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Quantocracy’s Daily Wrap for 08/29/2017

This is a summary of links featured on Quantocracy on Tuesday, 08/29/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Correlation Cointegration [Jonathan Kinlay]

    In a previous post I looked at ways of modeling the relationship between the CBOE VIX Index and the Year 1 and Year 2 CBOE Correlation Indices: The question was put to me whether the VIX and correlation indices might be cointegrated. Lets begin by looking at the pattern of correlation between the three indices: VIX-Correlation1 VIX-Correlation2 VIX-Correlation3 If you recall from my previous
  • Academic Research Insights: Global Equities and Overreaction [Alpha Architect]

    What are the research questions? Is there a consistent and reliable long term overreaction pattern in global equity markets? In US equity markets, buying long term losers and selling long term winners (also called long term price reversal) is a well-documented anomaly. Does it also exist in global equity markets? Do known risk characteristics explain all or part of the excess returns associated
  • Iron Condor Results Summary – Part 4 – Top Performers By Metric [DTR Trading]

    In this article we will look at a subset of the 3024 iron condor strategy variations that were tested between January 2007 and September 2016. Specifically, we will look at the 1512 iron condor strategy variations that used both stop losses and profit targets. Out of these 1512 variations we will look at the top performers in terms of the following metrics: P&L / Trade (total return) Largest
  • Statistical Arbitrage Using Pair Trading In The Mexican Stock Market [Quant Insti]

    There are very few algo trading firms/strategies that are operating in the Mexican stock exchange. I believe this should provide great opportunities as there is little competition. Contrary to a more developed market, arbitrage opportunities arent readily realized which suggests there might be opportunities for those looking and able to take advantage of them. This is the main motivation for

Quantocracy’s Daily Wrap for 08/28/2017

This is a summary of links featured on Quantocracy on Monday, 08/28/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Statistical Arbitrage on a Cross-border Soybean Crush Spread [Golden Compass]

    Pairs trading is one of the simplest forms of statistical arbitrage which involves exploiting relative mispricings between two similar assets. It operates based on the assumption of the law of one price; that anomalies among securities valuation will occur in the short run but in the long run, will be corrected by market efficiency. In academic literature, studies such as such as Bogomolov (2010)
  • An Interactive Dynamic Delta Hedging Example in R [Top of The Bell Curve]

    Delta hedging is a technique used by trades to reduce the directional risk of a position. This delta hedging strategy results in the reduction of the variability of the profit and loss (pnl) of the position. A position that is delta hedged is said to be delta neutral. In this blog we will look at delta hedging European options under the Black and Scholes framework. A European option is an option
  • Smart Beta and Factor Correlations to the S&P 500 [Factor Research]

    SUMMARY Most smart beta products exhibit correlations of > 0.9 to the S&P 500 Factors show correlations of zero on average However, factor correlations are highly volatile across the market cycle INTRODUCTION In our recent research note Smart Beta vs Factors in Portfolio Construction we analysed the impact of including Value & Growth smart beta ETFs in an equity-centric portfolio

Quantocracy’s Daily Wrap for 08/25/2017

This is a summary of links featured on Quantocracy on Friday, 08/25/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Definitive Guide To Momentum Investing and Trading [Signal Plot]

    During my review of several quantitative trading books and papers, I kept on seeing information on two classes of trading strategies: mean reversion and momentum. I thought the things I read explained mean reversion quite clearly, but I wasnt entirely clear on how to implement momentum investing and trading strategies, so I decided to research it more thoroughly. This post focuses on what I
  • Trend-Following with Valeriy Zakamulin: Testing Profitability of Trading Rules (Part 6) [Alpha Architect]

    The difficulty in testing the profitability of trend-following rules stems from the fact that the procedure of testing involves either a single- or multi-variable optimization. Specifically, any trading rule considered in Part 3 has at least one parameter that can take many possible values. For example, in the Moving Average Crossover rule, MAC(s,l), there are two parameters: the size of the
  • Theta and Weekends Again [Highly Evolved Vol]

