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Quantocracy’s Daily Wrap for 02/26/2017

This is a summary of links featured on Quantocracy on Sunday, 02/26/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Introduction to Hidden Markov Models with Python Networkx and Sklearn [Black Arbs]

    Who is Andrey Markov? What is the Markov Property? What is a Markov Model? What makes a Markov Model Hidden? A Hidden Markov Model for Regime Detection Conclusion References Who is Andrey Markov? Markov was a Russian mathematician best known for his work on stochastic processes. The focus of his early work was number theory but after 1900 he focused on probability theory, so much so that he taught

Quantocracy’s Daily Wrap for 02/23/2017

This is a summary of links featured on Quantocracy on Thursday, 02/23/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Potential Return-Free Risk of Bonds [EconomPic]

    I've read too many posts / articles that outline why a rise in rates is good for long-term bond investors (as that would allow reinvestment at higher rates). While this can be true depending on the duration of bonds owned and/or for nominal returns over an extended period of time, it is certainly not true over shorter periods of time and absolutely not true for an investor in most real return
  • How Short Positions Affect Factor Investing? [Quantpedia]

    The performances of factor investing rely heavily on short sales, not only for building the initial long-short strategy, but also for regularly rebalancing the positions. Since short selling is subject to both legal restrictions and substantial costs, this paper examines how severely restrictions on short positions affect the financial attractiveness of factor investing. To fill the gap between
  • Dual Momentum Analysis [Quant Dare]

    Why dual momentum? Because strategies based on highest relative momentum show great results in the long run, but can experience deep falls and have little participation in the posterior rebounds after large market falls. To sidestep these drawbacks, here it is laid out a strategy based on Gary Antonaccis studies about Dual Momentum and Absolute Momentum, with the difference that, while he used

Quantocracy’s Daily Wrap for 02/22/2017

This is a summary of links featured on Quantocracy on Wednesday, 02/22/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Zoo or Unicorn Ranch? [Dual Momentum]

    According to Morningstar, as of June 2016, the assets in smart beta exchange traded products totaled $490 billion. BlackRock forecasts smart beta using size, value, quality, momentum, and low-volatility will reach $1 trillion by 2020 and $2.4 trillion by 2025. This annual growth rate of 19% is double the growth rate of the entire ETF market. Are factors the cure-all for our investment needs? Or
  • Explaining the Low Risk Effect with @LarrySwedroe [Alpha Architect]

    As my co-author, Andrew Berkin, and I(1) explain in our new book, Your Complete Guide to Factor-Based Investing,(2) one of the big problems for the first formal asset pricing model developed by financial economists, the CAPM, was that it predicts a positive relation between risk and return. But empirical studies have found the actual relation to be flat, or even negative. Over the last 50
  • Country ETF Rotation Reader s Suggestions [Alvarez Quant Trading]

    My last post on Country ETF Rotation generated several ideas of what to test to improve the results. See the original post for the list ETFs being traded. One important test I left out from the original post was a baseline case. An idea applied to all the tests was trading more ETFS. For all tests, I will be showing results of trading (2,5,8) ETFs in the spreadsheet. Testing is from 1/1/2007 to

Quantocracy’s Daily Wrap for 02/21/2017

This is a summary of links featured on Quantocracy on Tuesday, 02/21/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Crisis Alpha: A Simple ETF Approach [Flirting with Models]

    Trend-following strategies such as managed futures and tactical equity have historically provided crisis alpha against sustained drawdowns. For short-horizon events (e.g. single day, week, or month events), the effectiveness of these approaches in managing risk is largely based on the luck of prior positioning. For more constant protection, option-based strategies can be applied by
  • Market Regime Detection using Hidden Markov Models in QSTrader [Quant Start]

    In the previous article on Hidden Markov Models it was shown how their application to index returns data could be used as a mechanism for discovering latent "market regimes". The returns of the S&P500 were analysed using the R statistical programming environment. It was seen that periods of differing volatility were detected, using both two-state and three-state models. In this
  • Modeling Risk With Bootstrapping Techniques In R [Capital Spectator]

    Limited data is the financial modelers biggest challenge. Making assumptions about risk is tough enough under the best of circumstances. All too often its even tougher when the historical record is thin. There are several ways to manage this challenge, including bootstrapping, aka resampling the available data to create historical records that might have occurred. Nothings perfect, of
  • New Feature: Historical Allocation Analysis [Allocate Smartly]

    Weve added a major new feature to our members area: historical allocation analysis. Every strategy that we track now includes a brand new subpage, which is updated daily and devoted to helping members better understand how each asset class has contributed to the strategys performance. In this blog post, we discuss this new feature. Note that all of the charts in this post require JavaScript,
  • Spx 1% low volatility range streaks [Voodoo Markets]

