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Quantocracy’s Daily Wrap for 12/14/2015

This is a summary of links featured on Quantocracy on Monday, 12/14/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Building a backtesting system in Python: or how I lost $3400 in two hours [Jon.IO]

    This is the another post of the series: How to build your own algotrading platform. Building a backtest system is actually pretty easy. Easy to screw up I mean. Even though there are tons of excellent libraries out there (and we'll go through them at some point), I always like doing this on my own in order to fine-tune it. From all the backtesting systems I have seen, we can assume that there
  • Don t Blame Lack Of Dispersion [Larry Swedroe]

    In a recent article, Advisor Perspectives editor Robert Huebscher noted: During the last 40 years, an average of 60% of equity funds underperformed the S&P 500. But, according to the SPIVA data, 86.4% of large-cap managers underperformed their benchmark in 2014. The percentages were not much better last year for mid-cap (66.2%) or small-cap (72.9%). The article goes on to cite work by
  • When ingredients spoil [Flirting with Models]

    Summary We break portfolio construction into two unique phases: signal generation and the rules that determine allocations. We use the analogy that this process is like cooking, where our signals are the ingredients and our allocation rules are the recipe. While most firms focus their research on generating the best ingredients, we believe it is important to acknowledge up front that the
  • A Poor Man’s Magic Formula [Relative Value]

    Here's a python script I made to rank stocks according to their return on equity and trailing EV/EBITDA. I have named it the poor mans magic formula because all of the fundamental data is scraped from yahoo finance. Yahoo are friendly enough to provide this data for 'free' but have been known to block ip addresses that make too many requests in one day (15k+). Unfortunately neither

Quantocracy’s Daily Wrap for 12/13/2015

This is a summary of links featured on Quantocracy on Sunday, 12/13/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Interview with Michael Bryant [Better System Trader]

    Creating robust trading strategies can be a difficult task, sometimes taking months or even years to generate something you find acceptable. Even then, once you start trading it live there is no guarantee itll work in the future. With strategy creation being such an involved process at times, how would you like it if you could just tell the computer the results you wanted and let it figure out
  • Maintaining a database of price files in R [R Trader]

    Doing quantitative research implies a lot of data crunching and one needs clean and reliable data to achieve this. What is really needed is clean data that is easily accessible (even without an internet connection). The most efficient way to do this for me has been to maintain a set of csv files. Obviously this process can be handled in many ways but I found very efficient and simple overtime to
  • The Halloween effect with python and pandas [Shifting Sands]

    The Halloween effect, aka sell in May and go away is the observation that equity market returns tend to be worse over summer time in the northern hemisphere. Anyone who has followed markets for a while has probably noticed a distinct lull over the summer period. But can we quantify this effect, does it really exist? We can and it does, and its simple to show with less than 10 lines of
  • Crude Oil and Trend Following [Wisdom Trading]

    Crude oil has been in the news a lot recently. Just last week, after the OPEC meeting, its price declined by over 10%. With a lot of volatility, we wanted to test how a trend following strategy would have performed on the futures contract in recent history. Here is a quick post showing our benchmark Wisdom State of Trend Following system and the performance it would have produced on a

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