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Quantocracy’s Daily Wrap for 10/02/2023

This is a summary of links featured on Quantocracy on Monday, 10/02/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Key insights: Imbalance in the order book [OS Quant]

    I summarise key insights from a few papers studying the limit order book. Youl learn how to measure volume imblanace in the limit order book and how well it predicts price moves. Author Adrian Letchford Published 1 October 2023 Length 5 minutes Like what you see? Follow Adrian on Twitter to be notified of new content. Follow This write up is my notes on a few papers looking at using order book
  • How to use VectorBT PRO to algorithmically find chart patterns [PyQuant News]

    VectorBT PRO (VBT) is a proprietary Python package designed for backtesting and analyzing quantitative trading strategies. In todays guest post, youll use VectorBT PRO to algorithmically detect chart patterns from 230 million unique pattern and window combinations. All in about 2 minutes. Ready? Using VectorBT PRO to algorithmically find chart patterns VectorBT PRO provides a comprehensive
  • AI case study: Long/Short strategy [Quant Dare]

    In todays post we will be using AI to improve a module of the Alternative Data-Driven Investment (ADDI) strategy developed by ETS Asset Management Factory, which is an automatic Long Short investment strategy that aims to obtain stable performance de-correlated from the market and with a limited drawdown risk. This comes after our recent posts, where we have been looking into key aspects of
  • Is $SPX Selloff Near An End? [Quantifiable Edges]

    This past week was the 4th week in a row that the SPX declined. It is quite unusual to see SPX close down for 4 weeks in a row, but still remain above its 40-week moving average. Below is a look at other times since 1975 that this action has occurred. SPX down 4 weeks in a row but above 40-week average These results are suggestive of an upside edge over the next several weeks. Below I have listed
  • ETF Evolution: what does it mean for investors? [Alpha Architect]

    The first ETFs emerged in 1993 and closely tracked broad-based indexes for a low fee. Since then, the competitive situation in the ETF industry today has differentiated itself by adding a new breed of ETFs that reflected specialization into popular investment themes. When the evolution of the ETF industry is compared to the evolution of mutual funds, the picture that emerges is different in two
  • ESG Preferences Negatively Affecting Market Efficiency [Alpha Architect]

    Environmental, social, and governance (ESG) investing continues to increase in popularity, with many institutional and individual investors incorporating ESG criteria into their investment decision-making process. Three main themes have driven this massive shift of assets: 1) Many investors are motivated by nonfinancial reasons to tilt their portfolios toward firms with robust ESG criteria; 2)

Quantocracy’s Daily Wrap for 09/28/2023

This is a summary of links featured on Quantocracy on Thursday, 09/28/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Researching the Quality Factor with Alphalens and Zipline [Quant Rocket]

    Buying high-quality stocks and avoiding low-quality ones can improve investment returns. In this post, I use Alphalens and Zipline to analyze the Piotroski F-Score, a composite measure of a firm's financial health and quality. This post is part of the fundamental factors series, which explores techniques for researching fundamental factors using Pipeline, Alphalens, and Sharadar US
  • AutoRegressive Moving Average (ARMA) models: Using Python [Quant Insti]

    In the first part of my ARMA article series, I covered the background theory of lag operators, the stationarity and invertibility of Autoregressive Moving Average models (ARMA) and the different types of versions you can create from it. Here, well explore theoretically these models using Python. Here you'll learn about ARMA model Python examples. From the simulation of these models to a

Quantocracy’s Daily Wrap for 09/27/2023

This is a summary of links featured on Quantocracy on Wednesday, 09/27/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • An Introduction to Machine Learning Research Related to Quantitative Trading [Quantpedia]

    Following the recent release of the popular large language model ChatGPT, the topic of machine learning and AI seems to have skyrocketed in popularity. The concept of machine learning is, however, a much older one and has been the topic of various research and technology projects over the last decade and even longer. In this article, we would like to discuss what machine learning is, how it can be
  • Reducing Whipsaws When Using 200-day Moving Average for Market Timing [Alvarez Quant Trading]

