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Quantocracy’s Daily Wrap for 09/13/2021

This is a summary of links featured on Quantocracy on Monday, 09/13/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Long Short Equity Strategy [Quant Insti]

    As the name suggests, long short equity strategy is one where we take both long and short positions in different equities. This strategy is normally used by hedge funds to generate greater risk adjusted returns due to its inherently low risk characteristics. In this article, you will learn about how this strategy works and how one should approach building such a strategy. You will also see its
  • Equal vs Market Cap-Weighted Portfolios in Stock Market Crashes [Factor Research]

    There is no consensus whether an equal or market cap-weighted allocation model for stocks is superior Both generated similar drawdowns during stock market crashes on average Theoretically, equal-weight is superior, but practically cap-weighted INTRODUCTION Diversify, reduce fees, avoid active trading, and keep it simple. Most investors would be well-served by following the above framework.
  • How To Reduce Lag In A Moving Average [Raposa Trade]

    Moving average indicators are commonly used to give traders a general idea about the direction of the trend by smoothing the price series. One of the big drawbacks to most common moving averages is the lag with which they operate. A strong trend up or down may take a long time to get confirmation from the series leading to lost profit. In 2005, Alan Hull devised the Hull Moving Average (HMA) to

Quantocracy’s Daily Wrap for 09/11/2021

This is a summary of links featured on Quantocracy on Saturday, 09/11/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 09/10/2021

This is a summary of links featured on Quantocracy on Friday, 09/10/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Use Lexical Density of Company Fillings [Quantpedia]

    The application of alternative data is currently a strong trend in the investment industry. We, too, analyzed few datasets in the past, be it ESG data, sentiment, or company fillings. This article continues the exploration of the alt-data space. This time, we use the research paper by Joenvr et al., which shows that lexically diverse hedge funds outperform lexically homogeneous as an
  • Optimizing implicitly using genetic algorithms [Quant Dare]

    Sometimes it is too costly, even impossible, to explicitly optimize an equation. Today we will see how to optimize implicitly using genetic algorithms. Sometimes, in finance as well as in other aspects of life, a problem presents itself in the most clear of terms: an explicit equation which we must optimize. In these cases, we must either maximize or minimize its value, given some variables which
  • Is The Value Premium Smaller Than We Thought? [Alpha Architect]

    From 2017 through March 2020, the relative performance of value stocks in the U.S. was so poor, experiencing its largest drawdown in history, that many investors jumped to the conclusion that the value premium was dead. It is certainly possible that what economists call a regime change could have caused assumptions to change about why the premium should exist/persist. For example, if the

Quantocracy’s Daily Wrap for 09/08/2021

This is a summary of links featured on Quantocracy on Wednesday, 09/08/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A New Return Asymmetry Investment Factor in Commodity Futures [Quantpedia]

    As mentioned several times, Quantpedia is a big fan of transferring ideas from one asset class to another. This article is another example; we use an idea originally tested on Chinese stocks and apply it to the commodity futures investment universe. The resultant return new asymmetry investment factor in commodities is an interesting trading strategy unrelated to other common factors and has a
  • Managing Data Outliers With Quantile Regression: Part I [Capital Spectator]

    One of the more difficult challenges for modeling is deciding how (or if) to deal with extreme data points. Its a common problem in economic and financial numbers. Fat tailed distributions are standard fare in stock market returns, for example. Meanwhile, the dramatic collapse in the economy during the pandemic last year is a reminder that outliers pop up in macro analytics too. That leads to
  • Do Cryptocurrencies Improve Portfolio Diversification? [Alpha Architect]

    Portfolio diversification benefits are often driven by correlation coefficients, but this analysis can get complicated, fast. Over time academics and practitioners have realized that it is not enough to simply calculate a correlation using short return intervals (daily?, monthly?) over a sample period (3 years?, 5 years?) and combine asset classes together based on the resulting correlation

Quantocracy’s Daily Wrap for 09/07/2021

This is a summary of links featured on Quantocracy on Tuesday, 09/07/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Introduction to Hedge Ratio Estimation Methods [Hudson and Thames]

