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Quantocracy’s Daily Wrap for 10/07/2022

This is a summary of links featured on Quantocracy on Friday, 10/07/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Random Forests and Boosting for ARCH-like volatility forecasts [Sarem Seitz]

    In the last article, we discussed how Decision Trees and Random Forests can be used for forecasting. While mean and point forecasts are the most obvious applications, they might not always be the most useful ones. Consider the classic example of financial returns, where the conditional mean is hard, if not impossible, to predict. Conditional variance on the other hand has been shown to exert some
  • Conditional Portfolio Optimization [EP Chan]

    Previously on this blog, we wrote about a machine-learning-based parameter optimization technique we invented, called Conditional Parameter Optimization (CPO). It appeared to work well on optimizing the operating parameters of trading strategies, but increasingly, we found that its greatest power lies in its potential to optimize portfolio allocations. We call this Conditional Portfolio
  • Momentum Everywhere, Including Emerging Markets [Alpha Architect]

    In order for investors to determine which of the hundreds of factors in what John Cochrane famously called the zoo of factors were worthy of investment, Andrew Berkin and I set out seven criteria in our book Your Complete Guide to Factor-based Investing. For a factor to be considered, it must meet all the following tests. To start, it must provide explanatory power to portfolio returns
  • Research Review | 7 Oct 2022 | Interest Rates and Inflation [Capital Spectator]

    The Factor Multiverse: The Role of Interest Rates in Factor Discovery Jules H. van Binsbergen (University of Pennsylvania), et al. September 2022 We study the importance of the decline in interest rates in the discovery of asset pricing anomalies. We investigate 153 discovered anomalies as well as 1,395 potential undiscovered anomalies and find that absent the decline in interest rates, the asset

Quantocracy’s Daily Wrap for 10/04/2022

This is a summary of links featured on Quantocracy on Tuesday, 10/04/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Multi Strategy Management for Your Portfolio [Quantpedia]

    If you follow Quantpedias blogs, you probably know that Quantpedia PRO already contains multiple risk management and portfolio construction tools for your quantitative investment strategies. For example, Crisis Hedge can find you suitable investment hedges for negative months and for bear markets. The Correlation Analysis report, on the other hand, reviews your model portfolios correlation
  • Factor Olympics Q3 2022 [Finomial]

    Value is leading the performance scoreboard in YTD 2022 Low volatility is the worst-performing factor Oddly, the value and low volatility factors are strongly positively correlated INTRODUCTION We present the performance of five well-known factors on an annual basis for the last 10 years. Specifically, we only present factors where academic research supports the existence of positive excess
  • Transaction costs and portfolio strategies [SR SV]

    Transaction costs are a key consideration for the development of trading strategies; and not just in final profitability checks. Indeed, disregard for trading costs at the design stage leads to excessive reliance on fleeting small-scale characteristics for return predictors. It also skews the conventional efficient frontier of portfolio choice towards risky trading strategies. A realistic

Quantocracy’s Daily Wrap for 09/27/2022

This is a summary of links featured on Quantocracy on Tuesday, 09/27/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Probabilistic Sharpe Ratio: Hypothesis Testing and Min Track Record Length [Portfolio Optimizer]

    In the first post of this series about the Sharpe ratio considered as a statistical estimator, I introduced a probabilistic framework to answer the question What is the probability that an estimated Sharpe ratio is statistically significantly greater than a reference Sharpe ratio? In this second post, I will present additional results, described in the paper Comparing Sharpe ratios: So where are
  • How Much Can You Lose with Bonds? [Factor Research]

    Bonds are typically considered safe investments However, there were decades of negative real returns Drawdowns reached 50% for U.S. Treasuries and Bonds INTRODUCTION Inflation greater than 10% was unknown for the majority of people in developed markets before this year, but it is nothing particularly new for emerging market citizens. Russia experienced 15.5% in 2015, India 10.1% in 2013, Brazil

Quantocracy’s Daily Wrap for 09/25/2022

This is a summary of links featured on Quantocracy on Sunday, 09/25/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Use pandas DateOffsets for easy date manipulation [Wrighters.io]

    So much useful data has a date or time component. Often, data has a timestamp to represent when the data was acquired, or when an event will take place, or as an identifying attribute like an expiration date. For this reason, understanding how to work with dates and times effectively can be a very useful skill. One common need is to select dates (and times) using rules based on their offset from
  • Inflation-Linked Bonds for Inflationary Periods? [Factor Research]

    Inflation-linked bonds are considered inflation-hedges However, these have lost almost as much as plain-vanilla bonds in 2022 The sensitivity to interest rates matters more than that to inflation INTRODUCTION Inflation is the biggest issue facing the U.S. and is more important to citizens than crime, health care, or immigration according to a Pew Research Centre survey from May 2022. Given
  • Sector vs Factor-based Benchmark Selection [Factor Research]

    Manager-selected benchmarks are suboptimal as they are not free of conflict of interests Investors can use sectors to identify more appropriate benchmarks However, this ignores factors, which are better at explaining investment returns INTRODUCTION In our last research article (Mirror, Mirror on the Wall, which is the fairest Benchmark of them All?) we highlighted that investors can use factor

Quantocracy’s Daily Wrap for 09/24/2022

This is a summary of links featured on Quantocracy on Saturday, 09/24/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Slava Ukraini! Latest from Quantocracy contributor in Ukraine: Jupyter Notebook To Download VIX Futures [Only VIX]

    I will be publishing some of my research notebooks, starting with downloader for VIX data, fitting Nelson-Siegel model for term structure ( static ) , and dynamic ( Kalman Filter ), and possibly some recent work I did on regime clusters in VIX and ML for VIX trading. Here is the link to the first one. Email me if you have any questions. In the last 2 days UN Human Right Commission published their
  • Consumer Spending Data and the Cross-Section of Stock Returns [Alpha Architect]

    Consumer demand drives the cash flows of consumer-oriented companies. Thus, they should serve as a reliable source of information to predict future fundamentals above and beyond the information contained in financial statements and readily available market data. For example, Jiekun Huang, author of the 2018 study The Customer Knows Best: The Investment Value of Consumer Opinions, analyzed
  • Should Levered and Inverse ETFs Even Exist? [Alpha Architect]

    In 2019, the SEC proposed that all brokers and advisors be required to determine whether or not their clients understood the risks of investing in levered and inverse exchange traded products before selling such products to them. The SEC moved on this requirement in response to a series of fund failures. For example, after losing 96% of its value in a single day, Credit Suisse closed its Daily

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!