    I was working on testing a market timing indicator that I read about it. It was showing some promise and the next step was to compare it to my benchmark. My benchmark is using the 200-day moving average. But an additional rule removes a lot of the whipsaws that can happen. After doing the comparison, the market timing indicator compared well. But then I realized I had not written a blog post about
  • Super-Secret Proprietary Black Box Strategies [Allocate Smartly]

    Note: This is a rare non-geeky, non-quantitative, stream of thought blog post. Because were so deep into this world of Tactical Asset Allocation (TAA), were sometimes asked for our thoughts on such-and-such black box TAA strategy. By black box we mean a strategy for which the trading rules are not disclosed to investors (nor to us). Black box strategies inherently sell on the idea that
  • Geographic investing: business activity vs. domicile [Alpha Architect]

    The article explores the limitations of traditional country-level stock market indexes constructed based on issuing firms domicile. Additionally, it introduces a new type of national stock market index called the EMindex, which is based on companies business activities rather than their domicile. The EMindex aims to provide a better representation of a countrys economic risk by

Quantocracy’s Daily Wrap for 09/25/2023

This is a summary of links featured on Quantocracy on Monday, 09/25/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Code Walkthrough for Alpha Simulator: Simple Trend Rule with Vol Targeting [Hanguk Quant]

    In the last post we did a line-by-line walkthrough of the alpha simulator, using a uniform random variable to represent the signal generating component of the alpha backtest. We also raised a few questions: How can we manage the varying levels of risk profiles of different stocks in our asset universe at a snapshot in time? How can we manage the time varying levels of risk profiles of the entire
  • The predictive power of real government bond yields [SR SV]

    Real government bond yields are indicators of standard market risk premia and implicit subsidies. They can be estimated by subtracting an estimate of inflation expectations from standard yields. And for credible monetary policy regimes, inflation expectations can be estimated based on concurrent information on recent CPI trends and the future inflation target. For a data panel of developed markets
  • R&D stocks – do asset pricing models do them justice? [Alpha Architect]

    Since the development of the CAPM, which explains about two-thirds of the variation of returns among diversified portfolios, academic research has attempted to find models that increase the explanatory power of the cross-section of stock returns. Models are not like cameras that provide an exact replica of the world. If models were perfectly accurate, they would be laws, like we have in physics.
  • Have Stock Markets Changed? [Finominal]

    Trading technology continues to make trading of stocks easier, cheaper, and faster However, despite this and other financial innovations like ETFs, the US stock market structure hasnt changed Likely explained by the fact that its core participants are unable to change INTRODUCTION Consider the following: The average holding period for an individual stock in the US was 5 years in the 1970s,

Quantocracy’s Daily Wrap for 09/22/2023

This is a summary of links featured on Quantocracy on Friday, 09/22/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation Performance During the 2022 Bear Market [Allocate Smartly]

    As a whole, Tactical Asset Allocation (TAA) did not manage losses during the 2022 bear market as well as it has during previous downturns. Individual strategies varied and some did well, but a primary function of TAA is loss management, and any failure to do so is worth analyzing further. In this post, were going to break down the reason for some strategies poor performance in 2022. Heres
  • Quant Signal Trade-Offs in the Real World [Robot Wealth]

    I want to discuss a couple of simple trade-off considerations around quant trading signals that may not be obvious. Heres the price of some asset: Our main job is to predict how its likely to move. To do this, you use information about it that you think is predictive. And at any point in time: New information is appearing (trades, quotes, events, chatter). Old information that used to be

Quantocracy’s Daily Wrap for 09/20/2023

This is a summary of links featured on Quantocracy on Wednesday, 09/20/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Range-Based Volatility Estimators: Overview and Examples of Usage [Portfolio Optimizer]

    Volatility estimation and forecasting plays a crucial role in many areas of finance. For example, standard risk-based portfolio allocation methods (minimum variance, equal risk contributions, hierarchical risk parity) critically depend on the ability to build accurate volatility forecasts1. Multiple methods for estimating volatility have been proposed over the past several decades, and in this

Quantocracy’s Daily Wrap for 09/18/2023

This is a summary of links featured on Quantocracy on Monday, 09/18/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Analysis of Price-Based Quantitative Strategies for Country Valuation [Quantpedia]

