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Quantocracy’s Daily Wrap for 01/04/2023

This is a summary of links featured on Quantocracy on Wednesday, 01/04/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Interview with ChatGPT about its book ‘From Data to Trade: A Machine Learning Approach to Quantitative Trading’ [Gautier Marti]

    Introducing the first book ever generated by an artificial intelligence on the subject of using machine learning for quantitative trading: From Data to Trade: A Quantitative Approach to Machine Learning! This groundbreaking work offers a unique perspective on the use of machine learning in the financial markets, as it was created by an advanced AI using its own machine learning algorithms to
  • Most popular posts 2022 [Eran Raviv]

    As per usual this point in time, I check my blogs traffic-analytics to see which were the most popular pieces last year. Without further ado.. First: Correlation and Correlation Structure (6) Distance Correlation (08:33 minutes average time on page) Second: Similarity and Dissimilarity Metrics Kernel Distance (11:28 minutes average time on page) Third: What is the Kernel Trick? (11:51
  • Factor Olympics 2022 [Finominal]

    2022 is the best year for factor investing over the last decade Value generated the best and size the worst return in 2022 Despite the strong returns, investors remain cautious on factor investing 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
  • Asset pricing theory & the role labor displacement plays [Alpha Architect]

    In this article about asset pricing theory, we examine the research on the impact of technological advances that displace human labor in favor of machine capital to asset pricing. Automation and the displacement of labor by capita: Asset pricing theory and empirical evidence Jir Knesl Journal of Financial Economics, 2023 A version of this paper can be found here Want to read our summaries of

Quantocracy’s Daily Wrap for 01/02/2023

This is a summary of links featured on Quantocracy on Monday, 01/02/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • When Point Forecasts Are Completely Useless [Sarem Seitz]

    In the last article, we discussed one advantage of probabilistic forecasts over point forecasts – namely, handling time-to-exceedance problems. In this post, we will examine another limitation of point forecasts: Higher order statistical properties. The ideas will be very familiar to those with a background in mathematics or statistics. Readers without formal training in either will therefore
  • Survivorship Bias [Jonathan Kinlay]

    The relprice Index in the Performance Data table shows the price of the stock relative to the S&P 500 index over a specified period. Lets look at the median relPrice for all stocks that are currently members of the S&P500 index, eliminating any for which the relevant Performance Data is missing: currentSP500 = Select [ allStocks , # [ Symbol Information ] [ SP500 ] && Length [ #
  • Why Technical Analysis Doesn’t Work [Jonathan Kinlay]

    Generally speaking, one of the major attractions of working in the equities space is that the large number of available securities opens up a much wider range of opportunities for the quantitative researcher than for, say, futures markets. The focus in equities tends to be on portfolio strategies since the scope of the universe permits an unparalleled degree of diversification. Single stock
  • Modified and balanced FX carry [SR SV]

    There are two simple ways to enhance FX carry strategies with economic information. The first increases or reduces the carry signal depending on whether relevant economic indicators reinforce or contradict its direction. The output can be called modified carry. It is a gentle adjustment that leaves the basic characteristics of the original carry strategy intact. The second method equalizes
  • Expected Returns to Green Stocks [Alpha Architect]

    The past decade has seen a dramatic growth in sustainable investingapplying environmental, social and governance (ESG) criteria to investment strategies. Investments considered environmentally friendly are often referred to as green, while brown denotes the opposite. Important questions for investors are: What are the expected returns to green stocks? What does their past

Quantocracy’s Daily Wrap for 12/29/2022

This is a summary of links featured on Quantocracy on Thursday, 12/29/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor’s Performance During Various Market Cycles [Quantpedia]

    We have already showed How to extend history of any asset, portfolio or strategy to a 100-year long history. Weve done this by introducing Quantpedias Multi-Factor Regression Model, which aims to replicate any portfolio and recreate what its 100-year history would have looked like. The model uses several factors, including Market (U.S. Equities), Bonds, Commodities, Trend factor, etc. In
  • Hundreds of quant papers from #QuantLinkADay in 2022 [Cuemacro]

    If you follow me on Twitter (@saeedamenfx), youll notice that I post a lot of stuff about burgers (yes, I do like them). In an effort to make sure that I at least regularly post quant content (as opposed to burger based tweets), I started posting a daily link under the #QuantLinkADay hashtag. Usually these are papers, sometimes they might also be code libraries, essentially anything that seems
  • Cryptocurrencies with Python: A new YouTube video series! [Quant at Risk]

