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Quantocracy’s Daily Wrap for 03/24/2016

This is a summary of links featured on Quantocracy on Thursday, 03/24/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • On the 60/40 portfolio mix [Eran Raviv]

    Not sure why is that, but traditionally we consider 60% stocks and 40% bonds to be a good portfolio mix. One which strikes decent balance between risk and return. I dont want to blubber here about the notion of risk. However, I do note that I feel uncomfortable interchanging risk with volatility as we most often do. I am not unhappy with volatility, I am unhappy with realized loss, that is
  • On Backtesting: An All-New Chapter from our Adaptive Asset Allocation Book [GestaltU]

    If you've been a regular reader of our blog, you already know that we recently published our first book Adaptive Asset Allocation: Dynamic Portfolios to Profit in Good Times – and Bad. As of this writing, it still stands as the #1 new release in Amazon's Business Finance category. We're pretty psyched about that. In our book, we spent a great deal of time summarizing the research
  • The Comprehensive Guide to Stock Price Calculation [Quandl]

    Adjusted stock prices are the foundation for time-series analysis of equity markets. Good analysts insist on properly-adjusted stock data. But the best analysts understand the adjustment process from first principles. This is Quandl's guide to the creation and maintenance of accurate adjusted historical stock prices. Introduction Adjustment Principles 1.Cash Dividends 2.Stock Dividends
  • Markov Chain Monte Carlo for Bayesian Inference – The Metropolis Algorithm [Quant Start]

    In previous discussions of Bayesian Inference we introduced Bayesian Statistics and considered how to infer a binomial proportion using the concept of conjugate priors. We discussed the fact that not all models can make use of conjugate priors and thus calculation of the posterior distribution would need to be approximated numerically. In this article we introduce the main family of algorithms,
  • Have benchmarks made us bad active investors? [Alpha Architect]

    Obsession with short-term performance against market cap benchmarks preordains the dysfunctionality of asset markets. The problems start when trustees hire fund managers to outperform benchmark indexes subject to limits on annual divergence Benchmarking causes, first, the inversion of the relationship between risk and return so that high volatile securities and asset classes offer lower returns
  • Responding to Your Comments on Our Adaptive Asset Allocation Book [SkewU]

    If you've been a regular reader of our blog, you already know that we recently published our first book Adaptive Asset Allocation: Dynamic Portfolios to Profit in Good Times – and Bad. As of this writing, it still stands as the #1 new release in Amazon's Business Finance category. We're pretty psyched about that. This has been – and continues to be – a very interesting experience.
  • The Dynamic Duo Of Risk Factors: Part I [Capital Spectator]

    The value and momentum factors have earned high praise in recent years as complementary sources of risk premia for designing and managing equity portfolios. AQRs widely cited paper Value and Momentum Everywhere a few years back helped popularize the idea, pointing to applications in equities and beyond. Theres no shortage of support from the wider world of investment management.
  • A Few Notes On Adaptive Asset Allocation [CXO Advisory]

    In the introductory text for Part I of their 2016 book, Adaptive Asset Allocation: Dynamic Global Porfolios to Profit in Good Times and Bad, Adam Butler, Michael Philbrick and Rodrigo Gordillo state: we have come to stand for something square and real, a true Iron Law of Wealth Management: We would rather lose half our clients during a raging bull market than half of our clients money
  • Smart Beta Strategies in Australia [Quantpedia]

    "Smart beta" investing is an alternative to the traditional active and passive approaches to funds management, whereby investors adopt a systematic method that provides exposure to factors that are argued to be related with expected returns at low cost. Therefore, the question of how smart is smart beta investing can be empirically examined by testing the performance of those factors
  • [Academic Paper] Optimal Delta Hedging for Options [@Quantivity]

    The practitioner Black-Scholes delta for hedging options is a delta calculated from the Black-Scholes-Merton model (or one of its extensions) with the volatility parameter set equal to the implied volatility. As has been pointed out by a number of researchers, this delta does not minimize the variance of changes in the value of a traders position. This is because there is a non-zero
  • Slides from Investing in Smart Beta Conference [Flirting with Models]

    Justin spoke at the Investing in Smart Beta conference this week in Fort Lauderdale, FL. He spoke alongside Research Affiliates in a session titled "The Smart Beta Checklist: Choosing The Best Strategy & Risk/Return Profile For Your Portfolio." Here's a quick description: Depending on market conditions and clients objectives, an array of disparate smart beta strategies can

Quantocracy’s Daily Wrap for 03/22/2016

This is a summary of links featured on Quantocracy on Tuesday, 03/22/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why Investors Should Combine Value and Momentum [Alpha Architect]

    In the past we have discussed how to combine value and momentum strategies to improve an equity allocation. In this piece we discuss why an investor should combine use value and momentum.* Many investors recognize that stand-alone value and momentum strategies have historically worked. Of course, these strategies don't work all the time and can have long streaks of terrible performance. But
  • The More Unique Your Portfolio, The Greater Its Potential [Investor’s Field Guide]

    If there is a lot of overlap between your portfolio and the market, there is only so much alpha you can earn. This is obvious. Still, when you visualize this potential it sends a powerful message. Active sharethe preferred measure of how different a portfolio is from its benchmarkis not a predictor of future performance, but it is a good indicator of any strategys potential excess return.

