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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

Quantocracy’s Daily Wrap for 08/20/2015

This is a summary of links featured on Quantocracy on Thursday, 08/20/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 08/19/2015

This is a summary of links featured on Quantocracy on Wednesday, 08/19/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Health of Stock Mean Reversion: Dead, Dying or Doing Just Fine [Alvarez Quant Trading]

    My second post on this blog was a look at mean reversion, Is mean reversion dead? Given I am using a new data provider(Premium Data), it has been almost two years since that post and there have been other articles on this recently, I figured it was time to check again. The research will focus on Russell 1000 stocks since 1995. The test is back to 1995 covers 3 bull markets and 2 bear ma
  • Correlation and correlation structure [Eran Raviv]

    Given a constant speed, time and distance are fully correlated. Provide me with the one, and Ill give you the other. When two variables have nothing to do with each other, we say that they are not correlated. You wish that would be the end of it. But it is not so. As it is, things are perilously more complicated. By far the most familiar correlation concept is the Pearson
  • Technical Analysis or Quantitative Analysis? [Factor Wave]

    Yesterday I had a coffee with a person I have known for 20 years. He has worked as a quantitative analyst, a trader and a finance professor for at least as that long. He is one of the most knowledgeable people I know. When he says something it is worth listening. What he said was (roughly), " I've come to believe that there is something to technical analysis".
  • Crisis Alpha: Surprising Ways to Hedge Stock Portfolio Risk [Alpha Architect]

    Investing in the current environment is difficult. Most, if not all, asset classes have high nominal prices, suggesting low nominal expected returns. Not exactly exciting. And for many investors who are retired and/or have near-term liquidity needs, investing in equity exposureswhile necessary to generate higher expected returnsalso prevents many investors from sleeping at night!
  • New academic paper related to #12 – Pairs Trading with Stocks [Quantpedia]

    We assume that the drift in the returns of asset prices consists of an idiosyncratic component and a common component given by a co-integration factor. We analyze the optimal investment strategy for an agent who maximizes expected utility of wealth by dynamically trading in these assets. The optimal solution is constructed explicitly in closed-form and is shown to be affine in the co-in

Quantocracy’s Daily Wrap for 08/18/2015

This is a summary of links featured on Quantocracy on Tuesday, 08/18/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Kalman Filter and Pairs Trading [MKTSTK]

    Imagine this scenario: you are a statistical arbitrage trader at a prop desk or HF. As such, you routinely hold an inventory of ETF exposure that you must hedge. The previous night, you instructed your overnight traders to calculate the hedge ratios for a matrix of ETFs. The next morning before the market opens, your junior traders eagerly present their result
  • The Effect of the Board of Directors [Factor Wave]

    The Quality factor is a composite measure designed to identify companies with the characteristics that typically lead to success. It is particularly useful to identify "value traps": companies which are cheap by current metrics but are losing money. I wrote a little about quality here. However, we are not so dogmatic that we won't admit that there are aspects of quality tha
  • Intertemporal PCA Analysis [John Orford]

    Taking a leaf out of Mike Harris' recent Momersion theme, here's a PCA point of view of the balance between momentum and mean reversion over the previous 10 years from the Lazy PCA tool. August 2015 – 2014 PCA fits momentum and mean reversion components to the daily returns, they balance each other out here, which means the market is somewhat efficient (i.e. 'momersio
  • State of Trend Following in July: UP [Au Tra Sy]

    July saw a bounce back up in the index after several months of downwards action. The YTD performance is still negative though. Please check below for more details. Detailed Results The figures for the month are: July return: 3.86% YTD return: -1.47% Below is the chart displaying individual system results throughout July:

Quantocracy’s Daily Wrap for 08/17/2015

This is a summary of links featured on Quantocracy on Monday, 08/17/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Introduction to Monte Carlo Analysis Part 1 [Quants Portal]

    The Monte Carlo, filled with a lot of mystery is defined by Anderson et al (1999) as the art of approximating an expectation by the sample mean of a function of simulated variables. Used as a code word between Stan Ulam and John von Neumann for the stochastic simulations they applied to building better atomic bombs (Anderson, 1999), the term Monte Carlo evolved into a method used in a
  • The Sustainable Active Investing Framework: Simple, But Not Easy [Alpha Architect]

    The debate over passive versus active investing is akin to Eagles vs. Cowboys or Coke vs. Pepsi. In short, once our preference for one style over the other is established, it becomes a proven fact or incontrovertible reality in our minds. This post is not meant to convert a passive investor into an active investor; however, we do explain why we believe some active investing
  • Strategy Gamma Overview [John Orford]

    Economics 101 tells us that people have 'convex' utility curves. Which means there are diminishing returns to having more, but losing what you currently have diminishes your well being precipitously. Convexity is such a human property, it shows up again and again in unexpected places. Eyeballs, boobs and butts to name a few examples. I
  • Interview with Larry Williams [Better System Trader]

    On the show this week we have Larry Williams who has been trading futures and stocks for over 50 years. In 1987 he won the world cup trading championship, turning $10,000 in to over $1.1 million in 12 months, that's a cool 11,000% return and the highest return to ever be achieved in that competition. 10 years later his daughter won the same competition with a 1000% return a
  • Downloading Stock Market News for Specific Symbols [Godel’s Market]

    Grabbing the data. How do you grab the latest news on your favorite ticker symbol? It all starts with the following URL. https://www.google.com/finance/company_news?q=SPY&output=rss You'll want to change "q=SPY" to whatever symbol you're interested in. You can add something like the following to the end if you'd like m
  • Can managed futures manage rising rates? [Flirting with Models]

    Summary Rising interest rates are on the horizon somewhere Yield curve dynamics including the absolute level of rates, their direction of change, and the slope of the yield curve all play an important role in the returns for managed futures The cost of carry in shorting fixed-income futures means that commodity trading advisors (CTAs) may fail
  • Autoregressive Moving Average ARMA(p, q) Models for Time Series Analysis – Part 1 [Quant Start]

    In the last article we looked at random walks and white noise as basic time series models for certain financial instruments, such as daily equity and equity index prices. We found that in some cases a random walk model was insufficient to capture the full autocorrelation behaviour of the instrument, which motivates more sophisticated models. In the next couple of articles w
  • Weather and the Markets [Factor Wave]

    It should be fairly obvious to anyone who has been involved with investing for any time, that traders decisions are heavily influenced by their mood. Actually, some interesting recent research has shown that people generally make decisions intuitively before using their conscious thought processes to justify them. No one is actually all that rational. So perhaps it should b
  • RUT Strangle – High Loss Threshold – 59 DTE [DTR Trading]

    This post reviews the backtest results of selling one-lot options strangles on the Russell 2000 Index (RUT), initiated at 59 days-to-expiration (DTE). The results in this post were derived from 2312 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