    We kicked off a new series of go-to solutions for #Cryptocurrencies with #Python. Subscribe to our YouTube channel for regular updates!
  • Geopolitical risk: novel econometric methods! [Alpha Architect]

    Traditional measures of geopolitical risk have been primarily qualitative. In this article, the authors describe and analyze not just new, but novel measures including textual analysis of news and expert reports, novel econometric methods and machine learning applications for measuring geopolitical risk and changes in geopolitical risk. Comparing Geopolitical Risk Measures Ahmet K. Karagozoglu, Na

Quantocracy’s Daily Wrap for 12/27/2022

This is a summary of links featured on Quantocracy on Tuesday, 12/27/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trading a 2s10s Inversion [Simplify]

    The latest inversion of the Treasury yield curve has been a popular topic of conversation among market pundits and participants alike. In this blog, we explain what a curve inversion is, why it is important, and what (if any) bearing it has on bond prices going forward. The 2s10s Yield Curve The 2s10s yield curve is a measure of the difference in interest rates between the two-year and ten-year
  • Research Compendium 2022 [Finominal]

    If we knew what it was we were doing, it would not be called research, would it? Albert Einstein December 2022. Reading Time: 10 Minutes. Author: Finominal RESEARCH COMPENDIUM 2022 In 2022, we published more than 50 research articles on a wide range of investing topics including CTA replication, inflation-linked bonds, performance benchmarking, tactical asset allocation, thematic
  • Multi-Factor Long-Short Portfolios how have they performed? [Alpha Architect]

    Multi-factor, long-short portfolios have provided significant portfolio diversification benefits by adding unique sources of risks that have historically produced premiums that meet the criteria Andrew Berkin and I established in our book Your Complete Guide to Factor-Based Investingthe premiums have been persistent, pervasive, robust to various definitions, survive transactions costs and

Quantocracy’s Daily Wrap for 12/22/2022

This is a summary of links featured on Quantocracy on Thursday, 12/22/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Slava Ukraini! Latest from Quantocracy contributor in Ukraine: MOVE Index [Only VIX]

    In the previous article I wrote about using VIX / MOVE index ratio as an indicator for SPX returns. Here is the google sheet for your reference and experiments. The ratio itself is very stable – in fact 11 years ago I wrote that VIX = EXP(-1.84+1.06*LN(MOVE)) As you can see the relationship has held up well, with only major dislocation during Covid period. That chart is on the second sheet in the

Quantocracy’s Daily Wrap for 12/20/2022

This is a summary of links featured on Quantocracy on Tuesday, 12/20/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Probabilistic alpha and beta: quantifying an uncertain edge [Artifact Research]

    In finance, the performance of an asset is often quantified by alpha (the excess returns above a benchmark return) and beta (the volatility or risk of the asset relative to a benchmark). These metrics are estimated from historical data and are often based on only short track records. Even if a long series of historical returns is available for an asset, older data may no longer represent the
  • A Balanced Portfolio and Trend-Following During Different Market States [Quantpedia]

    Whats the performance of a balanced portfolio during rising rates? How does it behave when inflation is high? What about a combination of these market states? And how do trend-following strategies fare in such an environment? These and even more questions we will attempt to resolve in our todays article. We will be looking at different market cycles and how a balanced portfolio and a typical
  • Alpha Vantage API Python Tutorial [Analyzing Alpha]

    This article explains how to call the Alpha Vantage API to retrieve stock market data in a Python application using the Python alpha_vantage library and the Python requests module. The documentation for the Python alpha_vantage client library is limited. It isnt easy to understand the mapping between the Alpha Vantage API endpoints and the Python alpha_vantage library classes and methods. The
  • Do Poor YTD Results Mean Late December Rally Will Flop? [Quantifiable Edges]

    Ive heard people saying recently that the typical 2nd half of December bullish tendency is unlikely to unfold this year. The theories suggest that the market is often up on the year. And people and institutions flush with profits tend to push it higher as the New Year approaches. There is also lots of buying chasing strong market returns heading into year end. But when it is a down year like
  • Scale in Active Management – a look at its Diseconomies [Alpha Architect]

    Pastor, Stambaugh, and Taylor (2015) and Zhu (2018) provide significant evidence of decreasing returns to scale (DRS) at both the fund and industry levels. The authors examine the robustness of their inferences after Adams, Hayunga, and Mansi (2021) critique the above two studies. What are the Academic Insights? The authors find robust evidence of DRS at fund and industry levels. Their evidence

Quantocracy’s Daily Wrap for 12/19/2022

This is a summary of links featured on Quantocracy on Monday, 12/19/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Zakamulin’s Optimal Trend Following [Allocate Smartly]