Quantocracy’s Daily Wrap for 03/21/2016

This is a summary of links featured on Quantocracy on Monday, 03/21/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Best Links of the Last Two Weeks [Quantocracy]

    The best quant mashup links for the two weeks ending Saturday, 03/19 as voted by our readers: How to Learn Advanced Mathematics Without Heading to University – Part 1 [Quant Start] When Measures Become Targets: How Index Investing Changes Indexes [Investor's Field Guide] Meet the DIY Quants Who Ditched Wall Street for the Desert (h/t Abnormal Returns) Evolving Neural Networks through
  • Beware bad multi-factor products [Flirting with Models]

    This post is available as a PDF here. Summary Multi-factor portfolios are a great way to diversify across multiple factors that can potentially create excess risk-adjusted returns while simultaneously smoothing out relative performance volatility. There are two ways we've seen manufacturers build multi-factor portfolios: (1) combining independent portfolios of factors, or (2) picking stocks
  • When Trading Detracts From Alpha [Larry Swedroe]

    As explained in my latest book, The Incredible Shrinking Alpha, which I co-authored with Andrew Berkin, accompanying the rapid growth of the actively managed mutual fund industry, the average performance of mutual funds has been trending downward over the past few decades. Teodor Dyakov, Hao Jiang and Marno Verbeekauthors of the study The Trading Performance of Active Mutual Funds,

Best Links of the Last Two Weeks

The best quant mashup links for the two weeks ending Saturday, 03/19 as voted by our readers:

We also welcome one blog making its first ever appearance on the mashup this week:

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Votes by Clickthroughs

[click graph to enlarge]

Your votes matter to the quant community.

The graph to the right shows the average number of clickthroughs a link receives from our website (excluding RSS, Twitter and Stocktwits), broken out by the number of votes cast by our readers.

A core goal of Quantocracy is to have a positive impact on our corner of the financial world by rewarding the best work, and encouraging the best minds to keep writing.

As the graph makes clear, the citizens of Quantocracy are doing just that (way to go guys). Links with 11 or more votes receive nearly 6-times as many clickthroughs as a link with no votes (wow).

If you haven’t done so already, we invite you to register to vote and be a part of the effort. Your votes matter to the quant community.

Read on Readers!
Mike @ Quantocracy

Quantocracy’s Daily Wrap for 03/20/2016

This is a summary of links featured on Quantocracy on Sunday, 03/20/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Predicting Stock Market Returns Lose the Normal and Switch to Laplace [Six Figure Investing]

    Everyone agrees the normal distribution isnt a great statistical model for stock market returns, but no generally accepted alternative has emerged. A bottom-up simulation points to the Laplace distribution as a much better choice. A well-known problem in financial risk assessment is the failure of the normal distribution (also known as the Gaussian distribution) to correctly predict big up or

Quantocracy’s Daily Wrap for 03/19/2016

This is a summary of links featured on Quantocracy on Saturday, 03/19/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Reflections on Careers in Quantitative Finance [Jonathan Kinlay]

    Carnegie Mellon's Steve Shreve is out with an interesting post on careers in quantitative finance, with his commentary on the changing landscape in quantitative research and the implications for financial education. I taught at Carnegie Mellon in the late 1990's, including its excellent Master's program in quantitative finance that Steve co-founded, with Sanjay Srivastava. The
  • Price Breakout with NR7 | Trading Strategy (Setup & Entry) [Oxford Capital]

    I. Trading Strategy Developer: Toby Crabel (Setup: NR7 Pattern); Laurence A. Connors, Linda B. Raschke (Entry: Price Breakout with NR7). Source: (i) Crabel, T. (1990). Day Trading with Short Term Price Patterns and Opening Range Breakout. Greenville: Traders Press, Inc; (ii) Laurence A. Connors, Linda B. Raschke (1995). Street Smarts | High Probability Short Term Trading Strategies. M. Gordon

Quantocracy’s Daily Wrap for 03/17/2016

This is a summary of links featured on Quantocracy on Thursday, 03/17/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Meet the DIY Quants Who Ditched Wall Street for the Desert (h/t @AbnormalReturns)

    In the high desert plain of New Mexico, Roger Hunter monitors automated trades on hog futures and currency pairs. Roger Hunter in his home office. Roger Hunter in his home office. Photographer: David Paul Morris/Bloomberg Four computer screens display a dizzying array of price charts and program codes in the office of his single-story, thatched adobe home in the town of Las Cruces. Out back, where
  • Covered Calls Uncovered [Quantpedia]

