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Quantocracy’s Daily Wrap for 12/15/2018

This is a summary of links featured on Quantocracy on Saturday, 12/15/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Modern backtesting with integrity [SR SV]

    Machine learning offers powerful tools for backtesting trading strategies. However, its computational power and convenience can also be corrosive for financial investment due to its tendency to find temporary patterns while data samples for cross validation are limited. Machine learning produces valid backtests only when applied with sound principles. These should include [1] formulating a logical

Quantocracy’s Daily Wrap for 12/14/2018

This is a summary of links featured on Quantocracy on Friday, 12/14/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Portfolio construction through handcrafting: implementation [Investment Idiocy]

    This post is all about handcrafting; a method for doing portfolio construction which human beings can do without computing power, or at least with a spreadsheet. The method aims to achieve the following goals: Humans can trust it: intuitive and transparent method which produces robust weights Can be easily implemented by a human in a spreadsheet Can be back tested Grounded in solid theoretical
  • The Most Wonderful Week of the Year 2018 edition [Quantifiable Edges]

    Over several time horizons op-ex week in December has been the most bullish week of the year for the SPX. The positive seasonality actually has persisted for up to 3 weeks. Ive shown the study below in the blog many times since 2008. It looks back to 1984, which was the first year that SPX options traded. The table is updated again this year. 2018-12-14-1 The stats are extremely strong. This
  • Estimating the Bid-Ask Spread [Dekalog Blog]

    Below I provide a vectorised Octave function to estimate the bid-ask spread from high, low and close prices according to "A Simple Way to Estimate Bid-Ask Spreads from Daily High and Low Prices," (Corwin and Schultz, 2012). The paper can be downloaded from one of the author's homepage at https://www3.nd.edu/~scorwin/, where one can also find a spreadsheet which shows the
  • Random Walk Simulation Of Stock Prices Using Geometric Brownian Motion [Quant Insti]

    In this blog on random walk simulation, we will learn how to simulate stock prices. Future stock prices are very hard to predict and are dependent on the past trend and volatility. While simulating the stock prices one has to give reasonable weightage to these two parameters. The random walk model helps incorporate these two features of a stock and simulate the stock prices in a very clear and
  • Does the Sunspot Cycle Predict Grain Prices? [CXO Advisory]

    As a follow-up to Sunspot Cycle and Stock Market Returns a reader asked: Sunspot activity does have a direct relationship to weather. Could one speculate on the agriculture market using the sunspot cycle? To investigate, we relate sunspot activity to the fairly long U.S. Producer Price Index (PPI) for grains. Using monthly averages of daily sunspot counts and monthly PPI for grains
  • Sunspot Cycle and Stock Market Returns [CXO Advisory]

    A reader asked whether Charles Nenner, self-described as the talk of Wall Street since accurately predicting some of the biggest moves in the Markets over the past few years, accurately forecasts equity and commodity markets. We consider the following: In his July 2007 discussion of the Nenner Methodology at the Bloomberg Studio, Charles Nenner cites sunspot activity as a specific key

Quantocracy’s Daily Wrap for 12/12/2018

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

  • More examples in Financial Visualisation [Quant Dare]

    In line with the previous post Group Funds with the Sun we continue exploring new ways to visualise and analyse financial data. We will take annual data from the current components of Dow Jones Industrial with data going back to 2000 to play around. Animated Risk Return scatter Risk-Return scatter is one of the most widely used plots in finance. The plot below shows the relationship between

Quantocracy’s Daily Wrap for 12/11/2018

This is a summary of links featured on Quantocracy on Tuesday, 12/11/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Mechanical Turk [Financial Hacker]

    We can see thinking machines taking over more and more human tasks, such as car driving, Go playing, or financial trading. But sometimes its the other way around: humans take over jobs supposedly assigned to thinking machines. Such a job is commonly referred to as a Mechanical Turk in reminiscence to Kempelens famous chess machine from 1768. In our case, a Mechanical Turk is an automated
  • After a Lost Decade, Will Value Get its Groove back in 2019? [Alpha Architect]

    Borne in academia and raised by fund managers seeking to outperform, value style mutual funds and ETFs today hold close to $2 trillion(1). But with poor returns over the past decade, the question of whether value is dead has become a popular topic of conversation.(2) The search term is value investing dead generates over 23 million results in less than 0.38 seconds! For comparison,
  • ETFs Have NOT Screwed Up Correlations, Liquidity, and Alpha Opportunities [Alpha Architect]

    What are the Research Questions? The paper investigates the following research question: Have ETFs flows affected the correlation structure of returns? Have ETFs flows affected the liquidity of underlying securities? Have ETFs flows affected the ability of managers to generate alpha? What are the Academic Insights? By examining these issues empirically, considering the longer historical and
  • Using Metals to Trade Bonds [System Trader Success]

    Sometimes things in the financial markets are pretty obvious if one is willing to see them. Despite all of the angst that has been exhausted since July of 2016 when the S&P 500 Index broke out to a new all-time high regarding the 2016 election, the economy, interest rates and the ultimate effect that all of it may have the stock market the market has just kept chugging

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