Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 09/20/2022

This is a summary of links featured on Quantocracy on Tuesday, 09/20/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Living Our Mission [Quant Connect]

    Were happy to share that today we published the code for 15 brokerage integrations to our open-source platform, LEAN. One step toward the future were building. LEAN handles all of the data and brokerage infrastructure for you so you can focus on what matters most: creating brilliant strategies. Giving independent quant investors the tools needed to compete. In the future, LEAN will be the OS
  • Has the Stock Market Systematically Changed? [Alpha Architect]

    The past few years in the stock market have been pretty crazy. And the pinnacle of crazy was during March 2020 peak chaos in the stock market. Below is a chart of US large-cap stocks and small-cap stocks in 2020. Note the monster crash in March watch out below! Source: koyfin.com As an individual investor in the late 90s internet bubble burst, and having launched a hedge fund in

Quantocracy’s Daily Wrap for 09/19/2022

This is a summary of links featured on Quantocracy on Monday, 09/19/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Forecasting with Decision Trees and Random Forests [Sarem Seitz]

    Today, Deep Learning dominates many areas of modern machine learning. On the other hand, Decision Tree based models still shine particularly for tabular data. If you look up the winning solutions of respective Kaggle challenges, chances are high that a tree model is among them. A key advantage of tree approaches is that they typically don't require too much fine-tuning for reasonable results.
  • Hierarchical PCA x Hierarchical clustering on crypto perpetual futures [Gautier Marti]

    PCA is a useful tool for quant trading (stat arb) but in its naive implementation suffers from several forms of instabilities which yield to unnecessary turnover (trading cost) and spurious trades. In order to regularize the model, several techniques are available: Sparse PCA Robust PCA Kernel PCA Probabilistic PCA Bayesian PCA In this blog, we will discuss one in particular: The
  • The Linear Regression-Adjusted Exponential Moving Average [Financial Hacker]

    There are already uncounted variants of moving averages. Vitali Apirine invented another one in his article in the Stocks&Commodities September issue. The LREMA is an EMA with a variable period derived from the distance of the current price and a linear regression line. This ensures an optimal EMA period at any point at least in theory. Will this complex EMA variant beat the standard EMA
  • Crypto PCA First Eigenvector [Gautier Marti]

    This short blog to illustrate an interesting fact that I found in An Analysis of Eigenvectors of a Stock Market Cross-Correlation Matrix by Nguyen and co-authors: The first eigenvector is not THE market portfolio (market-cap or uniformly weighted) as people usually believe, but a correlation-weighted market portfolio. import numpy as np import pandas as pd from scipy.stats import rankdata import
  • How Did Momentum Investing Perform After the Previous Two Valuation Peaks? [Alpha Architect]

    Near the end of 2021, I wrote an article noting that value portfolios looked historically cheap based on valuation spreads. I found that in the next five years (after the peak), Value investing performed quite well.(1) Following this post, I have received numerous questions related to the following question: How did Momentum investing perform after the previous two valuation peaks? This article

Quantocracy’s Daily Wrap for 09/16/2022

This is a summary of links featured on Quantocracy on Friday, 09/16/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Cross-Asset Signals and Time Series Momentum [Allocate Smartly]

    This is a test of concepts from the paper Cross-Asset Signals and Time Series Momentum. Standard time series momentum is a well-documented feature of financial markets. Assets going up tend to continue going up. In this paper, the authors show that stocks and treasuries can be used to time each other. This is cross-asset momentum. Treasury momentum is a positive predictor of stock
  • The Probabilistic Sharpe Ratio [Portfolio Optimizer]

    The Sharpe ratio1 is one of the most commonly used measure of financial portfolio performance, but because it is deeply rooted in mean-variance theory, its usage with return distributions deviating from normality (e.g. hedge funds, cryptocurrencies) is frequently questioned2. One solution to this issue is to switch to a probabilistic framework, under which the Sharpe ratio computed from a finite
  • Three Factor ETF Rotation Strategy [Alvarez Quant Trading]

