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Quantocracy’s Daily Wrap for 03/16/2021

This is a summary of links featured on Quantocracy on Tuesday, 03/16/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 03/15/2021

This is a summary of links featured on Quantocracy on Monday, 03/15/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Introduction to Sell-Off Analysis for Crypto-Assets: Triggered by Bitcoin? [Quant at Risk]

    They say that small fishes buy and sell driven by unstable waters but only big whales make the waves really huge. Recently, this quite popular phrase, makes sense when it comes to cryptocurrency trading influenced by sudden dives of the Bitcoin price. The strategies of buying and selling executed by the whales, often referred to as the institutional buying/selling, may differ in their
  • How to Measure the Liquidity of Cryptocurrency? [Alpha Architect]

    n January 2020, trading in bitcoin exceeded $930 billion and has certainly grown over the past year. Unlike nearly any other asset, bitcoin can be traded 24 hours a day, 7 days a week on trading platforms around the globe. While trading cryptocurrencies has become relatively frequent, the high number of exchanges combined with the lack of regulated data makes determining the liquidity of these
  • Hierarchical Clustering in Python [Quant Insti]

    With the abundance of raw data and the need for analysis, the concept of unsupervised learning became popular over time. The main goal of unsupervised learning is to discover hidden and exciting patterns in unlabeled data. The most common unsupervised learning algorithm is clustering. Applications for cluster analysis ranges from medical to face recognition to stock market analysis. In this blog,

Quantocracy’s Daily Wrap for 03/12/2021

This is a summary of links featured on Quantocracy on Friday, 03/12/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Activate sigmoid! [OSM]

    In our last post, we introduced neural networks and formulated some of the questions we want to explore over this series. We explained the underlying architecture, the basics of the algorithm, and showed how a simple neural network could approximate the results and parameters of a linear regression. In this post, well show how a neural network can also approximate a logistic regression and
  • Z-Score Factor Portfolio Weighting [Philipp Kahler]

    Factor investing has been around for some years and has shown to be a valid concept for portfolio strategies. Usually the investor selects a few factors and then goes long the 10% of stocks with the highest factors and goes short (if he wants to trade delta neutral) the 10% of stocks with the lowest factors. But I think this approach misses a lot of performance, so I would like to show you a

Quantocracy’s Daily Wrap for 03/09/2021

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

  • An Introduction to Volatility Targeting [Quantpedia]

    One of the most popular reports in the Portfolio Analysis section of our Quantpedia Pro tool is Volatility Targeting. In this article, we will explain some theory behind this portfolio management method. And then, we will go more in-depth, pick several examples and explain some common volatility targeting variants. Introduction Volatility is the most common risk metric of a stock. The main

Quantocracy’s Daily Wrap for 03/08/2021

This is a summary of links featured on Quantocracy on Monday, 03/08/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Detecting Volume Breakouts [Financial Hacker]

    It is estimated that about 6000 different technical indicators have been meanwhile published, but few of them are based on volume. In his article in Stocks & Commodities April 2021, Markos Katsanos proposed a new indicator for detecting high-volume breakouts. And he tested it with a trading system that I believe is the most complex one ever posted on this blog. The VPN indicator calculates the
  • Autoregression: Model, Autocorrelation and Python Implementation [Quant Insti]

    Time series modelling is a very powerful tool to forecast future values of time-based data. Time-based data is data observed at different timestamps (time intervals) and is called a time series. These time intervals can be regular or irregular. Based on the pattern, trend, etc. observed in the past data, a time series model predicts the value in the next time period. The time series models
  • Low Volatility Factor Investing: Risk-Based or Behavioral-Based or Both? [Alpha Architect]

    The low-risk effect (aka low volatility) is based on the empirical observation that assets with low risk have high alpha. Specifically in this research, the effect is defined as the risk-adjusted return spread between low-risk and high-risk portfolios and not just low-risk stocks. Since the low-risk effect confounds traditional asset pricing models, various researchers have developed competing

Quantocracy’s Daily Wrap for 03/06/2021

This is a summary of links featured on Quantocracy on Saturday, 03/06/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • NER For Stock Mentions on Reddit (h/t @PyQuantNews)

    eddit has been at the epicenter of one of the biggest movements in the world of finance, and although it seemed like an unlikely source of such a movement its hardly surprising in hindsight. The trading-focused subreddits of Reddit are the backdrop for a huge amount of discussion about what is happening in the markets so it is only logical to tap into this huge data source. When

Quantocracy’s Daily Wrap for 03/04/2021

This is a summary of links featured on Quantocracy on Thursday, 03/04/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Does it make sense to change your trading behaviour in different periods of volatility? [Investment Idiocy]

