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Quantocracy’s Daily Wrap for 07/27/2017

This is a summary of links featured on Quantocracy on Thursday, 07/27/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Vigilant Asset Allocation from Dr. Wouter Keller and JW Keuning [Allocate Smartly]

    This is a test of the Vigilant Asset Allocation (VAA) strategy from the recently published paper: Breadth Momentum and Vigilant Asset Allocation, by Dr. Wouter Keller and JW Keuning. This is an aggressive momentum trading model, similar in spirit to Keller and Keunings popular Protective Asset Allocation strategy. Results versus the 60/40 benchmark from 1971 to the present, net of
  • Trend and Carry Everywhere [Cantab Capital]

    Simple rules on macro assets can create very attractive returns. We present a simple trend system and a simple carry system and show how the combination of the two return streams appears very attractive. Summary Trend following in one form or another has been an investment style for decades. Surprisingly to us, there is still a considerable amount of mystery about how trend works and the important
  • Financialization of Crude Oil Market [Quantpedia]

    The financialization of crude oil markets over the last decade has changed the behavior of oil prices in fundamental ways. In this paper, we uncover the gradual transformation of crude oil from a physical to a financial asset. Although economic demand and supply factors continue to play an important role, recent indicators associated with financialization have emerged since 2008. We show that
  • Derivatives Pricing II: Volatility Is Rough [Quant Start]

    In this new 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 discusses how the assumption of constant volatility in the Black-Scholes model can be

Quantocracy’s Daily Wrap for 07/26/2017

This is a summary of links featured on Quantocracy on Wednesday, 07/26/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why Today s Fed Day Setup May Not Be As Bullish As Most [Quantifiable Edges]

    Wednesday is a Fed Day. Fed Days have historically shown an upside tendency. I have documented this tendency in great detail over the years, with the most complete documentation coming in The Quantifiable Edges Guide to Fed Days. Based on what the market did Tuesday, this does not seem to be the most favorable Fed Day setup. A big reason for this is that SPX closed at a 20-day high on Tuesday. Fed
  • Stochastic portfolio theory, revisited! [Quant Dare]

    Im here today to talk about the Stochastic Portfolio Theory (SPT). SPT is a relatively new portfolio management theory. It was first introduced in 1999 by Robert Fernholz. In my opinion, SPT is very attractive for several reasons: its theoretical, its not very well known and, most importantly, its cool. Lets begin this post with a gentle introduction to the topic. This theory is all
  • Testing Equity Factor Allocation Strategies With Random Portfolios [Capital Spectator]

    Designing and managing asset allocation strategies based on factors is promoted in some corners as a better way to build portfolios. Not surprisingly, theres no shortage of studies that support this view. But the jurys still out on whether its prudent to throw out the standard asset-class buckets. Factor-based investing can play a productive role in enhancing a conventionally designed

Quantocracy’s Daily Wrap for 07/24/2017

This is a summary of links featured on Quantocracy on Monday, 07/24/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Information Content in the Limit Order Book for Crude Oil Futures (WTI) [Golden Compass]

    Order book imbalance strategies have been a big alpha source in automated market making. Tick by tick observations provide important information about general market sentiment and direction, and high frequency trading firms (HFTs) have been very efficient at trading on this information at very low latency intervals. In their recent paper on HFT strategies, Goldstein, Kwan and Philip analyzed six
  • Separating Positions from Allocations [Following the Trend]

    Most trading models I see are missing an important concept. Its not a terribly difficult concept, but it is an important one. Its not at all strange that most traders, in particular on the retail side, are missing this point. Most trading books skip over it. Most books gloss over it, or just dont mention it at all. My books included. The Traditional Way Heres how a regular type of
  • Trick Question: How is the Momentum Factor Performing YTD? [Alpha Architect]

    If you ask your typical long-only investor (or financial advisor) how momentum is doing this year theyll likely say, Amazing! This statement will almost surely be based on the fact they own (or know about) the iShares Momentum Factor Fund (Ticker: MTUM). MTUM is on fire year to date (through 5/31/2017):(1) 17.42% based on market prices versus 8.66% for the S&P 500 Total Return
  • The Case for the Harmonic Mean P/E Calculation [EconomPic]

    The most recent "analysis" seemingly spreading like wildfire across the perma-bear community was performed by Horizon Kinetics in their most recent quarterly commentary. Their claim is that the price-to-earnings of the Nasdaq (or any index really) is much higher than reported because we are being fed a manipulated harmonic mean rather than arithmetic mean calculation for the price to
  • Managing Capital Market Assumption Risk [Flirting with Models]

