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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

Quantocracy’s Daily Wrap for 08/07/2023

This is a summary of links featured on Quantocracy on Monday, 08/07/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quant_rv part 8: a multi-vol approach [Babbage9010]

    Sum up: by combining all the vols into one strategy and randomizing key parameters, we can generate useful signals that yield a decent return with some consistency. Were not meeting all the quant_rv goals yet, but were making progress on all the fronts. ~ Links to earlier parts ~ Part 1: jumping in, Part 2: cleanup, Part 3: new goals, Part 4: heatmaps, Part 5: param exploration, Part 6:
  • Quant And Machine Learning Links: 20230806 [Machine Learning Applied]

    Portfolio Management: A Deep Distributional RL Approach David Pacheco Aznar This thesis presents the development and implementation of a novel Deep Distributional Reinforcement Learning (DDRL) approach in the field of quantitative finance: the Distributional Soft Actor-Critic (DSAC) with an LSTM embedding. The model is built to further stabilize the performance of the widely used deep
  • Statistical Shrinkage (2) [Eran Raviv]

    During 2017 I blogged about Statistical Shrinkage. At the end of that post I mentioned the important role signal-to-noise ratio (SNR) plays when it comes to the need for shrinkage. This post shares some recent related empirical results published in the Journal of Machine Learning Research from the paper Randomization as Regularization. While mainly for tree-based algorithms, the intuition
  • Investor demand: can it explain returns? [Alpha Architect]

    The traditional financial theory attributes security returns to market- or factor-based risk, with no role ascribed to other influences. In this research, the authors argue for including investor demand as an additional variable in explaining returns. Can changes in investor demand generate systematic changes in security returns? Overall, our analysis shows that demand effects caused by

Quantocracy’s Daily Wrap for 08/05/2023

This is a summary of links featured on Quantocracy on Saturday, 08/05/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Integrating the No-Code Quant Backtester into the Russian Doll Engine [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
  • Why Backtests Run Fast or Slow: A Comparison of Zipline, Moonshot, and Lean [Quant Rocket]

    Backtest speed can significantly affect research friction. The ability to form a hypothesis and quickly get an answer from a backtest allows you to investigate more hypotheses. In this article, I explore several factors that affect backtest speed and compare the performance of 3 open-source backtesters. The backtesters I compare are: Moonshot, a vectorized backtester written in Python Zipline, an
  • The Low-Beta Anomaly: are its returns justified? [Alpha Architect]

    The low-beta anomaly for the capital asset pricing model (CAPM)low-beta stocks outperform high-beta stockswas first documented more than 50 years ago by Fischer Black, Michael Jensen, and Myron Scholes in their 1972 paper, The Capital Asset Pricing Model: Some Empirical Tests. In our 2016 book, Your Complete Guide to Factor-Based Investing, Andrew Berkin and I presented evidence

Quantocracy’s Daily Wrap for 07/30/2023

This is a summary of links featured on Quantocracy on Sunday, 07/30/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Realistic Backtester for Perpetual Futures (Part 1/2) (With Code) [Taiwan Quant]

    Introduction Simulator/backtester architecture Preparing the data Simulating a single market Simulating market orders Part 2: Simulating trading costs Simulating funding Simulating many markets Finish Subscriber materials (source code) Introduction In the last article, we looked at how markets work and at simulating them in theory. Today we will write a complete event-driven simulator/backtester
  • Pure macro FX strategies: the benefits of double diversification [SR SV]

    Pure macro(economic) strategies are trading rules that are informed by macroeconomic indicators alone. They are rarer and require greater analytical resources than standard price-based strategies. However, they are also more suitable for pure alpha generation. This post investigates a pure macro strategy for FX forward trading across developed and emerging countries based on an external
  • Quant And Machine Learning Links: 20230730 [Machine Learning Applied]

    Quantocracy: This is a curated mashup of quantitative trading links. Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling Masanori Hirano, Kentaro Minami, Kentaro Imajo Deep hedging is a deep-learning-based framework for derivative hedging in incomplete markets. The advantage of deep hedging lies in its ability to handle various realistic market conditions, such as market

Quantocracy’s Daily Wrap for 07/28/2023

This is a summary of links featured on Quantocracy on Friday, 07/28/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Square root of a portfolio covariance matrix [OS Quant]

    The square root of your portfolios covariance matrix gives you a powerful way of understanding where your portfolio variance is coming from. Here I show how to calculate the square root and provide an interactive example to explore how it works. Author Adrian Letchford Published 27 July 2023 Length 4 minutes Like what you see? Follow Adrian on Twitter to be notified of new content. Follow
  • Retail attention metrics: do they produce differences in returns? [Alpha Architect]

    Abstract: We find that by using a novel measure of investor attention, generated from InvestingChannels clickstream data on online financial news consumption, we can identify broad groups of stocks which are less efficiently priced and therefore where anomalies such as Value and Momentum are likely to produce greater cross-sectional differentiation in returns. We also apply these groupings to