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Quantocracy’s Daily Wrap for 04/02/2023

This is a summary of links featured on Quantocracy on Sunday, 04/02/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Improving Hedged Equity With a Short-Dated Ladder [Simplify]

    A costless collar, sometimes referred to as a hedged equity or defined outcome strategy, is a risk management strategy that combines holding a long position in a stock or index with buying a put spread defined by a specific set of strikes (e.g. 5% OTM long put, 20% OTM short put). This provides downside protection for the long stock position if the stock falls within the put spreads option
  • Is a Naive 1/N Diversification Strategy Efficient? [Alpha Architect]

    Investment strategy should be based on three fundamental principles. First, markets are highly, though not perfectly, efficient. That leads to the conclusion that active management is the losers game. Second, if markets are efficient, it must follow that you should believe that all unique sources of risk have similar risk-adjusted returns. Third, if all individual sources of risk have similar

Quantocracy’s Daily Wrap for 03/31/2023

This is a summary of links featured on Quantocracy on Friday, 03/31/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Can We Backtest Asset Allocation Trading Strategy in ChatGPT? [Quantpedia]

    Its always fun to push the boundaries of technology and see what it can do. The AI chatbots are the hot topic of actual discussion in the quant blogosphere. So we have decided to test OpenAIs ChatGPT abilities. Will we persuade it to become a data analyst for us? While we may not be there yet, its clear that AI language models like ChatGPT can soon revolutionize how we approach to finance

Quantocracy’s Daily Wrap for 03/30/2023

This is a summary of links featured on Quantocracy on Thursday, 03/30/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Investing & Unintended Consequences [Finominal]

    Simple equity ETFs often have exposures to other asset classes Gold stocks are bond proxies & growth stocks are short commodities Investors may have unintended bets in their portfolios INTRODUCTION ETFs used to be like surgical instruments. StateStreets SPY tracks the stocks of the S&P 500, iShares AGG the universe of US investment-grade bonds, and so on. Investors knew exactly what

Quantocracy’s Daily Wrap for 03/23/2023

This is a summary of links featured on Quantocracy on Thursday, 03/23/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Webinar recordings and notebook [Robot Wealth]

    Towards the end of last year, we ran a couple of free Zoom webinars on: The Basics of Edge Extraction the trader smarts of getting an edge Data Analysis for Traders an interactive research session. Here are the recordings: Basics of Edge Extraction Data analysis for Traders The colab research notebook for the second session can be found here. (To make sense of it youll want to
  • Volume and Mean Reversion Part 2 [Alvarez Quant Trading]

    From the Volume and Mean Reversion post, a reader sent a suggestion to instead use the ratio of 10 day moving average of the Close times Volume divided by the 63-day moving average of the Close times Volume (CV10/63). I had not tried this before and wanted to see how well it would work. First Steps I decided to follow the same path as the previous post. First testing on a very simple mean
  • Myth-Busting: The Economy Drives the Stock Market [Finominal]

    US real GDP growth and US stock market returns were positively correlated since 1900 However, the correlation was not consistent and even turned negative The evidence of this relationship from other countries is mixed INTRODUCTION Switch on Bloomberg TV or CNBC at any time of the day, and there is a good possibility the host will be explaining the daily ups and downs of the stock market as a
  • Generative Adversarial Networks: A rivalry that strengthens [Quant Dare]

    How does ChatGPT work? What is behind deep fake images of celebrities? How do we deal with the lack of data in finance? All these issues have in common the same underlying concept; they are based on generative models. Generative models are algorithms that create new instances of data that mimic the data on which they have been trained. Depending on their task, different generative models are

Quantocracy’s Daily Wrap for 03/20/2023

This is a summary of links featured on Quantocracy on Monday, 03/20/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 03/19/2023

This is a summary of links featured on Quantocracy on Sunday, 03/19/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Avoid Equity Bear Markets with a Market Timing Strategy Part 3 [Quantpedia]

    In the last third installment, we will finish exploring the world of market timing strategies (see parts 1 & 2). We will focus on yield curve predictors and incorporate all three ideas (price-based, macro-economic, and yield curve predictors) into one final trading strategy that yields an annual return above that of the stock market while doubling its Sharpe ratio and reducing maximal drawdown
  • R & D, Expected Profitability, and Expected Returns [Alpha Architect]