    Calculating an optimal portfolio from a set of capital market assumptions (CMAs) is a straightforward quantitative exercise, but the results are highly dependent on the assumptions holding in the future. Any portfolio that is initially assumed to be optimal will be sub-optimal if any single assumed parameter turns out to be different. By utilizing multiple sets of capital market assumptions, we
  • Academic Research Insight: When Does International Investing Make Sense? [Alpha Architect]

    What are the research questions? Market globalization is said to be the culprit of decreased benefits of international diversification. In 1995, a US investor investing in Vodafone had exposure to 99% of UK based sales. The same investor in 2012 is exposed to UK sales only for 8% while at the same time he is exposed to 30% of US sales, his home country. Vodafone is an example of a multinational

Quantocracy’s Daily Wrap for 07/21/2017

This is a summary of links featured on Quantocracy on Friday, 07/21/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Building an Insider Trading Database and Predicting Future Equity Returns [EP Chan]

    Ive long been interested in the behavior of corporate insiders and how their actions may impact their companys stock. I had done some research on this in the past, albeit in a very low-tech way using mostly Excel. Its a highly compelling subject, intuitively aligned with a companys equity performance – if those individuals most in-the-know are buying, it seems sensible that the stock
  • Trend-Following with Valeriy Zakamulin: Types of Moving Averages (Part 2) [Alpha Architect]

    In my previous blog post we considered the general weighted moving average. In this post we aim to give an overview of some specific types of moving averages. Specifically, we cover ordinary moving averages and mention some examples of exotic moving averages. Ordinary Moving Averages These are the most common types of moving averages used to time the market.(1) Simple Moving Average The

Quantocracy’s Daily Wrap for 07/19/2017

This is a summary of links featured on Quantocracy on Wednesday, 07/19/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Run Trading Algorithms on Google Cloud Platform in 6 Easy Steps [Robot Wealth]

    Earlier this year, I attended the Google Next conference in San Francisco and gained some first hand perspective into whats possible with Googles cloud infrastructure. Since then, Ive been leaning on Google Cloud Platform (GCP) to run my trading algorithms (and more) and it has become an important tool in my workflow. In this post, Im going to show you how to set up a GCP cloud compute
  • How to Improve Shiller’s CAPE Ratio [Quantpedia]

    The accuracy of U.S. stock return forecasts based on the cyclically-adjusted P/E (CAPE) ratio has deteriorated since 1985. The issue is not the CAPE ratio, but CAPE regressions that assume it reverts mechanically to its long-run average. Our approach conditions mean reversion in the CAPE ratio on real (not nominal) bond yields, reducing out-of-sample forecast errors by as much as 50%. At present,

Quantocracy’s Daily Wrap for 07/18/2017

This is a summary of links featured on Quantocracy on Tuesday, 07/18/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Backtesting Systematic Trading Strategies in Python: Considerations and Open Source Frameworks [Quant Start]

    In this article Frank Smietana, one of QuantStart's expert guest contributors describes the Python open-source backtesting software landscape, and provides advice on which backtesting framework is suitable for your own project needs. Backtesting is arguably the most critical part of the Systematic Trading Strategy (STS) production process, sitting between strategy development and deployment
  • EconomVIX…A Summary of Past VIX Posts [EconomPic]

    RCM Alternatives has a great piece (HT Tadas) out outlining what the VIX is, the market for VIX related products, and how to think about volatility as an asset class. It also happens to contain my new favorite quote for anyone thinking about trading volatility: Still, if you cannot see the VIX futures curve in your head, burning $100 bills is probably more profitable than trading them. I'll
  • Academic Research Insights: Does the Scope of the Sell-Side Analyst Industry Matter? [Alpha Architect]

    What are the research questions? Do variations in aggregate measure of size and activity of sell-side analysts affect the quality of research produced by that industry? Do those same variations in aggregate measures of size and activity of sell-side analysts affect optimism bias in the research produced? Do those variations extend to sectors where information is easily obtained and modelled? Do

Quantocracy’s Daily Wrap for 07/17/2017

This is a summary of links featured on Quantocracy on Monday, 07/17/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Combining Tactical Views with Black-Litterman and Entropy Pooling [Flirting with Models]

