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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

Quantocracy’s Daily Wrap for 06/20/2023

This is a summary of links featured on Quantocracy on Tuesday, 06/20/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is Managed Futures Value-able? [Flirting with Models]

    In Return StackingTM: Strategies for Overcoming a Low Return Environment, we advocated for the addition of managed futures to traditionally allocated portfolios. We argued that managed futures low empirical correlation to both equities and bonds and its historically positive average returns makes it an attractive diversifier. More specifically, we recommended implementing managed futures as an
  • Index Replication: avoid the negatives! [Alpha Architect]

    There are several significant, well-documented benefits of index funds. In addition to outperforming a large majority of actively managed funds, they tend to have low fees, low turnover (resulting in low trading costs and high tax efficiency), broad diversification, high liquidity, and near-zero tracking error (generally assumed to mean that they incur negligible trading costs). However, there are
  • Merchandise import as predictor of duration returns [SR SV]

    Local-currency import growth is a widely underestimated and important indicator of trends in fixed-income markets. Its predictive power reflects its alignment with economic trends that matter for monetary policy: domestic demand, inflation, and effective currency dynamics. Empirical evidence confirms that import growth has significantly predicted outright duration returns, curve position returns,
  • Preferential Times for Preferred Income Strategies? [Finominal]

    Preferred income funds offer exceptionally high yields However, the higher the yield, the lower the total return The diversification benefits of these funds were limited INTRODUCTION Although the job of a stock analyst is not easy, fixed-income analysts have it arguably harder. Sure, there might be multiple share classes for a few stocks like Alphabet or Berkshire Hathaway, but equity is perpetual

Quantocracy’s Daily Wrap for 06/16/2023

This is a summary of links featured on Quantocracy on Friday, 06/16/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Long-Only Value Investing: Size Doesn’t Matter! [Alpha Architect]

    Many factor investors are familiar with small-cap value investing, which is a reasonable allocation for long-term investors who can tolerate a lot of volatility. Why are there so many small-cap value investors? Small-cap value investors have been told that the value premium is higher, on average, in small stocks versus larger stocks. Unfortunately, this is not true if you are a long-only
  • Exploratory Data Analysis of Fundamental Factors [Quant Rocket]

    When researching fundamental factors, analyzing alpha shouldn't be your first step. You can save time and spot issues early by starting with a basic exploration of your factor's distribution and statistical properties, a process known as exploratory data analysis (EDA). This post looks at operating margin, a profitability ratio, to demonstrate what you can learn from exploratory data
  • Linking Impact in Divergence Attribution II [Quant Dare]

    In my post Linking Impact in Divergence Attribution I explained the need to use linking algorithms in order to aggregate single-period returns. I ended my exposition by setting out the formula for adjusted returns using Andrew Frongellos algorithms (arguably the ones with best qualities in the industry). If you found this final expression of Frongello-adjusted attribution factors quite nasty,

Quantocracy’s Daily Wrap for 06/15/2023

This is a summary of links featured on Quantocracy on Thursday, 06/15/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Enhance your portfolio analysis framework with carbon emissions attributions [DileQuante]

    As a portfolio manager, of a mutual or dedicated fund, you have to regularly report the performance of your fund on a specific time frame (monthly, quarterly, yearly, etc.). One of the common tools is the performance attribution analysis, which is a framework that allows to isolate the effect between allocation and selection processes. Several methods can be used (Brinson, Top-Down, Geometric,

Quantocracy’s Daily Wrap for 06/12/2023

This is a summary of links featured on Quantocracy on Monday, 06/12/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Industry classification and the role it plays in momentum strategies [Alpha Architect]

    Momentum strategies have been popular since the original Jagadeesh and Titman article was published in 1993. Variations on the strategies have employed calculating momentum on an individual and industry basis. For instance, in a 1999 study, Moskowitz and Grinblatt produced a positive and significant excess return from a long/short strategy buying the top three winning industries and selling
  • Did COVID ruin Opex week? [Quantifiable Edges]

    This week is options expiration week. And we have known for a long time that opex is often a bullish week for the market. Interestingly, that seasonal tendency has not seemed to hold true since the COVID crash in 2020. Below is a look at performance of all opex weeks since 1984. Opex week performance has floundered since 2020 There has been a clear shift in the curve over the last few years. The
  • Diversification versus Hedging II [Finominal]

    Ideally diversifying funds are uncorrelated and generate positive returns However, identifying such funds is more challenging than expected Creating a diversified portfolio requires thoughtful fund and asset class selection INTRODUCTION In our last research note (read Diversification versus Hedging), we explored creating a diversification strategy by selecting funds that exhibit negative downside
  • How do AI exposures impact future stock returns? [Alpha Architect]

    In this article we examine the research about how artificial intelligence influences stock returns by analyzing a measurement of firm-level AI exposures called Alness. AI Narrative and Stock Mispricing Arka Bandyopadhyay, Dat Mai, Kuntara Pukthuanthong SSRN, Working Paper A recent version of the paper can be found here Want to read our summaries of academic finance papers? Check out our Academic

Quantocracy’s Daily Wrap for 06/09/2023

This is a summary of links featured on Quantocracy on Friday, 06/09/2023. To see our most recent links, visit the Quant Mashup. Read on readers!