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Quantocracy’s Daily Wrap for 02/05/2021

This is a summary of links featured on Quantocracy on Friday, 02/05/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Copula for Pairs Trading: Sampling and Fitting to Data [Hudson and Thames]

    This is the second article of the copula-based statistical arbitrage series. You can read the first article: Copula for Pairs Trading: A Detailed, But Practical Introduction. Overview Whether it is for pairs trading or risk management, two natural questions to ask before putting copula for use are: How to draw samples from a copula? How should one fit a copula to data? The necessity of fitting is
  • Improving time series animations in matplotlib (from 2D to 3D) [Quant Dare]

    Animating time series is a very powerful tool to show evolution over time, but matplotlib default animations are boring and they are not well suited for comparison purposes. Along this blog, animations are widely used: from explaining how neural networks train, to showing synthetic time-series statistics or indicating which funds are selected by the low volatility anomaly. Imagine that you want to
  • Heatmap Plot of Forex Temporal Clustering of Turning Points [Dekalog Blog]

    Following up on my previous post, below is the chart of the temporal turning points that I have come up with. This particular example happens to be 10 minute candlesticks over the last two days of the GBP_USD forex pair. The details I have given about various turning points over the course of my last few posts have been based on identifying the "ix" centre value of turning point
  • Do Security Analysts Follow the Academic Evidence? [Alpha Architect]

    As my co-author Andrew Berkin and I explain in our new book Your Complete Guide to Factor-Based Investing, there is considerable evidence of cross-sectional return predictability. Citing more than 100 academic papers, we presented evidence of predictability for both equity and bond factors. And since the research is well known, one would think that sophisticated professional investors would

Quantocracy’s Daily Wrap for 02/02/2021

This is a summary of links featured on Quantocracy on Tuesday, 02/02/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • When a correlation matrix is not a correlation matrix and what can be done about it [Portfolio Optimizer]

    Estimating how individual assets are moving together is an important part of many financial applications1 and the most commonly used measure for this is the Pearson correlation. Unfortunately, for a variety of reasons, what sometimes appears to be a correlation matrix is actually not a valid correlation matrix, which might prevent algorithms using such a matrix in input from providing meaningful
  • Understanding Variance Explained in PCA – Matrix Approximation [Eran Raviv]

    Principal component analysis (PCA from here on) is performed via linear algebra functions called eigen decomposition or singular value decomposition. Since you are actually reading this, you may well have used PCA in the past, at school or where you work. There is a strong link between PCA and the usual least squares regression (previous posts here and here). More recently I explained what does
  • The failure of anomaly indicators in finance [Mathematical Investor]

    Recent public reports have underscored a crisis of replicability in numerous fields of science: In 2012, Amgen researchers reported that they were able to replicate fewer than 10 of 53 cancer studies. In March 2014, physicists announced with fanfare that they had detected evidence of gravitational waves from the inflation epoch of the big bang. However, other researchers were unable to

Quantocracy’s Daily Wrap for 02/01/2021

This is a summary of links featured on Quantocracy on Monday, 02/01/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • So you want to be a quant/systematic trader? [Investment Idiocy]

    One of the upsides of having a (very, very minor) public profile is that you get a lot of people asking you for advice, which is flattering (and if you say otherwise, you need to consider just how first world that particular 'problem' is). The only downside of this is you get asked the same sort of question a number of different times. At some point it becomes worth writing a blog
  • Myth-Busting: Low Rates Don’t Justify High Valuations [Factor Research]

    High equity valuations are frequently justified by low interest rates There is no long-term evidence in the US to support this theory P/E ratios in Japan and Europe have remained low, despite zero or negative yields INTRODUCTION One of the more peculiar transactions I worked on as an investment banker at Citigroup was the initial public offering (IPO) of a Kuwaiti property company. This was during
  • Hot Topic: Does Gamma Hedging Actually Affect Stock Prices? [Alpha Architect]

    More and more evidence seems to suggest that social Media impacts daily momentum and volatility. Some hedge funds that were short GME the past couple of months should have read these blog posts. In a similar vein, there is plenty of twitter chatter on the topic and anecdotal evidence that during the last week of February 2020 ( when the US market crashed more than 10%), market volatility was

Quantocracy’s Daily Wrap for 01/31/2021

This is a summary of links featured on Quantocracy on Sunday, 01/31/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Parsing portfolio optimization [OSM]

    Our last few posts on risk factor models havent discussed how we might use such a model in the portfolio optimization process. Indeed, although weve touched on mean-variance optimization, efficient frontiers, and maximum Sharpe ratios in this portfolio series, we havent discussed portfolio optimization and its outputs in great detail. If we mean to discuss ways to limit our exposure to
  • Are there sources of free data for markets? [Cuemacro]

    So wheres the best place to get a burger? I get asked that a lot. Ill try to give my best answer, but if you live in a place I havent visited, Ill probably draw a blank. Yes, you can read reviews, but the only real way to tell if a burger joint is good, is to try it. Everyone has their own different taste, some prefer greasier burgers, others prefer lots of cheese on a burger etc. When
  • Probing Price Momentum of Bitcoin during its Bull Runs with a Piecewise Linear Model [Quant At Risk]

    In 2020 Bitcoin delivered us another spectacular bull run. It was as impressive as the one we witnessed in 2017. The analysis of Bitcoin price time-series during its bull runs can uncover interesting results. By comparing a selected set of characteristics we could find some commonalities in trading. In todays learning note we will have a look at the most recent Bitcoins bull run and fit its
  • Temporal Clustering Times on Forex Majors Pairs [Dekalog Blog]

    In the following code box there are the results from the temporal clustering routine of my last few posts on the four forex majors pairs of EUR_USD, GBP_USD, USD_CHF and USD_JPY. This is based on 10 minute bars over the last year or so. Readers should read my last few previous posts for background. The first set of results, EUR_USD, are what the charts of my previous posts were based on and

Quantocracy’s Daily Wrap for 01/30/2021

This is a summary of links featured on Quantocracy on Saturday, 01/30/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Trend Persistence Indicator [Financial Hacker]

    Financial markets are not stationary: price curves can swing all the time between trending, mean reverting, or entire randomness. Without a filter for detecting trend regime, any trend following strategy will bite the dust sooner or later. In Stocks & Commodities February 2021, Richard Poster proposed a trend persistence indicator for avoiding unprofitable market periods. The TPR indicator

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.