    I am drawn to ETF rotation strategies. What likely draws me to them is that in general, these are simple strategies that do not trade that often. My goal with these strategies is to match buy and hold with less drawdown. What follows is a strategy I have known about for a while and tested but never written about. The Concept From a set of ETFs, select the one to three that have had the best
  • Adversarial examples and quant quakes [Alex Chinco]

    Imagine youre a quantitative long-short equities trader. If you can predict which stocks will have above-average returns next period and which will have below-average returns, then you can profit by buying the winners and selling short the losers. Return predictability and trading profits are two sides of the same coin. Your entire job boils down to solving this classification problem. Ideally,
  • The Short-Duration Equity Premium [Alpha Architect]

    The objective of research into asset pricing is to determine which characteristics are most important for predicting returns and then build simplified models using as few factors as possibleto tame the so-called zoo of factorswhile still providing a high level of explanatory power. In recent years we have seen heightened interest in the ability of the duration of equity cash

Quantocracy’s Daily Wrap for 09/14/2022

This is a summary of links featured on Quantocracy on Wednesday, 09/14/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • NEW Contributor: Multivariate GARCH with Python and Tensorflow [Sarem Seitz]

    In an earlier article, we discussed how to replace the conditional Gaussian assumption in a traditional GARCH model. While such gimmicks are a good start, they are far from being useful for actual applications. One primary limitation is the obvious restriction to a single dimensional time-series. In reality, however, we are typically dealing with multiple time-series. Thus, a multivariate GARCH
  • Optimal Allocation to Cryptocurrencies in Diversified Portfolios [Artur Sepp]

    Cryptocurrencies have been acknowledged as an emerging asset class with a relatively low correlation to traditional asset classes. One of the most important questions for allocators is how much to allocate to Bitcoin and to a portfolios cryptocurrency assets within a broad portfolio which includes equities, bonds, and other alternatives. I wrote a research paper addressing this questions. I will
  • Analyzing U.S. election cycle seasonality in the S&P 500 [Quant Dare]

    Everyone is aware of the importance of the U.S. elections and we take it for granted that, like many other things, financial markets will end up being affected in some way. But have you ever wondered if there is any seasonality throughout those elections that we can take advantage of when making investment decisions? Lets find out! Introduction The following charts show the historical evolution
  • Momentum – a separate factor or does it subsume stock risk? [Alpha Architect]

    Breaking new ground, the authors present a novel view on the nature and source of momentum that differs from our current understanding of momentum, whether it be industry momentum, residual, or any other version of momentum. Explanations of the source of profitability for momentum strategies have traditionally relied on behavioral biases on the part of investors, time-varying risk premiums,

Quantocracy’s Daily Wrap for 09/11/2022

This is a summary of links featured on Quantocracy on Sunday, 09/11/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 09/09/2022

This is a summary of links featured on Quantocracy on Friday, 09/09/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Own a Part of QuantConnect [Quant Connect]

    Today were launching the next step of our journey. Were proud to be opening ownership of QuantConnect to our community. We want to align our investors and users to continue pioneering as the worlds leading cloud quantitative analytics platform. With more than 210,000 engineers, quants, traders, and scientists in the community we believe you are our best partners going forward. The world
  • Brand Values and Long-Term Stock Returns [Alpha Architect]

    Despite the fact that a companys internally generated intangible investments create future value, (Summary) under current U.S. generally accepted accounting principles, internally developed intangibles are not included in reported assets. While research and development (R&D) is an important intangible asset, so too is branding. Omission of an increasingly important class of assets reduces

Quantocracy’s Daily Wrap for 09/08/2022

This is a summary of links featured on Quantocracy on Thursday, 09/08/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bootstrap Simulation with Portfolio Optimizer: Usage for Financial Planning [Portfolio Optimizer]

    In statistics, a bootstrap method, also called bootstrapping, is a compute-intensive procedure that allows to estimate the distribution of a statistic through repeated resampling from a single observed sample of data1. Bootstrapping has several applications in quantitative finance, for example to test the robustness of a trading strategy, to compute a portfolio value at risk, etc. In this post, I
  • Self-organizing maps for an investment strategy [Quant Dare]

