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Quantocracy’s Daily Wrap for 07/10/2023

This is a summary of links featured on Quantocracy on Monday, 07/10/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Selected ML Papers from ICML 2023 [Gautier Marti]

    This blog post serves as a summary and exploration of ~100 papers, providing insights into the key trends presented at ICML 2023. The papers can be categorized into several sub-fields, including Graph Neural Networks and Transformers, Large Language Models, Optimal Transport, Time Series Analysis, Causality, Clustering, PCA and Autoencoders, as well as a few miscellaneous topics. Graph Neural
  • Covered Call Strategies Uncovered [Finominal]

    Covered call strategies aim to offer index-like returns with lower volatility and higher yields They have underperformed their benchmarks significantly over longer periods They are tools for market timing, but that is difficult to execute successfully INTRODUCTION JP Morgan has been a late-comer to the ETF industry, but achieved remarkable success in the actively-managed ETF space as it manages

Quantocracy’s Daily Wrap for 07/07/2023

This is a summary of links featured on Quantocracy on Friday, 07/07/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Structure Function: Forgotten Detection Tool for Periodic Signals [Quant at Risk]

    In time-series analysis we often examine signals for specific volatility patterns. The simplest one is a periodic or quasi-periodic modulation. In finance these modulations are of paramount importance allowing for signal decomposition, separating short-term variations from long-term trends. Regardless of the time-scales, it is possible to build a trading model analysing the signals content in
  • And the Winner Is: Examining Alternative Value Metrics [Alpha Architect]

    Value as an investment strategy has long been popular in both academia and among practitioners and is supported by valuation theory, which provides a framework for identifying the drivers of expected returns: the prices investors pay and the expected future cash flows investors will receive. Unfortunately, theory does not tell us the best way to extract information about expected returns from

Quantocracy’s Daily Wrap for 07/06/2023

This is a summary of links featured on Quantocracy on Thursday, 07/06/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Simulation from a Multivariate Normal Distribution with Exact Sample Mean Vector and Sample Covariance Matrix [Portfolio Optimizer]

    In the research report Random rotations and multivariate normal simulation1, Robert Wedderburn introduced an algorithm to simulate i.i.d. samples from a multivariate normal (Gaussian) distribution when the desired sample mean vector and sample covariance matrix are known in advance2. Wedderburn unfortunately never had the opportunity to publish his report3 and his work was forgotten until Li4
  • Parameter exploration with quant_rv and heatmap [Babbage9010]

    For v1.2.0 we take a step back from 1.1.0 to meet some of the new goal requirements right off the bat, and to play explore. In particular, we remove the code to test QQQ (or other ETFs) and related vars. Next we change code to make it easy to explore parameters (like the volatility threshold) to see how it works and what it actually does. Finally we craft a heatmap tool to help us explore the

Quantocracy’s Daily Wrap for 07/05/2023

This is a summary of links featured on Quantocracy on Wednesday, 07/05/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Jumping into quant_rv [Babbage9010]

    So we need some starter code, and some goals for where were going. The starter code comes from a blog post by Learning Machines back in April 2023. Hes got some great stuff on his blog (well use some of his ideas here), so take a good look through his Quantitative Finance category, at least. Well start with the final code that he ended up getting from a rather elegant interaction with
  • Clustering Forex Market [Quant Dare]

    The Forex Market is the global marketplace where currencies are bought and sold. It is the largest and most liquid financial market in the world, with trillions of dollars traded daily. A currency pair is an asset composed of two currencies traded on the financial market. Its price represents the relative value of one currency against the other. For example, in the case of the EUR/USD pair, a

Quantocracy’s Daily Wrap for 07/03/2023

This is a summary of links featured on Quantocracy on Monday, 07/03/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Selected ML Papers from ICML 2023 [Gautier Marti]

    This blog post serves as a summary and exploration of ~100 papers, providing insights into the key trends presented at ICML 2023. The papers can be categorized into several sub-fields, including Graph Neural Networks and Transformers, Large Language Models, Optimal Transport, Time Series Analysis, Causality, Clustering, PCA and Autoencoders, as well as a few miscellaneous topics. Graph Neural
  • Factor Olympics 2023 1H [Finominal]

    All popular factors generated negative excess returns in 1H 2023 Small caps performed best, low-risk stocks worst Somewhat surprisingly, long-short multi-factor products produced positive returns 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

Quantocracy’s Daily Wrap for 06/30/2023

This is a summary of links featured on Quantocracy on Friday, 06/30/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Taking your MLFinLab strategy live [Hudson and Thames]

    Executing a live trading strategy can be a daunting task. From analyzing market data and identifying trading signals to deploying and monitoring trades in real-time, the process requires precision, speed, and accuracy. Fortunately, advancements in technology have paved the way for innovative platforms and tools that assist traders in their pursuit of profitable trading strategies. One such
  • Financial Statements Effect [Quant Dare]

    Effect J. Gonzlez 28/06/2023 No Comments In a previous post we saw how avoiding being in the market during Earnings publications could be a zero-sum game in the long run. In this post our purpose is to study if it is possible to take advantage of the effect in the stock prices based on the behavior of the prices during financial statements publication dates. Discussion Fundamental researchers
  • Performance of Factors: what the research says [Alpha Architect]