    Last week we stated that market makers don't fully account for weekend decay in equity options. Today we show specific results. Christopher Jones and Joshua Shemesh studied this issue and presented the findings in a paper that they presented to the 2010 American Finance Association meeting. They looked at the returns of long option portfolios on U.S. equities from 1996 to 2007 and found the
  • Podcast: Building entries without curvefitting [Better System Trader]

    You may have noticed over the past few weeks of Thursday Trading Thoughts that weve been following a theme. In episode 113 we heard about a test Kevin Davey calls the Monkey test, which can be used to measure the effectiveness of entries and exits. Then in episode 114 we reviewed a technique that Dave Bergstrom shared to measure the decay of a trading edge so that we can determine

Quantocracy’s Daily Wrap for 08/24/2017

This is a summary of links featured on Quantocracy on Thursday, 08/24/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Timing Luck and Portfolio Tranching [Allocate Smartly]

    In this post we discuss portfolio tranching (i.e. dividing a portfolio into overlapping slices of the same underlying strategy) to minimize timing luck. This is an under discussed but important topic in tactical asset allocation. For more smart thoughts on portfolio tranching, see this excellent piece from Newfound Research. For our test case, well use a TAA strategy particularly

Quantocracy’s Daily Wrap for 08/23/2017

This is a summary of links featured on Quantocracy on Wednesday, 08/23/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Portfolio Allocations using Enterprise Multiples (and others) [Alpha Architect]

    A common question asked in the factor investing field is the following how much of the models performance is driven by sector allocations, and how much is driven by security selection? Our answer is to simply buy Value stocks or Momentum stocks, regardless of sector constraints. Why? Well a nice anecdote (but not data) is that investing in cheap technology stocks was not a great
  • Sector trading using the 200-day moving average Part 2 [Alvarez Quant Trading]

    Several readers asked for additional tests to be done on the strategy on Sector trading using the 200-day moving average. We will be testing allocated 11% per ETF instead of 10%, using asymmetric number of days and adding IEF to the SPY MA200 10 day test. SPY MA200 10 day Buy Rule: Buy SPY when it is above the 200-day MA for 10 or more days. Sell IEF. Sell Rule: Sell SPY when it is below the
  • Episode #68 with @CHoffstein “Risk Cannot Be Destroyed, Only Transformed” [Meb Faber]

    Guest: Corey Hoffstein. Corey is the founder and CIO of Newfound Research. Hes a frequent speaker on industry panels and contributes to ETF.com, ETF Trends, and Forbes.coms Great Speculations blog. He was named a 2014 ETF All Star by ETF.com. Corey holds a Master of Science in Computational Finance from Carnegie Mellon University and a Bachelor of Science in Computer Science, cum laude, from
  • Are Short Out-of-the-Money Put Options Risky-Leverage Increases Risks [Relative Value Arbitrage]

    Traders often debate whether short out-of-the-money (OTM) or at-the-money (ATM) puts are riskier. The argument for OTM put options being riskier is that their Speeds (or dGamma/dspot) are higher than the ATMs ones, thus the Gamma, which is negative, can increase (in absolute value) substantially during a market downturn. In this post, we will quantify and compare the risks of short OTM and ATM
  • Famous Theorems In Mathematical Finance [Koppian Adventures]

    In this post, I want to explain the intuition behind some famous theorems in mathematical finance. I will not explain any proofs, since you can find these in books, but rather for what the theorems can be used. Girsanovs theorem Let us start with Girsanovs theorem. We need a Brownian motion Wt under measure P, and a sufficiently reasonable function v. Then there is an equivalent

Quantocracy’s Daily Wrap for 08/22/2017

This is a summary of links featured on Quantocracy on Tuesday, 08/22/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • High Frequency Trading I: Introduction to Market Microstructure [Quant Start]

