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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

Quantocracy’s Daily Wrap for 08/14/2017

This is a summary of links featured on Quantocracy on Monday, 08/14/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Impact of High Equity Valuations on Safe Retirement Withdrawal Rates [Flirting with Models]

    While valuation-based market timing is notoriously difficult, present and future retirees should prepare for muted U.S. stock and bond returns relative to historical experience. High valuations suggest that retirement withdrawal rates that were once safe may now deliver success rates that are no better – or even worse – than a coin flip. This outlook is by no means a call for despair, but rather
  • My new book: Smart Portfolios [Investment Idiocy]

    … is now ready for pre-order. For more information see the website, here: https://www.systematicmoney.org/smart To pre-order you can go here: https://www.harriman-house.com/smart-portfolios
  • Battle of the Oscillators…Round 1 [System Trader Success]

    In a recent article, Predictive Indicators written by John Ehlers he highlighted a unique indicator used to time market cycles. This indicator is a heavily modified Stochastic Oscillator and was demonstrated on the S&P. In this article I want to put Johns Oscillator to the test by comparing it to another popular indicator used for timing to stock index markets.

Quantocracy’s Daily Wrap for 08/12/2017

This is a summary of links featured on Quantocracy on Saturday, 08/12/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 08/10/2017

This is a summary of links featured on Quantocracy on Thursday, 08/10/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Derivatives Pricing III: Models driven by L vy processes [Quant Start]

    In this article series QuantStart returns to the discussion of pricing derivative securities, a topic which was covered a few years ago on the site through an introduction to stochastic calculus. Imanol Prez, a PhD researcher in Mathematics at Oxford University, and an expert guest contributor to QuantStart describes how Lvy processes can be utilised to make the Black-Scholes model more

Quantocracy’s Daily Wrap for 08/09/2017

This is a summary of links featured on Quantocracy on Wednesday, 08/09/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Monte Carlo Simulation for your Portfolio PL [Open Source Quant]

    Once you have a system, the biggest obstacle is trusting it. Tom Wills What is the last thing you do before you climb on a ladder? You shake it. And that is Monte Carlo simulation. Sam Savage, Stanford University Introduction In my early days of looking at trading strategies, getting to the equity curve felt like the final piece of the puzzle. The piece that would provide enough
  • Beyond Efficient Markets [Larry Swedroe]

    Andrew Lo is a professor of finance and the director of the Laboratory for Financial Engineering at MITs Sloan School of Management. His research spans a wide range of topics, including the empirical validation and implementation of financial asset pricing models; the pricing of options and other derivative securities; financial engineering and risk management; trading technologies and market