    The hedge ratio estimation problem is one of the most important issues for portfolio managers. The key concept of the hedging problem can be posed as the following equation: S_{t}=P_{1, t}+sum_{n=2}^{N} omega_{n} P_{n, t} where P_1 represents the market value at observation t of a portfolio we wish to hedge and P_n represents a set of variables(instruments or portfolios) available for building a
  • Hierarchical Risk Parity: Introducing Graph Theory and Machine Learning in Portfolio Optimizer [Portfolio Optimizer]

    In this short post, I will introduce the Hierarchical Risk Parity portfolio optimization algorithm, initially described by Marcos Lopez de Prado1, and recently implemented in Portfolio Optimizer. I will not go into the details of this algorithm, though, but simply describe some of its general ideas together with their associated implementation tweaks in Portfolio Optimizer. Hierarchical risk
  • Why you need more data than you think in your backtest [Raposa Trade]

    How many years does it take before you can be confident in a trading strategy? Does one great year mean you have a tremendous strategy? Does one bad year mean you should pack it up and try something else? How soon can you tell that a system is flawed and needs changing? These arent easy questions, but theyre incredibly important to any investor, whether youre systematic or not! While we
  • Truth and Liebor [Investment Idiocy]

    This will be a bit different from my normal posts. It's basically some personal reflections on the LIBOR fixing scandal, prompted by having just read this book written by Stelios Contogoulas: This post isn't really a book review, although I will say that the book is definitely worth buying. Most of you have probably already read the excellent Spider Network. That is arguably better

Quantocracy’s Daily Wrap for 09/05/2021

This is a summary of links featured on Quantocracy on Sunday, 09/05/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Trade the MACD: Four Strategies with Backtests [Raposa Trade]

    The Moving Average Convergence-Divergence (MACD) is a popular and versatile indicator that appears in a number of trading systems. In its most basic form, we have the difference between two exponential moving averages (EMA), one fast and the other slow. The MACD is the difference between these two EMAs. From this simple beginning a host of other indicators such as signal lines and MACD bars are
  • Ten things investors should know about nowcasting [SR SV]

    Nowcasting in financial markets is mainly about forecasting forthcoming data reports, particularly GDP releases. However, nowcasting models are more versatile and can be used for a range of market-relevant information, including inflation, sentiment, weather, and harvest conditions. Nowcasting is about information efficiency and is particularly suitable for dealing with big messy data. The
  • Matrix profile: Using Weakly Labeled Time Series to Predict Outcomes [Dekalog Blog]

    Back in May of this year I posted about how I had intended to use Matrix Profile (MP) to somehow cluster the "initial balance" of Market Profile charts with a view to getting a heads up on immediately following price action. Since then, my thinking has evolved due to my learning about the paper "Matrix profile: Using Weakly Labeled Time Series to Predict Outcomes" and its

Quantocracy’s Daily Wrap for 09/03/2021

This is a summary of links featured on Quantocracy on Friday, 09/03/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Handling Big Data [Jonathan Kinlay]

    One of the major challenges that users face when trying to do data science is how to handle big data. Leaving aside the important topic of database connectivity/functionality and the handling of data too large to fit in memory, my concern here is with the issue of how to handle large data files, which are often in csv format, but which are not too large to fit into available memory. It is well
  • A Streamlit Dashboard for the @AlpacaHQ API (h/t @PyQuantNews)

    The Alpaca brokerage service is very useful for algorithmic traders that comes with an API to retrieve data and execute trades in a paper or live environment. While you can also check the status and returns of your positions through the API, Alpaca has spent some time creating a frontend where users can visually check their live and paper accounts. Seeing that Alpaca is more focused on building
  • Factor Timing Is Tempting [Alpha Architect]

    Academic research has found that factor premiums are both time-varying and dependent on the economic cycle. For example, Arnav Sheth, and Tee Lim, authors of the December 2017 study Fama-French Factors and Business Cycles, examined the behavior of six Fama-French factorsmarket beta (MKT), size (SMB), value (HML), momentum (MOM), investment (CMA) and profitability (RMW)across business