    In last weeks commentary, we outline a number of problems faced by tactical asset allocators in actually implementing their views. This week, we explore popular methods for translating a combination of strategic views and tactical views into a single, comprehensive set of views that can be used as the foundation of portfolio construction. We explore Black-Litterman, which can be used to

Quantocracy’s Daily Wrap for 07/16/2017

This is a summary of links featured on Quantocracy on Sunday, 07/16/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The birth of a strategy pt. 2 extending VXX history and other data concerns [Quant Bear]

    This is the third part in a series describing how to approach the creation of a new trading strategy, including everything from idea generation, universe selection, data generation, proper in/out of sample testing, necessary considerations before live trading and the eventual big decision: do I want to trade that? The first posts can be found here: Introduction Part 1 For this series it has been

Quantocracy’s Daily Wrap for 07/14/2017

This is a summary of links featured on Quantocracy on Friday, 07/14/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trend-Following with Valeriy Zakamulin: Moving Average Basics (Part 1) [Alpha Architect]

    One of the basic principles of technical analysis is that prices move in trends. Traders firmly believe that these trends can be identified in a timely manner and used to generate profits and limit losses. Consequently, trend following is arguably one of the most widespread market timing strategies; it tries to jump on a trend and ride it. Specifically, when stock prices are trending upward
  • Trend Following Research [Dual Momentum]

    There have been hundreds of research papers on relative strength momentum since the seminal work by Jegadeesh and Titman in 1993. [1] Relative momentum has been shown to work in and out-of-sample within and across most asset classes. Theoretical results have been consistent, persistent, and robust. Research on trend following absolute momentum got a much later start. The first paper on Time
  • Breadth Momentum and Vigilant Asset Allocation (VAA) [TrendXplorer]

    Breadth momentum extends traditional absolute momentum approaches for crash protection. Breadth momentum quantifies risk at the universe level by the number of assets with non-positive momentum relative to a breadth protection threshold. Vigilant Asset Allocation matches breadth momentum with a responsive momentum filter for targeting offensive annual returns with defensive crash protection.
  • Is Equity Premium Predictable? [Quantpedia]

    We study the performance of a comprehensive set of equity premium forecasting strategies that have been shown to outperform the historical mean out-of-sample when tested in isolation. Using a multiple testing framework, we find that previous evidence on out-of-sample predictability is primarily due to data snooping. We are not able to identify any forecasting strategy that produces robust and
  • Identifying Asset Pairs For Pairs Trading [Koppian Adventures]

    Last time, we talked about how to identify stationary time series. Today we continue this line of thought by defining cointegration and looking at its usage in trading. In particular, we will discuss how a pairs trading strategy works. Motivation If we remember that stationarity assumes that a mean of a time series exist, we can conclude that if the time series wanders too far off its mean, it
  • The profitability factor [Investing For A Living]

    What would you think of a quant strategy that only invests in the most profitable companies? Would it under perform the market or beat the market? If youre an efficient market person you may think that higher profitability must be priced into equities and therefore at best the strategy would match the market. Not so. Turns out that profitability is quite a durable factor and is only beaten by

Quantocracy’s Daily Wrap for 07/12/2017

This is a summary of links featured on Quantocracy on Wednesday, 07/12/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Out-of-sample testing and luck [Alvarez Quant Trading]

    Continuing from the last post, I will show how using different definitions of passing our out-of-sample test can change our results. How luck can play a role if you use only one strategy to test in out-of-sample. How you split your in-sample(IS) and out-of-sample(OOS) can change results. The Strategy I will be using a stock mean reversion strategy with an average hold of three days. Some of my
  • Avoiding Overpriced Winners: A Better Way to Capture the Momentum Premium? [Alpha Architect]

    Any frequent reader of our blog knows we are fans of momentum investing. At this point, investment professionals should know that momentum historically works, that momentum is painful, and we have our own opinions on how to implement momentum investing via our Quantitative Momentum Index. Sometimes we feel there is nothing new when it comes to momentum. However, a new momentum investing paper,
  • “Past performance is no guarantee of future results”, but helps a bit [Quant Dare]

    We are rather used to reading this disclaimer (or some variation thereof) in mutual fund prospectuses or investment vehicle webpages. Despite warnings, investors and advisors insist on considering past performance (and some other related metrics) as important factors in asset selection. But, are they really wrong? In this post, I will try to shed some light on this topic by means of some metrics