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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

Quantocracy’s Daily Wrap for 01/17/2021

This is a summary of links featured on Quantocracy on Sunday, 01/17/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Research impact of delta hedging with Python [Cuemacro]

    One of the things which I found confusing at first with options, is the fact that lots of the folks trading them, dont really have a view about whether the spot price will go up and down. They are basically trading the volatility parameter from options pricing models (Emanuel Derman explains the point about trading parameter much better than me here and also here). One of the most common

Quantocracy’s Daily Wrap for 01/16/2021

This is a summary of links featured on Quantocracy on Saturday, 01/16/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • More factors, more variance…explained [OSM]

    Risk factor models are at the core of quantitative investing. Weve been exploring their application within our portfolio series to see if we could create such a model to quantify risk better than using a simplistic volatility measure. That is, given our four portfolios (Satisfactory, Naive, Max Sharpe, and Max Return) can we identify a set of factors that explain each portfolios variance

Quantocracy’s Daily Wrap for 01/15/2021

This is a summary of links featured on Quantocracy on Friday, 01/15/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How To Create A Fully Automated AI Based Trading System With Python (h/t @PyQuantNews)

    A couple of weeks ago I was casually chatting with a friend, masks on, social distance, the usual stuff. He was telling me how he was trying to, and I quote, detox from the broker app he was using. I asked him about the meaning of the word detox in this particular context, worrying that he might go broke, but nah: he told me that he was constantly trading. If a particular stock has been going
  • How to Get Historical Market Data Through Python Apis [Quant Insti]

    As a quant trader, you are always on the lookout to create and optimise your trading strategies. Backtesting forms a very important part of this process. And for backtesting, access to historical data is a necessity. But its a very daunting task to find decent historical price data for backtesting your trading strategies. While a simple google search can give you the end of day data for any
  • Research Review | 15 January 2021| Forecasting [Capital Spectator]

    Long-Term Stock Forecasting Magnus Pedersen (Hvass Laboratories) December 17, 2020 When plotting the relation between valuation ratios and long-term returns on individual stocks or entire stock-indices, we often see a particular pattern in the plot, where higher valuation ratios are strongly correlated with lower long-term stock-returns, and vice versa. Moreover the plots often show a particular