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Quantocracy’s Daily Wrap for 12/02/2015

This is a summary of links featured on Quantocracy on Wednesday, 12/02/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Case for an Allocation to Dollar Based EM Debt [EconomPic]

    While the underperformance of high yield bonds since my post The Case Against High Yield has certainly made high yield bonds more attractive (yields went from sub 6% to north of 8%), I still prefer the risk/return profile of a stock/bond allocation (more here). For those that are looking for a higher yielding fixed income alternative with limited currency risk and the potential for U.S. interest
  • Exploring mean reversion and cointegration with Zorro and R: part 1 [Robot Wealth]

    This series of posts is inspired by several chapters from Ernie Chans highly recommended book Algorithmic Trading. The book follows Ernies first contribution, Quantitative Trading, and focuses on testing and implementing a number of strategies that exploit measurable market inefficiencies. Im a big fan of Ernies work and have used his material as inspiration for a great deal of my own
  • Putting TWAP to the Test [Flirting with Models]

    A few weeks ago, we discussed a method that we implemented to construct more realistic indices using an estimate of time weighted average prices (TWAP) for trade execution. Prior to October, whenever our models required a rebalance, we assumed that it occurred at the opening price on the following day in our hypothetical index calculation. Making this assumption is not necessarily bad, especially
  • Trend Following Via Slope [Relative Value]

    Linear regression slope is another tool one can use to sidestep potentially sticky situations. The following algorithms hold a full position in SPY but liquidate whenever the slope of closing prices turn negative. Starting Capital of $100,000 and backtest period from 2/2003 and 11/2015. Strategy End Bal Gain CAGR Sharpe Sortino Drawdown Buy and Hold 316400 216.4% 9.4% -54.9% 1 Month 182561 82.6%
  • ‘predictions’, ‘forecasts’ or ‘projections’? [Eran Raviv]

    Perhaps it is the different jargon used in different disciplines, not sure. But for some reason, the terms predictions, forecasts and projections are frequently used interchangeably. There should be at least some distinction, here is what I entertain: The word predictions should be reserved for situations where the future is already here. For example, given the
  • Trend Following UP in November and YTD [Wisdom Trading]

    November 2015 Trend Following: UP +6.06% / YTD: +9.86% The Wisdom trend following index gradually made its way up last month to post a strong result for November, and recover a large part of the losses occurred in October. This helped cement the YTD number further in the black. Now nearly in double-digit territory, there is a strong chance that trend following will end 2015 positive. Stay tuned
  • Predictable & Skewed Returns [Larry Swedroe]

    There has been a lot of research recently that investigates the link between stock returns and higher moments of the return distribution, specifically the skewness of returns. This link, unfortunately, is frequently ignored by more standard measures of market risk and volatility. Skewness, if youll recall, measures the asymmetry of a distribution. In terms of the stock market, the asymmetric
  • Student t Distributed Linear Value-at-Risk [Quant at Risk]

    One of the most underestimated feature of the financial asset distributions is their kurtosis. A rough approximation of the asset return distribution by the Normal distribution becomes often an evident exaggeration or misinterpretations of the facts. And we know that. The problem arises if we investigate a Value-at-Risk (VaR) measure. Within a standard approach, it is computed based on the

Quantocracy’s Daily Wrap for 12/01/2015

This is a summary of links featured on Quantocracy on Tuesday, 12/01/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Quantitative Momentum Investing Philosophy [Alpha Architect]

    Our Quantitative Momentum (QM) system seeks to identify stocks with the highest quality momentum. We consider the term momentum to mean a continuation of past returnspast winners tend to be future winners, while past losers tend to be future losers. How can we exploit this phenomenon? At Alpha Architect, we have designed a system to identify the quality of momentum by examining how
  • Reversals and Momentum [Factor Wave]

    Ive recently written a few posts about the persistency of cross-sectional momentum. But it seems that eventually stocks that go up have to come down. Not only is this somewhat intuitive, but the existence of stock price reversals is also well documented. But actually momentum persistence and price reversals are completely compatible if we look at the entire cross section of stocks. In
  • Central Limit Theorem: Visual demonstration [Quant Dare]

    Everybody knows about the Central Limit Theorem, but have you ever seen a visual demonstration? The central limit theorem states that, given certain conditions, the mean of a large number of iterates of independent random variables, will be approximately normally distributed, regardless of the underlying distribution. Formally, Let {X1, , Xn} be a sequence of independent and identically

Quantocracy’s Daily Wrap for 11/30/2015

This is a summary of links featured on Quantocracy on Monday, 11/30/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Announcing the QuantStart Advanced Trading Infrastructure Article Series [Quant Start]