    Since the discovery of the size, value, and momentum effects in the 1980s and 1990s, a plethora of other factors have been identified in the asset pricing literature, which led John Cochrane to coin the phrase zoo of factors. It has raised questions and led to research into how many factors are needed, the replicability of originally reported results, and the decay of factor performance over

Quantocracy’s Daily Wrap for 06/29/2023

This is a summary of links featured on Quantocracy on Thursday, 06/29/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Analyzing the Profitability Factor with Alphalens [Quant Rocket]

    How does a company's profitability affect its stock returns? In this post, I use Alphalens, a Python library for analyzing alpha factors, to investigate the relationship between operating margin, a profitability ratio, and future returns. This is the second post in the fundamental factors series, which explores techniques for researching fundamental factors using Pipeline, Alphalens, and
  • Calculating Realised Volatility with Polygon Forex data [Quant Start]

    In the previous article we wrote a Python function which utilised the Polygon API to extract a month of minutely data for both a major (EURUSD) and exotic (MZXZAR) FX pair. We plotted the returns series and looked at some of the issues that can occur when working with this type of data. This article is part of series where we will be creating a machine learning model which uses realised volatility
  • BloombergGPT: Where Large Language Models and Finance Meet [Alpha Architect]

    Developments in the use of Large Language Models (LLM) have successfully demonstrated a set of applications across a number of domains, most of which deal with a very wide range of topics. While the experimentation has elicited lively participation from the public, the applications have been limited to broad capabilities and general-purpose skills. Only recently have we seen a focus on
  • Can AI Explain Company Performance? [Finominal]

    The rapid evolution of language models has the potential to revolutionise financial analysis GPT outperformed when analyzing earnings calls, followed by Word2Vec and BERT However, overall models should be selected carefully as each has its pros and cons ABSTRACT This paper aims to evaluate the quality of word vectors produced by different word embedding models on two text similarity related

Quantocracy’s Daily Wrap for 06/24/2023

This is a summary of links featured on Quantocracy on Saturday, 06/24/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quant Infrastructure #5 – Order Executor [Taiwan Quant]

    In the previous article of the main series, we looked at robustly tracking our trading inventory and built an Inventory component for our Quant Infrastructure. In this article, we look at tracking and managing orders and build an OrderExecutor for this purpose. Orders require a different approach from the Inventory because we actively control them as opposed to passively taking information from

Quantocracy’s Daily Wrap for 06/23/2023

This is a summary of links featured on Quantocracy on Friday, 06/23/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Intangible Value: Modernizing the Factor Portfolio [Alpha Architect]

    Abstract: The Intangible Value Factor (IHML) can play an additive role in factor portfolios alongside the established market, size, value, quality, and momentum factors. This Six-Factor Model avoids the problematic anti-innovation bias of traditional factor portfolios and can be easily implemented using ETFs. This post is a summary of the recently published paper Intangible Value: A
  • Research Review | 23 June 2023 | Forecasting Equity Returns [Capital Spectator]

    The Realized Information Ratio and the Cross-Section of Expected Stock Returns Mehran Azimi (University of Massachusetts Boston) January 2023 This study investigates the predictability of asset returns with the information ratio and its specific variant, the Sharpe ratio. We find that the realized Sharpe ratio (rsr ) negatively predicts the cross-section of stock returns. The predictability is not
  • Attenuation of Anomalies: what role do fundamentals play? [Alpha Architect]

    The article aims to explore the possibility that changes in fundamentals play a role in the attenuation of stock market anomalies, offering an alternative explanation to the prevailing arbitrage-based explanation. Can the changes in fundamentals explain the attenuation of anomalies? Choi, Lewis and Tan Journal of Financial Economics, 2023 A version of this paper can be found here Want to read our

Quantocracy’s Daily Wrap for 06/21/2023

This is a summary of links featured on Quantocracy on Wednesday, 06/21/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Several Key PerformanceAnalytics Functions From R Now In Python [QuantStrat TradeR]

    So, thanks to my former boss, and head of direct indexing at BNY Mellon, Vijay Vaidyanathan, and his Coursera course, along with the usual assistance from chatGPT (I officially see it as a pseudo programming language), I have some more software for the Python community now released to my github. As wordpress now makes it very difficult to paste formatted code, Ill be linking more often to
  • Negative Hypergeometric Distribution and USDT [Quant at Risk]

    In crypto market, stablecoins are meant to maintain their constant value with respect to the underlying currency. At least in theory. The problem begins with an idea of stablecoins value to be stable or being stabilised over time. Different backup mechanisms are at work. For example, Tether tokens are called stablecoins because they offer price stability as they are pegged at 1-to-1 to a fiat
  • Introduction to Matching Pursuit Algorithm with Stochastic Dictionaries [Quant at Risk]

    There is a huge number of ways how one can transform financial times-series in order to discover new information about changing price dynamics. We talk here about certain transformation that takes price time-series (or return-series) and transforms it into a new domain. Every solid textbook on Time-Series Analysis lists ample examples. 1. Fourier Transform Interestingly, there is little to few