Quantocracy

Quant Blog Mashup

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Quantocracy’s Daily Wrap for 12/30/2016

This is a summary of links featured on Quantocracy on Friday, 12/30/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • PortfolioCharts’ Golden Butterfly [Allocate Smartly]

    This is a test of the Golden Butterfly, the homegrown buy & hold strategy from PortfolioCharts.com. PortfolioCharts is to buy & hold what AllocateSmartly is to tactical asset allocation, an independent and unbiased catalog of strategy performance, so when they put their stamp of approval on a portfolio, it deserves consideration. This strategy is similar in concept to Brownes

Quantocracy’s Daily Wrap for 12/28/2016

This is a summary of links featured on Quantocracy on Wednesday, 12/28/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Most popular posts – 2016 [Eran Raviv]

    Another year. Looking at my google analytics reports I cant help but wonder how is it that I am so bad in predicting which posts would catch audience attention. Anyhow, top three for 2016 are: On the 60/40 portfolio mix The case for Regime-Switching GARCH Most popular machine learning R packages And my personal favorites: ASA statement on p-values Why bad trading strategies may perform well?

Quantocracy’s Daily Wrap for 12/27/2016

This is a summary of links featured on Quantocracy on Tuesday, 12/27/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Reflecting on Research in 2016 [Flirting with Models]

    On behalf of the entire Newfound Research team, we would like to wish you and yours a happy holiday season. We treat this weekly research commentary as a sacred part of our investment process. We continue to be honored and humbled by the vast and growing number of readers it reaches, a sign of the trust and confidence you place in our work. To remain transparent can often be uncomfortable, as new
  • Recommended Quant Readings for you Best of 2016! [Quant Insti]

    As 2016 nears its finish line, here we are with the list of recommended reading on our blog with the top-rated blog posts, as voted by you! Enjoy the last few days doing what you love most! Read on. System Architecture of Algorithmic Trading This one is straight out of a lecture in the curriculum of QuantInstis Executive Programme in Algorithmic Trading (EPAT). It compares the traditional

Quantocracy’s Daily Wrap for 12/24/2016

This is a summary of links featured on Quantocracy on Saturday, 12/24/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean Reversion Volatility Strategy [Milton FMR]

    Ever wondered if you can design a profitable trading strategy by trading volatility ETFs ? Well, yes you can. Those ETFs are highly ineffective vehicles on a long term investment horizon. However short term strategies have shown to be a rewarding way to trade these ETFs. Before we move onto strategy design we have to choose two volatility ETFs for backtesting. We will backtest our strategies with

Quantocracy’s Daily Wrap for 12/23/2016

This is a summary of links featured on Quantocracy on Friday, 12/23/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Using Absolute Momentum to Positively Skew Calendar Year Returns [EconomPic]

    There are instances where I "borrow" an idea from someone (actually… most of my posts were at a minimum inspired by someone else). In this case, I am stealing the initial concept from Ryan Detrick who posted the following chart of annual U.S. stock returns going back ~200 years as there is a lot of interesting information in his chart. As Ryan pointed out in a supporting post most

Quantocracy’s Daily Wrap for 12/22/2016

This is a summary of links featured on Quantocracy on Thursday, 12/22/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Time Series Momentum, Volatility Scaling, and Crisis Alpha [Alpha Architect]

    If you couldnt tell from our recent monster commodity futures post, weve been thinking a lot about futures recently. The futures research area is relatively fresh, and a lot more exciting than hacking through equity stock selection research where we already understand the basic answer buy cheap/quality, buy strength, and embrace relative performance pain. As part of our research
  • Applying Genetic Algorithms to define a Trading System [Quant Dare]

    When talking about quantitative trading, there are a large number of indicators and operators we can use as a buy/sell rule. But apart from deciding what indicator we will follow, the most important part would be setting the correct parameters. So, one method we can use to find adequate parameters without spending a lot of time in the simulation of a lot of combinations would be using a genetic
  • An Effect of Monetary Conditions on Carry Trades [Quantpedia]

    This paper investigates the relation between monetary conditions and the excess returns arising from an investment strategy that consists of borrowing low-interest rate currencies and investing in currencies with high interest rates, so-called "carry trade". The results indicate that carry trade average excess return, Sharpe ratio and 5% quantile differ substantially across expansive and
  • Sorting Through The Factor Zoo [Larry Swedroe]