    A few days ago I was browsing on the elitetrader.com forum site when someone posted this: I am interested to know if anyone change their SMA/EMA/WMA/KAMA/LRMA/etc. when volatility changes? Let say ATR is rising, would you increase/decrease the MA period to make it more/less sensitive? And the bigger question would be, is there a relationship between volatility and moving average? Interesing I
  • Momentum Factor Investing: What’s the Right Risk-Adjustment? [Alpha Architect]

    The momentum factor represents one of our core investment beliefs: buy winners. So when research presents itself that may contradict our beliefs it provides the opportunity to dig deeper and think harder about the factors we hold so dearly. Erik Theissen and Can Yilanci begin their paper by warming us up to the idea that momentum does outperform, and when measured on a portfolio level
  • Adding candlesticks to mean reversion setup [Alvarez Quant Trading]

    My preferred chart style is a candlestick chart but I have never investigated candlestick formations to see if they can help provide an edge in my trading. I recently ran into this blog post, Do Candlesticks Work? A Quantitative Test Of 23 Candlestick Formations, where he did his own investigation. Even better he shared the code for the formations in AmiBroker which would make it a lot easier. You

Quantocracy’s Daily Wrap for 03/02/2021

This is a summary of links featured on Quantocracy on Tuesday, 03/02/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Testing a Risk Premium Value Strategy [Allocate Smartly]

    This is a test of a Risk Premium Value strategy (RPV) that allocates to major US asset classes based on current risk premium valuations relative to historical norms. Readers will note the similarity between RPV and other related strategies, such as CXO Advisorys SACEVS. Backtested results from 1987 net of transaction costs follow (see backtest assumptions). Results are shown in two flavors:
  • Does Crowdsourced Investing Work? [Alpha Architect]

    Historically, as Richard Thaler pointed out in his book Misbehaving, financial academics have looked at humans as Econs. An Econ, unlike a human, values everything down to a penny before they make a decision, knows all possible alternatives, weighs them accurately, and always optimizes. 1 In recent years weve moved away from thinking of humans as Econs. We are now left with the age-old

Quantocracy’s Daily Wrap for 03/01/2021

This is a summary of links featured on Quantocracy on Monday, 03/01/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Does X work, some brief thoughts and choose your adventure [Investment Idiocy]

    When I was a spotty teenager I was a walking nerd cliche. I liked computers; both for programming and games. I was terrified of girls. I was rubbish at nearly all sports*. And I played D&D (and Tunnels and Trolls, and Runequest). * Nearly all: Not, I'm not talking about the 'sport' of Chess: I was also rubbish at Chess and still am. But due to some weird anomaly I was a dinghy

Quantocracy’s Daily Wrap for 02/26/2021

This is a summary of links featured on Quantocracy on Friday, 02/26/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Nothing but (neural) net [OSM]

    We start a new series on neural networks and deep learning. Neural networks and their use in finance are not new. But are still only a fraction of the research output. A recent Google scholar search found only 6% of the articles on stock price price forecasting discussed neural networks.1 Artificial neural networks, as they were first called, have been around since the 1940s. But development was
  • VIX and More: The Evolution of the VIX (1) [VIX and More]

    Volatility is notorious for clustering in the short-term, mean-reverting in the medium-term and settling into multi-year macro cycles over the long-term. I have chronicled each of these themes in this space in the past. Apart from volatility, I have also taken great pains to talk about the movements of the VIX, which is one of the most famous instances of implied volatility and represents investor
  • A Robust Approach to Multi-Factor Regression Analysis [Quantpedia]

    Practitioners widely use asset pricing models such as CAPM or Fama French models to identify relationships between their portfolios and common factors. Moreover, each asset class has some widely-recognized asset pricing model, from equities through commodities to even cryptocurrencies. However, which model can we use if our portfolio is complex and consists of many asset classes? Which factors
  • Correlation and correlation structure (5) a new coefficient of correlation [Eran Raviv]

    This is the fifth post which is concerned with quantifying the dependence between variables. When talking correlations one usually thinks about linear correlation, aka Pearsons correlation. One serious limitation of linear correlation is that its, well.. linear. By construction its not useful for detecting non-monotonic relation between variables. Here I share some recent academic
  • The Forecasting Power of Value, Profitability, and Investment Spreads [Alpha Architect]

    Studies such as the 2019 paper Value Return Predictability Across Asset Classes and Commonalities in Risk Premia, have demonstrated that while it is difficult to time investments based on their value spreads 1 which weve covered occasionally here and here, value spreads do contain information on the returns to value strategies in individual equities, industries, commodities, currencies,
  • Research Review | 26 February 2021 | Inflation [Capital Spectator]

    The Increased Toxicity of the U.S. Treasury Security Market Scott E. Hein (Texas Tech University) January 2, 2021 This short research paper documents the fact that exclusively watching for rising yields on conventional U.S. Treasury securities to reflect increased inflationary fears in the U.S. is no longer appropriate. With the Federal Reserve seeking to keep short-term nominal yields near zero