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Quantocracy’s Daily Wrap for 11/16/2022

This is a summary of links featured on Quantocracy on Wednesday, 11/16/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Reviewing Patent-to-Market Trading Strategies [Quantpedia]

    The following article is a short distillation of the research paper Leveraging the Technical Competence of a Stock for the Purpose of Trading written by Rishabh Gupta. The author spent a summer internship at Quantpedia, investigating the Patent-to-Market (PTM) ratio developed by Jiaping Qiu, Kevin Tseng, and Chao Zhang. The PTM ratio uses public information about the number and dates of patents
  • New Quant Podcast: So, you want to be a Quant? [Quant at Risk]

    Here is the first episode in a new series of podcasts entitled Break into Finance. We will be talking about what it takes to launch your career in finance, what does it mean to become a quant, and where to start. Any questions welcomed!
  • Beyond linear II: the Unscented Kalman Filter [Quant Dare]

    The Unscented Kalman Filter allows to deal with nonlinear systems in a different way than the Extended Kalman Filter. Find how it works in this post. This is not the first time we talk about the Kalman Filter (and it probably wont be the last); I recommend you check this and this posts to understand the standard and extended versions of this algorithm and the notation we are going to use. The

Quantocracy’s Daily Wrap for 11/14/2022

This is a summary of links featured on Quantocracy on Monday, 11/14/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • If you’re so smart, how come you’re not Sam Bankman-Fried? [Investment Idiocy]

    There has been a very interesting discussion on twitter, relating to some stuff said by Sam Bankman-Fried (SBF), who at the time of writing has just completely vaporized billions of dollars in record time via the medium of his crypto exchange FTX, and provided a useful example to future school children of the meaning of the phrase nominative determinism*. * Sam, Bank Man: Fried. Geddit? Read the
  • Impact of Dataset Selection on the Performance of Trading Strategies [Quantpedia]

    We have previously mentioned that not all models (such as CAPM) that work well for developed markets (DM, such as the U.S. and Europe) are suited to be applicable in other world parts. The following article is a short analysis that shows that investing in Emerging Markets (EM) has its peculiarities. Especially investing in Chinese equities can sometimes be complicated with its mix of mainland
  • Creating a CTA from Scratch [Finominal]

    CTAs have become popular again given their positive returns in 2022 However, they are typically difficult to grasp given their ever-changing portfolios Building a CTA from scratch is not complicated and reduces the opaqueness INTRODUCTION 2022 has been a long year of disappointments for investors. Growth stocks do not go up forever. TIPS do not protect against high inflation. And cryptocurrencies
  • Industry and factor momentum: is there a theoretical foundation? [Alpha Architect]

    This post is the second and final portion of the review on momentum published on Momentum literature. The seminal article on momentum was published by Jegadeesh and Titman in 1993. Although the Jegadeesh article foreshadowed much of the research on cross-sectional and time series momentum at the stock level, it wasnt until the mid-to-late-2000s that investigators turned to study momentum at the

Quantocracy’s Daily Wrap for 11/13/2022

This is a summary of links featured on Quantocracy on Sunday, 11/13/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Simple Approach to Market-Timing Strategy Replication [Quantpedia]

    In previous articles, we discussed the ideas behind portfolio replication with market factors and presented Quantpedias approach to Multi-Factor Regression. Additionally, we examined the methods of market factor data extension used in construction of our historic factor universe we utilize to mimic portfolios during the past century (Extending Historical Daily Bond Data to 100 Years, Extending
  • Top 7 blogs on Sentiment Trading | 2022 [Quant Insti]

    The intelligent investor is a realist who sells to optimists and buys from pessimists. – Benjamin Graham Sentiment Trading strategies work on market sentiment and the trends around them. The strategies are often determined by the price and value of an asset that may fluctuate. Market sentiments influence the behavior of investors for a specific asset or financial market. It is the emotional
  • Macro factors of the risk-parity trade [SR SV]

