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Quantocracy’s Daily Wrap for 04/24/2019

This is a summary of links featured on Quantocracy on Wednesday, 04/24/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Avoiding Trades Before Earnings [Alvarez Quant Trading]

    Over my last 16 years of research, one of the most asked questions is should you not take trades before an earnings release. I could never answer this question because I did not have the data. I can easily recall trades were a stock came out with poor earnings and crashed 25%. But without testing this, I would still take stocks into earnings. Because that is how the testing was done. A few months
  • Meta-Labeling (A Toy Example) [Quants Portal]

    Welcome to the concept of Meta-Labeling. This blog post investigates the idea and tries to help build an intuition for what is taking place. The idea of meta-labeling is first mentioned in the textbook Advances in Financial Machine Learning by Marcos Lopez de Prado and promises to improve model and strategy performance metrics by helping to filter-out false positives. In this blog post we make use
  • P-hacking and backtest overfitting [Mathematical Investor]

    Recent public reports have underscored a crisis of reproducibility in numerous fields of science. Here are just a few of recent cases that have attracted widespread publicity: In 2012, Amgen researchers reported that they were able to reproduce fewer than 10 of 53 cancer studies. In 2013, in the wake of numerous recent instances of highly touted pharmaceutical products failing or disappointing
  • Podcast: Gary Antonacci: combining relative strength price momentum with absolute momentum [System Trader Show]

    Imagine that you spend a few minutes a month to manage your investment. All is rule-based, statistically significant, simple and logical. No place for discretionary decisions, no guessing, no gut feeling, no forecasting. And in the long-term, you are almost sure to beat all the actively managed investment funds on the market. Sounds like a scam? Well, everyone should verify everything, but once

Quantocracy’s Daily Wrap for 04/23/2019

This is a summary of links featured on Quantocracy on Tuesday, 04/23/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Replicating Famous Hedge Funds [Factor Research]

    Diverse hedge fund strategies can be replicated via factor-mimicking portfolios The analysis highlights that most returns are explained by factors, not alpha However, hedge funds can create value by harvesting factor returns efficiently via portfolio construction INTRODUCTION In 1973, the U.S. Food and Drug Administration (FDA) published the first regulations that required the nutrition labeling
  • The Recent $RUT / $SPX Divergence And Why It Might Be Bullish [Quantifiable Edges]

    One aspect of recent market action that is interesting is the weakness in the Russell vs the SPX over the last few days. While some may worry the divergence is concerning, an old Quantifinder study that appeared last night indicates the setup is likely suggestive of an upside edge. It looked at times the RUT closed down 3 or more days in a row and the SPX closed at a 3-day high. 2019-04-23-1 As

Quantocracy’s Daily Wrap for 04/22/2019

This is a summary of links featured on Quantocracy on Monday, 04/22/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bond ETFs in an Era of Rising Rates [Better Buy And Hold]

    Bonds are key to a well-diversified portfolio; theyve provided both consistent returns and consistent diversification against riskier asset classes like stocks and real estate. But bonds face stiff headwinds in the coming years. Thats not prognostication, its a mathematical certainty. Its imperative that our portfolio designs account for this less optimistic future. Failing to do so
  • mlfinlab on PyPi Index [Quants Portal]

    mlfinlab is a living and breathing project in the sense that it is continually enhanced with new code from the chapters in the Advanced Financial Machine Learning book. We have built this on lean principles with the goal of providing the greatest value to the quantitative community. Currently the package contains code from the following chapters: Chapter 2: Financial Data structures: The
  • The Path-Dependent Nature of Perfect Withdrawal Rates [Flirting with Models]

    The Perfect Withdrawal Rate (PWR) is the rate of regular portfolio withdrawals that leads to a zero balance over a given time frame. 4% is the commonly accepted lower bound for safe withdrawal rates, but this is only based on one realization of history and the actual risk investors take on by using this number may be uncertain. Using simulation techniques, we aim to explore how different
  • 12 Quant Business Practices to Improve [Two Centuries Investments]