    In this new article series Imanol Prez, a PhD researcher in Mathematics at Oxford University, and an expert guest contributor to QuantStart delves into high-frequency trading and introduces the concept of market microstructure. Nowadays, a significant number of financial instruments are traded in electronic markets, and alternatives that used to be popular in the past, such as open outcry stock
  • Explaining the FOMC Drift [Quantpedia]

    I propose a theoretical explanation for the puzzling pre-announcement positive drift that has been empirically documented before scheduled Federal Open Market Committee (FOMC) meetings. I construct a general equilibrium model of disagreement (difference-of-opinion) where two groups of agents react differently to the information released at the announcement and to signals available between two

Quantocracy’s Daily Wrap for 08/21/2017

This is a summary of links featured on Quantocracy on Monday, 08/21/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Accounting for Autocorrelation in Assessing Drawdown Risk [Flirting with Models]

    Under a simple model of asset prices, expected returns and volatilities can be used to calculate expected maximum drawdowns over a given timeframe. However, these expected drawdowns do not line up with the drawdowns investors have experienced. Simple models have underestimated drawdown risk in equities, low volatility equities, and income strategies, and overestimated historical drawdown risk in
  • Making Python massively parallel (and burgers) [Cuemacro]

    I like burgers. I suspect I start most of my blog articles with a similar sentence. Most burgers are sufficiently large, such that a single burger will suffice for a meal. However, occasionally you get burger sliders, mini burgers of different flavours, which are also easier to share. It is an obvious point that a plate of burger sliders is likely to end up getting finished quicker than a single
  • Modeling Volatility and Correlation [Jonathan Kinlay]

    In a previous blog post I mentioned the VVIX/VIX Ratio, which is measured as the ratio of the CBOE VVIX Index to the VIX Index. The former measures the volatility of the VIX, or the volatility of volatility. A follow-up article in ZeroHedge shortly afterwards pointed out that the VVIX/VIX ratio had reached record highs, prompting Goldman Sachs analyst Ian Wright to comment that this could signal
  • Academic Research Insight: Abusing ETFs [Alpha Architect]

    What are the research questions? By studying the trading data (provided by a German brokerage house) of a large (6,949) group of individual self-directed investors over the period from 2005-2010, the authors attempt at answering:(1) Do ETFs provide performance benefits to individual investor portfolios? If not, what are the reasons? Does investors heterogeneity (specifically, overconfident
  • Smart Beta vs Factors in Portfolio Construction [Factor Research]

    SUMMARY Investors seek smart beta products for risk reduction However, smart beta products are effectively long-only products with full equity risk Only factor products, i.e. long-short portfolios, offer true diversification benefits and downside protection INTRODUCTION FTSE Russells 2017 Smart Beta Investor Survey showed that the Nr 1 objective for evaluating smart beta strategies was for risk

Quantocracy’s Daily Wrap for 08/20/2017

This is a summary of links featured on Quantocracy on Sunday, 08/20/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Speculation in a Truth Chamber [Philosophical Economics]

    In this piece, Im going to share a mental exercise that we can use to increase the truthfulness of our thinking. The exercise is intended primarily for traders and investors, given their obvious (financial) reasons for wanting to think more truthfully about the world, but it has the potential to be useful for anyone in any field who has that goal. Background: Motivated Cognition As intelligent

Quantocracy’s Daily Wrap for 08/18/2017

This is a summary of links featured on Quantocracy on Friday, 08/18/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 08/17/2017

This is a summary of links featured on Quantocracy on Thursday, 08/17/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pump-and-Dump via Twitter [CXO Advisory]

    Do stock scammers use Twitter to manipulate prices of microcap stocks? In his August 2017 paper entitled Market Manipulation and Suspicious Stock Recommendations on Social Media, Thomas Renault performs an event study to analyze returns for microcap stocks around spikes in Twitter activity about the stocks. He identifies tweets about a stock as those containing a dollar sign ($) before its