    Value investing originated as an investment strategy in which investors try to beat the stock market by looking for stocks that trade at a price below their intrinsic value or book value. Value investors do not subscribe to the efficient-market hypothesis, which suggests that stock prices always reflect their intrinsic value. Instead, value investors believe stocks can be overvalued or undervalued
  • A New Wolf in Town? Pump-and-Dump Manipulation in Cryptocurrency Markets [Alpha Architect]

    Pump-and-Dump (P&D) schemes to manipulate the prices of cryptocurrencies are unlike the P&D schemes found in the equity market. They produce very large price distortions on the order of 65%, very large trading volumes of 13.5x the average, and generate very large profits to cryptocurrency manipulators. They target illiquid coins but only have temporary, short-term impact on prices. In
  • Don’t Convert to Convertible Bonds [Finominal]

    Convertible bonds are typically viewed as debt rather than equity instruments However, these are highly correlated to equities The diversification benefits are limited as these just represent diluted equity proxies INTRODUCTION Lets say youre the CEO of a small listed company that isnt doing well. The stock price of your company is depressed, so issuing equity would be highly dilutive.

Quantocracy’s Daily Wrap for 09/13/2023

This is a summary of links featured on Quantocracy on Wednesday, 09/13/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Seasonality of Bitcoin [Quantpedia]

    Seasonality effects, one of the most fascinating phenomena in the world of finance, have captured the attention of investors and researchers worldwide. Since these anomalies are often driven by factors other than general market trends, they usually dont correlate strongly with market movements, which can help reduce the portfolios overall risk. Following the theme of our previous article Are

Quantocracy’s Daily Wrap for 09/11/2023

This is a summary of links featured on Quantocracy on Monday, 09/11/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • There IS a low vol anomaly in SPY [Babbage9010]

    TL;DR There really is a low volatility anomaly in the SPY data; low volatility today predicts low volatility tomorrow and risk-adjusted returns are higher investing daily in the lower vol half of predicted market days. Same data, new analysis, better graphs and youll see it too. First up, a mea culpa. I misused my shallow understanding of stats::lag() in the last post and ended up
  • Momentum turning points and their impact on market cycles [Alpha Architect]

    The article investigates time-series (TS) momentum strategies and their performance in financial markets based on various speeds or lookback horizons. The study aims to understand the connections between different speeds of TS momentum, unobservable variables like trend, turning points, and noise levels in realized returns, and their impact on market cycles. Momentum Turning Points Goulding,
  • K-Nearest Neighbors Algorithm: Steps to Implement in Python [Quant Insti]

    Machine Learning (ML) has emerged as a powerful tool in the field of Artificial Intelligence, revolutionising various aspects of our lives. Whether it's recognising human handwriting or enabling self-driving cars, ML has become an integral part of our daily routines. With the exponential growth of data, the prevalence and importance of ML are only expected to increase in the coming years. ML
  • Fixed Income Factors II [Finominal]

    There are style factors like value and traditional fixed income factors like term premium The correlations of these factors has been low However, it is not clear which are better suited for a factor exposure analysis INTRODUCTION In our last research article, we compared fixed income factors from two asset managers, namely AQR Capital Management and Robeco, which highlighted different security

Quantocracy’s Daily Wrap for 09/09/2023

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

  • How to backtest 2,000,000 simulations for the best exits [PyQuant News]

    If youve been a reader of this newsletter for a while, or a student of Getting Started With Python for Quant Finance, youll recognize this statement: Backtests are not a way to brute force optimize parameters to maximize a performance metric. Doing that leads to overfitting and losses. But optimization does play an important part in building trading strategies. Today, well see how. How to
  • Equity versus fixed income: the predictive power of bank surveys [SR SV]

    Bank lending surveys help predict the relative performance of equity and duration positions. Signals of strengthening credit demand and easing lending conditions favor a stronger economy and expanding leverage, benefiting equity positions. Signs of deteriorating credit demand and tightening credit supply bode for a weaker economy and more accommodative monetary policy, benefiting long-duration