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Quantocracy’s Daily Wrap for 08/28/2023

This is a summary of links featured on Quantocracy on Monday, 08/28/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 15 Ideas, Frameworks, and Lessons from 15 Years [Flirting with Models]

    Today, August 28th, 2023, my company Newfound Research turns 15. It feels kind of absurd saying that. I know Ive told this story before, but I never actually expected this company to turn into anything. I started the company while I was still in undergrad and I named it Newfound Research after a lake my family used to visit in New Hampshire. I fully expected the company to be shut down within a
  • The determinants of inflation [Alpha Architect]

    The research questions of the article are as follows: How can a Hidden Markov Model be applied to identify regimes of shifting inflation? What are the characteristics and descriptive information of the identified inflation regimes? Which economic variables are the determinants of inflation and how can their relative importance be measured? What implications do the findings have for policymakers in
  • Quant And Machine Learning Links: 20230827 [Machine Learning Applied]

    AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment Tianping Zhang, Yuanqi Li, Yifei Jin, Jian Li The multi-factor model is a widely used model in quantitative investment. The success of a multi-factor model is largely determined by the effectiveness of the alpha factors used in the model. This paper proposes a new evolutionary

Quantocracy’s Daily Wrap for 08/26/2023

This is a summary of links featured on Quantocracy on Saturday, 08/26/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Intro to Black-Scholes, implied volatility and hedging [OS Quant]

    Im a little embarrassed to admit this, I was recently in a quant interview and the interviewer quickly realised that I didnt know the Black-Scholes formula! That was definitely a moment when imposter syndrome became reality. To fix the situation, Ive written up an easy intro to the Black-Scholes model here. I hope this helps you as much as it helped me. Black-Scholes setup The
  • How to Launch Your Career as a Risk Quant in 2024? [Quant at Risk]

    Launching a career as a risk quant requires a well-thought-out strategy that combines a strong educational foundation, technical skills, and an understanding of the evolving landscape of risk management. To embark on this journey, aspiring risk quants should start by building a solid educational background. Pursuing a bachelors or Masters degree in mathematics, statistics, finance, economy,
  • Business sentiment and commodity future returns [SR SV]

    Business sentiment is a key driver of inventory dynamics in global industry and, therefore, a powerful indicator of aggregate demand for industrial commodities. Changes in manufacturing business confidence can be aggregated by industry size across all major economies to give a powerful directional signal of global demand for metals and energy. An empirical analysis based on information states of
  • Structured notes: Wall Street fairy tales that should be avoided! [Alpha Architect]

    As a general rule of thumb, the more complexity that exists in a Wall Street creation, the faster and farther investors should run. David Swensen, Unconventional Success Structured products are packages of synthetic investment instruments specifically designed to appeal to needs that investors perceive are not being met by available securities. They are often packaged as asset allocation tools

Quantocracy’s Daily Wrap for 08/23/2023

This is a summary of links featured on Quantocracy on Wednesday, 08/23/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Correlation Matrix Stress Testing: Random Perturbations of a Correlation Matrix [Portfolio Optimizer]

    In the previous posts of this series, I detailed a methodology to perform stress tests on a correlation matrix by linearly shrinking a baseline correlation matrix toward an equicorrelation matrix or, more generally, toward the lower and upper bounds of its coefficients. This methodology allows to easily model known unknowns when designing stress testing scenarios, but falls short with unknown
  • Design Crypto-Asset to Avoid Structural Failures Due to Random Vibrations [Quant at Risk]

    Although the relationship is not immediately clear and obvious, the structural engineering has a lot in common with financial assets. In both cases we deal with the objects under stress over their entire lifetimes. There are two possible outcomes: something can break or perform well. The engineers run lots of analyses and tests before a given element is ready for its use and service. It is
  • Sector Neutralization: Why It Matters and How to Use It [Quant Rocket]

    Sector neutralization is a technique to hedge out sector bets and reduce the impact of sector-specific risks on the portfolio by ranking factors within sectors rather than across sectors. This post uses the debt-to-equity ratio to show why sector neutralization is important and how to perform it in Pipeline. This post is part of the fundamental factors series, which explores techniques for
  • How to make amazing dashboards to easily power alpha analysis [PyQuant News]

    Principal component analysis (PCA) is used widely in data science. Its a way to reduce the number of dimensions in a data set. Its also used in quant finance to find alpha. In a stock portfolio, a dimension might be a column of returns for one of the stocks. Once you get the model built, you could spend your time tweaking the code. Or, you can do it in an interactive dashboard to power your

