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Quantocracy’s Daily Wrap for 09/20/2020

This is a summary of links featured on Quantocracy on Sunday, 09/20/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Algorithmic Trading Using Logistic Regression (h/t @PyQuantNews) [Hands Off Investing]

    With the increasing popularity of machine learning, many traders are looking for ways in which they can teach a computer to trade for them. This process is called algorithmic trading (sometimes called algo-trading). Algorithmic trading is a hands off strategy for buying and selling stocks that leverages technical indicators instead of human intuition. In order to implement an algorithmic
  • Is the Weakest Week Still Weak When There is Weakness Prior to the Weakest Week? [Quantifiable Edges]

    As I have shown many times in the past, there isnt a more reliable time of the year to have a selloff than this upcoming week. I have often referred to is as The Weakest Week. Since 1960 the week following the 3rd Friday in September has produced the most bearish results of any week. Below is a graphic to show how this upcoming week has played out over time. 2019-09-20-1 As you can see
  • R tidyverse for macro trading research [SR SV]

    The tidyverse is a collection of packages that facilitate data science with R. It is particularly powerful for macro trading research because [a] it supports efficient and standardized work with Rs vast universe of econometric models, [b] is well adapted for analyzing data vintages (i.e. data series that change over time), and [c] supports code in form of visually clean chains of statistical

Quantocracy’s Daily Wrap for 09/18/2020

This is a summary of links featured on Quantocracy on Friday, 09/18/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Sequential satisficing [OSM]

    In our last post, we ran simulations on our 1,000 randomly generated return scenarios to compare the average and risk-adjusted return for satisfactory, naive, and mean-variance optimized (MVO) maximum return and maximum Sharpe ratio portfolios.1 We found that you can shoot for high returns or high risk-adjusted returns, but rarely both. Assuming no major change in the underlying average returns

Quantocracy’s Daily Wrap for 09/17/2020

This is a summary of links featured on Quantocracy on Thursday, 09/17/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Effective Market Regimes Techniques with @AlvarezQuant LIVE this weekend [Better System Trader]

    This weekend is the very first episode of the new BST Live show. On the show this week were going to discuss how to improve your trading performance and reduce risk by trading only when the market conditions are best for your strategies. Ive got Cesar Alvarez from Alvarez Quant Trading joining me to talk about his own Market Regime research and how he applies Market Regime techniques with
  • Aspect Partners’ Risk Managed Momentum [Allocate Smartly]

    This is an independent test of Aspect Partners flagship tactical asset allocation strategy Risk Managed Momentum (RMM). By tactical standards, RMM is a very active, very aggressive strategy. It has done an excellent job navigating this difficult year so far. Backtested results from 1970 follow. Results are net of transaction costs (see backtest assumptions). Learn about what we do and follow
  • Accruals and Momentum and Their Implications for Factor Investors [Alpha Architect]

    The price momentum and accruals (the difference between accounting earnings and cash flowsadjustments made for revenue that has been earned but not received, and costs that have been incurred but not paid) anomalies are two well-documented financial phenomena. Recent research has focused on whether there is a relationship between the two anomalies, an idea Jack previously wrote about in his

Quantocracy’s Daily Wrap for 09/16/2020

This is a summary of links featured on Quantocracy on Wednesday, 09/16/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Positional Option Trading by Euan Sinclair: A Review [Robot Wealth]

    Trading books set a low bar for the reviewer. 99% are full of facile feel-good advice (dont fight the trend, always use a protective stop). The 1% that are useful tend to either be dry technical treatments (quants who dont trade), or sporadically helpful insights from traders who make money but dont know why (traders who dont quant.) This book is a practical book by an experienced
  • Announcement: What’s next for BST [Better System Trader]

    Theres been alot of speculation about whats next for BST. This video explains all the details you need to know, including whats happening to BST this Sunday
  • QuantStart News – August 2020 [Quant Start]

    A couple of months ago we started a new set of posts designed to keep the QuantStart community aware of what the QuantStart team had been up to in previous month. In last month's post we discussed what we had been working on in July 2020. Articles and Tutorials In August we once again reviewed our Content Survey for 2020 and noted that while Machine Learning took the top spot in terms of
  • Can the Best Stock Pickers Still Beat the Market? An Out of Sample Test [Alpha Architect]

    We all intuitively know we are better than average drivers, and if were investors, well we know were better than average there too. Frankly, we all put ourselves easily in the top 10%, at least. Those that are in the know will quote Buffets, The Superivestors of Graham-and-Doddsville, but academics will counter with a cite for Kosowski, Timmermann, Wermers, and White (2006). In

Quantocracy’s Daily Wrap for 09/14/2020

This is a summary of links featured on Quantocracy on Monday, 09/14/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Networks with MlFinLab: Minimum Spanning Tree (MST) [Hudson and Thames]

    Network analysis can provide interesting insights into the dynamics of the market, and the continually changing behaviour. A Minimum Spanning Tree (MST) is a useful method of analyzing complex networks, for aspects such as risk management, portfolio design, and trading strategies. For example, Onnela et al. (2003) notices that the optimal Markowitz portfolio is found at the outskirts of the tree
  • Keeping data vendor APIs simple [Cuemacro]