    Since the development of the CAPM, academic research has attempted to find models that increase the explanatory power of the cross-section of stock returns. We moved from the single-factor CAPM (market beta) to the three-factor Fama-French model (adding size and value), to the Carhart four-factor model (adding momentum), to Lu Zhangs q-factor model (beta, size, investment, profitability), to

Quantocracy’s Daily Wrap for 03/16/2023

This is a summary of links featured on Quantocracy on Thursday, 03/16/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is it Possible to Know the Daily High or Low Intraday with 80% Accuracy? [Black Arbs]

    This is an old concept concerning the opening range. The idea is that the opening range often sets the days high or low within the first hour of cash equities trading (9:30 am – 10:30 am EST). Recently a trader on [Youtube] made the claim that you can know with 88% probability the high or low of the day after the first hour of trading. He managed to successfully repopularize the idea of using

Quantocracy’s Daily Wrap for 03/15/2023

This is a summary of links featured on Quantocracy on Wednesday, 03/15/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Avoid Equity Bear Markets with a Market Timing Strategy Part 2 [Quantpedia]

    In this second installment in a series of three articles, we will continue with our goal to construct a market timing strategy that would sidestep the equity market during bear markets. A few days ago, we started with price-based market timing strategies. Today, we will focus on macroeconomic indicators and predictors derived from the movements in the commodity markets. Market Timing Using Trend
  • More Intuitive Joins in dplyr 1.1.0 [Robot Wealth]

    dplyr 1.1.0 was a significant release that makes several common data operations more syntactically intuitive. The most significant changes relate to joins and grouping/aggregating operations. In this post well look at the changes to joins. First, install and load the latest version of dplyr: install.packages("dplyr") library(dplyr) Copy A new approach to joins The best way to explore

Quantocracy’s Daily Wrap for 03/14/2023

This is a summary of links featured on Quantocracy on Tuesday, 03/14/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Avoid Equity Bear Markets with a Market Timing Strategy – Part 1 [Quantpedia]

    In this series of three articles, our goal is to construct a market timing strategy that would reliably sidestep the equity market during bear markets, thereby reducing market volatility and boosting risk-adjusted returns. We will build trading signals based on price-based indicators, macroeconomic indicators, and a leading indicator, a yield curve, that would try to predict recessions and bear
  • Twitter Sentiment Analysis Using Zero-Shot Classification [Analyzing Alpha]

    Are you looking for a way to quickly assess the sentiment of public companies through their tweets without previously training any ML models? The OpenAI API provides powerful, zero-shot classification capabilities so that text data can be classified into multiple categories regardless of whether or not the model has encountered those categories. This guide explains each step using excellent
  • Multi-Strategy Hedge Funds: Jack of All Trades? [Finominal]

    A few select multi-strategy hedge funds generated outsized returns in 2022 However, the average fund lost money The average fund can be simply replicated via the S&P 500 & cash INTRODUCTION Citadel made $16 billion in profits in 2022, Millenium $8.0 billion, and Point 72 $2.4 billion. These returns are spectacular as all three are multi-strategy funds that allocate capital to hundreds of

Quantocracy’s Daily Wrap for 03/11/2023

This is a summary of links featured on Quantocracy on Saturday, 03/11/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • SetFit: Fine-tuning a LLM in 10 lines of code and little labeled data [Gautier Marti]

    This blog is a follow-up to the series of posts Snorkel Credit Sentiment – Part 1 (May 2019) May the Fourth: VADER for Credit Sentiment? (May 2019) Experimenting with LIME – A tool for model-agnostic explanations of Machine Learning models (May 2019) Using LIME to explain Snorkel Labeler (August 2019) which share a common dataset of portfolio managers comments focused on the CDS market.
  • Algorithmic Trading in Python with Machine Learning: Walkforward Analysis [Ed West]

    Implementing a successful trading strategy with code can be a challenging task. While some traders prefer to use basic trading rules and indicators, a more advanced approach involving predictive modeling may be necessary. In this tutorial, I will guide you through the process of training and backtesting machine learning models in PyBroker, an open-source Python framework that I developed for
  • Research Review | 10 March 2023 | ETFs [Capital Spectator]

    ETF Dividend Cycles Pekka Honkanen (University of Georgia), et al. February 2023 Exchange-traded funds (ETFs) collect approximately 7% of all U.S. corporate dividends, which they are required to redistribute to investors. How do the funds manage these dividend flows, and does such management have spillover effects on other financial markets? In this paper, we document a new stylized fact of the