Quantocracy’s Daily Wrap for 08/21/2023

This is a summary of links featured on Quantocracy on Monday, 08/21/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Revisiting Link s Global Growth Cycle Strategy [Allocate Smartly]

    Weve previously covered Links Global Growth Cycle strategy, which uses OECD Composite Leading Indicator (CLI) data to time the market. The strategy has navigated the market gyrations over the last few years well, so naturally its gotten the attention of members. Recent strategy results follow. Learn about what we do and follow 70+ asset allocation strategies like this one in near
  • A Case Study in Finding Edge [Robot Wealth]

    In 2021, James, I, and a small team decided to set up a crypto trading venture. We faced several problems, but knowing almost nothing about crypto was the most significant. We sensed that the fractured, developing nature of the crypto market would likely be a good place to seek out inefficiencies, but beyond that, we were winging it. That turned out to be very true inefficiencies abounded in
  • Factor seasonality – an independent risk factor? [Alpha Architect]

    Factor seasonality always seemed to be an idea that was too close to factor timing to help build factor strategies. Surprisingly, the authors find a substantial factor seasonality effect across global markets, suggesting that the assumption is unwarranted. This is the first study I have encountered that tested the proposition that portfolios sorted twice on a specific factor first and high returns
  • Quant And Machine Learning Links: 20230820 [Machine Learning Applied]

    Portfolio Selection via Topological Data Analysis Petr Sokerin, Kristian Kuznetsov, Elizaveta Makhneva, Alexey Zaytsev Portfolio management is an essential part of investment decision-making. However, traditional methods often fail to deliver reasonable performance. This problem stems from the inability of these methods to account for the unique characteristics of multivariate time series data

Quantocracy’s Daily Wrap for 08/18/2023

This is a summary of links featured on Quantocracy on Friday, 08/18/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Avoid Equity Bear Markets with a Market Timing Strategy – Revisiting Our Research [Quantpedia]

    In March, we posted a series of three articles where our goal was to construct a market timing strategy that would reliably sidestep the equity market during bear markets. Each article focused on trading signals based on a specific group of indicators, namely, price-based indicators, macroeconomic indicators, and a leading indicator, a yield curve, that can predict recessions and bear markets in
  • Research Review | 18 August 2023 | Factor Risk Premia Analysis [Capital Spectator]

    Expanding the Fama-French Factor Model with the Industry Beta Anatoly B. Schmidt (NYU Tandon School of Engineering) August 2023 Recently it was shown that the news-based stock pricing model (NBSPM) outperforms the momentum-enhanced five-factor Fama-French model (FF5M) for a representative list of holdings of the major US equity sector ETFs both in-sample (Schmidt 2023) and out-of-sample (Schmidt

Quantocracy’s Daily Wrap for 08/17/2023

This is a summary of links featured on Quantocracy on Thursday, 08/17/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Technical Analysis Report Methodology + Double Bottom Country Trading Strategy [Quantpedia]

    We cannot start without a cheap quip: Technical analysis is an astrology for men. Market technicians believe that prices currently contain all information about any asset. It is undoubtedly an oversimplified assumption, as the market is much more complex than that. But suppose you try to use fundamental analysis too harshly. In that case, you assume that you have all the possible information about
  • Quant_rv part 9: why realized vol? [Babbage9010]

    A big issue for me with this project is: how do we validate this whole approach? We started out with a super simple vol-based timing strategy (long/flat market exposure). I rather glibly state that realized (or perhaps more properly, historical) volatility is a sensible, logical, statistically meaningful market observation. But is it? The real low volatility anomaly (documented in

Quantocracy’s Daily Wrap for 08/14/2023

This is a summary of links featured on Quantocracy on Monday, 08/14/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Feature: 10-Year Stock Market Return Forecast [Allocate Smartly]

    We are often asked about stock market valuation models such as Shillers CAPE Ratio and the Buffet Indicator. These models predict long-term returns, usually forecasting the next 10 years. Our recent analysis of one such valuation model, the Aggregate Investor Allocation to Equities, motivated us to take our our own deep dive into the subject. Our goal is two-fold: (a) analyze valuation models
  • Post-Mortem: Losing Money At 36k-Feet Above Sea Level and How Not To [Taiwan Quant]

