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Quantocracy’s Daily Wrap for 12/10/2018

This is a summary of links featured on Quantocracy on Monday, 12/10/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Risk in the Risk-Free Rate [Flirting with Models]

    The risk-free rate is an important concept in financial theory, but the risk-free rate accessible to most investors can vary significantly in level. The variation in risk-free rate not only has an important impact on the theoretically optimal portfolio, but it can have a very real impact upon portfolio returns. We demonstrate that recent generational lows in short-term Treasuries had made this
  • Factor Optimisation [Factor Research]

    Equity factors exhibit sector biases and exposures to other common factors A factor optimisation process allows investors to create pure factors Risk-adjusted returns do not increase, but pure factors are attractive from analytical, risk and allocation perspectives INTRODUCTION When large quantities of organisms like zooplankton and algae are buried underneath sedimentary rock and subjected to
  • Commodity carry [SR SV]

    Across assets, carry is defined as return for unchanged prices and is calculated based on the difference between spot and futures prices (view post here). Unlike other markets, commodity futures curves are segmented by obstacles to intertemporal arbitrage. The costlier the storage, the greater is the segmentation and the variability of carry. The segmented commodity curve is shaped prominently by

Quantocracy’s Daily Wrap for 12/07/2018

This is a summary of links featured on Quantocracy on Friday, 12/07/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is there a signal in the noise? Yield Curves, Economic Growth and Stock Prices [Musings on Markets]

    The title of this post is not original and draws from Nate Silver's book on why so many predictions in politics, sports and economics fail. It reflects the skepticism with which I view many 'can't fail" predictors of economic growth or stock markets, since they tend to have horrendous track records. Over the last few weeks, as markets have gyrated, market commentators have been
  • Portfolio construction through handcrafting: The method [Investment Idiocy]

    This post is all about handcrafting; a method for doing portfolio construction which human beings can do without computing power (although realistically you'd probably need a spreadsheet unless you're some kind of weird masochist). The method aims to achieve the following goals: Humans can trust it: intuitive and transparent method which produces robust weights Can be easily implemented
  • Trend Following on Steroids [Alpha Architect]

    Trend following is well-known and the simplest version is as follows: you buy an asset when it has positive momentum (the price goes up) and you sell it and go to cash (or any other safe haven) when the momentum turns negative.(1) The best-known example of trend following is on the monthly ETF SPY (based on the SP500 index) and the 10-month SMA momentum (SMA10).(2) In this article, we will
  • 90 Years Of Death Crosses [Quantifiable Edges]

    The SPX could complete a Death Cross formation today or tomorrow, in which the 50-day moving average crosses below the 200-day moving average. In the past I have looked back to 1960 when examining Death Crosses. This time I decided to use Amibroker with my Norgate database, which goes back to 1928, and examine Death Crosses back as far as I can. This made for an interesting starting point,
  • Weekly Recap: Trend-Following, Portfolios, and Risk Factors [Alpha Architect]

    You can watch the video via the link below: This week Ryan and I discuss three posts. First, we examine a guest post titled, Trend Following on Steroids, which examines ways to enhance a simple trend-following strategy. Second, we examine a post I wrote regarding how to use trend-following within a standard stock/bond portfolio. Last, we examine a post by Tommi on an AQR paper regarding how

Quantocracy’s Daily Wrap for 12/06/2018

This is a summary of links featured on Quantocracy on Thursday, 12/06/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • RNN, LSTM, GRU For Trading [Quant Insti]

    In my previous article, we have developed a simple artificial neural network and predicted the stock price. However, in this article, we will use the power of RNN (Recurrent Neural Networks), LSTM (Short Term Memory networks) & GRU (Gated Recurrent Unit Network) and predict the stock price. We are going to use TensorFlow 1.12 in python to coding this strategy. You can access all python code

Quantocracy’s Daily Wrap for 12/05/2018

This is a summary of links featured on Quantocracy on Wednesday, 12/05/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Portfolio construction through handcrafting: motivating [Investment Idiocy]