    This is a test of a novel trend-following strategy from the paper Optimal Trend Following Rules in Two-State Regime-Switching Models by Valeriy Zakamulin and Javier Giner. These results arent as eye catching as many we track, but the paper contributes some important ideas to the study of tactical asset allocation. Results trading the S&P 500 from 1956 follow, compared to two popular

Quantocracy’s Daily Wrap for 12/18/2022

This is a summary of links featured on Quantocracy on Sunday, 12/18/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Serverless architecture for crypto trading [Gautier Marti]

    I recently asked on LinkedIn about advice and opinions on infrastructure for collecting, storing, processing, and storing back derived data (features, signals) for some simple mid freq / stat arb trading strategies. I did not expect to receive so much feedback about infrastructure for trading data pipelines: Many opinions on infra I am currently using a very simple stack based on serverless AWS
  • MOVE Index and SPX returns [Only VIX]

    MOVE index (Merrill Lynch Option Volatility Estimate) was developed by Merrill Lynch to measure implied volatility of US Treasury markets. ML became a part of Bank of America in 2008, and then indexes were sold to ICE in 2019, so now the index is called "ICE BofAML MOVE Index" The index is a yield-curve weighted average of normalized implied volatility of 30-day options. The index has

Quantocracy’s Daily Wrap for 12/16/2022

This is a summary of links featured on Quantocracy on Friday, 12/16/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 100 Years of Historical Market Cycles [Quantpedia]

    Which assets perform best when rates are rising, and inflation is high? And what happens if rates are still rising but inflation is already falling? And whats the impact of the business cycle? These are the questions that everyone is currently trying to answer. Today, we will start a longer series of articles with the goal of giving an exact quantitative answer to all questions related to
  • The Informativeness: Measuring the Homogeneity of a Universe of Assets [Portfolio Optimizer]

    In this post, I will describe a measure of the homogeneity of a universe of assets, called the informativeness, introduced by Brockmeier et al.1 in their paper Quantifying the Informativeness of Similarity Measurements. After quickly going through the associated mathematics, I will present two examples of usage of this measure – one as potential indicator of systemic risk and the other as as a
  • Machine Learning and Emerging Market Stock Returns [Alpha Architect]

    More specifically, the paper differentiates between: Traditional linear models (ordinary least squares regression and elastic net) and Machine learning methods that allow for non-linearities and interactions (tree-based models such gradient boosted regression trees and random forest and neural networks with one to five layers) What are the main results? #1 Return forecasts based on machine

Quantocracy’s Daily Wrap for 12/13/2022

This is a summary of links featured on Quantocracy on Tuesday, 12/13/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Experimental Design and Common Pitfalls of Machine Learning in Finance [Hudson and Thames]

    The first lecture from the Experimental Design and Common Pitfalls of Machine Learning in Finance series addresses the four horsemen that present a barrier to adopting the scientific approach to machine learning in finance. The second lecture focuses on a protocol for backtesting and how to avoid the seven sins of backtesting. By implementing the research protocol outlined in these articles, an
  • CoinGecko API Python Tutorial [Analyzing Alpha]

    This article will show you how to access the CoinGecko API endpoints in Python to retrieve live cryptocurrency information. You will use the pycoingecko and the Python requests library to fetch data from CoinGecko API. The official CoinGecko API and pycoingecko libraries documentations lack concrete examples and explanations. Despite having over a decade of Python programming experience, It
  • Beware of Spurious Factors [Eran Raviv]

    The word spurious refers to outwardly similar or corresponding to something without having its genuine qualities. Fake. While the meanings of spurious correlation and spurious regression are common knowledge nowadays, much less is understood about spurious factors. This post draws your attention to recent, top-shelf, research flagging the risks around spurious factor analysis. While formal
  • Myth Busting: Alts’ Uncorrelated Returns Diversify Portfolios [Finominal]

    Alternatives with lower correlations to equities & bonds did not lead to greater diversification benefits Correlations often break when markets crash Better metrics are required to measure the diversification potential of alternatives INTRODUCTION Alternative investments accounted for $13 trillion in assets under management (AUM) in 2021, nearly twice what it was 2015. By 2026, that figure is
  • Volatility scaling: is it useful for factor timing? [Alpha Architect]

    The research summarized here is built upon a documented risk management strategy applied to factor investing (Barroso and Santa-Clara, 2015; Moreira and Muir, 2017). The idea was to overlay a scaled volatility measure designed to change risk exposures and hopefully produce higher Sharpe ratios. That basic research is tweaked in this article by analyzing the effect of scaling on portfolios