    Spx is on a low volatility streak, taking a look at how long the streaks usually last and how the current streak relates to past instances. Also looking at Spx returns once the spell breaks as do probably most others, i expected volatility to pick up, that does not seem to be the case. Bill Luby of Vix And More had a recent post supporting the case for low volatility feeding low volatility on

Quantocracy’s Daily Wrap for 02/20/2017

This is a summary of links featured on Quantocracy on Monday, 02/20/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Modeling Asset Processes [Jonathan Kinlay]

    Over the last twenty five years significant advances have been made in the theory of asset processes and there now exist a variety of mathematical models, many of them computationally tractable, that provide a reasonable representation of their defining characteristics. While the Geometric Brownian Motion model remains a staple of stochastic calculus theory, it is no longer the only game in town.
  • President’s Day Factor Investing Geekout [Alpha Architect]

    Our epic piece on factors from a few weeks ago is still ringing in our own ears: Are factors even real? Or just data-mining? The conclusion: who knows. We need more data. And more data we can find. To include a recent masters thesis on nordic country equities, which looks at Size, value, momentum, profitability and investment in a stock market that hasnt been data-dredged as heavy as the US.

Quantocracy’s Daily Wrap for 02/19/2017

This is a summary of links featured on Quantocracy on Sunday, 02/19/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Outliers and Loss Functions [Eran Raviv]

    A few words about outliers In statistics, outliers are as thorny topic as it gets. Is it legitimate to treat the observations seen during global financial crisis as outliers? or are those simply a feature of the system, and as such are integral part of a very fat tail distribution? I recently read a paper where the author chose to remove forecasts which produced enormous errors: (some) models

Quantocracy’s Daily Wrap for 02/17/2017

This is a summary of links featured on Quantocracy on Friday, 02/17/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Will ETFs Destroy Factor Investing? Nope. [Alpha Architect]

    One of the popular investing truisms is the following (inspired by Bill Sharpe): For somebody to beat the market (win) someone else has to lag the market (lose). This becomes an even more daunting (efficient market) statement when changed to the following: For someone to consistently beat the market (win) someone else has to be consistently willing to lag the market (lose). This correctly implies

Quantocracy’s Daily Wrap for 02/16/2017

This is a summary of links featured on Quantocracy on Thursday, 02/16/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation Insights via the Geeks from @ThinkNewfound [Alpha Architect]

    The Alpha Architect mission is to empower investors through education.(1) We cant accomplish our mission without help. Fortunately, finance twitter and an explosion of bloggers are helping us achieve our goal. Awesome! Of course, with so many new blogs hitting the scene, we now face an information overload problem: too many blogs and too many writers. How do we identify who is a flash in

Quantocracy’s Daily Wrap for 02/15/2017

This is a summary of links featured on Quantocracy on Wednesday, 02/15/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Random forest: many is better than one [Quant Dare]

    Random forest is one of the most well-known ensemble methods and it came up as a substantial improvement of simple decision trees. In this post, we are going to explain how to build a random forest from simple decision trees and to test how they actually improve the original algorithm. Maybe you first need to know more about a simple tree; if that is the case, take a look at my previous post.
  • Ehlers s Autocorrelation Periodogram [QuantStrat TradeR]

    This post will introduce John Ehlerss Autocorrelation Periodogram mechanisma mechanism designed to dynamically find a lookback period. That is, the most common parameter optimized in backtests is the lookback period. Before beginning this post, I must give credit where its due, to one Mr. Fabrizio Maccallini, the head of structured derivatives at Nordea Markets in London. You can find the
  • Timing the Stock Market with the Inflation Rate [iMarketSignals]

    Stocks usually perform poorly when inflation is on the rise. Using the inflation rate, we developed a market timer according to two simple rules. Switching according to the Timer signals between the S&P500 with dividends and a money-market fund would have provided from Aug-1953 to end of Jan-2016 and annualized return of 12.48%. Over the same period buy-and-hold of the S&P500 with

Quantocracy’s Daily Wrap for 02/12/2017

This is a summary of links featured on Quantocracy on Sunday, 02/12/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Speed up Python data access by 30x and more [Cuemacro]

    Lets say you send a letter from London to Tokyo. How long would it take to get a reply? At the bare minimum, it takes 12 hours for a letter to fly there, and then another 12 hours for a reply to fly back, so 1 day at least (and this ignoring the time it takes for your letter to be read, the time it takes to write a reply, the time it takes to post it etc.). We could of course use faster means
  • Using PMI Data For Tactical Asset Allocation [Backtest Wizard]

    The 200 day moving average is perhaps one of the most well-known tactical asset allocation filters and many analysts suggest that you should be long the stock market if the Index is greater than the 200 day MA, and flat the stock market if the Index is less than the 200 day MA. For example, the following chart plots the buy and hold performance of the SPY (the black Line), and the performance of