    I've talked around a type of portfolio construction called "Handcrafting" for some time now, in both of my first two books, and in the odd blog post. I thought it would be useful to explain how the technique works in a more thorough and complete series of blog posts, and also share some code that implements the method. I intend to do four posts on this topic. The first, which you
  • The Emotional Quant Curve [Alvarez Quant Trading]

    While writing my presentation for TradersFest 2018, I wanted to add the traders emotional curve. But looking at it closer, it did not capture my feelings as I go through the cycle of up and downs of trading a strategy. Here is my curve. I have been on every part of this curve multiple times. October and November caused several strategies to go into the red part of the curve. The top box of the
  • MACD: Moving Average Convergence Divergence (Part 1) [Oxford Capital]

    I. Trading Strategy Developer: Gerald Appel. Source: Appel, G. (2005). Technical Analysis. NJ: Pearson Education, Inc; Star, B., PhD (2016). Zero In On The MACD. Stocks & Commodities, May 2016. Concept: Trend following trading strategy based on the MACD (Moving Average Convergence Divergence) line. Research Goal: Performance verification of momentum signals. Specification: Table 1. Results:
  • A Portfolio of Leveraged Exchange Traded Funds vs. Benchmark Asset Allocation [Quantpedia]

    A new interesting financial research paper gives an idea to build a diversified portfolio of leveraged ETFs (scaled down to have the same risk as a benchmark asset allocation built from a non-leveraged ETFs) to beat benchmark asset allocation. However, caution is needed as the most of the outperformance is due to inherent leveraged position in bonds because excess ratio of cash in portfolio (which

Quantocracy’s Daily Wrap for 12/04/2018

This is a summary of links featured on Quantocracy on Tuesday, 12/04/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Use Trend Following within a Portfolio [Alpha Architect]

    A question we have been receiving recently is the following: How should I use trend following within a portfolio? Generally, the questions are related to our Global Value, Momentum, and Trend Index, which allocates to the (1) Value, (2) Momentum, and (3) Trend factors. A big difference between the Global Value Momentum Trend (GVMT) portfolio and many other smart-beta products is the

Quantocracy’s Daily Wrap for 12/03/2018

This is a summary of links featured on Quantocracy on Monday, 12/03/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • GARCH and a rudimentary application to Vol Trading [QuantStrat TradeR]

    This post will review Kris Boudts datacamp course, along with introducing some concepts from it, discuss GARCH, present an application of it to volatility trading strategies, and a somewhat more general review of datacamp. So, recently, Kris Boudt, one of the highest-ranking individuals pn the open-source R/Finance totem pole (contrary to popular belief, I am not the be-all end-all of coding R
  • The relationship between ATR and standard deviation [Investment Idiocy]

    Let's begin this post with a gross generalisation: Professional traders tend to measure risk and target risk using standard deviation. Amateur traders tend to use a funky little number called the ATR: 'Average True Range'. Both try and achieve the same aim: summarise the typical movement in the price of something using a single number. However they are calculated differently. Can we
  • Maximizing Diversification [Flirting with Models]

    Diversification within a portfolio can be quantified using the diversification ratio, which measures how much the volatility is reduced relative to a scenario where all assets are perfectly correlated. By maximizing the diversification ratio, we can construct the most diversified portfolio for a given investment universe. We construct the most diversified portfolio using data from 1973 and look at
  • Measuring Factor Exposures: Uses and Abuses [Alpha Architect]

    What are the research questions? USES: Can investors really separate alpha from beta? What are the ins-and-outs of understanding the exposures in a portfolio and their contribution to alpha? ABUSES: Are there differences in the way strategies are constructed in academic articles vs. the way practitioners actually implement those strategies that are consequential for investors?
  • Private Equity: The Emperor Has No Clothes [Factor Research]

    This research note was originally published by the CFA Institutes Enterprising Investor blog. Here is the link. SUMMARY Private equity returns can be replicated with small cap equities Small, cheap and levered stocks would have achieved higher returns since 1988 Valuation and debt multiples are at all-time-highs, lowering expected returns FROM BUST TO BOOM The private equity industry had an
  • Free Data and the Collapse of Trading Costs [CXO Advisory]