Quantocracy’s Daily Wrap for 09/02/2021

This is a summary of links featured on Quantocracy on Thursday, 09/02/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The three kinds of (over) fitting [Investment Idiocy]

    This post is something that I've banged on about in many presentations at several conferences* (most complete slides are here), and in various interviews, but never actually formally described in a blog post. In fact this post has existed in draft form since 2015 (!). * you know, when you leave your house and listen to someone else speaking. Something that in late 2021 is a distant memory,

Quantocracy’s Daily Wrap for 09/01/2021

This is a summary of links featured on Quantocracy on Wednesday, 09/01/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Purchasing Power Parity [Quant Dare]

    Purchasing Power Parity (PPP) is a well-known measure used to compare the currencies of different countries in terms of price levels. So, in this post, we are going to explain PPP and study, through an example, its relation with the currency pairs. PPP is based on the law of one price (LOOP). For that reason, in order to understand PPP, first, we are going to explain the LOOP. This law states that
  • VVIX/VIX as a Return Indicator? [CXO Advisory]

    Is the ratio of implied volatility of implied volatility (CBOE VVIX Index), interpretable as a measure of changes in investor fear level, to CBOE VIX Index itself a useful indicator of future stock market returns? To investigate, we relate monthly VVIX/VIX and monthly change in VVIX/VIX to monthly SPDR S&P 500 (SPY) total returns. Using end-of-month levels of both VVIX and VIX and
  • Mutual Funds: Negative $125B in Value-Add? [Alpha Architect]

    Elton, Gruber, and Busse (2004) as well as Hortacsu and Syverson (2004) suggest that mutual fund markets are not perfectly competitive and that fees do matter to investors. In contrast, the neoclassical view of mutual funds (see for example Berk and Green, 2004; Pastor, Stambaugh and Taylor, 2019 and others) implies that fees do not matter because with competitive markets and rational investors,

Quantocracy’s Daily Wrap for 08/30/2021

This is a summary of links featured on Quantocracy on Monday, 08/30/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Caveats in Calibrating the OU Process [Hudson and Thames]

    This is a series where we aim to cover in detail various aspects of the classic Ornstein-Uhlenbeck (OU) model and the Ornstein-Uhlenbeck Jump (OUJ) model, with applications focusing on mean-reverting spread modeling under the context of pairs trading or statistical arbitrage. Given the universality and popularity of those models, the techniques discussed can easily be applied to other areas where
  • Training Neural Networks: Why, As With Humans, Teaching Methods Matter [Enjine]

    I achieved my lifes biggest accomplishment in 2004, when I defeated dozens of other contestants to clinch the Canadian Settlers of Catan championship. Settlers, as its called by its enthusiasts, is a strategy board game where players collect resources, build settlements, trade with and rob from each other to reach 10 points first. It remains a favourite among many board game
  • The Best Systematic Trading Strategies in 2021: Part 3 [Quantpedia]

    Finally, what are the five top-performing quantitative trading strategies in 2021? In part 1 of our article, we analyzed tendencies and trends among the Top 10 quantitative strategies of 2021. Thanks to Quantpedia Pros screener, we published several interesting insights about them. In part 2 of our article, we got deeper into the first five specific strategies, which are significantly
  • Building an Inflation Portfolio Using Asset Classes [Factor Research]

    We recently explored using stocks to create an inflation-proxy portfolio that resulted in a collection of stocks with strong sector and factor biases. Specifically, the portfolio exhibited overweights in energy and financial stocks, perhaps as expected, as well as a long position in the value and short positions in the momentum and quality factors. However, despite selecting stocks based on their
  • Dijkstra algorithm [Quant Insti]

    Start learning all about the Dijkstra algorithm for finding the shortest path. We briefly review the Kruskal algorithm, Prim algorithm, Johnson algorithm and Bellman algorithm as well. We'll cover: What is the Dijkstra algorithm? How does the Dijkstra algorithm work? Pseudo code of Dijkstra algorithm Dijkstra algorithm table Dijkstra algorithm time complexity When to use the Dijkstra