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Quantocracy’s Daily Wrap for 11/09/2022

This is a summary of links featured on Quantocracy on Wednesday, 11/09/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 11/08/2022

This is a summary of links featured on Quantocracy on Tuesday, 11/08/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • MOIC: Investing Holy Grail [Quant Dare]

    Many investors are looking for the holy grail of investing. They all want a magic formula that tells them which stocks to buy, and which ones to sell. But experienced investors know that there is no such a thing. I was convinced of it until I discovered the MOIC formula. MOIC Multiple on Invested Capital (MOIC) is a metric used to describe the performance of an investment relative to its
  • Equity Research in the Wolfram Language [Jonathan Kinlay]

Quantocracy’s Daily Wrap for 11/07/2022

This is a summary of links featured on Quantocracy on Monday, 11/07/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Matching data between data sources with Python [Wrighters.io]

    Data is often messy and rarely in perfect shape. This is especially true if the data comes from many different sources and the specifications are loosely defined. If you have access to data that is in great shape, its probably because someone else did the dirty work of validating it, cleaning it up, and normalizing it for you. One particular type of data problem is matching data between data
  • Skewness: the fallacy of the expected return [Artifact Research]

    In this post we will take a closer look at the expected return that is often stated for investments like stocks and other financial assets, or for certain outcomes in gambling. The point we want to convey is that the expected return is only valid for one period or a single iteration (say, one year, or one round of a game such as Blackjack), but that the expected return can be highly
  • The Cross Section of Stock Returns Pre CRSP data [Alpha Architect]

    What are the Research Questions? Several studies reveal variables that predict cross-sectional differences in stock returns but mainly rely on a sample of U.S. stocks, mostly covering the post-1963 period. These studies are often criticized for potential data mining issues since the database never changes, but new findings crop up all the time. This paper studies the cross-section of U.S.
  • Top 10 blogs on Machine Learning in 2022 [Quant Insti]

    Algorithmic Trading is seeing a rapid expansion of the application of artificial intelligence (AI) and machine learning (ML). These technological developments have completely transformed Algo trading. Making informed decisions requires carefully analyzing both current and historical market data. In order to analyze data and make effective forecasts for effective trading decisions, artificial
  • Sector & Factor Performance During Wartime [Finominal]

    The S&P 500 increased during two of the three largest wars of the United States Value, size, and momentum factors had positive returns during WW II The top and worst-performing industries during WW II were diverse INTRODUCTION Before 2020, the threat of a global pandemic shutting down the world economy was not a top-of-mind concern for most investors. Pandemics were nothing new, of course, but
  • Market Risk and Speculative Factors [Alpha Architect]

    There are basically two types of investors, those that are risk averse and, thus, both demand risk premiums for taking risk and diversify their holdings, and those who are risk seekers who have a preference for positively skewed (lottery-like) returns which leads them to speculate and concentrate risks. The psychological preferences of risk seekers drives up the valuations of the lottery-like

Quantocracy’s Daily Wrap for 11/02/2022

This is a summary of links featured on Quantocracy on Wednesday, 11/02/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Optimal trend following allocation under conditions of uncertainty [Investment Idiocy]

    Few people are brave enough to put their entire net worth into a CTA fund or home grown trend following strategy (my fellow co-host on the TTU podcast, Jerry Parker, being an honorable exception with his 'Trend following plus nothing' portfolio allocation strategy). Most people have considerably less than 100% – and I include myself firmly in that category. And it's probably true
  • How to Replicate Any Portfolio [Quantpedia]

    Would you like to see the performance of your portfolio 100 years back in history? Do you want to analyze the risk of your strategy under 100 years of real historical scenarios? All of these, and much more, will be soon (in a few days) available for Quantpedia Pro subscribers. How? We will explain today how we can model a 100-year history of your portfolio. Replicating Portfolios with Factors When
  • Momentum literature: an analysis of 30 years [Alpha Architect]

    n this article, the author examines the research published over the last 30 years on momentum and its theoretical credibility. One of the original momentum articles was published by Jegadeesh and Titman in 1993, and is considered the seminal work on the topic. The research review contained in this publication begins with the 1993 work and confines itself to only the highest quality journals among