    Just imagine that every time you went into a shop, you had to change your clothes, because they had a different dress code? Say your grocer would insist on only people wearing yellow, whilst your supermarket would require red clothes only.. Ok, thats a somewhat ridiculous example, which would never happen. In practice, we can wear whatever we want (within reason), and go into whichever shop

Quantocracy’s Daily Wrap for 09/11/2020

This is a summary of links featured on Quantocracy on Friday, 09/11/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Momentum Turning Points [Allocate Smartly]

    This is a test of two recent papers: Momentum Turning Points and Breaking Bad Trends. Learn more about what we do and follow 50+ asset allocation strategies like these in near real-time. Successful trend-following strategies must balance the speed of the trading signal. If the signal is too slow, the strategy will not adapt quickly enough to changes in the actual underlying trend. If its
  • Liquidity Cascades: The Coordinated Risk of Uncoordinated Market Participants [Flirting with Models]

    This paper is unlike any research weve shared in the past. Within we dive into the circumstantial evidence surrounding the weird behavior many investors believe markets are exhibiting. We tackle narratives such as the impact of central bank intervention, the growing scale of passive / indexed investing, and asymmetric liquidity provisioning. Spoiler: Individually, the evidence for these

Quantocracy’s Daily Wrap for 09/10/2020

This is a summary of links featured on Quantocracy on Thursday, 09/10/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Positive Similarity of Company Filings and the Cross-Section of Stock Returns [Quantpedia]

    The usage of alternative data is now a main-stream topic in investment management and algorithmic trading. So lets together explore the textual analysis of 10-K & 10-Q filings and analyze how these datasets could be used as a profitable part of investment portfolios. We invite you to read this short summarization of the research. Full version can be found on the SSRN. Introduction A 10-K or
  • Can We Use the Shiller CAPE Ratio to Forecast Country Returns? [Alpha Architect]

    We all became relatively aware of the CAPE ratio when Shiller predicted the 2000 internet bubble in his book Irrational Exuberance, then after he added a touch of robustness when he called the 2007 housing crisis 1 we all became intimately aware of it. Since that time CAPE has been utilized to mark Historically, the CAPE ratio, Shillers PE, has primarily be utilized over long periods of
  • Kelly criterion: Part 2 [Quant Dare]

    When investing, we spend plenty of time thinking about which securities should we buy but we rarely wonder how much money should we allocate in each asset. Although it does not seem like an important aspect, it is crucial when defining a strategy, up to the point that it can determine the hole performance of your portfolio. In this sense, the Kelly criterion helps us selecting the optimal

Quantocracy’s Daily Wrap for 09/09/2020

This is a summary of links featured on Quantocracy on Wednesday, 09/09/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Predicting Bond Returns? Focus on GDP Growth and Inflation Indicators [Alpha Architect]

    Can the excess returns of government bonds be predicted? This is a classic research question in finance academic research circles. Sometimes practitioners, who are often buried in the more exciting parts of the financial markets, tend to forget that government bonds (do they even belong in your portfolio) are a large global asset class, representing a whopping 30% of market capitalizations across

Quantocracy’s Daily Wrap for 09/07/2020

This is a summary of links featured on Quantocracy on Monday, 09/07/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • CorrGAN: Realistic Financial Correlation Matrices [Hudson and Thames]

    There are 6 properties that empirical correlation matrices exhibit that no synthetic generation method has been able to replicate, until now. Enabling researchers to backtest strategies on an abundance of data would make our algorithms and strategies more robust, accurate, and efficient. Since historical data can be biased and does not have enough high-stress events to test multiple scenarios,
  • Volatility Hedge Funds: The Good, the Bad, and the Ugly [Factor Research]

    Volatility hedge funds provided attractive diversification benefits for equity portfolios However, long were preferable over short volatility strategies Some scepticism is required for the hedge fund index performance INTRODUCTION In finance 101, there is usually little doubt on what constitutes the major asset classes in the investment industry, i.e. there are the traditional ones like equities

Quantocracy’s Daily Wrap for 09/06/2020

This is a summary of links featured on Quantocracy on Sunday, 09/06/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Quant Blog: A Primer on State Space Models [Patrick Aschermayr]

    In my first series of posts, I will give a primer on state space models (SSM) that will lay a foundation in understanding upcoming posts about their variants, usefulness, methods to apply inference and forecasting possibilities. When talking about a state space model (SSM), people usually refer to a bivariate stochastic process , where is an unobserved Markov chain and is an observed sequence of
  • Nowcasting with MIDAS regressions [SR SV]

    Nowcasting macro-financial indicators requires combining low-frequency and high-frequency time series. Mixed data sampling (MIDAS) regressions explain a low-frequency variable based on high-frequency variables and their lags. For instance, the dependent variable could be quarterly GDP and the explanatory variables could be monthly activity or daily market data. The most common MIDAS predictions