    Risk-parity positioning in equity and (fixed income) duration has been a popular and successful investment strategy in past decades. However, part of that success is owed to a supportive macro environment, with accommodative refinancing conditions and slow, disinflationary, or even deflationary economies. Financial and economic shocks, as opposed to inflation shocks, dominated markets, leading to

Quantocracy’s Daily Wrap for 11/10/2022

This is a summary of links featured on Quantocracy on Thursday, 11/10/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Controversial Post: QuantConnect and the Challenge of Democratizing Finance [Quant Rocket]

    QuantConnect, a quantitative backtesting and trading platform serving mostly retail traders, recently announced a crowdfunding campaign seeking to raise money from its community of users. The company's associated regulatory disclosures (required by the SEC for equity crowdfunding offerings) reveal that the fundraising is a matter of survival. In this article, I explore what
  • The decline in interest rates: its role in asset pricing anomalies [Alpha Architect]

    At its most basic level, factor-based investing is simply about defining, and then systematically following, a set of rules that produce diversified portfolios. An example of factor-based investing is a value strategy, buying cheap (low valuation) assets and selling expensive (high valuation) assets. A problem with factor-based investing is that smart people with powerful computers can find

Quantocracy’s Daily Wrap for 11/09/2022

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

Quantocracy’s Daily Wrap for 11/08/2022

This is a summary of links featured on Quantocracy on Tuesday, 11/08/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • MOIC: Investing Holy Grail [Quant Dare]

    Many investors are looking for the holy grail of investing. They all want a magic formula that tells them which stocks to buy, and which ones to sell. But experienced investors know that there is no such a thing. I was convinced of it until I discovered the MOIC formula. MOIC Multiple on Invested Capital (MOIC) is a metric used to describe the performance of an investment relative to its
  • Equity Research in the Wolfram Language [Jonathan Kinlay]

Quantocracy’s Daily Wrap for 11/07/2022

This is a summary of links featured on Quantocracy on Monday, 11/07/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Matching data between data sources with Python [Wrighters.io]

    Data is often messy and rarely in perfect shape. This is especially true if the data comes from many different sources and the specifications are loosely defined. If you have access to data that is in great shape, its probably because someone else did the dirty work of validating it, cleaning it up, and normalizing it for you. One particular type of data problem is matching data between data
  • Skewness: the fallacy of the expected return [Artifact Research]

    In this post we will take a closer look at the expected return that is often stated for investments like stocks and other financial assets, or for certain outcomes in gambling. The point we want to convey is that the expected return is only valid for one period or a single iteration (say, one year, or one round of a game such as Blackjack), but that the expected return can be highly
  • The Cross Section of Stock Returns Pre CRSP data [Alpha Architect]

    What are the Research Questions? Several studies reveal variables that predict cross-sectional differences in stock returns but mainly rely on a sample of U.S. stocks, mostly covering the post-1963 period. These studies are often criticized for potential data mining issues since the database never changes, but new findings crop up all the time. This paper studies the cross-section of U.S.
  • Top 10 blogs on Machine Learning in 2022 [Quant Insti]

    Algorithmic Trading is seeing a rapid expansion of the application of artificial intelligence (AI) and machine learning (ML). These technological developments have completely transformed Algo trading. Making informed decisions requires carefully analyzing both current and historical market data. In order to analyze data and make effective forecasts for effective trading decisions, artificial
  • Sector & Factor Performance During Wartime [Finominal]

    The S&P 500 increased during two of the three largest wars of the United States Value, size, and momentum factors had positive returns during WW II The top and worst-performing industries during WW II were diverse INTRODUCTION Before 2020, the threat of a global pandemic shutting down the world economy was not a top-of-mind concern for most investors. Pandemics were nothing new, of course, but
  • Market Risk and Speculative Factors [Alpha Architect]

    There are basically two types of investors, those that are risk averse and, thus, both demand risk premiums for taking risk and diversify their holdings, and those who are risk seekers who have a preference for positively skewed (lottery-like) returns which leads them to speculate and concentrate risks. The psychological preferences of risk seekers drives up the valuations of the lottery-like