    How have costs of U.S. stock trading data evolved in recent years? In his October 2018 paper entitled Retail Investors Get a Sweet Deal: The Cost of a SIP of Stock Market Data, James Angel examines costs of U.S. stock market data. He also describes the production of these data and their consolidation/distribution via Securities Information Processors (SIP). Using data for U.S. trading costs

Quantocracy’s Daily Wrap for 12/01/2018

This is a summary of links featured on Quantocracy on Saturday, 12/01/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in November [Allocate Smartly]

    This is a summary of the recent performance of a wide range of excellent tactical asset allocation strategies, net of transaction costs. These strategies are sourced from books, academic papers, and other publications. While we dont (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Learn more about what we do or let AllocateSmartly help you
  • Listen to the whole quant album [Cuemacro]

    Its 50 years since the Beatles released the White Album. To celebrate it has been reissued with new mixes of the original tracks. I have to admit, its rarer these days to listen to an album the whole way through. Instead, I invariably listen to playlists on iTunes which select tracks from all manner of different artists and albums and mix them together. iTunes and Spotify have revolutionized
  • Understanding the correlation of equity and bond returns [SR SV]

    The correlation of equity and high grade sovereign bond returns is a powerful driver of portfolio construction and the term premia of interest rates. This correlation has turned from positive in the 1970s-1990s to negative in the 2000s-2010s, on the back of similar shifts in the correlation between inflation and economic growth and between inflation and real interest rates. The structural

Quantocracy’s Daily Wrap for 11/30/2018

This is a summary of links featured on Quantocracy on Friday, 11/30/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Research Review | 30 November 2018 | Risk Factors [Capital Spectator]

    Factor Investing: Get Your Exposures Right! Franois Soup (BNP Paribas Asset Management), et al. October 26, 2018 This paper is devoted to the question of optimal portfolio construction for equity factor investing. The first part of the paper focusses on how to make sure that a given equity portfolio has the targeted factor exposures, even before imposing any constraints. We show that such

Quantocracy’s Daily Wrap for 11/28/2018

This is a summary of links featured on Quantocracy on Wednesday, 11/28/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Stiffness Indicator Analysis [Alvarez Quant Trading]

    A reader pointed me the November 2018 issue of Technical Analysis of Stocks & Commodities to an article about a trend following indicator on S&P500 stocks. I liked the concept of the indicator and the article had backteted results and AmiBroker code. How could I resist not looking into this? Little did I realize this would lead to Backtesting is Hard and How much does not having
  • Deep Reinforcement Trading [Quant Dare]

    Deep Reinforcement Learning applications in finance are still largely unknown. Nonetheless, recent developments in other fields have pushed researchers towards exciting new horizons. I believe that there is a huge potential for Reinforcement Learning in finance. As investment guru Ray Dalio, founder of Bridgewaters, defends in his book Life and Work Principles, investment is an iterative process.
  • When SPX Closes Higher On Bad Breadth [Quantifiable Edges]

    While the SPX closes higher on Tuesday, NYSE breadth was weak both from an % Up Issues and % Up Volume standpoint. This triggered the study below from the Quantifinder. I also discussed it in last nights subscriber letter. 2018-11-28-1 Here we see numbers suggesting a substantial bearish edge over the next 1-4 days. Below is the full list of instances and their 4-day returns. 2018-11-28-2

Quantocracy’s Daily Wrap for 11/27/2018

This is a summary of links featured on Quantocracy on Tuesday, 11/27/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Create own Recession Indicator using Mixture Models [Eran Raviv]

    Broadly speaking, we can classify financial markets conditions into two categories: Bull and Bear. The first is a todo bien market, tranquil and generally upward sloping. The second describes a market with a downturn trend, usually more volatile. It is thought that those bull\bear terms originate from the way those animals supposedly attack. Bull thrusts its horns up while a bear swipe its