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Quantocracy’s Daily Wrap for 01/29/2021

This is a summary of links featured on Quantocracy on Friday, 01/29/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Complete Guide to Portfolio Optimization in R Part 2 [Milton FMR]

    Congratulations you made it to part2 of our tutorial. Give yourself a round of applause. If you stumbled upon part2 before reading part1 we advise you to start from the beginning and read part1 first. In Part2 we dive into mean variance portfolio optimization, mean CVar portfolios and backtesting. As mentioned in part1 we conclude this tutorial with a full blown portfolio optimization process with
  • Do Candlesticks Work? A Quantitative Test Of 23 Candlestick Formations [Quantified Strategies]

    This article explains candlesticks and why we like to use candlesticks when displaying charts. Moreover, we test quantitatively 23 different candlestick formations. Perhaps surprisingly, some of the formations work pretty well. Some of the formations can highly likely be improved by adding one more variable. Candlesticks are a popular way to display quotes on a chart, something we have done since
  • The Quality Factor What Exactly Is It? [Alpha Architect]

    The existence of a quality premium in stocks that has been persistent over time, pervasive around the globe, and robust to various definitions have been well documented by studies such as Buffetts Alpha, Global Return Premiums on Earnings Quality, Value, and Size, and The Excess Returns of Quality Stocks: A Behavioral Anomaly. While there is no consistent definition of
  • Why is data cleaning important and how to do it the right way? [Quant Insti]

    Data cleaning is the time-consuming but the most important and rewarding part of the data analysis process. The process of data analysis is incomplete without cleaning data. But what happens if we skip this step? Suppose we had certain erroneous data in our price data. The incorrect data formed outliers in our dataset. And our machine learning model assumed that this part of the dataset (maybe the
  • New Research Tries To Solve For Beta Risk s Failure For Stocks [Capital Spectator]

    At the core of modern finance is the proposition that beta (market) risk is the dominant factor that drives performance. But numerous empirical tests of the capital asset pricing model (CAPM) over the decades suggest otherwise. There have be various attempts to adjust CAPM to find a closer mapping of risk and return, but the results have been mixed. Perhaps two new research papers move us closer

Quantocracy’s Daily Wrap for 01/27/2021

This is a summary of links featured on Quantocracy on Wednesday, 01/27/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Correct Vectorized Backtest Methodology for Pairs Trading [Hudson and Thames]

    Whilst backtesting architectures is a topic on its own, this article dives into how to correctly backtest a pairs trading investment strategy using a vectorized (quick methodology) rather than the more robust event-driven architecture. This is a technique that is very common amongst analysts and is rather straightforward for long-only portfolios, however, when you start to construct long-short
  • Infrastructure of algorithmic trading systems [Trade With Science]

    A development processs infrastructure can be understood as a step-by-step guide when working on a trading project. Every developer has a bit different approaches, but the skeleton of the process is usually the same. This article is an introduction to building your trading system from scratch. Each topic is crucial and contains steps you should not forget to do. Depending on your previous
  • A Review of Ben Graham s Famous Value Investing Strategy: “Net-Nets” [Alpha Architect]

    Benjamin Graham, often considered a strong candidate for the the father of quantitative value investing, developed an investment strategy that involved purchasing securities for less than their current-asset value, a rough index of the liquidating value. We uncovered ten research papers that examined the returns achieved by investing in such securities which were conducted over a
  • Fundamental and Sentiment analysis with different data sources [Quant Insti]

    Technical analysis of price and volume history wont cut it alone nowadays. When we want to perform value investing and/or measure a securitys intrinsic value, we need to make a fundamental analysis of the security. To perform fundamental analysis we need data, lots of data. We want fundamental data in the form of ratios, financial statements, earnings, etc. On top of that we can also use

Quantocracy’s Daily Wrap for 01/26/2021

This is a summary of links featured on Quantocracy on Tuesday, 01/26/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Machine Learning for Trading Pairs Selection [Hudson and Thames]