    To date on QuantStart we have considered two major quantitative backtesting and live trading engines. The first arised from the Event-Drive Backtesting series I wrote back in March 2014. The second is QSForex, an open-source backtest and live trading engine that hooks into the OANDA Forex Broker API, which is still being used by many of you. I've had a lot of requests recently for a more
  • Momentum Investing: Why Does Seasonality Matter for Momentum? [Alpha Architect]

    With Januaries (a month in which lagged "losers" typically outperform lagged "winners") excluded, the average monthly return to a momentum strategy for U.S. stocks was found to be 59 bps for non-quarter-ending months but 310 bps for quarter-ending months. The pattern was stronger for stocks with high levels of institutional trading and was particularly strong in December. The
  • Overnight Trading in the E-Mini S&P 500 Futures [Jonathan Kinlay]

    Jeff Swanson's Trading System Success web site is often worth a visit for those looking for new trading ideas. A recent post Seasonality S&P Market Session caught my eye, having investigated several ideas for overnight trading in the E-minis. Seasonal effects are of course widely recognized and traded in commodities markets, but they can also apply to financial products such as the
  • Recovery of Financial Price-Series based on Daily Returns Matrix in Python [Quant at Risk]

    As a financial analyst or algo trader, you are so often faced with information on, inter alia, daily asset trading in a form of a daily returns matrix. In many cases, it is easier to operate with the return-series rather than with price-series. And there are excellent reasons standing behind such decision, e.g. the possibility to plot the histogram of daily returns, the calculation of daily
  • Momentum Based Strategies for Low and High Frequency Trading [Quant Insti]

    It is important to know the difference between high frequency and low frequency trading before discussing the specific trading strategies. Opinions tend to differ on what constitutes high frequency but by and large there is a consensus that the duration of asset holding period is very low, ranging from seconds to minutes. High frequency trading revolves around market microstructure and order book
  • Longer Lives Lower Interest Rates [Larry Swedroe]

    Ever since the global financial crisis, the real interest rates of developed economies have remained in negative territory. Nominal interest rates hover near zero, and inflation rates, although quite low for historical standards, have remained positive (in most countries, at least on average). Whats more, negative nominal interest rates have even been observed in some developed countries for
  • D3 – Javascript for Financial Analysts – Chapter 10 [John Orford]

    First draft of 'JavaScript for Financial Analysts' Chapter 10. ~ D3 is a foreboding beast. It eschews classic programming styles in favour of a more functional approach. Luckily however, if you have come this far, get ready to sit back and enjoy of the fruits of your labour. Almost every charting library is descriptive, they give you several chart templates which you can configure and
  • Real Estate = A Real Good Time [Jay On The Markets]

    OK, I will admit I am a bit late with this one. Ill go ahead and blame The Holidays. Anyway, if you were wondering when it might be a good time to hold real estate stocks, the answer might well be, um, Now. (Jay Kaeppel Interview at BetterSystemTrader.com) Favorable Seasonal Period for Real Estate Stocks *A favorable seasonal period for real estate stocks tends to occur between the
  • Ivy Portfolio December 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 11/29/2015

This is a summary of links featured on Quantocracy on Sunday, 11/29/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Best Links of the Week [Quantocracy]

    The best quant mashup links for the week ending Saturday, 11/28 as voted by our readers: Frog in the Pan: Identifying the Highest Quality Momentum Stocks [Alpha Architect] Better Tests with Oversampling [Financial Hacker] Bring More Data [Dual Momentum] A framework for rapid and robust system development based on k-means clustering [Robot Wealth] Predicting volatility [EP Chan] * * * My fellow
  • [Academic Paper] Stop-Loss Strategies with Serial Correlation, Regime Switching, and Transactions Costs [@Quantivity]

    Stop-loss strategies are commonly used by investors to reduce their holdings in risky assets if prices or total wealth breach certain pre-specified thresholds. We derive closed-form expressions for the impact of stop-loss strategies on asset returns that are serially correlated, regime switching, and subject to transactions costs. When applied to a large sample of individual U.S. stocks, we show
  • [Academic Paper] Dissecting Investment Strategies in the Cross Section and Time Series [@Quantivity]

    We contrast the time-series and cross-sectional performance of three popular investment strategies: carry, momentum and value. While considerable research has examined the performance of these strategies in either a directional or cross-asset settings, we offer some insights on the market conditions that favor the application of a particular setting.
  • [Academic Paper] Rethinking Performance Evaluation [@Quantivity]

