Quantocracy

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Quantocracy’s Daily Wrap for 01/12/2022

This is a summary of links featured on Quantocracy on Wednesday, 01/12/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to get serious about making money trading [Robot Wealth]

    In Australia, if youre serious about getting the job done effectively and efficiently, you might say: Im not here to f*** spiders. Many traders act like they are, indeed, here to f*** spiders. If youre making soup, you first need a good stock. Stock isnt exciting. Everyone has stock. Garnish is exciting, but you cant make soup from just garnish. Similarly, you need some stock
  • Finding Alpha on the Internet – Part 2 [Derek Wong]

    I was continuing from the last post. I will explain why I picked the paper I did, answer the questions from the previous post, and show my note-taking and thought process. After reading hundreds if not thousands of whitepapers, blogs, or articles, this is my distilled version of how I approach it. I will document this for the first time. It may be different from how others approach it. Over time
  • Trend-Following Filters Part 4 [Alpha Architect]

    Previous articles in this series examine, from a digital signal processing (DSP) frequency domain perspective, various types of digital filters used by quantitative analysts and market technicians to analyze and transform financial time series for trend-following purposes. An Introduction to Digital Signal Processing for Trend Following Trend-Following Filters Part 1 Trend-Following Filters
  • Analyzing S&P 500 Constituents Returns by Sector [Quant Dare]

    In a previous post we analyzed the performance of US Sectors using SPDR Sector ETFs. Now, lets dive into the analysis of sectors using S&P 500 components and some measures of its performance. Before going any further, it is important to note that there are some differences between the Sector SPDR and the figures that we are going to see. In this analysis, we weight each company equally but

Quantocracy’s Daily Wrap for 01/11/2022

This is a summary of links featured on Quantocracy on Tuesday, 01/11/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How backtest overfitting in finance leads to false discoveries [Mathematical Investor]

    The present author, together with Marcos Lpez de Prado, has just published the article How backtest overfitting in finance leads to false discoveries in Significance, a journal of the British Statistical Society. The published article is now available at the Significance (Wiley) website. This article is condensed from the following manuscript, which is freely available from SSRN: Finance is Not
  • Asset Allocation and Private Market (i.e. illiquid) Investing [Alpha Architect]

    Allocations to illiquid assets(1) have become increasingly popular, thus requiring asset managers to consider portfolio-wide liquidity characteristics. Although determining the price of illiquidity is a challenge for investors, the construction of a portfolio that includes liquidity constraints can be even more daunting. Improvised, or other less formal approaches to manage illiquidity, are less

Quantocracy’s Daily Wrap for 01/10/2022

This is a summary of links featured on Quantocracy on Monday, 01/10/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Modeling Intent in R and/or Python [Open Source Quant]

    Learning or experimenting with Tidytext has been on my radar for at least a few years. Only recently did i have a need to pick it up. As with most learnings, they lead you down a path of more knowledge (read: rabbit holes) than you foresaw. This post is a hat-tip to the resources i used, knitting them together in a sample use case with an extension using parallel processing for the R
  • Research Compendium 2021 [Factor Research]

    There is nothing new in the world except the history you do not know Harry S. Truman January 2022. Reading Time: Several hours. Author: FactorResearch. RESEARCH COMPENDIUM 2021 In 2021 we published more than 50 research notes on mostly quantitative strategies, but also on topics like venture capital, catastrophe bonds, inflation, long volatility strategies, and direct indexing. The
  • Variance risk premia for patient investors [SR SV]

    The variance risk premium manifests as a long-term difference between option-implied and expected realized asset price volatility. It compensates investors for taking short volatility risk, which typically comes with a positive correlation with the equity market and occasional outsized drawdowns. A recent paper investigates a range of options-related strategies for earning the variance risk
  • Oversold $NDX does not bounce as reliably as $SPX [Quantifiable Edges]

    The NDX was hit especially hard last week. It fell 4.5% on the week and Friday was the lowest close since October. Many times we will see multi-day pullbacks and/or intermediate-term lows during a long-term uptrend suggest the market is primed for a bounce. But in running some studies on NDX this weekend, I found results like below. NDX oversold shows no hint of upside edge Such setups have been a

Quantocracy’s Daily Wrap for 01/07/2022

This is a summary of links featured on Quantocracy on Friday, 01/07/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The matrix effective rank: measuring the dimensionality of a universe of assets [Portfolio Optimizer]

    Quantifying how diversified is a universe of assets is an open problem in quantitative finance, partly because there is no definite formula for diversification1. Lets make the (reasonable) assumption that the way assets are moving together within a universe is important for its diversification. This in turn makes asset correlations within a universe important in determining how diversified it
  • What Do Mutual Fund Investors Really Care About? [Alpha Architect]

    Itzhak Ben-David, Jiacui Li, Andrea Rossi, and Yang Song contribute to the literature on the behavior of individual investors with their July 2021 study What Do Mutual Fund Investors Really Care About?, published in the July 2021 issue of The Review of Financial Studies. They began by noting that while researchers agree that fund flows are strongly correlated with past performance, there is

Quantocracy’s Daily Wrap for 01/05/2022

This is a summary of links featured on Quantocracy on Wednesday, 01/05/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Finding Alpha on the Internet Part 1 [Derek Wong]