    In this post, we will investigate and showcase a machine learning selection framework that will aid traders in finding mean-reverting opportunities. This framework is based on the book: A Machine Learning based Pairs Trading Investment Strategy by Sarmento and Horta. A time series is known to exhibit mean reversion when, over a certain period, it reverts to a constant mean. A topic of
  • The Importance Of Stress Tests & Robustness Tests 10/12 [Trade With Science]

    If you developed a given futures market strategy, in an ideal world, it would perform well on all markets (from metals, energies, currencies, bonds, stock indices, grains, softs). However, from our experience, we know that this is a challenging task. You would be happy if it worked for markets from the same segment. Stress tests and robustness tests are crucial to understand in order to choose the
  • Recent Weaknesses of Factor Investing [CXO Advisory]

    How have value, quality, low-volatility and momentum equity factors, and combinations of these factors, performed in recent years. In their October 2020 paper entitled Equity Factor Investing: Historical Perspective of Recent Performance, Benoit Bellone, Thomas Heckel, Franois Soup and Raul Leote de Carvalho review and put into context recent performances of these these

Quantocracy’s Daily Wrap for 01/25/2021

This is a summary of links featured on Quantocracy on Monday, 01/25/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Market Timing via the VRP? [Factor Research]

    Stock market returns were highly positive when the variance risk premium (VRP) was negative Returns were slightly negative across markets when the VRP was positive This relationship can not be exploited for market timing INTRODUCTION The US stock market in 1999 and 2020 had probably more similarities than differences. In both years the market was up considerably, retail investors were highly
  • Macro uncertainty as predictor of market volatility [SR SV]

    Market volatility measures the size of variations of asset returns. Macroeconomic uncertainty measures the size of unpredictable disturbances in economic activity. Large moves in macroeconomic uncertainty are less frequent and more persistent than shifts in market volatility. However, macroeconomic uncertainty is an important driver of market volatility because it is related to future earnings and

Quantocracy’s Daily Wrap for 01/23/2021

This is a summary of links featured on Quantocracy on Saturday, 01/23/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Complete Guide to Portfolio Optimization in R Part 1 [Milton FMR]

    The purpose of portfolio optimization is to minimize risk while maximizing the returns of a portfolio of assets. Knowing how much capital needs to be allocated to a particular asset can make or break an investors portfolio. In this article we will use R and the rmetrics fPortfolio package which relies on four pillars: Definition of portfolio input parameters, loading data and setting constraints.

Quantocracy’s Daily Wrap for 01/22/2021

This is a summary of links featured on Quantocracy on Friday, 01/22/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Amazing Efficacy of Cluster-based Feature Selection [EP Chan]

    One major impediment to widespread adoption of machine learning (ML) in investment management is their black-box nature: how would you explain to an investor why the machine makes a certain prediction? What's the intuition behind a certain ML trading strategy? How would you explain a major drawdown? This lack of "interpretability" is not just a problem for financial ML, it is a
  • Is the Market Getting more Efficient? [Alpha Architect]

    In 1998, Charles Ellis wrote Winning the Losers Game, in which he presented evidence that while it is possible to generate alpha and win the game of active management, the odds of doing so were so poor that its not prudent for investors to try. At the time, roughly 20 percent of actively managed mutual funds were generating statistically significant alphas (they were able to outperform

Quantocracy’s Daily Wrap for 01/21/2021

This is a summary of links featured on Quantocracy on Thursday, 01/21/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Analyze Volume Profiles With Python (h/t @PyQuantNews) [Minh Nguyen]

    When trading in markets such as equities or currencies it is important to identify value areas to inform our trading decisions. One way to do this is by looking at the volume profile. In this post, we explore quantitative methods for examining the distribution of volume over a period of time. More specifically, well be using Python and statistical and signal processing tools in SciPys suite
  • Trend-Following Filters Part 2/2 [Alpha Architect]

