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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

Quantocracy’s Daily Wrap for 08/08/2017

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

  • Exploring Our Scraped Options Data Bid-Ask Spreads [Black Arbs]

    Compared to the equity market, the options market is a level up in complexity. For each symbol there are multiple expiration dates, strike prices for each expiration date, implied volatilities, and that's before we get to the option greeks. The increased complexity presents us with more opportunity. More complexity means less ground truth, more errors, more gaps, and more structural
  • Volatility Premium, Covered Call Selling, and Knowing What You Own [Alpha Architect]

    The folks at AQR are top-notch researchers and have written a ton of great papers. Some of their more famous papers are the following: Value and Momentum Everywhere A Century of Evidence on Trend Following Size Matters If you Control Your Junk (my favorite title of any paper ever published) In this post, I wanted to highlight a number of lesser known papers by the fine folks at AQR that deal with
  • Visualizing Tail Risk [Eran Raviv]

    Tail risk conventionally refers to the risk of a large and sharp draw down of the portfolio. How large is subjective and depends on how you define what is a tail. A lot of research is directed towards having a good estimate of the tail risk. Some fairly new research also now indicates that investors perceive tail risk to be a stand-alone risk to be compensated for, rather than bundled together
  • Trend Following 140 Years of Data Supports its Value [Wisdom Trading]

    AQR updates it paper on Trend Following performance over the last century. Despite the strategy experiencing poor recent performance, it brings tremendous value to stock and bond portfolios over time by 1) increasing returns and 2) lowering volatility and max loss. A win-win-win in my book. Trend Following aint perfect. No investment strategy is, but the data proves Trend Followings

Quantocracy’s Daily Wrap for 08/07/2017

This is a summary of links featured on Quantocracy on Monday, 08/07/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Measuring Market Divergence for Systematic Trend Following [Golden Compass]

    In his 2012 book, Nicholas Nassim Taleb defined the term antifragile for representing things that benefit from disorder. When this concept is applied to trading, an obvious example can be seen in the relative outperformance of systematic trend following strategies during market crises. In the CTA and technical analysis domain, divergence is defined as the strength of directional shifts in market
  • A Gentle Guide to Global Tactical Asset Allocation [Flirting with Models]

    Two questions we frequently receive are: what is global tactical asset allocation? and what are style premia (factors)? In this commentary, we aim to provide a very high-level answer to those questions, incorporating as little math or financial theory as possible and avoiding nuanced discussion. This is not meant as a practitioners guide, but simply a very basic introduction. At the
  • Iron Condor Results Summary – Part 3 – 2017 Results [DTR Trading]

    In this article we'll look more deeply at the following iron condor (IC) strategy variations: 38 DTE, 25 pt. wings, 20 delta shorts, 100% stop loss, 50% profit taking 80 DTE, 25 pt. wings, 20 delta shorts, 100% stop loss, 50% profit taking 80 DTE, 75 pt. wings, 12 delta shorts, 200% stop loss, 50% profit taking These strategy variations appeared to be the strongest based on their metrics, and
  • Academic Research Insight: Diagonal Models versus 1/N Diversification [Alpha Architect]

    In spite of several efforts by researchers to overcome the estimation-risk problem (the use of estimate inputs based on sample information as if they were representative of the true population) which produces the so-called wacky weights, DeMiguel, Garlappi and Uppal (2009) present striking evidence that favors a simple 1/N nave portfolio strategy. The authors challenge the results by

Quantocracy’s Daily Wrap for 08/04/2017

This is a summary of links featured on Quantocracy on Friday, 08/04/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trend-Following with Valeriy Zakamulin: Anatomy of Trading Rules (Part 4) [Alpha Architect]

    In our context, a technical trading indicator can be considered as a combination of a specific technical trading rule with a particular moving average of prices. In two preceding blog posts we showed that there are many technical trading rules, as well as there are many popular types of moving averages. As a result, there exist a vast number of potential combinations of a specific trading rule
  • US Stock Multiples Properly Reflect Sentiment, But It Doesn’t Make Them Attractive [EconomPic]

    GMO's latest quarterly commentary is a must read, especially the second half where Jeremy Grantham attempts to model / answer the question "Why Are Stock Market Prices So High?". His first bullet point in the whole piece provides a good summary: Contrary to theory, the market P/E level does not primarily reflect future prospects. It reflects current conditions. Go read the whole

Quantocracy’s Daily Wrap for 08/02/2017

This is a summary of links featured on Quantocracy on Wednesday, 08/02/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Podcast with David Varadi (@CSSAnalytics): Managing Risk is Absolutely Critical [Meb Faber]

    Guest: David Varadi. David is the Co-Founder and Portfolio Manager of QuantX Funds and has been the Director of Research at Blue Sky Asset Management since 2014. Previously, David was the Vice President of Economic Research and Strategic Development for Flexible Plan Investments overseeing quantitative strategy development. Before joining Flexible Plan Investments, David was the Quantitative
  • Sector trading using the 200-day moving average [Alvarez Quant Trading]

    A user commented on ETF Sector Rotation post about a simple idea for trading the sector ETFs, which I cant believe I have never tried. I like keeping things simple just like my Brazilian Jiu-Jitsu game. Rules If the Select Sector SPDR ETF (XLY, XLP, XLF, XLE, XLV, XLI, XLB, XLK, XLU) is above its 200-day moving average for the last 5 days, then buy 10% of that ETF. If it is below the 200-day