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Quantocracy’s Daily Wrap for 09/11/2015

This is a summary of links featured on Quantocracy on Friday, 09/11/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is Technical Analysis Folk Medicine? [Factor Wave]

    I've been thinking more about technical analysis. Not so much how to do it, but more about what it actually is. Some of it can be tested scientifically, but a lot can't (for more on this distinction refer to the excellent book by David Aronson, "Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals"). So TA isn't
  • Factor Models Can Only Tell You So Much [Alpha Architect]

    Factor analysis has taken the professional consultant world by storm and we are slowly seeing this analysis being used more and more by sophisticated retail investors and investment advisors. And thats greatfactor analysis is a great tool. In fact, we discuss the use of the tool and how it is useful in a recent post called, Basic Factor Analysis: Simple Tools to Understand What Drives
  • Dual ETF Momentum September Update [Scott’s Investments]

    Scotts Investments provides a free Dual ETF Momentum spreadsheet which was originally created in February 2013. The strategy was inspired by a paper written by Gary Antonacci and available on Optimal Momentum. Antonaccis book, Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk, also details Dual Momentum as a total portfolio strategy. My

Quantocracy’s Daily Wrap for 09/08/2015

This is a summary of links featured on Quantocracy on Tuesday, 09/08/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Book Review: DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth [Dual Momentum]

    I have always looked favorably upon do-it-yourself investing (DIY). It was a prominent feature of my own book. So Ive been looking forward to DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth by Wes Gray, Jack Vogel, and David Foulke (GVF), the managing members of Alpha Architect. GVF took on an ambitious project since they cover a broad range of su
  • Hypothesis-Driven Development Part II [QuantStrat TradeR]

    This post will evaluate signals based on the rank regression hypotheses covered in the last post. The last time around, we saw that rank regression had a very statistically significant result. Therefore, the next step would be to evaluate the basic signals whether or not there is statistical significance in the actual evaluation of the signalnamely, since the strategy fr
  • The Case for Put Writing / Further Improving PutWrite Performance [EconomPic]

    Jesse Livermore of the always interesting Philosophical Economics outlines the case for writing puts in his recent post The Worlds Best Investment For the Next 12 Months. Given this has been an area of focus for me professionally for the better part of the last 5 years (sneak preview… I love the concept), I thought I could add to the conversation. Note that some of the post below dup
  • Skewed By Randomness: Testing Arbitrary Rebalancing Dates [Capital Spectator]

    How much influence do investors have over their portfolios? Perhaps it's less than commonly assumed. The notion that randomness plays a role in money management has been widely studied in finance-Nassim Taleb's popular treatment in Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets is one example. The concept is a staple in the money game, although it's
  • Will You Be Able to Retire Without Tactical Asset Allocation? [Flirting with Models]

    Frugal Fran is a 25-year old investor at the beginning of her career making $40,000 per year. Financially savvy, she has already started planning for her retirement. She plans to retire at age 65 and follow an "own your age" policy for her stock/bond mix. Fran projects a salary increase of 1.5% per year, after inflation. When she retires, Fran would like to replace 85% of her income
  • Intro to Hidden Markov Chains [Quants Portal]

    In a situation where you wish to determine the returns on an investment, one may have all the expertise to do this but without certain information (missing pieces) it would not be possible to derive to a conclusive figure. In practical terms assume you have the value of all returns of all assets in your portfolio; without the rate at which each asset produces the returns we will not ha
  • SPX Performance Following Selloffs Into 3-Day Weekends [Quantifiable Edges]

    One of my former studies I looked at over the weekend examined how the market performed following large selloffs before U.S.-only three day weekends. These include Labor Day, Martin Luther King Day, Presidents Day, Memorial Day, and Fourth of July. Since 2000 there have been 12 instances where there was a greater than 1% selloff prior to the US-only 3-day weekend. Statistics from 1-5 d
  • State of Trend Following in August [Au.Tra.Sy]

    A positive return for the State of Trend Following index, bringing the YTD performance nearly exactly on neutral level. The big spike up seen in the last part of the month, which brought the index close to +10%, was short-lived. The index quickly reverted it with a quick spike down. Please check below for more details. Detailed Results The figures for the
  • How bad was August 2015? [Flirting with Models]

    The SPDR S&P 500 ETF SPY fell more than 6% in August Measured against other monthly returns, August 2015 was the worst month since 2012 Monthly returns are arbitrary and skew our understanding of market moves Since 2012, there have been several -6%, or near -6%, return periods Recently, in reference to August 2015, this headline appeare
  • A way to an improved Size and Value Factors [Quantpedia]

    Authors: Lambert, Fays, Hubner Title: Size and Value Matter, But Not the Way You Thought Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2647298 Abstract: Fama and French factors do not reliably estimate the size and book-to-market effects. We demonstrate inconsistent pricing of those factors in the US stock market. We replace Fama and Frenchs independent rankings with the

Quantocracy’s Daily Wrap for 09/04/2015

This is a summary of links featured on Quantocracy on Friday, 09/04/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Strategy Replication Nonlinear SVMs can systematically identify stocks with high and low future returns [Mintegration]