    Only showing the latest backtest versions without disclosing their out-of-sample degradation Backtesting todays static holdings (managers, asset allocations, sub-asset-classes) into the past – filled with look-ahead bias Charging fees that are on par with the tracking error of the strategy Asking candidates at job interviews to reveal interesting new factors and data-sets Publishing quant
  • Compound Your Knowledge Episode 9: Investor Confidence & Issues with Factor Investing [Alpha Architect]

    In this weeks post, we discuss two posts. The first post, written by Elisabetta, examines a new method attempting to directly measure aggregate investor overconfidence. The second post, written by Larry Swedroe, examines issues that plague Factor Investing.

Quantocracy’s Daily Wrap for 04/19/2019

This is a summary of links featured on Quantocracy on Friday, 04/19/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Multi-threading Trading Strategy Back-tests and Monte Carlo Simulations in Python [Python For Finance]

    In this post I will be looking at a few things all combined into one script you ll see what I mean in a moment Being a blog about Python for finance, and having an admitted leaning towards scripting, backtesting and optimising systematic strategies I thought I would look at all three at the same timealong with the concept of multithreading to help speed things up. So the script
  • Factor Investing is Simple, But Not Easy (Video) [Alpha Architect]

    We are creating a series of long-form educational videos that present materials often covered in our white papers. The intent of these videos is make our content more accessible to visual learners. The video below is a presentation related to a long-form post we have on a post called, The Sustainable Active Investing Framework: Simple, But Not Easy, which is a discussion about identifying

Quantocracy’s Daily Wrap for 04/18/2019

This is a summary of links featured on Quantocracy on Thursday, 04/18/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Daily Extremes – Significance of time [Philipp Kahler]

    Analysing at which time daily market extremes are established shows the significance of the first and last hours of market action. See how different markets show different behaviour and see what can be learned from this analysis. Probability of Extremes A day of trading usually starts with a lot of fantasies for the future, then we try to survive the day and end it with a lot of hope for tomorrow.
  • Gini Index For Decision Trees [Quant Insti]

    Decision trees are often used while implementing machine learning algorithms. The hierarchical structure of a decision tree leads us to the final outcome by traversing through the nodes of the tree. Each node consists of an attribute or feature which is further split into more nodes as we move down the tree. But how do we decide which attribute/feature should be placed at the root node, which
  • SPX Strangle – 2018 Review [DTR Trading]

    I've been a little curious how the SPX strangle has been performing since I last analyzed it's results back in 2015. For this article, we'll just look at the following variations and how they performed from January 2007 through December 2018: 59 DTE – 16 Delta Short Strikes (100:50) / 2 DTE – exit if the trade has a loss of 100% of its initial credit OR if the trade has a profit of

Quantocracy’s Daily Wrap for 04/17/2019

This is a summary of links featured on Quantocracy on Wednesday, 04/17/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Reliably download historical market data from Yahoo! Finance with Python [Ran Aroussi]

    Ever since Yahoo! Finance decommissioned their historical data API, Python developers looked for a reliable workaround. As a result, my library, fix-yahoo-finance, gained momentum and was downloaded over 100,000 acording to PyPi. fix-yahoo-finance aimed to offer a temporary fix to the problem by scraping the data from Yahoo! Finance and returning a the data in the same format as
  • Trading and investing performance – year five [Investment Idiocy]

    Hard to believe, but it has been five and a half years since I had to go to an office to manage other peoples money, and exactly five years since I began systematically trading my own. Time then for another annual review. Perhaps it is confusing for overseas readers, but these reviews follow the UK tax year which runs from 6th April to 5th April, rather than any logical period like a calendar year
  • Classification of Market Regimes [Quant Dare]

