Quantocracy

Quant Blog Mashup

ST

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

Quantocracy’s Daily Wrap for 08/24/2022

This is a summary of links featured on Quantocracy on Wednesday, 08/24/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Computation of Theory-Implied Correlation Matrices [Portfolio Optimizer]

    In this short post, I will provide an overview of the TIC algorithm1 introduced by Marcos Lopez de Prado in his paper Estimation of Theory-Implied Correlation Matrices2, which aims to compute a forward-looking asset correlation matrix blending both empirical and theoretical inputs. I will also describe the associated implementation tweaks in Portfolio Optimizer. Notes: A Google sheet corresponding

Quantocracy’s Daily Wrap for 08/23/2022

This is a summary of links featured on Quantocracy on Tuesday, 08/23/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Probabilistic programming in finance: a robust Sharpe ratio estimate [Artifact Research]

    In this post, we will develop a time-varying, probabilistic extension of the Sharpe ratio as a widely used performance metric for financial assets. In particular, we devise a Bayesian regime-switching model to capture different market conditions and infer the full distribution the Sharpe ratio as it changes over time using the probabilistic programming framework bayesloop. We show that by focusing
  • Forecasting Market Indices Using Stacked Autoencoders and LSTM [Jonathan Kinlay]

    The stem paper for this post is: Bao W, Yue J, Rao Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE 12(7): e0180944. https://doi.org/10.1371/journal.pone.0180944 The chief claim by the researchers is that 90% to 95% 1-day ahead forecast accuracy can be achieved for a selection of market indices, including the S&P500
  • Long Volatility versus Tactical Asset Allocation [Factor Research]

    Long volatility strategies are attractive diversifiers, but complex and not easily accessible Tactical asset allocation for equities may be considered as an alternative There is no clear winner between these two options INTRODUCTION Risk management in portfolio construction is primarily achieved via diversification or rules-based frameworks. The former simply means combining asset classes that
  • Is Relative Sentiment an Anomaly? [Alpha Architect]

    Relative sentiment is an indicator that measures the positions, flows, and attitudes of institutional investors compared to those of individual investorswhere institutions typically consist of large asset managers, insurance companies, pension funds, and endowments. In some instances, howeverdepending on the dataset and the asset class under considerationinstitutions might also include

Quantocracy’s Daily Wrap for 08/18/2022

This is a summary of links featured on Quantocracy on Thursday, 08/18/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Value and momentum yes, size and CAPM no [Klement on Investing]

    In recent weeks, I have probably ODed my readers with philosophical and highbrow topics. Whether it was my Hitchhikers Guide to Investment Research or yesterdays post on the Fiscal Theory of the Price Level. Since this is my last post before my summer break, I wanted to bring it down to earth and do some good old-fashioned testing of investment returns. I have been going on about the
  • Alpha from Short-Term Signals [Alpha Architect]

    In Your Complete Guide to Factor-Based Investing Andrew Berkin and I provided six criteria that had to be met in order to determine which exhibits in the factor zoo are worthy of investment. For a factor to be considered, it must meet all of the following tests. To start, it must provide explanatory power to portfolio returns and have delivered a premium (higher returns). Additionally,

Quantocracy’s Daily Wrap for 08/17/2022

This is a summary of links featured on Quantocracy on Wednesday, 08/17/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Geometric Brownian Motion Simulation with Python [Quant Start]

    Generating synthetic data is an extremely useful technique in quantitative finance. It provides the ability to assess behaviour on models using data with known behaviours. This has a myriad of applications, such as testing backtesting simulators for correct functional behaiour as well as allowing potential "what if?" scenarios to be evaluated, such as for simulated crises. Synthetic data
  • Protected equity fund: Split your portfolio to better fit your hedging instruments [DileQuante]

    Imagine you are an European insurer. One of your funds is an equity portfolio of EMU stocks. Under Solvency II framework, you might want to reduce your Solvency Capital Requirement (SCR) thanks to the use of derivatives to hedge some of your equity risk. However, due to your size, the only sufficiently liquid contracts that you can use are Euro Stoxx 50 (E50) options. The problem is that your fund

Quantocracy’s Daily Wrap for 08/16/2022

This is a summary of links featured on Quantocracy on Tuesday, 08/16/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 100-Years of the United States Dollar Factor [Quantpedia]

    Finding high-quality data with a long history can be challenging. We have already examined How To Extend Historical Daily Bond Data To 100 years, How To Extend Daily Commodities Data To 100 years, and How To Build a Multi-Asset Trend-Following Strategy With a 100-year Daily History. Following the theme of our previous articles, we decided to extend historical data of a new factor, the Dollar
  • Outperformance Ain t Alpha [Factor Research]

    Outperformance and alpha are not the same One is the difference from a benchmark, the other is the unexplained return A contribution analysis helps understanding the return drivers INTRODUCTION Almost 90% of US drivers rate themselves safer and more skillful than average. Obviously, such perceptions do not reflect reality. After all, nine out of 10 people cant all be above average.

Quantocracy’s Daily Wrap for 08/15/2022

This is a summary of links featured on Quantocracy on Monday, 08/15/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Correlation and Correlation Structure (6) Distance Correlation [Eran Raviv]

    While linear correlation (aka Pearson correlation) is by far the most common type of dependence measure there are few arguably better ways to characterizeestimate the degree of dependence between variables. This is a fascinating topic I keep coming back to. There is so much for a typical geek to appreciate: non-linear dependencies, should we consider the noise in the data or rather just focus on
  • Three Strategies for Trading the Donchian Channel in Python [Raposa Trade]

    In the 1970's, Richard Donchian began the trend following trend by introducing a simple breakout trading system that would make him millions over the following decades. This system was predicated on an indicator that came to bear his name the Donchian Channel. We're going to show you how to calculate and trade the Donchian Channel with three example strategies so you can incorporate it
  • Mining Credit Card Data for Stock Returns [Alpha Architect]

    In this article, the authors explore an alternative measure of consumer demand from a unique dataset of individual credit and debit card daily transactions ( available one week after the transaction was made on average) from January 2013 to December 2019. They ask the following: Can more timely information such as daily transactions information on sales predict future earnings surprises? Can more