    Ive replicated the following academic paper from my favourite journal; Title: Nonlinear support vector machines can systematically identify stocks with high and low future returns Authors: Ramon Huerta, Fernando Corbacho, and Charles Elkan Journal: Algorithmic Finance (2013) 45-58 45, DOI 10.3233/AF-13016, IOS Press, http://algorithmicfinance.org/2-
  • Economics, Mathematics, & Common Sense [Alphamaximus]

    Almost all calculations in finance involve using log returns rather than % returns. There are a number of reasons why log returns are preferred. Estimating beta doesnt seem like a special case. But when you go through the math, something doesnt quite add up. rS1M1Pt=?+?rM+?t=S0exp(r)=M0exp(rM)=S0+wM0 Because we want to zero out market risk, so want to s
  • Benford’s Law [Factor Wave]

    Benford's Law states that in many naturally occurring groups of numbers, the small digits are seen disproportionately often. This is often applied to the leading digits of data but it is more general than that. This was first noticed by the astronomer Simon Newcomb (who also should be famous for an awesome beard!) in 1881 when he saw that the first pages in a library book of logarithms
  • Backtesting Data Independence [John Orford]

    Light is the most precious resource to a photographer, everything you can do with your camera is budgeted by the amount of light available. Financial analysis is similarly constrained by the amount of data available. So more available data is always good. With Big 'O' Sharpe you can generate as much data as the data is granular. E.g. i

Quantocracy’s Daily Wrap for 09/02/2015

This is a summary of links featured on Quantocracy on Wednesday, 09/02/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Systems building – Checks and balances [Investment Idiocy]

    Driverless cars are, apparently, very close to commercial reality. I don't know about you but there is something pretty scary about a computer being completely in control of a complex process, which could have catastrophic consequences if it went wrong. Ah it was nothing. You should have seen the other guy… (From autospies.com) That might seem a strange atti
  • How Can a Strategy Everyone Knows About Still Work? [AQR]

    Some assert that once a strategy is discovered it cant work anymore. Others, often implicitly, assume the future will look as wonderful as the past. Perhaps not surprisingly, we stake out a middle ground. Were going to argue that certain well-known classic strategies that have worked over the long term will continue to work going forward, though perhaps not at the same level and wit
  • Gray et al., DIY Financial Advisor [Reading the Markets]

    Models beat expertsor, stated more cautiously, models typically beat experts. This is the rallying cry of DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth (Wiley, 2015) by Wesley R. Gray, Jack R. Vogel, and David P. Foulke, all managing members of Alpha Architect. Whether or not you believe this claimand despite the seeming preponderance of evidence in its fav
  • Stock Returns Around Labor Day [CXO Advisory]

    Does the Labor Day holiday, marking the end of summer vacations, signal any unusual return effects by refocusing U.S. stock investors on managing their portfolios? By its definition, this holiday brings with it any effects from the turn of the month. To investigate the possibility of short-term effects on stock market returns around Labor Day, we analyze the historical behavior of the s

Quantocracy’s Daily Wrap for 08/29/2015

This is a summary of links featured on Quantocracy on Saturday, 08/29/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Multivariate volatility forecasting [Eran Raviv]

    Last time we showed how to estimate a CCC and DCC volatility model. Here I describe an advancement labored by Engle and Kelly (2012) bearing the name: Dynamic equicorrelation. The idea is nice and the paper is well written. Departing where the previous post ended, once we have (say) the DCC estimates, instead of letting the variance-covariance matrix be, we force some struc
  • Introduction to Monte Carlo Analysis Part 2 [Quants Portal]

    Markov Chains, Central Limit Theorem and the Metropolis-Hastings In the previous article I gave a generic overview of Monte Carlo as well as introduced importance sampling. We now dive deeper by giving strict definitions of some of the widely used and yet misunderstood or rather commonly neglected concepts due to its perceived importance. There after we explore the Metropol
  • Steady Vol & Big ‘O’ Sharpe [John Orford]

    Big 'O' Sharpe changes backtest starting dates day by day until the lowest Sharpe is found. When strategies rebalance on periodic basis it turns out that such very small changes cause very large differences in results. Big 'O' Sharpe is the pessimistic grumpy brother of the happy-go-lucky Sharpe ratio. On the plus side if you do get a good Big 'O' Sharpe number
  • Multiscale Noisy-Rational-Expectations Equilibrium [Alex Chinco]

    1. Motivation Evolutionarily Slow. In modern financial markets, people simultaneously trade the exact same assets on vastly different timescales. For example, a Jegadeesh and Titman (1993)-style momentum portfolio turns over half its holdings once every 6 months. By contrast, Kirilenko, Kyle, Samadi, and Tuzun (2014) estimate that high-frequency traders (HFTs) reduce half

Quantocracy’s Daily Wrap for 08/28/2015

This is a summary of links featured on Quantocracy on Friday, 08/28/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Avoiding Stock Market Crashes with the Hi-Lo Index of the S&P500 [iMarketSignals]

    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 acco
  • Are Spikes Predictive? [Factor Wave]