    We show that the standard equation-by-equation OLS used in performance evaluation ignores information in the alpha population and leads to severely biased estimates for the alpha population. We propose a new framework that treats fund alphas as random effects. Our framework allows us to make inference on the alpha population while controlling for various sources of estimation risk. At the
  • Interview with Andrew Gibbs [Better System Trader]

    Andrew Gibbs has been involved in the financial markets since 2001 and is the founder and CEO of Halifax New Zealand. Andrew has extensive experience in all forms of equity and derivative contracts, managing millions of dollars and trading a number of markets around the world. In this episode we discuss volatility and methods to trading the VIX plus the benefits and methods of including

Quantocracy’s Daily Wrap for 11/27/2015

This is a summary of links featured on Quantocracy on Friday, 11/27/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Persistent Momentum [Factor Wave]

    Somewhat related to the idea of acceleration that I have been writing about recently, is the concept of persistent momentum. That is, do stocks that have performed well over several periods, beat those that have done well for only one period? This idea was tested by Hong-Yi Chen, Pin-Huang Chou and Chia-Hsun Hsieh in their paper Persistency of the Momentum Effect: The Role of Consistent Winners
  • Predicting volatility [EP Chan]

    Predicting volatility is a very old topic. Every finance student has been taught to use the GARCH model for that. But like most things we learned in school, we don't necessarily expect them to be useful in practice, or to work well out-of-sample. (When was the last time you need to use calculus in your job?) But out of curiosity, I did a quick investigation of its power on predicting the

Quantocracy’s Daily Wrap for 11/26/2015

This is a summary of links featured on Quantocracy on Thursday, 11/26/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • PDF: The PCA Model in the FX Market: Economic Factors and Volatility Modelling [Kevin Pei]

    The PCA Model in the FX Market: Economic Factors and Volatility Modelling
  • When Risk Goes Unrewarded [Larry Swedroe]

    Risk-based asset pricing theory suggests, simply, that assets bearing a higher risk should compensate investors with higher returns. While most papers investigating the risk-return relationship of assets are focused on equity markets, surprisingly few studies explore this phenomenon in currency markets (which are among the deepest and most liquid markets in the world). In fact, the FX markets are
  • Is Momentum Effect Result of Over- of Under-reaction? [Quantpedia]

    Several studies have attributed the high excess returns of the momentum strategy in the equity market to investor behavioral biases. However, whether momentum effects occur because of investor underreaction or because of investor overreaction remains a question. Using a simple model to illustrate the linkage between idiosyncratic volatility and investor overreaction as well as the stock turnover
  • Visualisation (Now with 3D!) – JavaScript for Financial Analysts [John Orford]

    First draft of 'JavaScript for Financial Analysts' Chapter 9. ~ The web dominates our communication. The driver of this crushing victory? The humble webpage increasingly coupled with JavaScript. Up until now we have focused on the basics of how to code JavaScript in a functional manner, now for some fun. The next chapters will explore JavaScript's rich ecosystem of libraries. The

Quantocracy’s Daily Wrap for 11/25/2015

This is a summary of links featured on Quantocracy on Wednesday, 11/25/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is The Acceleration Factor A Better Way To Measure Momentum? [Capital Spectator]

    Momentum has received a lot of attention in the asset-pricing literature over the past several decades, and for good reason. Trending behavior is a staple in markets. In contrast with other pricing anomalies, short-term return persistencepositive and negativeis a robust factor across asset classes. The fact that momentum is deployed far and wide in the money management industry and
  • RUT Straddle – 38 DTE – Results Summary [DTR Trading]

    This is the first article in a series where we will look at the backtest results of selling at-the-money (ATM) options straddles on the Russell 2000 index (RUT). In the prior series, we looked at the performance of this same strategy on the SPX. For background on the setup for the backtests, as well as the nomenclature used in the tables below, please see the introductory article for this series:

Quantocracy’s Daily Wrap for 11/24/2015

This is a summary of links featured on Quantocracy on Tuesday, 11/24/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A framework for rapid and robust system development based on k-means clustering [Robot Wealth]

    Important preface: This post is in no way intended to showcase a particular trading strategy. It is purely to share and demonstrate the use of the framework Ive put together to speed the research and development process for a particular type of trading strategy. Comments and critiques regarding the framework and the methodology used are most welcome. Backtest results presented are for
  • International Evidence for our favorite Value metric: Enterprise Multiples [Alpha Architect]