Quantocracy’s Daily Wrap for 11/02/2022

This is a summary of links featured on Quantocracy on Wednesday, 11/02/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Optimal trend following allocation under conditions of uncertainty [Investment Idiocy]

    Few people are brave enough to put their entire net worth into a CTA fund or home grown trend following strategy (my fellow co-host on the TTU podcast, Jerry Parker, being an honorable exception with his 'Trend following plus nothing' portfolio allocation strategy). Most people have considerably less than 100% – and I include myself firmly in that category. And it's probably true
  • How to Replicate Any Portfolio [Quantpedia]

    Would you like to see the performance of your portfolio 100 years back in history? Do you want to analyze the risk of your strategy under 100 years of real historical scenarios? All of these, and much more, will be soon (in a few days) available for Quantpedia Pro subscribers. How? We will explain today how we can model a 100-year history of your portfolio. Replicating Portfolios with Factors When
  • Momentum literature: an analysis of 30 years [Alpha Architect]

    n this article, the author examines the research published over the last 30 years on momentum and its theoretical credibility. One of the original momentum articles was published by Jegadeesh and Titman in 1993, and is considered the seminal work on the topic. The research review contained in this publication begins with the 1993 work and confines itself to only the highest quality journals among

Quantocracy’s Daily Wrap for 10/31/2022

This is a summary of links featured on Quantocracy on Monday, 10/31/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Slava Ukraini! Latest from Quantocracy contributor in Ukraine: Volatility and Price of a Straddle, Are They The Same? [Only VIX]

    Yesterday I found another piece of ignorance on Medium: Stop Watching The VIX, Just Make Your Own tl;dr : Just use ATM straddles. This is of course not correct. As I have written before on this blog that (skipping mathematical rigor) the value of ATM straddle is or about 80% of the expected volatility. So if SPY = $400 and VIX = 20, the expected volatility is $400 * 20/100 = $80, then 1 year ATM
  • Volatility-based Equity Allocations [Finominal]

    The VIX currently trades within its top quartile since 1990 Using volatility to time equity allocations is a widely used strategy However, it is challenging to pursue this over the long-term INTRODUCTION The One Ring from J.R.R. Tolkiens Lord of the Rings saga is a plain gold ring unless it is thrown into a fire, when Elvish runes appear that roughly translate into One Ring to rule them all,

Quantocracy’s Daily Wrap for 10/29/2022

This is a summary of links featured on Quantocracy on Saturday, 10/29/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Building Candlesticks in Rust [Mark Best]

    Candlesticks are a common way to represent price and volume of an asset over a period of time. There are various common types of bars such as time, volume, tick bars, hieken-ashi, renko to name a few. There is a lot of information about the implementations of these on the internet so their details will not be covered here. The aim of this article is to share some tips for implementation and also a
  • Momentum Gap – its role in reducing crashes [Alpha Architect]

    This article discusses the academic research about the Momentum Gap and the role that its predictive potential may have in reducing momentum crashes, hence possibly improving performance. In our book Your Complete Guide to Factor-Based Investing, Andrew Berkin and I presented the evidence demonstrating that momentum, both cross-sectional (CSMOM) and time-series (TSMOM), has provided a
  • Identifying market regimes via asset class correlations [SR SV]

    A recent paper suggests identifying financial market regimes through the correlations of asset class returns. The basic idea is to calculate correlation matrixes for sliding time windows and then estimate pairwise similarities. This gives a matrix of similarity across time. One can then perform principal component analysis on this similarity matrix and extract the axes of greatest relevance.
  • Asynchronous Trading Revisited: Practical Implications [Alpha Architect]

    In this article, the author examines several important questions related to asynchronous trading, or the variation in trading frequency that occurs when trading stocks or other assets. Timo Wiedemann, University of Muenster (Germany) The newest version of the paper can be found here. What are the Research Questions? Trading is not continuous, leading to asynchronous trading times for different