    Equity index covered calls have historically provided attractive risk-adjusted returns largely because they collect equity and volatility risk premia from their long equity and short volatility exposures. However, they also embed exposure to an uncompensated risk, a nave equity market reversal strategy. This paper presents a novel performance attribution methodology, which deconstructs the
  • EP 064: The casino edge, mean reversion strategies, and how to develop robust trading systems w/ Nick Radge [Chat With Traders]

    For this episode I spoke with returning guest Nick Radge, who was originally on episode number 4. But in case you missed it; Nick is a systematic trend follower and momentum trader, most active in Australian and US equity markets. This time around, we discussed mean reversion strategies and why they may appeal to certain traders, the importance of trade frequency when developing a system, which
  • Let s make a deal : from TV shows to identifying trends [Quant Dare]

    How about trying to find any use of the famous Monty Hall problem in a stock index context? Let your imagination run First of all, some of you may be confused because neither Monty Hall problem nor Lets make a deal are familiar to you so I will refresh you what these names are concerned to. Monty Hall was a TV presenter for Lets make a deal, a famous American show in the
  • Justin’s Take: Building a Portfolio for Resolve’s March Madness Challenge [Flirting with Models]

    A Newfound, we try to embrace March Madness as an opportunity to foster some good-natured competition within the company. This year we decided to mix things up and go with our own version of ReSolve's unique March Madness Challenge. When Corey originally suggested the idea, my initial reaction is probably best conveyed with this Puff Daddy lyric: "Put your money on the table and get your

Quantocracy’s Daily Wrap for 03/16/2016

This is a summary of links featured on Quantocracy on Wednesday, 03/16/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Corey’s Take: Building a Portfolio for ReSolve s March Madness Challenge [Flirting with Models]

    The team over at ReSolve recently posted about their very unique March Madness Challenge. The crux of their idea is that the rules governing a more traditional bracket system is fundamentally flawed since it inherently reduces the sample size upon which skill is measured. For example, nearly everyone in the bracket will choose the #1 seed to beat the #16 seed in each region, eliminating the
  • Never Book a Loss (And Why That s Bad For You) [Throwing Good Money]

    I have got a great trading system for you. I mean, look at that equity curve! Its very straight, no drawdowns, and $30,000, compounded, became almost $120,000 over time. Whats the catch? They say (and Im not sure who they are) that the average retail investor hates to book a loss, and takes profits too soon. The reverse of the cut your losses early and let your profits run
  • Adding Stops and Scaling Out to a Mean Reversion Strategy [Alvarez Quant Trading]

    I came on an idea recently that I had tested. I have tested adding max loss stops to a mean reversion strategy, with no success. See this post for more on that. About eight years ago, I tested scaling out of trades. But this person claimed that adding the two together was how to improve a mean reversion strategy. Interesting idea I had not tested. I have a one question poll below about what to do
  • The Seven Deadly Sins of Quantitative Data Analysts [Quandl]

    Sooner or later, every quant is tempted by forbidden fruit. These all-too-human traits can permeate even the most sophisticated analysis. Keep these tips in mind as you develop strategies, and you just may turn vice into virtue. Quandl_7_sins_v04 (1) Download the printable version here.
  • Price Breakout with NR7 | Trading Strategy (Filter & Exit) [Oxford Capital]

    I. Trading Strategy Developer: Toby Crabel (Setup: NR7 Pattern); Laurence A. Connors, Linda B. Raschke (Entry: Price Breakout with NR7). Source: (i) Crabel, T. (1990). Day Trading with Short Term Price Patterns and Opening Range Breakout. Greenville: Traders Press, Inc; (ii) Laurence A. Connors, Linda B. Raschke (1995). Street Smarts | High Probability Short Term Trading Strategies. M. Gordon
  • Testing The Beta Premise [Larry Swedroe]

    One of the most important issues in finance concerns the relationship between risk and expected return. John Lintner, William Sharpe and Jack Treynor are generally given most of the credit for introducing the first formal asset pricing model, the capital asset pricing model (CAPM), which was developed in the early 1960s. The CAPM provided the first precise definition of risk and how it drives
  • Limits of Machine Learning Part 2 [MKTSTK]

    Last weeks podcast was pretty negative on the value of machine learning in trading, so this week I wanted to provide my own counterpoint and explore life within the limits I identified earlier. Specifically, I wanted to begin mapping the things machine learning algorithms might really be great at doing in the world of trading and finance. This podcast is presented in a slightly different manner

Quantocracy’s Daily Wrap for 03/13/2016

This is a summary of links featured on Quantocracy on Sunday, 03/13/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Understanding Modern Portfolio Construction (h/t @AbnormalReturns) [Pragmatic Capitalism]

    My newest research paper, Understanding Modern Portfolio Construction, is available on SSRN. This paper is the culmination of years of work and I consider it to be the most important piece of research Ive published. I wrote this paper in much the same way that I wrote my paper, Understanding the Modern Monetary System, however, since Im not an academic economist, this paper is more along the