    The internet is teeming with resources for potential alpha. How do you separate the wheat from the chaff? In this series of blog posts, I will explain my process for identifying sources worth diving deeper into and extracting some kind of benefit to a portfolio or strategy. This will be a side-by-side view of my workflow as an individual. As of now, I have no idea what even to research or how I

Quantocracy’s Daily Wrap for 01/04/2022

This is a summary of links featured on Quantocracy on Tuesday, 01/04/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is Hedged Equity a Good Replacement for the 40 ? [Simplify]

    Equity investors are drawn to the asset class with the expectation of an attractive and consistent risk premium. But with this risk premium comes the real risk of downside volatility. Traditionally, investors have paired stocks with defensive assets like bonds to provide anti-correlative benefits, but this has become increasingly more difficult in the current environment. In earlier blog posts, we
  • Most popular posts 2021 [Eran Raviv]

    Kind of sad, but the same intro which served last year, befits this year also. Littered with Corona, this year was not easy. But looking around me, I feel grateful. The following quote by Socrates comes to mind: If all our misfortunes were laid in one common heap whence everyone must take an equal portion, most people would be content to take their own and depart. On topic, as with previous
  • Factor Olympics Q4 2021 [Factor Research]

    2021 was a year of moderate factor performance Value, Quality, and Low Volatility factors generated positive returns Momentum and Size were negative INTRODUCTION We present the performance of five well-known factors on an annual basis for the last 10 years. Specifically, we only present factors where academic research supports the existence of positive excess returns across market cycles and asset

Quantocracy’s Daily Wrap for 01/02/2022

This is a summary of links featured on Quantocracy on Sunday, 01/02/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Asset Price Dynamics and Trading Strategy’s PnL Volatility [Relative Value Arbitrage]

    In a previous post, we discussed how the dynamics of assets are priced in the options prices. We recently came across a newly published article [1] that explored the same topic but from a different perspective that does not involve options. The conclusion of the new article [1] is consistent with the previous one [2]; that is, the volatilities of mean-reverting assets are smaller than those of
  • Top Ten Blog Posts on Quantpedia in 2021 [Quantpedia]

    As usual, at this time of the year, let us do a short recapitulation of posts on our blog in the previous 12 months. We have published nearly 70 short analyses of academic papers and our own research articles on this blog in 2021. We want to use this opportunity to summarize 10 of them, which were the most popular (based on the Google Analytics tool). Maybe you will be able to find something you

Quantocracy’s Daily Wrap for 12/30/2021

This is a summary of links featured on Quantocracy on Thursday, 12/30/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Using OECD Composite Leading Indicator Data to Time the Market [Allocate Smartly]

    This is a test of the Global Growth Cycle strategy from Grzegorz Link that uses OECD Composite Leading Indicator (CLI) data to time the market. Were going to present these results in an odd way. First, were going to replicate Grzegorzs test, which (as he discusses) includes a degree of lookahead bias. Due to the nature of CLI data revisions, the strategy is peeking into the
  • Hundreds of quant papers from #QuantLinkADay in 2021 [Cuemacro]

    Over the past few years Ive been tweeting a daily quant paper (or quant related library or similar) every day with the #QuantLinkADay tag. The initial objective was to make sure that my Twitter account @saeedamenfx posted at least some useful quant link of information (and to prevent Twitter followers being overrun by burger tweets, which I admittedly do tweet about and yes, I do like burgers
  • Understanding Momentum Investing [Alpha Architect]

    Momentum, the tendency of past winner stocks to outperform past loser stocks over the next several months, is one of the most well-documented and well-researched asset pricing anomalies. In our book, Your Complete Guide to Factor-Based Investing, Andrew Berkin and I present the evidence of a premium that has been persistent across long periods of time, pervasive around the globe and across

Quantocracy’s Daily Wrap for 12/29/2021

This is a summary of links featured on Quantocracy on Wednesday, 12/29/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Backtesting Is Zipline Dead? Or does it just need a reload? [Following the Trend]

    In the past year, that is by far the most common question I get. Now that Quantopian went the way of the Dodo, is Zipline dead? Should we switch to a different backtesting engine? The short answer is that the rumors of Ziplines demise are exaggerated, but lets take it from the start. My 2019 book Trading Evolved focuses on teaching you how to use Python to backtest trading strategies. In

Quantocracy’s Daily Wrap for 12/27/2021

This is a summary of links featured on Quantocracy on Monday, 12/27/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Primer on Grid Trading Strategy [Quantpedia]

    Grid trading is an automated currency trading strategy where an investor creates a so-called price grid. The basic idea of the strategy is to repeatedly buy at the pre-specified price and then wait for the price to rise above that level and then sell the position (and vice versa with shorting and covering). We will explore the basics and show favorable and unfavorable scenarios in the first
  • Classifying market regimes [SR SV]

    Market regimes are clusters of persistent market conditions. They affect the relevance of investment factors and the success of trading strategies. The practical challenge is to detect market regime changes quickly and to backtest methods that may do the job. Machine learning offers a range of approaches to that end. Recent proposals include [1] supervised ensemble learning with random forests,