Quantocracy’s Daily Wrap for 10/31/2022

This is a summary of links featured on Quantocracy on Monday, 10/31/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Slava Ukraini! Latest from Quantocracy contributor in Ukraine: Volatility and Price of a Straddle, Are They The Same? [Only VIX]

    Yesterday I found another piece of ignorance on Medium: Stop Watching The VIX, Just Make Your Own tl;dr : Just use ATM straddles. This is of course not correct. As I have written before on this blog that (skipping mathematical rigor) the value of ATM straddle is or about 80% of the expected volatility. So if SPY = $400 and VIX = 20, the expected volatility is $400 * 20/100 = $80, then 1 year ATM
  • Volatility-based Equity Allocations [Finominal]

    The VIX currently trades within its top quartile since 1990 Using volatility to time equity allocations is a widely used strategy However, it is challenging to pursue this over the long-term INTRODUCTION The One Ring from J.R.R. Tolkiens Lord of the Rings saga is a plain gold ring unless it is thrown into a fire, when Elvish runes appear that roughly translate into One Ring to rule them all,

Quantocracy’s Daily Wrap for 10/29/2022

This is a summary of links featured on Quantocracy on Saturday, 10/29/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Building Candlesticks in Rust [Mark Best]

    Candlesticks are a common way to represent price and volume of an asset over a period of time. There are various common types of bars such as time, volume, tick bars, hieken-ashi, renko to name a few. There is a lot of information about the implementations of these on the internet so their details will not be covered here. The aim of this article is to share some tips for implementation and also a
  • Momentum Gap – its role in reducing crashes [Alpha Architect]

    This article discusses the academic research about the Momentum Gap and the role that its predictive potential may have in reducing momentum crashes, hence possibly improving performance. In our book Your Complete Guide to Factor-Based Investing, Andrew Berkin and I presented the evidence demonstrating that momentum, both cross-sectional (CSMOM) and time-series (TSMOM), has provided a
  • Identifying market regimes via asset class correlations [SR SV]

    A recent paper suggests identifying financial market regimes through the correlations of asset class returns. The basic idea is to calculate correlation matrixes for sliding time windows and then estimate pairwise similarities. This gives a matrix of similarity across time. One can then perform principal component analysis on this similarity matrix and extract the axes of greatest relevance.
  • Asynchronous Trading Revisited: Practical Implications [Alpha Architect]

    In this article, the author examines several important questions related to asynchronous trading, or the variation in trading frequency that occurs when trading stocks or other assets. Timo Wiedemann, University of Muenster (Germany) The newest version of the paper can be found here. What are the Research Questions? Trading is not continuous, leading to asynchronous trading times for different

Quantocracy’s Daily Wrap for 10/24/2022

This is a summary of links featured on Quantocracy on Monday, 10/24/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean Reversion Check Up 2022 [Alvarez Quant Trading]

    A common question I get is whether mean reversion is still working. My response is I am still trading a mean reversion strategy but the edges seem to get smaller. Over the year I have investigated this. I was asked again recently and wanted to investigate again. Here are the results of my 2022 investigation. The Rules Test date range 1/1/2000 to 9/30/2022. I wanted to keep the rules simple. I
  • Live Algo Trading on the Cloud – Vultr [Algo Trading 101]

    What does live algorithmic trading on the Cloud mean? Rerequisite Basic Guide What are the pros of deploying your trading strategies to the Cloud? What are the cons of deploying your trading strategies to the Cloud? What is the Cloud Service? What is the Cloud used for? What cloud providers are good? What is Vultr? Why should I use Vultr? Why shouldnt I use Vultr? What does Vultr offer? How
  • Fast Logging for HFT In Rust [Mark Best]

    In this article well be discussing a fast way of logging in Rust and its application to high frequency trading. The code presented here solves two problems, one is well known, the latter less so. It is a imperative to avoid using IO operations within the strategy thread, but logging operations can hide a lot of memory operations that should also be avoided. Logging from the strategy is useful
  • Are hedge funds losing their hedge? [Mathematical Investor]

