Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 07/27/2023

This is a summary of links featured on Quantocracy on Thursday, 07/27/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • XRP-based Crypto Investment Portfolio Inspired by Ripple vs SEC Lawsuit [Quant at Risk]

    Crypto-market price actions often revolves around the news. Good or bad? It does not matter. However, the recent long-term battle between the SEC and Ripple seemed to reignite the markets. On July 13, 2023, XRP/USDT suddenly shoot up, dragging a number of not so obvious cryptos up along. This was the implication of the courts verdict in the lawsuit. This observation led me to formulate a
  • Top Models for Natural Language Understanding (NLU) Usage [Quantpedia]

    In recent years, the Transformer architecture has experienced extensive adoption in the fields of Natural Language Processing (NLP) and Natural Language Understanding (NLU). Google AI Researchs introduction of Bidirectional Encoder Representations from Transformers (BERT) in 2018 set remarkable new standards in NLP. Since then, BERT has paved the way for even more advanced and improved models.
  • Building a No Code Quantitative Backtest Engine for Machine Trading [Hanguk Quant]

    We started off with the conceptualisation of trading alpha in different abstract representations, such as mathematical formulas, graphs and visual representations: Alpha-Encoding Data Structures Alpha-Encoding Data Structures HangukQuant Jun 30 Read full story For machine trading this would require a convenient translation between the different representations onto computer bits, and we
  • Regression is a tool that can turn you into a fool [Alpha Architect]

    Running regressions on past returns is a great tool for academic researchers who understand this approachs nuance, assumptions, pitfalls, and limitations. However, when factor regressions become part of a sales effort and/or are put in the hands of investors/advisors/DIYers, the tool can quickly turn you into a fool. Dont get me wrong, running regressions on return series is useful for

Quantocracy’s Daily Wrap for 07/26/2023

This is a summary of links featured on Quantocracy on Wednesday, 07/26/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 07/25/2023

This is a summary of links featured on Quantocracy on Tuesday, 07/25/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • All the vols, for quant_rv [Babbage9010]

    Its just too easy to do all the volatility measures, with quantmod (well, with TTR actually). Lets skip all the preliminaries and have a look. And, a Pearson pairs table: C2C Parkinson Rogers-Satchell Garman-Klass,Yang-Zhang C2C 1.0000000 0.4395541 0.2619220 0.3573710 Parkinson 0.4395541 1.0000000 0.8322215 0.8214485 Rogers-Satchell 0.2619220 0.8322215 1.0000000 0.8649953

Quantocracy’s Daily Wrap for 07/23/2023

This is a summary of links featured on Quantocracy on Sunday, 07/23/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Recursive least-squares linear regression [OS Quant]

    I first learned about this algorithm in the book Kernel Adaptive Filter: A Comprehensive Introduction1 sometime in 2012 or 2013. This book goes in depth into how to build kernel filters and does a fantastic job of easing you into the mathematics. I highly recommend having a read if you can. In my trading algorithms, at each time period, I use a linear regression to predict future returns of each
  • Quant And Machine Learning Links: 20230723 [Machine Learning Applied]

    Reinforcement Learning for Credit Index Option Hedging Francesco Mandelli, Marco Pinciroli, Michele Trapletti, Edoardo Vittori In this paper, we focus on finding the optimal hedging strategy of a credit index option using reinforcement learning. We take a practical approach, where the focus is on realism i.e. discrete time, transaction costs; even testing our policy on real market data. We
  • Research Review | 21 July 2023 | Forecasting Markets [Capital Spectator]

    Betting on War? Oil Prices, Stock Returns and Extreme Geopolitical Events Knut Nygaard (Oslo Metropolitan U.) and L.Q. Srensen (Storebrand Asset Mgt.) July 2023 We show that the ability of oil price changes to predict stock returns is largely limited to five extreme geopolitical events: the 2022 invasion of Ukraine, the 2003 invasion of Iraq, the 1990/91 Persian gulf war, the 1986 OPEC collapse,
  • Risk of Momentum Crashes: can it be reduced? [Alpha Architect]

