Quantocracy

Quant Blog Mashup

ST

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

Quantocracy’s Daily Wrap for 02/10/2017

This is a summary of links featured on Quantocracy on Friday, 02/10/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: Market Microstructure Theory [Amazon]

    After an introduction to the general issues and problems in market microstructure, the book examines the main theoretical models developed to address inventory-based issues. There is then an extensive examination and discussion of the information-based models, with particular attention paid to the linkage with rational expectations model and learning models. The concluding chapters are concerned
  • Podcast: A deeper understanding of optimization with Andrea Unger [Better System Trader]

    One of the most common uses of optimization is to find the best values for a trading strategy, but is this approach only giving us part of the picture? Are there other uses for optimization that we can leverage to create better trading strategies? Today were going to have a quick chat with World Cup Trading Champion Andrea Unger, the only trader to ever win the competition 3 years in a
  • Drop Out for OOS Sanity [Beyond Backtesting]

    The vexing problem facing every system developer is the need to validate their backtest. One rigorous way to do that is to use walk forward optimization. However, an argument can be made that the alternative approach of taking all of the data into consideration can also make sense, and, in fact, some highly experienced system developers prefer that approach to WFA. The most commonly used way to
  • Roll em! How to calculate futures rolls (and why you care) [Adam H Grimes]

    This post will be a bit more technical than most, but its an important subject to understand. Today, lets talk about rolling and back-adjusting futures prices: why we did it. How we do it, and what it means when we look at historical charts. Futures pricing First, a little quick background. When you look at historical charts, the prices you see may not be the price at which the asset traded.
  • Research Review | 10 February 2017 | Portfolio Strategy [Capital Spectator]

    Liquid Alternative Mutual Funds versus Hedge Funds Jonathan S. Hartley (University of Pennsylvania) February 1, 2017 Despite the rapid rise of the number of liquid alternative mutual funds (LAMFs) available to retail investors in recent years, few studies have compared how their return and risk characteristics differ from their hedge fund counterparts across their entire history. Being among the
  • Are Hedge Funds Betting Against Low-Volatility Stocks? [Quantpedia]

    The low-volatility anomaly is often attributed to limits to arbitrage, such as leverage, short-selling and benchmark constraints. One would therefore expect hedge funds, which are typically not hindered by these constraints, to be the smart money that is able to benefit from the anomaly. This paper finds that the return difference between low- and high-volatility stocks is indeed a highly

Quantocracy’s Daily Wrap for 02/09/2017

This is a summary of links featured on Quantocracy on Thursday, 02/09/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why TAA Has Been So Successful in Crises [Allocate Smartly]

    Most Tactical Asset Allocation (TAA) strategies have followed the same basic storyline. They keep pace with the market during the good times (like we find ourselves in right now), and shine during the bad times. To illustrate, the graphs below shows the average return of all of the TAA models that we track (orange) versus the 60/40 benchmark (grey) during the Dot-Com Bust of 2000-02 and Global
  • A Curious Intraday Pattern in Brazilian Stock Index Futures [Quantogo]

    Since the first article of this blog (Technical Analysis for intraday stocks trading? FORGET IT!), im pointing to the fact that there is a lot of cross correlation between stocks and between stocks and the future index. Thats not new to anyone and even those who are starting at the quantitative trading/analysis come to realize this on their own. But, is there some way we can explore that
  • Podcast: Strategy objectives, statistical significance and market behavior w/ @Alphatative [Chat With Traders]

    Returning to Chat With Traders for a second time is David Bushfirst on episode 23. David began as a discretionary trader, more than 20-years ago, but over time hes developed into a quant trader. And hes exceptionally good at what he does; Davids been the first place winner of two (real money) trading competitions in recent years. Last time David was on we spoke fairly extensively about