    Hedge funds were pioneered some 70 years ago by Australian financier Alfred Winslow Jones. His idea was to combine a long position (i.e., one that profits if the securities go up in price), typically a set of growth stocks, with a short position (i.e., one that profits if the securities go down in price) on the other part of the portfolio. Jones argued that this long-short
  • Thematic versus Momentum Investing [Finominal]

    Thematic products underperform the stock market on average The exposure to the momentum factor was low to negative recently Systematic performance chasing beats performance chasing with a narrative INTRODUCTION Space: the final frontier. Where no man has gone before. Well, wealthy folks can now go there by booking a ticket with Virgin Galactic. It will not be deep space and it could be debated
  • The Effect of Indexing on Price Discovery and Limits to Arbitrage [Alpha Architect]

    The rise of stock indexing has raised concerns that index investing distorts stock pricesindexers are free riders who rely on prices without contributing to price discovery, thus reducing price efficiency. Byung Ahn and Panos Patatoukas, authors of the study Identifying the Effect of Stock Indexing: Impetus or Impediment to Arbitrage and Price Discovery? published in the August 2022 issue

Quantocracy’s Daily Wrap for 10/21/2022

This is a summary of links featured on Quantocracy on Friday, 10/21/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • QuantConnect Integration with MlFinLab [Hudson and Thames]

    Announcing that MlFinLab is fully integrated into the powerful backtesting and execution platform of QuantConnect! At the start of 2022, we set out to improve the user experience across all of our products and to improve the accessibility of our libraries. This meant integrations into platforms that have a strong community, historical simulations, data feeds, and live execution. QuantConnect was a
  • Correlation Matrices Denoising: Results from Random Matrix Theory [Portfolio Optimizer]

    The estimation of empirical correlation matrices in finance is known to be affected by noise, in the form of measurement error, due in part to the short length of the time series of asset returns typically used in their computation1. Worse, large empirical correlation matrices have been shown to be so noisy that, except for their largest eigenvalues and their associated eigenvectors, they can

Quantocracy’s Daily Wrap for 10/19/2022

This is a summary of links featured on Quantocracy on Wednesday, 10/19/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Best Defensive Asset Class [Allocate Smartly]

    In this post we look at what major asset classes have proven to be the best defensive choice in months when the market has fallen over the last 50+ years. Well look at multiple government and corporate bond assets, diversified commodities, gold and the US dollar. The results? As expected, a mixed bag. Investors who blindly assumed any defensive asset was a sure thing in times of market stress
  • Stock-Bond Correlation, an In-Depth Look [Quantpedia]

    The recent surge in global inflation sent shock waves across financial markets and affected the complicated relationship between stocks and bonds. Today, we would like to present you with a review of two interesting papers, which provide both a deep and easy-to-understand examination of the correlation structure of those two main asset classes. The first paper reviews specifics in various parts of
  • Causality: interest rates and fixed income assets [Quant Dare]

    The blog has previously addressed interest rates in a post that splits the yield rate curve into three relevant components. This time this post tries to identify the influence of interest rates on fixed income assets by using the Granger causality test. Interest rates obviously have a strong impact on fixed income assets because they are the base to compute their prices. However, can we find any

Quantocracy’s Daily Wrap for 10/18/2022

This is a summary of links featured on Quantocracy on Tuesday, 10/18/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Finding and analyzing free stock index data with Python and EDGAR [Wrighters.io]

    A stock index is just a list of stocks. But an index is a special list because investors use it to make investing decisions. An index is constructed via rules about stocks to include, how much to include, and when to include (or remove it). Finding this data, especially for more obscure indexes, can be difficult. This is especially the case if you want to find this data for free. This article will
  • Democracy: is it better for the stock market? [Alpha Architect]

    In this article, we examine the research that addresses the question of whether or not democracy leads to better possible outcomes for the stock market. Democracy and Stock Returns Xun Lei and Tomasz Piotr Wisniewski SSRN Working Paper A version of this paper can be found here Want to read our summaries of academic finance papers? Check out our Academic Research Insight category. What are the