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Quantocracy’s Daily Wrap for 12/11/2015

This is a summary of links featured on Quantocracy on Friday, 12/11/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Research Review | 11 2015 Dec | Portfolio Management [Capital Spectator]

    Buffetts Asset Allocation Advice: Take it With a Twist Javier Estrada October 26, 2015 One of the most important decisions retirees need to make is the asset allocation of their portfolios. They can have a static or a dynamic allocation, and simplicity usually favors the former. Warren Buffett recently added another vote for static allocations by revealing that he had advised a trustee to
  • December Spikes In The VIX Bring Traders Year-End Gifts [Dana Lyons]

    Jumps in the VIX during the month of December have (almost) always led to year-end stock market gains. It is the time of year when research houses trot out all matters of bullish seasonal stock market patterns pertaining to year-end. It seems as though each year brings more and more attention, and giddiness, to the idea of a year-end rally (though, that is likely due to the greater reach of
  • International Evidence for The Acquirer’s Multiple Investment Strategy [Greenbackd]

    A new paper from Christian Walkshusl and Sebastian Lobe* called The Enterprise Multiple Investment Strategy: International Evidence examines the performance of the enterprise multiple (EV/EBITDA) in international markets, including Australia, Canada, France, Germany, hong Kong, Japan, Singapore, Sweden, Switzerland and the UK. The paper, published in the Journal of Financial and Quantitative
  • The Volatility Anomaly Uncovered [Larry Swedroe]

    Recent academic papers have shown that low-volatility stocks have provided better returns than higher volatility stocks. Whats more, this is a global phenomenon. These findings, however, run counter to economic theory, which predicts that higher expected risk should be compensated with greater expected returns, resulting in the low volatility anomaly. Of interest is that this finding holds true
  • An Analysis of Expected Returns of Trend-Following Strategies [Quantpedia]

    This paper describes how to create ex-ante expectation for generalized trend-following rules. This report first study the effect of trend-following rules applied to random data with varying degrees of drift and autocorrelation. There is a positive relationship between drift, autocorrelation and the theoretically extractable Sharpe ratio for a trend following strategy. Drift is more important,

Quantocracy’s Daily Wrap for 12/10/2015

This is a summary of links featured on Quantocracy on Thursday, 12/10/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Value Investing Requires Patience…a LOT of Patience-Ask Cliff Asness [Alpha Architect]

    Value investing was a lot easier in 2012 and 2013 when our value approach beat the market by a substantial margin and we had a reasonable edge on other active value players. But now that we have lived through 2014 and 2015, most value approaches (simple, complex, quant, human, small, large, etc.) have some degree of suckiness. If you dont believe us, check our post on The Value Pain
  • Attention and Acceleration [Factor Wave]

    One of our readers, Corey Hoffstein, told me about an interesting paper, Investor Attention, Visual Price Pattern, and Momentum Investing, by Li-Wen Chen and Hsin-Yi Yu. Their basic idea is that investors are drawn to stocks that have attention grabbing behavior. And the thing that most grabs attention is gains, particularly consistent gains. Under this hypothesis, momentum is
  • A Whole Lot Of New Lows For A Market Near Its High [Dana Lyons]

    Another day, another piece of evidence that the stock market is not what it may appear to be by looking at the major averages. I place the quotes around market as the word has a very different meaning to us than it seems to have for the superficial market observer or at least mainstream financial media types. When you hear on TV, etc., what the market did on a particular day, it

Quantocracy’s Daily Wrap for 12/09/2015

This is a summary of links featured on Quantocracy on Wednesday, 12/09/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: Computational Intelligence: An Introduction [Amazon]

    Computational Intelligence: An Introduction, Second Edition offers an in-depth exploration into the adaptive mechanisms that enable intelligent behaviour in complex and changing environments. The main focus of this text is centred on the computational modelling of biological and natural intelligent systems, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial
  • It’s Generally Smart to Avoid Credit Risk [EconomPic]

    I've previously outlined that high yield credit risk is typically less ideal than simply gaining credit exposure through stocks and rate exposure through bonds. Now Larry Swedroe outlines the case for avoiding investment grade credit risk altogether. There are many well-documented anomalies in finance. Among them is the surprisingly small return that investors historically have earned for
  • Expanding the Efficient Frontier with Value and Momentum Strategies [Alpha Architect]

    Awwwmodern portfolio theorythat feel-good construct I teach to all of my graduate-level finance students each year. Simply input 1) a vector of expected returns and 2) a covariance matrix into your computer, and voil, you have your optimal portfolio weights. Like all things viewed with the benefit of hindsight, it sounds so easy, but the underlying theory and assumptions earned Harry
  • Rising Rates Don t Doom REITs [Larry Swedroe]

    As we have discussed many times, much of the conventional wisdom on investing is simply wrong. For our purposes, we can define conventional wisdom as those ideas that become so commonly accepted that they go unquestioned. Today well look at the idea that rising interest rates would doom returns to real estate investments, specifically the returns to real estate investment trusts (REITs).
  • RUT Straddle – 52 DTE – Results Summary [DTR Trading]