    My August 4, 2022, Alpha Architect article examined the research demonstrating that cross-sectional momentum has provided a premium that has been found to be persistent across time and economic regimes, pervasive around the globe and across sectors and asset classes (stocks, bonds, commodities and currencies), robust to various definitions, and survives transactions costs. And within equities,

Quantocracy’s Daily Wrap for 07/20/2023

This is a summary of links featured on Quantocracy on Thursday, 07/20/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A different measure of volatility for quant_rv [Babbage9010]

    Everybody knows what volatility is. But theres more than one way to measure it. The last couple of posts Ive been trying to document a little more about the plain vanilla standard way to measure vol in the context of my efforts toward finding an ETF switching strategy to use realized volatility as the primary signal, especially looking at being out of the market during higher volatility
  • Fund Concentration: Does it impact return? [Alpha Architect]

    This study explores the degree to which fund concentration as measured by high tracking error or active share, affects the magnitude of excess returns and whether or not the likelihood of outperformance or underperformance are distributed similarly. Three methods of analysis were used to examine the relationship between dispersion of fund excess returns and the degree of portfolio concentration

Quantocracy’s Daily Wrap for 07/16/2023

This is a summary of links featured on Quantocracy on Sunday, 07/16/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quant_rv: more exploration of strategy parameters [Babbage9010]

    There is grave danger in tying your strategy to one selected set of parameters, particularly if those parameters are cherry picked to give more exciting results than other possible choices. Im trying to working to avoid that in quant_rv. So far, quant_rv has two main parameters that can vary: the lookback_period for calculating realized volatility, and the volatility threshold used as a cutoff
  • Quant And Machine Learning Links: 20230716 [Machine Learning Applied]

    Financial Machine Learning Bryan T. Kelly, Dacheng Xiu We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping machine learning tools, as well as for
  • Are Sustainable Investors Compensated Adequately? [Alpha Architect]

    While sustainable investing continues to gain in popularity, economic theory suggests that if a large enough proportion of investors choose to favor companies with high sustainability ratings and avoid those with low sustainability ratings (sin businesses), the favored companys share prices will be elevated and the sin stock shares will be depressed. In equilibrium, the screening out of certain
  • A model for bond risk premia and the macroeconomy [SR SV]

    An empirical analysis of the U.S. bond market since the 1960s emphasizes occasional abrupt regime changes, as defined by yield levels, curve slopes, and related volatility metrics. An arbitrage-free bond pricing model illustrates that bond risk premia can be decomposed into two types. One is related to continuous risk factors, traditionally summarized as the level, slope, and curvature of the

Quantocracy’s Daily Wrap for 07/14/2023

This is a summary of links featured on Quantocracy on Friday, 07/14/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What’s Better, High Profit Margins or Improving Profit Margins? [Quant Rocket]

    Should investors prefer companies with high profit margins or companies with improving profit margins? Is it better to own an unprofitable company that's getting better, or a profitable company that's getting worse? This post explores these questions by analyzing the profitability growth factor and how it interacts with the profitability and size factors to impact stock performance. This
  • Visual Quantitative Analysis of Dow 30 Stocks [GCBC Ventures]

    Using the input data as described in Quantitative And Machine Learning Asset Analysis: Single Moving Average (SMA) (current price N day average)/N day average, where N = 21, 42, 63, , 231, 252, formed into an array. Dual Moving Average (DMA) Same as SMA with 21 day average substituted for the current price. Bollinger Band (BB) Same as SMA with the denominator replaced with the N
  • The Powerful Advantages of Investing in Conglomerate Stocks [Quant Dare]

    The conventional wisdom suggests that by spreading your investments across a wide range of assets, you can mitigate risk and achieve greater long returns. In this article, we will explore the diversification benefits of conglomerate stocks and why they can be valuable additions to a stock portfolio. Introduction Diversification has long been heralded as a fundamental principle of investing.

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