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Quantocracy’s Daily Wrap for 03/22/2021

This is a summary of links featured on Quantocracy on Monday, 03/22/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Modelling Slippage for Limit Orders using Adaptive KDE-based Loss Severity Distribution [Quant at Risk]

    Placing limit orders for trade execution is both quite popular and handy method in (algo)trading. A trader expects that the executed price of his buy/sell trade will ideally match the one requested in his limit order. Unfortunately, depending on a momentary market/asset liquidity, the difference between both prices can vary or vary significantly. This difference, by many known as an executed
  • An Economic Framework for ESG Investing [Alpha Architect]

    The 2018 Global Sustainable Investment Review reports over $30 trillion invested with explicit ESG goals as of the beginning of 2018. In the words of the authors: There is a clear tendency for many investors to own ethical companies in a saintly effort to promote good corporate behavior while hoping to do so in a guiltless way that does not sacrifice returns. To empower future investors in the ESG
  • Building a real-time market distress index [SR SV]

    A new Fed paper explains how to construct a real-time distress index, using the case of the corporate bond market. The index is based on metrics that describe the functioning of primary and secondary markets and, unlike other distress measures, does not rely on prices and volatility alone. Thus, it includes issuance volumes and issuer characteristics on the primary side and trading volumes and

Quantocracy’s Daily Wrap for 03/21/2021

This is a summary of links featured on Quantocracy on Sunday, 03/21/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Conditional Volatility Targeting [Alpha Architect]

    Financial economists have long known that volatility and returns are negatively correlated. Fischer Black documented this in his 1976 paper Studies of Stock Price Volatility Changes. This relationship results in the tendency to produce negative equity returns in times of high volatility. In addition, the research, including the 2017 study Tail Risk Mitigation with Managed Volatility
  • Research Review | 19 March 2021 | Forecasting [Capital Spectator]

    Predictable Financial Crises Robin Greenwood (Harvard University), et al. March 2021 Using historical data on post-war financial crises around the world, we show that crises are substantially predictable. The combination of rapid credit and asset price growth over the prior three years, whether in the nonfinancial business or the household sector, is associated with about a 40% probability of

Quantocracy’s Daily Wrap for 03/17/2021

This is a summary of links featured on Quantocracy on Wednesday, 03/17/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 4 simple ways to label financial data for Machine Learning [Quant Dare]

    We have seen in previous posts what is machine learning and even how to create our own framework. Combining machine learning and finance always leads to interesting results. Nevertheless, in supervised learning, it is crucial to find a set of appropriate labels to train your model. In todays post, we are going to see 3 ways to transform our data into a classification problem and 1 to transform
  • How to Predict Stock Returns (using a simple model) [Alpha Architect]

    Jack Bogle, the founder of Vanguard, created a simple explanation for predicting future stock returns. The so-called Occams razor (law of parsimony) approach is an attempt to explain projected returns as simple as possible. Mr. Bogles model is pretty simple: Expected returns (nominal, annualized over the next 10 years) = Starting Dividend Yield + Earnings Growth rate + Percentage

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