    Picture this: you're about to board a 10-hour flight. As you board the plane (or maybe some time waiting at the gate), a notification pops up in your pocket. You're busy with other things, so you ignore it and forget about it (in fact, you're used to ignoring notifications because you thought at one point that's the only healthy way to approach them). You walk into the
  • NASDAQ no longer leading the SPX what this means for the market [Quantifiable Edges]

    One particularly notable indicator change that occurred at the close on Friday is that out NASDAQ/SPX Relative Leadership indicator flipped so that it is now showing the SPX as leading and the NASDAQ as lagging. This can be seen in the chart below. NASDAQ/SPX Relative Strength shows NASDAQ faltering now Whenever the solid (green/red) line is above the blue dashed line that means the NASDAQ is
  • GARP Investing: Golden or Garbage? II [Finominal]

    Buying cheap growth stocks is intuitively appealing to investors Almost 50% of the US stocks are trading below a PEG ratio of 1 currently However, GARP stocks have not generated positive excess returns since 2005 INTRODUCTION In 2019, we published a research note on growth-at-reasonable-price (GARP) investing (Garp Investing: Golden or Garbage?), where we concluded that the strategy had some nice
  • Quant And Machine Learning Links: 20230813 [Machine Learning Applied]

    AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting Oleksandr Shchur, Caner Turkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, Yuyang Wang We introduce AutoGluon-TimeSeries an open-source AutoML library for probabilistic time series forecasting. Focused on ease of use and robustness, AutoGluon-TimeSeries enables users to generate accurate point and quantile

Quantocracy’s Daily Wrap for 08/13/2023

This is a summary of links featured on Quantocracy on Sunday, 08/13/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Business Cycle Sector Timing [CSS Analytics]

    The business cycle is a pattern that captures changes in economic activity over time. The changes in the business cycle occur in a sequential or serial manner, moving through a predictable sequence of phases. These cycles are consistent but vary in both duration and intensity. The phases of the business cycle are: Expansion: This is the phase where the economy is growing. During an expansion,
  • Generation of Syntactic Quantitative Signals and Alpha Factories [Hanguk Quant]

    This is the last of the advanced quant dev series post – next week, we will go back to the basics, and cover the details in how we arrive at the advanced quant backtesting library, which evolved from a rudimentary system consisting of a single signal, single model strategy to a multi signal, multi model strategy system. Most of the readers who struggle with our current advanced code should really
  • How to use capture ratios to improve investment performance [PyQuant News]

    In todays newsletter, well cover the up-market capture ratio, a framework for evaluating investment performance in rising markets. Even though the ratio is used by professional money managers, you can use it to better gauge your own investment performance. Lets dive in! How to use capture ratios to improve investment performance The up-market capture ratio is a way to evaluate how well an
  • Nowcasting macro trends with machine learning [SR SV]

    Nowcasting economic trends can make use of a broad range of machine learning methods. This not only serves the purpose of optimization but also allows replication of past information states of the market and supports realistic backtesting. A practical framework for modern nowcasting is the three-step approach of (1) variable pre-selection, (2) orthogonalized factor formation, and (3)

Quantocracy’s Daily Wrap for 08/11/2023

This is a summary of links featured on Quantocracy on Friday, 08/11/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 8 ways pandas is losing to Polars for quick market data analysis [PyQuant News]

    In todays newsletter, youll use Polars, a high-speed data-handling tool thats becoming essential in quantitative finance and algorithmic trading. Youll see how to compare its performance to pandas for many common data manipulation techniques. By the end of this post, youll understand how Polars can improve your data processing speed, especially when working with large datasets. So
  • Value and Profitability/Quality: Complementary Factors [Alpha Architect]

    In his 2012 paper The Other Side of Value: The Gross Profitability Premium, Robert Novy-Marx demonstrated that profitability, as measured by gross profits-to-assets, had roughly the same power as book-to-market (value factor) in predicting the cross-section of average returns profitable firms generated significantly higher returns than unprofitable firms despite having significantly

Quantocracy’s Daily Wrap for 08/09/2023

This is a summary of links featured on Quantocracy on Wednesday, 08/09/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Forecasting currency rates with fractional brownian motion [OS Quant]

    Fractional Brownian motion is defined as a stochastic Gaussian process XtXt that starts at zero X0=0X0=0 has an expectation of zero E[Xt]=0E[Xt]=0 and has the following covariance1: E[XtXs]=212(t2H+s2Hts2H)(1) E[XtXs]=221(t2H+s2Hts2H)(1) where is the volatility parameter and H(0,1)H(0,1) is the Hurst exponent. The