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Quantocracy’s Daily Wrap for 01/14/2023

This is a summary of links featured on Quantocracy on Saturday, 01/14/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Can ChatGPT Self-Improve Self-Written Python Code for Cholesky Decomposition? [Quant at Risk]

    It is needless to say about next big thing in the field of artificial intelligence (AI) known as ChatGPT. ChatGPT is a large language model developed by OpenAI. It is based on the GPT (Generative Pre-training Transformer) architecture and is trained on a massive dataset of text data. This allows it to generate human-like text and perform a wide range of natural language processing tasks, such as
  • Building a sector rotation strategy based on Fed s interest rate policy [Quant Dare]

    The Feds interest rate actions, which have been a topic of much discussion recently, can be very valuable information when making investment decisions. In particular, this post shows how to improve our sector allocation following the Feds announcements. Introduction The Federal Reserve System (FRS), better known as the Fed, is the central bank of the United States and, among all its tasks,
  • Detecting trends and mean reversion with the Hurst exponent [SR SV]

    The Hurst exponent is a statistical measure of long-term memory of time series. The existence and form of such memory are of great interest in financial markets, as financial returns are not generally governed by random walks. The Hurst exponent is a single scalar value that indicates if a time series is purely random, trending, or rather mean reverting. Thus, it can validate either momentum or
  • The Value Factor and Deleveraging [Alpha Architect]

    In his 2011 presidential address to the American Finance Association, John Cochrane coined the term zoo of factors, reflecting concerns about the quality of financial research. How do you separate the signal from the noise? In our book Your Complete Guide to Factor-Based Investing, Andrew Berkin and I provided five criteria investors could use to minimize the risk of data mining

Quantocracy’s Daily Wrap for 01/09/2023

This is a summary of links featured on Quantocracy on Monday, 01/09/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Robust Log-normal Stochastic Volatility for Interest Rate Dynamics [Artur Sepp]

    The volatility of interest rates in 2022 has been indeed extreme. In Figure 1, I show the dependence the between the MOVE index (which measures the implied volatility of one-month options on UST bond futures and which is constructed similarly to the VIX index for implied volatilities of the S&P index futures), realized 10y UST rate volatility over the 6 months rolling window, and the level of
  • Alternative Credit Funds: Credible Alternatives? [Finominal]

    Alternative credit funds aim to provide returns uncorrelated to traditional fixed income markets However, most of the performance can be explained by equities and plain-vanilla bonds All funds have lost money in the last 12 months, indicating that they are not alternative enough INTRODUCTION As savvy marketers say, the best way to sell a $2,000 watch is to place it next to a $10,000 one. In the

Quantocracy’s Daily Wrap for 01/07/2023

This is a summary of links featured on Quantocracy on Saturday, 01/07/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

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

    Russias economy was already in decline before the WW1 began, and its involvement in the conflict only exacerbated the situation. By 1917, russia was on the verge of collapse, with widespread poverty and a deep political divides. In March Tsar Nicholas II was forced to abdicate, and a provisional government was formed in his place. In 1918 bolsheviks signed a peace treating withdrawing from WW1
  • Defining Market Cycles Out of Sample [Quantpedia]

    We have already published a few articles about how the different market cycles affect the performance of your portfolio and performance of market factors. So far, these states of the market were identified in-sample, with the benefit of hindsight. The full methodology of how we defined bull/ bear market, low/ high inflation, and rising/ falling interest rates is described in this article. Today,

Quantocracy’s Daily Wrap for 01/05/2023

This is a summary of links featured on Quantocracy on Thursday, 01/05/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Scream if you want to go faster [Investment Idiocy]

    Happy new year. I didn't post very much in 2022, because I was in the process of writing a new book (out in April!). Save a few loose ends, my work on that project is pretty much done. Now I have some research topics I will be looking at this year, with the intention of returning to something like a monthly posting cycle. To be clear this is *not* a new years resolution, and therefore is
  • Which System Has The Lowest Risk of Ruin? [Relative Value Arbitrage]

    Would you rather choose a trading system that wins small amounts most of the time but when it loses, the loss is big? Or would you rather choose a trading system that loses small amounts most of the time but when it wins, the gain is big? In this blog post, we will examine such systems from the risk of ruin perspective. The risk of ruin is the probability of an investors eventual bankruptcy due
  • Optimal Trend Following with Transaction Costs [Alpha Architect]

    Despite the widespread popularity of trend-following investing, little is known about optimal trend-following with transaction costs. A few existing studies consider this question using a continuous-time model within the stochastic optimal control theory framework. However, despite being theoretically the most appropriate, this approach makes the problem intractable analytically, and the numerical

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