    Understanding classification of market regimes is fairly important in finance. It all comes down to correctly predicting the way prices are going to move. But prediction isnt the only crucial thing; knowing how to describe what has already happened is also of great importance. In this QuantDare post we look at types of classification of markets. We concentrate on their differences and suggest

Quantocracy’s Daily Wrap for 04/16/2019

This is a summary of links featured on Quantocracy on Tuesday, 04/16/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Factors that Plague Factor Investing [Alpha Architect]

    For those interested in the literature on factor-based investing, a new paper by Robert Arnott, Campbell Harvey, Vitali Kalesnik and Juhani Linnainmaa, Alices Adventures in Factorland: Three Blunders That Plague Factor Investing, focuses on why, in some ways, it has failed to live up to its promise (or hype). Their study covers the period July 1963-June 2018. The authors raise the
  • The seven reasons most econometric investments fail [Mathematical Investor]

    Marcos Lopez de Prado, recently named 2019 Quant of the Year by the Journal of Portfolio Management, has released a presentation entitled The seven reasons most econometric investments fail. Lopez de Prados overall point is that many widely used econometric approaches in finance either rely on misleading p-value statistics, or else rely on strong assumptions that are typically not satisfied by
  • Warren Buffet: The Greatest Factor Investor of All Time? [Factor Research]

    A factor exposure of Berkshire Hathaway reveals structural factor tilts Long Value, Size, Quality, and Low Volatility factors and short Growth and Dividend Yield Warren Buffet generated little alpha, but is highly skilled at harvesting factor returns SAINTS AND STAR INVESTORS The Vatican waits at least five years after a person died before it begins considering whether they are worthy of
  • Aggregate Investor Confidence in the Stock Market [Alpha Architect]

    What are the Research Questions? A common assumption in finance theory is that agents in the stock market behave rationally. Even if temporary mispricing occurs, due to irrational beliefs or incomplete information of some agents, arbitrageurs swiftly restore equilibria. In contrast, the history of stock markets yields rich evidence of events ( Crash of 1929, the Black Monday in 1987, the dot-com

Quantocracy’s Daily Wrap for 04/15/2019

This is a summary of links featured on Quantocracy on Monday, 04/15/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Speed Limit of Trend [Flirting with Models]

    Trend following is mechanically convex, meaning that the convexity profile it generates is driven by the rules that govern the strategy. While the convexity can be measured analytically, the unknown nature of future price dynamics makes it difficult to say anything specific about expected behavior. Using simulation techniques, we aim to explore how different trend speed models behave for

Quantocracy’s Daily Wrap for 04/11/2019

This is a summary of links featured on Quantocracy on Thursday, 04/11/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Investment Strategy in an Uncertain World [Alpha Architect]

    In 1921, University of Chicago Professor Frank Knight wrote the classic book Risk, Uncertainty, and Profit. An article from the Library of Economics and Liberty described Knights definitions of risk and uncertainty as follows: Risk is present when future events occur with measurable probability. Uncertainty is present when the likelihood of future events is indefinite or incalculable. In

Quantocracy’s Daily Wrap for 04/10/2019

This is a summary of links featured on Quantocracy on Wednesday, 04/10/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Learning to Rank with TensorFlow [Quant Dare]

    Alphabet, the largest Internet-based company, has based its success on sophisticated information retrieval algorithms since its origins. Now, 20 years later, one of its divisions is open-sourcing part of its secret sauce, drawing attention from developers all over the world. Since Google was founded back in 1998, it has grown from a simple Ph.D. research project to one of the largest companies in
  • The Problem With Unfilled Gaps Down From Intermediate-Term Highs [Quantifiable Edges]

    I saw some bullish studies emerge last night. But there was a study below that was not favorable that I thought readers would find interesting. One potential issue with Tuesdays decline is that it included an unfilled gap down. Generally, an unfilled gap down from a high has more trouble quickly rebounding that a decline that does not include an unfilled gap. Unfilled gaps from high levels will