Quantocracy

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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

Quantocracy’s Daily Wrap for 12/12/2016

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

  • A Dynamic Approach to Factor Allocation [EconomPic]

    ETF Trends (hat tip Josh) showed the following "quilt" of large cap factor calendar year returns in the post Low Volatility is Not a Buy and Hold Strategy. Author John Lunt's takeaway (bold mine): It is reasonable to conclude that low volatility is not a buy and hold strategy. This is not because it is unlikely to outperform over the long term, but rather because few investors are
  • New Book Added (Fin Math): Quantitative Risk Management: A Practical Guide to Financial Risk

    State of the art risk management techniques and practicessupplemented with interactive analytics All too often risk management books focus on risk measurement details without taking a broader view. Quantitative Risk Management delivers a synthesis of common sense management together with the cutting-edge tools of modern theory. This book presents a road map for tactical and strategic decision
  • The Ghost of GDP Past [Flirting with Models]

    Summary Economic growth is a key driver of long-term stock and bond returns. Economic growth comes from two main sources: demographic changes (i.e. increases in the number of workers) and productivity growth (i.e. each worker producing more output). Historically, approximately 55% of growth has come from productivity growth and 45% has come from demographic changes. Slowing population growth
  • Interest Rates and Value Investing [Alpha Architect]

    There is still no value in bonds today. Many readers just had a knee-jerk reaction and theyve determined that I fall into one of two categories: A total idiot A total genius But lets dig a bit deeper into the claim that bonds lack value, even with this quarters 85 basis point back-up in 10 year treasury note yields. One way to view value within non-credit fixed income assets is to
  • Hacking True Random Numbers in Python: Blockchain Miners [Quant at Risk]

    The magnitude and importance of random numbers in finance does not have to be explained. We need them. Either it is an option pricing or a Monte Carlo simulation, random numbers are with us. However, we make a trade-off: the speed in their generation versus uniqueness. That is why a widely accepted use of, inter alia, Mersenne Twister algorithm as a source of pseudo-random numbers has established
  • The Most Wonderful Weeeeek Of The Yeeeaaaarrrrr!! [Quantifiable Edges]

    Over several time horizons op-ex week in December has been the most bullish week of the year for the SPX. The positive seasonality actually has persisted for up to 3 weeks. Ive shown the study below in the blog many times since 2008. It looks back to 1984, which was the first year that SPX options traded. The table is updated again this year.

Quantocracy’s Daily Wrap for 12/11/2016

This is a summary of links featured on Quantocracy on Sunday, 12/11/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Cryptocurrencies and Machine Learning with @BMouler [Better System Trader]

    As markets become more mature and more efficient, it can be become increasingly difficult to find sustainable edges. Many traders are looking at the same data and using the same techniques, so what are our options here? 2 of the obvious options we have are: Try to find a unique approach to the markets or at least something that isnt so popular, Explore alternative markets where inefficiencies
  • Sources of Return for CTAs – A Brief Survey of Relevant Research [Quantpedia]

    This survey paper will discuss the (potential) structural sources of return for both CTAs and commodity indices based on a review of empirical research articles from both academics and practitioners. The paper specifically covers (a) the long-term return sources for both managed futures programs and for commodity indices; (b) the investor expectations and the portfolio context for futures
  • Reading Fundamental Data from Yahoo Finance [Copula.de]

    Recently I read a blogpost and someone was recommending the book "DIY Financial Advisor "by Wesley R. Gray, Jack Vogel and David Foulke. I believe it was the QuantStrat blog but I might be wrong. The book is a good read and also suggest a couple of simple systems any investor can implement and follow. One system requires fundamental data like P/E or EBITDA/TEV ratios and I could trace