    This is the third article in a series looking at the backtest results of selling at-the-money (ATM) options straddles on the Russell 2000 index (RUT). 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: Option Straddle Series – P&L Exits This post reviews the backtest results for 4120 options
  • A Seasonal Play in Financial Stocks [Jay On The Markets]

    As I wrote about here, here, here and yes, here (and, um, also here), the holiday season tends to be a good time to invest in the stock market in general and in specific sectors in particular (with that pesky fly in the ointment, monkey in the wrench, pain in the rear caveat that there are no guarantees this time around). So lets add one more to the mix. (Jay Kaeppel Interview at

Quantocracy’s Daily Wrap for 12/06/2015

This is a summary of links featured on Quantocracy on Sunday, 12/06/2015. 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, 12/05 as voted by our readers: Predicting volatility [EP Chan] Announcing the QuantStart Advanced Trading Infrastructure Article Series [Quant Start] Exploring mean reversion and cointegration with Zorro and R: part 1 [Robot Wealth] The Quantitative Momentum Investing Philosophy [Alpha Architect] * * * My fellow traders, ask not what
  • Predicting Heavy and Extreme Losses in Real-Time for Portfolio Holders (2) [Quant at Risk]

    This part is awesome. Trust me! Previously, in Part 1, we examined two independent methods in order to estimate the probability of a very rare event (heavy or extreme loss) that an asset could experience on the next day. The first one was based on the computation of the tail probability, i.e.: given the upper threshold Lthr find the corresponding ? such that Pr(L In both cases, the lack of

Quantocracy’s Daily Wrap for 12/05/2015

This is a summary of links featured on Quantocracy on Saturday, 12/05/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Using the LASSO to Forecast Returns [Alex Chinco]

    1. Motivating Example A Popular Goal. Financial economists have been looking for variables that predict stock returns for as long as there have been financial economists. For some recent examples, think about Jegadeesh and Titman (1993), which shows that a stocks current returns are predicted by the stocks returns over the previous 12 months, Hou (2007), which shows that the current returns
  • High Valuations and Low Yields [Sharpe Returns]

    This is how your average buy-and-hold investor probably feels right now if they are looking to deploy new capital for the long run. Today, bond yields are puny while stock valuations are rich. In fact, we currently have one of the worst yield and value combinations in history as seen in the charts below dating back to 1880: Source: multpl.com Notice on these charts the years 1921 and 1982, when
  • When can income be growth? [Flirting with Models]

    Summary Traditionally, stocks have been used for growth and bonds for safety Therefore, investors looking for long-term capital appreciation tended to allocate heavily towards stocks while investors concerned about preservation allocate more heavily towards bonds With an anemic forecast for equity returns over the next decade, could the best place to find growth actually be with income? It is

Quantocracy’s Daily Wrap for 12/04/2015

This is a summary of links featured on Quantocracy on Friday, 12/04/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A First Attempt At Applying Ensemble Filters [QuantStrat TradeR]

    This post will outline a first failed attempt at applying the ensemble filter methodology to try and come up with a weighting process on SPY that should theoretically be a gradual process to shift from conviction between a bull market, a bear market, and anywhere in between. This is a follow-up post to this blog post. So, my thinking went like this: in a bull market, as one transitions from
  • Factors in Other Products [Factor Wave]

    Most of what Ive written about have been equity factors. But factors, persistent price predictors, apply to other investments as well. FactorWave will also offer analyses in volatility, equity options and commodity futures. Volatility Equity volatility (tradeable through the VIX) displays two major pricing factors: The futures tend to collapse towards the cash index. This doesnt make a great
  • Ignore Liquidity At Your Peril [Larry Swedroe]

    Liquidity is valuable to investors. Therefore, investors demand higher expected returns for less liquid stocks. The liquidity of an asset market refers to the ability of investors to buy and sell significant quantities of that asset, quickly, at low cost and without a major price concession. Thus, liquidity risk can be thought of as the risk to investors that an investment cannot be bought or sold
  • Statistics – JavaScript for Financial Analysts [John Orford]

    First draft of 'JavaScript for Financial Analysts' Chapter 11. ~ Finance is a super slow mo movie of investment decision logic colliding into chaotic markets. The narrow space in between is inhabited by statistics. JavaScript has a capable statistics library called jStat which offers a wide range of statistical functions, as well as some very neat features. Async jStat supports

Quantocracy’s Daily Wrap for 12/03/2015

This is a summary of links featured on Quantocracy on Thursday, 12/03/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Markowitz portfolio optimization with VBA code [RRSP Strategy]

    Wouter, Butler and Kipnis [2015] recently demonstrated Classical Asset Allocation (CAA) for long only portfolios, based on Markowitz concepts. The method uses only two parameters thus minimizing the chances of curve-fitting and data snooping. The parameters are lookback period (12 months) and target volatility. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2606884 Main results from the
  • State of Trend Following in November [Au Tra Sy]

    Last months results for trend following were positive, with a strong performance that took the index back into positive territory for the year. The strategy goes into the last month of the year holding modest gains but 2015 will obviously not be a repeat of the runaway performance from last year. Please check below for more details. Detailed Results The figures for the month are: November

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