    In a previous post, we explained how self-organizing maps work, with a very simple example. In this post, we will explain how to implement self-organizing maps for an investment strategy. Last time, we gave a simple example with a map of colors to explain in detail how self-organizing maps (SOM) work. As we saw, similar colors tend to stick together. As such, we may use this algorithm for some
  • Mirror, Mirror on the Wall, Which is the Fairest Benchmark of Them All? [Factor Research]

    Evaluating manager performance is difficult as it requires an appropriate benchmark The managers benchmark selection is often not objective given conflicts of interests Factor exposure analysis can be used to systematically identify the best benchmark INTRODUCTION Although information asymmetries have largely disappeared in capital markets, there are plenty found in the asset management

Quantocracy’s Daily Wrap for 09/02/2022

This is a summary of links featured on Quantocracy on Friday, 09/02/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Automated Trading Edge Analysis [Quantpedia]

    Have you ever wondered if your trading asset trends or mean-reverts? Everyone involved in trading or investments daily solves the task of What trading strategy should I apply to my assets to generate profits? As always, we at Quantpedia will try to help you a bit with this never-ending task with our new tool/report, which will be unveiled next week for all Quantpedia Pro subscribers. The
  • How You Sort Matters in Sorting Factor Portfolios [Alpha Architect]

    In Your Complete Guide to Factor-Based Investing, Andrew Berkin and I established criteria that must be met before considering investing in a factor-based strategy. We established the criteria to minimize the risks that any findings were the result of data-mining exercises. Data mining occurs when, instead of beginning with a hypothesis, researchers torture the data until it confesses.
  • Research Review | 2 Sep 2022 | Trading Costs and Market Frictions [Capital Spectator]

    The Avoidable Costs of Index Rebalancing Robert D. Arnott (Research Affiliates), et al. May 2022 Traditional capitalization-weighted indices generally add stocks with high valuation multiples after persistent outperformance and sell stocks at low valuation multiples after persistent underperformance. For the S&P 500 Index, in the year after a change in the index, additions lose relative to

Quantocracy’s Daily Wrap for 08/29/2022

This is a summary of links featured on Quantocracy on Monday, 08/29/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bold Asset Allocation [Allocate Smartly]

    This is a test of Dr. Wouter Kellers tactical strategy Bold Asset Allocation (BAA) from his paper Relative and Absolute Momentum in Times of Rising/Low Yields. Backtested results from 1970 follow. Results are net of transaction costs see backtest assumptions. Learn about what we do and follow 60+ asset allocation strategies like this one in near real-time. Logarithmically-scaled.
  • What s wrong with Inverse ETFs? [Factor Research]

    Inverse ETFs come with significant risk disclosure Analyzing the performance of these products justifies the warnings There is a significant difference between performance of inverse ETFs & the inverse underlying indices INTRODUCTION In May 2022, Allianz, the large German insurance company, agreed to pay $6 billion in damages to investors and have its U.S. asset management arm plead guilty to

Quantocracy’s Daily Wrap for 08/28/2022

This is a summary of links featured on Quantocracy on Sunday, 08/28/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Don’t append rows to a pandas DataFrame [Wrighters.io]

    Most pandas users encounter a situation where choosing to append rows to a pandas DataFrame seems like a good idea. A quick search of the API (or your favorite search engine) reveals that pandas has an append method in DataFrame. You may be tempted to use it. In this article Ill show you why you should not use append, how you should grow your DataFrame, and a tip to make it faster. Since pandas
  • Are There Intraday and Overnight Seasonality Effects in China? [Quantpedia]

    At the moment, there is a lot of attention surrounding overnight anomalies in various types of financial markets. While such effects have been well documented in research, especially in US equities and derivatives, there are other asset classes that are not as well addressed. We previously compiled the most influential studies and built strategy upon them and also examined if similar