    Yesterday I looked at stock market returns in the week and month after a large daily decline and found that it is usually a good time to buy more equities. Especially because, as long-term investors, our strongly held prior is that equity markets appreciate over time. But what about spikes? Do large one day rallies tell us anything in particular about subsequent returns?
  • Missing the Best and the Worst [Flirting with Models]

    Numerous marketing pieces circulating around the web show the detriment that trying to time the market can have on a portfolio. These pieces often look similar to this chart, which shows the cumulative growth of $1 invested in the S&P 500 ETF (SPY) assuming that a given number of best days are missed over the period from 1995-2014. Missing 0 days is equivalent to simply buying and holdi
  • Visualizing Stock Market Risk: 7/1926 to 6/2015 [Alpha Architect]

    How crazy is current market action? Not that crazy. and if you lived through 2008, definitely not that crazy. Seeing a -3%+ or a +3% observation is roughly a 1/100 event, or ~ 2.5 times a year. Obviously, return events are not independent and volatility tends to cluster, but the numbers above establish a basic starting point for discussions about
  • 5 Ways to Plot Returns [John Orford]

    There's an infinite number of ways to plot financial time series, let's look at the main ones. The most basic way is just plonking returns on a plot. This has one very nice feature. The returns are comparable across time, which makes a lot of sense, right? A return at the beginning of our backtest should probably be as important as one at the end.
  • Why Thursday s Volume Was Disappointing For Bulls [Quantifiable Edges]

    Thursdays rally was accompanied by the lightest volume in 5 days. The relatively low volume could be worrisome for bulls. The importance of volume can be seen in the studies below. The first one looks at 2%+ SPX gains when volume comes in relatively high. 2015-08-28 image1 A week later of the instances closed higher and the average instance saw the SPX up ab

Quantocracy’s Daily Wrap for 08/26/2015

This is a summary of links featured on Quantocracy on Wednesday, 08/26/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Trajectory of a Crash [Philosophical Economics]

    Its amazing to think that just last Monday, August 17th, the S&P 500 closed at 2102. Today, it closed at 1868, falling 11.1% in 6 trading days. The shocking speed of the decline has injected a level of fear into markets not scene since the fall of 2011, when the Eurozone debt crisis was reaching its apex. Many traders have referenced 1987 as a paradigm for what might happen in a wor
  • Quant-Trader or Trader-Quant? [MKTSTK]

    The term quant trader gets thrown around a lot these days. For any trader who has been in the industry for more than a decade, the adoption of the term is driven by survival. Theres a running joke in some HFT circles: these days, older traders would never get past HR using the same criteria by which junior traders are hired. Junior traders must be data scientists and traders.
  • Let’s talk “Year-to-Date” [Flirting with Models]

    We have a pretty arbitrary practice in the financial services industry: we reset the performance clock of portfolios to zero every January. Consider this hypothetical scenario: its December and markets are up 20% for the year. They even got a nice 5% pop in the last month. The clock strikes midnight on December 31st. We roll into January and the markets proceed to tumbl
  • Super Reliable Backtesting [John Orford]

    Big 'O' is a measure of many things, with respect to backtesting it helps because results are always ambiguous. Backtesting results are almost iffy for a variety of reasons, but a salient one is the 'day bump' problem. Say, I have a strategy that trades at the 'beginning of every month' and the results look promising. What happens to the results if

Quantocracy’s Daily Wrap for 08/21/2015

This is a summary of links featured on Quantocracy on Friday, 08/21/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Parallels Of Betting & Investing [Larry Swedroe]

    Two of the most-well-known factors that help explain stock returns are the value effect (where equities with lower prices relative to metricssuch as book value, earnings, cash flow, sales and dividendstend to outperform the equities with higher prices relative to those metrics), and the momentum effect (where assets that have outperformed in the recent past tend to continue to outperf
  • Lazy Financial Strategies [John Orford]

    One of the major themes of War and Peace will resonate with all practitioners of stochastic finance. Essentially, Napoleon's nemesis, the Russian general Kutuzov, keeps dropping back before the invading French until at last he spots a weakness in the French and pounces. Tolstoy tells us that indecision and chaos is the natural state of things, and the logical c
  • Market Efficiency Hates Bad Weather [Alpha Architect]

    Building on research in psychology, we predict that unpleasant weather negatively affects capital market participants moods and activity levels, causing a muted response to information events The table below highlights that unpleasant weather seems to be correlated with slower market reactions. For example, in columns 5-8, the authors look at PEAD, or post earnings an
  • Moods and the Market [Factor Wave]

    At the start of the week I wrote a post about the effect of weather and the markets. Leo Cheng thought (quite reasonably) that this might just be data mining. If you look at enough things, some will appear to have an influence on the market just by chance. I've done a little more reading and I think that is not the case. I think the effect is real but it is weak.
  • RUT Strangle – High Loss Threshold – 66 DTE [DTR Trading]

    This post reviews the backtest results of selling one-lot options strangles on the Russell 2000 Index (RUT), initiated at 66 days-to-expiration (DTE). The results in this post were derived from 2336 individual trades entered by the backtester. The results are grouped by the delta of the short strikes. For example, a 4 delta strangle is constructed by selling a -4 delta put, and selli