upside and Downside Risks in Momentum Returns [Quantpedia]

    I provide a novel risk-based explanation for the profitability of momentum strategies. I show that the past winners and the past losers are differently exposed to the upside and downside market risks. Winners systematically have higher relative downside market betas and lower relative upside market betas than losers. As a result, the winner-minus-loser momentum portfolios are exposed to extra
  • Great Academic Research is Bursting at the Seams [Alpha Architect]

    Having been a full-time academic financial economist in a former life (still dabble, when able), I became accustomed to my annual pilgrimage to the annual American Finance Association (AFA) meeting. For the uninitiated, the AFA annual meeting is a gathering of all the major brainpower in academic finance. Getting a paper accepted into the program is a big deal for academic researchers (Ive had
  • Tis the Season for strange effects in the stock market [Alpha Architect]

    The efficient market hypothesis suggests that stock prices are always right in the sense that stock prices reflect all available information. Of course, during tax season, fundamentals go out the window: Im selling my losers, and letting my winners ride! And Im not the only investor thinking like this. But how can savvy investors leverage seasonality effects for their own