    As Professor John Cochrane observed, the literature on investment factors now fills a veritable factor zoo with hundreds of options. How do investors select from among this huge array of possibilities? Noah Beck, Jason Hsu, Vitali Kalesnik and Helge Kostka, authors of the paper Will Your Factor Deliver? An Examination of Factor Robustness and Implementation Costs, which appears in the
  • 38 DTE Iron Condor Results Summary [DTR Trading]

    The introduction to this series, here, described the different variations of SPX iron condors (IC) and exits that were tested at 38 days to expiration (DTE). Recall, the tests covered 9 IC variations, with short strike deltas at four locations, utilizing 12 exits. In all, there were 432 test runs (9 variations x 4 deltas x 12 exits). Each test run executed more than 200 SPX IC trades between the

Quantocracy’s Daily Wrap for 12/20/2016

This is a summary of links featured on Quantocracy on Tuesday, 12/20/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Volatility Trading Strategies, A Comparison of VRP and RY Strategies [Relative Value Arbitrage]

    In previous posts, we presented 2 volatility trading strategies: one strategy is based on the volatility risk premium (VRP) and the other on the volatility term structure, or roll yield (RY). In this post we present a detailed comparison of these 2 strategies and analyze their recent performance. The first strategy (VRP) is based on the volatility risk premium. The trading rules are as follows
  • Ex-ante and Ex-post Risk Model An Empirical Test [Alphaism]

    Whenever constructing a quant portfolio or managing portfolio risk, the risk model is at the heart of the process. A risk model, usually estimated with a sample covariance matrix, has 3 typical issues. Not positive-definite, which means[not invertible] Exposed to extreme values in the sample, which means[no stable] Ex-post tracking error is always larger than the ex-ante tracking error,

Quantocracy’s Daily Wrap for 12/18/2016

This is a summary of links featured on Quantocracy on Sunday, 12/18/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • An Interesting Analysis of Shiller’s CAPE Ratio [Quantpedia]

    Robert Shiller shows that Cyclically Adjusted Price to Earnings Ratio (CAPE) is strongly associated with future long-term stock returns. This result has often been interpreted as evidence of market inefficiency. We present two findings that are contrary to such an interpretation. First, if markets are efficient, returns on average, even when conditional on CAPE, should be higher than the risk-free

Quantocracy’s Daily Wrap for 12/15/2016

This is a summary of links featured on Quantocracy on Thursday, 12/15/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Price Is Wrong [Basis Pointing]

    In this piece, we compare U.S. equity mutual funds annual expenses to our estimate of their potential future pre-fee excess returns. We demonstrate that many funds are priced to failtheir fees approach or exceed their potential future pre-fee excess returns. Whereas investors might have tolerated overpriced funds like these in the past, theyre unlikely to do so in the future. Given this,

Quantocracy’s Daily Wrap for 12/14/2016

This is a summary of links featured on Quantocracy on Wednesday, 12/14/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • TAA Exposure to Rising Interest Rates [Allocate Smartly]

    Some of the tactical asset allocation strategies that we track have significant exposure to rising interest rates, or more specifically, to the types of assets that are most negatively affected by rising rates. While we dont (yet) track every published TAA model, the strategies that we do track are broadly representative of the TAA space, so I think its fair to draw some broader conclusions
  • Betting on Perfection [EconomPic]

    Just how perfect do circumstances need to be going forward for an investor in the S&P 500 to make money? Let's take a look at one measure. The first chart plots forward 10-year returns for the S&P 500 at various 5 point "CAPE" valuation buckets (i.e. less than 10x P/E all the way through above 30x) against the change in the starting P/E relative to the ten year forward P/E
  • Asset Pricing using Extreme Liquidity with Python (Part-2) [Black Arbs]

    POST OUTLINE Part-1 Recap Part-1 Error Corrections Part-2 Implementation Details, Deviations, Goals Prepare Data Setup PYMC3 Generalized Linear Models (GLM) Evaluate and Interprate Models Conclusions References part-1 recap In part 1 We discussed the theorized underpinnings of Ying Wu of Stevens Institute of Technology – School's asset pricing model. Theory links the catalyst of systemic risk
  • Escaping randomness, and turning to data for an edge w/ @DBurgh [Chat With Traders]

    On this episode, Im joined by a quant trader who works at a high frequency trading firmthough you might be surprised to hear, he started out on the same path that many retail traders dohis name is; Dave Bergstrom. The thing that makes Dave unique from most traders whove been on this podcast previously, is how he uses data-mining techniques to develop trading strategies. Though