    The enterprise multiple (EM) predicts the cross section of international returns. The return predictability of EM is similarly pronounced in developed and emerging markets and likewise strong among small and large firms. An international portfolio of low-EM firms outperforms a portfolio of high-EM firms by about 1% per month. The EM value premium is individually significant for the majority of
  • Improving A Hedge Fund Investment – Cantab Capital’s Quantitative Aristarchus Fund [Jonathan Kinlay]

    In this post I am going to take a look at what an investor can do to improve a hedge fund investment through the use of dynamic capital allocation. For the purposes of illustration I am going to use Cantab Capitals Aristarchus program a quantitative fund which has grown to over $3.5Bn in assets under management since its opening with $30M in 2007 by co-founders Dr. Ewan Kirk and Erich
  • Migration from Good Factor Exposures [Factor Wave]

    There are a number of ways to become more confident in the idea of factor investing. The simplest is to just compare the results of factor portfolios to those based on other methods. This will show outperformance but it wont give us a reason for the outperformance. So anything that can give us more reason to believe is always welcome. Eugene Fama and Kenneth French wrote a paper,
  • Visualisation Pt 1 – Javascript for the Financial Analyst Chapter 9 [John Orford]

    First draft of 'JavaScript for Financial Analysts' Chapter 9. ~ The web dominates our communication. The driver of this crushing victory? The humble webpage increasingly coupled with JavaScript. Up until now we have focused on the basics of how to code JavaScript in a functional manner, now for some fun. The next chapters will explore JavaScript's rich ecosystem of libraries. The

Quantocracy’s Daily Wrap for 11/23/2015

This is a summary of links featured on Quantocracy on Monday, 11/23/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Valuations Do Matter (Even Over Shorter Time Frames) / Momentum Driven Valuation Timing [EconomPic]

    I often read that valuations don't matter over the short-term (a case often cited against market timing). Over very short periods (hours, days, etc…) this certainly may be true, but while there can be a lot of variability around month-to-month or year-to-year performance, I completely disagree with the sentiment that it doesn't matter. That said, there are better ways than just using
  • Frog in the Pan: Identifying the Highest Quality Momentum Stocks [Alpha Architect]

    We test a frog-in-the-pan (FIP) hypothesis that predicts investors are inattentive to information arriving continuously in small amounts. Intuitively, we hypothesize that a series of frequent gradual changes attracts less attention than infrequent dramatic changes. Consistent with the FIP hypothesis, we find that continuous information induces strong persistent return continuation that does not
  • Better Tests with Oversampling [Financial Hacker]

    The more data you use for testing or training your strategy, the less bias will affect the test result and the more accurate will be the training. The problem: price data is always in short supply. Even shorter when you must put aside some part for out-of-sample tests. Extending the test or training period far into the past is not always a solution. The markets of the 1990s or 1980s were very
  • Great online courses for learning R [R for Traders]

    The last few months have seen a flurry of activity in terms of new courses being created for the R programming language. Udemyis one such online venue that provides a surprisingly broad array of topics related to the R language. These topics include statistical analysis, regression, data science, machine learning, quantitative trading, data visualization and more. As an adjunct instructor in the
  • Due Diligence: Ask This, Not That [Flirting with Models]

    Summary Due diligence is an important practice in our industry and one that should be ever-evolving. There are some questions we receive on due diligence questionnaires that are well intentioned, but we think can be improved. Finally, in doing due diligence, we think that after the question how, there should almost always be a follow-up question of why? We answer a lot of due
  • Avoiding Stock Market Crashes with the Hi-Lo Index of the S&P500 [System Trader Success]

    This daily indicator is calculated as the ratio of the number of S&P500 stocks that have reached new 3-month-highs minus those that have reached new 3-month-lows, divided 500. Exiting and entering the stock market according the indicators signals would have avoided major drawdowns of the market during the backtest period from Jan-2000 to Aug-2015. Switching according to the signals between
  • Is the Stock Market Different? [Quintuitive]

    Overall, we expect the stock market to go higher. There is a good reason for that the stock market is positive close to 54% of the days. A natural questions is whether this holds for other markets as well. There is inflation after all. Looks like the stock market is more or less unique in that regard. A little bit of R magic over the last 10 years of back-adjusted data from CSIData, and we
  • Another Look At Thanksgiving Week [Quantifiable Edges]

    Historically Thanksgiving week has shown some very strong tendencies. The table below is one I have shown a few times over the years. I decided to update it again this year. 2015-11-23 image1 Monday and Tuesday dont show anything suggesting an edge. Mondays total return was actually negative until 2008 when it posted a gain of over 6%. Wednesday and Friday, on the other hand, appear to be