    Part 1 of this analysis, which is available here, examines filters modeled on second-order processes from a digital signal processing (DSP) perspective to illustrate their properties and limitations. To briefly recap, a time series based on a second-order process consists of a mean a and a linear trend b which is contaminated with random normally distributed noise (t) where (t) ~ N(0, 2):
  • Copula for Pairs Trading: A Detailed, But Practical Introduction [Hudson and Thames]

    Suppose that you encountered a promising pair of stocks that move closely together, the spread zig-zagged around 0 like some fine needle stitching that sure looks like a nice candidate for mean-reversion bets. Whats more, you find out that the two stocks prices for the past 2 years are all nicely normally distributed. Great! You can avoid some hairy analysis for now. Therefore you fit them

Quantocracy’s Daily Wrap for 01/20/2021

This is a summary of links featured on Quantocracy on Wednesday, 01/20/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • An Introduction to Cointegration for Pairs Trading [Hudson and Thames]

    Cointegration, a concept that helped Clive W.J. Granger win the Nobel Prize in Economics in 2003 (see Footnote 1), is a cornerstone of pairs and multi-asset trading strategies. Anecdotally, forty years have passed since Granger coined the term cointegration in his seminal paper Some properties of time series data and their use in econometric model specification (Granger, 1981), yet one
  • Avoiding Gap Trades [Alvarez Quant Trading]

    Should you avoid trades that have recently gapped? What if you are trading a mean reversion strategy and a stock has recently had a large gap? Is that a good trade to take? Avoid? Does it depend on the direction of the gap? I did research on this about 15 years ago. Lets see what the current research says. Definition of Gap and Lap A gap is when a stock opens above the previous days high or
  • Volatility as an essential risk metric [Trade With Science]

    In this article, we will explain the basic concept of volatility, what it is, how it is calculated, implied and historical volatility, and how to model it. I believe youve already heard about volatility, so we dont want to just copy and paste the information you already know. In our articles, we are always trying to go through more exciting stuff for you. Before we dive deeper into

Quantocracy’s Daily Wrap for 01/19/2021

This is a summary of links featured on Quantocracy on Tuesday, 01/19/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Keller’s Resilient Asset Allocation [Allocate Smartly]

    This is a test of the latest tactical strategy from Dr. Wouter Keller: Resilient Asset Allocation (RAA). RAA is intended to be a low turnover strategy, only shifting from a balanced risk portfolio to a defensive portfolio during the most potentially bearish of times. Backtested results from 1970 follow. Results are net of transaction costs (see backtest assumptions). Learn about what we do and
  • Extracting Interest Rate Bounds from Option Prices [Sitmo]

    In this post we describe a nice algorithm for computing implied interest rates upper- and lower-bounds from European option quotes. These bounds tell you what the highest and lowest effective interest rates are that you can get by depositing or borrowing risk-free money through combinations of option trades. Knowing these bounds allows you to do two things: 1. Compare implied interest rate levels

Quantocracy’s Daily Wrap for 01/18/2021

This is a summary of links featured on Quantocracy on Monday, 01/18/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Oh, Quality, Where Art Thou? [Factor Research]

    Quality and quality income ETFs have underperformed the S&P 500 since 2005 The most recent underperformance is explained by an underweight to technology stocks However, more importantly, quality ETFs have not reduced drawdowns during stock market crashes INTRODUCTION Investing is never easy, but it is sometimes easier. Buying US government bonds at 10%+ yields when inflation was steadily
  • Statistics of Point&Figure Charts [Philipp Kahler]

    Point&Figure charts have been around for more than a 100 years and they are still quite popular, especially with commodities and forex traders. This article will do some statistical analysis of the most basic Point&Figure signal. Point&Figure Charts price movements only Unless bar and candlestick charts, which draw a price marker every day, Point&Figure charts are only updated
  • Historical Returns for Newly Elected Presidents [Quantifiable Edges]

    Back in the 1/20/2009 blog I looked at inauguration day returns. I wondered at the time whether a new president brought about new hope and optimism for the market. I have decided to update that study today. I limited the instances to only those inaugurations where a new president was entering office. I dont think re-elections carry a sense of new hope the way a new president does. I also