Quantocracy

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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

Quantocracy’s Daily Wrap for 08/16/2017

This is a summary of links featured on Quantocracy on Wednesday, 08/16/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Insights from the World’s Top Academic FX Researcher [Alpha Architect]

    Last week I had the privilege of co-hosting Behind the Markets with my friend Jeremy Schwartz. We had the honor of sitting down with one of my University of Chicago PhD classmates, Nick Roussanov. Nick has gone on to become a stellar academic and is currently tenured at The Wharton School (not too shabby!). Our conversation is here. If you ever wanted to dig deep into understanding and thinking
  • Supercointegrated Pairs Trading [Quantpedia]

    This paper uses S&P100 data to examine the performance of pairs trading portfolios that are sorted by the significance level of cointegration between their constituents. We find that portfolios that are formed with highly cointegrated pairs, named as "supercointegrated", yield the best performance reflecting a positive relationship between the level of cointegration and pairs trading

Quantocracy’s Daily Wrap for 08/15/2017

This is a summary of links featured on Quantocracy on Tuesday, 08/15/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bitcoin, Ethereum and Altcoins: How to get free daily and intraday Bitcoin historical prices [Sanz Prophet]

    In order to analyze and build crypto based trading strategies we need to get historical data for Bitcoin and other large-cap coins such as Ether, Ripple, Dash, Monero, etc. But also for up and coming coins such as Neo, Stratis, IOTA and many more. In this post I will point you to two solutions: 1. Using simple Python scripts originally posted by QuantAtRisk.com 2. Using QuantShare
  • What Alternative Career Paths Exist For Quants? [Quant Start]

    Recent graduates, postgraduates and those in early-career positions with a technical background are now faced with a wide choice of exciting and well-compensated career paths in a diverse set of industries. Quantitative finance remains an attractive option but the competition for top talent is growing from technology firms outside of the financial industry. The chance to "have an
  • Smart Beta vs Factor Returns [Factor Research]

    SUMMARY Smart beta ETFs are based on factor investing research Excess returns from smart beta ETFs are different from factor returns Investors need to be aware that smart beta ETFs offer little diversification for an equity-centric portfolio INTRODUCTION Blackrock, a provider of active and passive funds, estimates that smart beta ETFs will reach $1 trillion in assets by 2020 and $2.4 trillion by
  • Academic Research Insight: Can Bond Portfolios Be “Factorized”? [Alpha Architect]

    What are the research questions? Can the concepts contained in equity factors translate to the corporate bond market? Do single factor bond portfolios generate alpha? Do multifactor bond portfolios contribute additional value? What are the Academic Insights? YES. Using bond characteristics only, definitions for Value (based on differential between actual vs fair credit spread), Low