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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

Quantocracy’s Daily Wrap for 04/09/2019

This is a summary of links featured on Quantocracy on Tuesday, 04/09/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tail risk of systematic investment strategies and risk-premia alpha [Artur Sepp]

    Everyone knows that the risk profile of systematic strategies can change considerably when equity markets turn down and volatilities spike. For an example, a smooth profile of a short volatility delta-hedged strategy in normal regimes becomes highly volatile and correlated to equity markets in stressed regimes. Is there a way to systematically measure the tail risk of investment products including
  • S&P500 – when to be invested [Philipp Kahler]

    S&P500 when to be invested The stock market shows some astonishingly stable date based patterns. Using a performance heat map of the S&P500 index, these patterns are easily found. Date based performance The chart below shows the profit factor of a long only strategy investing in the S&P500. Green is good, red is bad. The strategy is strictly date based. It always buys and sells on
  • A Remarkable New Factor: The Cash Conversion Cycle [Alpha Architect]

    The barrier to entry into the factor zoo has increased exponentially. Prof. Harvey (now working with RAFI) made this clear at the 2017 AFA address, when he highlighted the issue with data-mining in front of a room full of academics from top-flight research programs in the country. Prof. Harvey and his colleague Yan Liu go a bit further in their recent working paper, stating the following: The rate
  • Equity Factor Census [CXO Advisory]

    Should investors trust academic equity factor research? In their February 2019 paper entitled A Census of the Factor Zoo, Campbell Harvey and Yan Liu announce a comprehensive database of hundreds of equity factors from top academic journals and working papers through January 2019, including a link to citation and download information. They distinguish among six types of common factors and

Quantocracy’s Daily Wrap for 04/08/2019

This is a summary of links featured on Quantocracy on Monday, 04/08/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The First Risk and Opportunity in Active Investing [Two Centuries Investments]

    What is the most significant risk in quant (and all active) investing today? The First Moment (the mean) The Second Moment (under-estimating tracking error) The Third Moment (skewness, left tails, crash risk) Mis-specified risk model (hidden factor biases, factors eating alphas) Sub-optimal portfolio construction methodology (error maximization) Higher than expected transaction costs
  • Compound Your Knowledge Episode 7: Momentum & Short Sellers [Alpha Architect]

    In todays video, we examine three articles from last week. The first article, written by Larry Swedroe, examines the Momentum of News. The second article, written by Wes, examines an out-of-sample test on Momentum by looking at Russian stocks in the 19th century. The third article, written by Tommi, examines the impacts that short sellers have within marketsare they manipulative or do they
  • Revisiting The Weird Portfolio [Flirting with Models]

    A few years ago, we blindly applied mean-variance optimization to a set of capital market assumptions, and The Weird Portfolio was born. This portfolio is weird because it does not look like typical investor portfolios since it tilts heavily toward credit-based and alternative asset classes. Despite having weird allocations, the portfolio actually performed in line with the iShares Core Moderate
  • Multi-Factor Smart Beta ETFs [Factor Research]

    Investors have leaned towards multi-factor over single-factor products in recent years The factor selection and portfolio construction of multi-factor ETFs can be challenged Multi-factor ETFs often feature factors, such as growth, which are not supported by academic research while lacking exposure to established ones such as quality and momentum INTRODUCTION Investors showed a strong preference

Quantocracy’s Daily Wrap for 04/05/2019

This is a summary of links featured on Quantocracy on Friday, 04/05/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The most overlooked aspect of algorithmic trading [EP Chan]

    Many algorithmic traders justifiably worship the legends of our industry, people like Jim Simons, David Shaw, or Peter Muller, but there is one aspect of their greatness most traders have overlooked. They have built their businesses and vast wealth not just by sitting in front of their trading screens or scribbling complicated equations all day long, but by collaborating and managing other
  • A Simple Mean Reversion Stock Trading Script in C# [Trevor Thackston]

    Python is not the only language In the past, Ive published stories on Medium showing how to write algorithms that trade stocks based on company fundamentals and how to run a technical analysis day trading algorithm in the cloud. Both of those articles assumed that: Python was the language the reader wanted to use. You had access to an Alpaca brokerage account and could therefore use Polygons
  • Low Volume At Highs Does Not Provide The Short-Term Bearish Edge It Once Did [Quantifiable Edges]

    Years ago, strong overbought readings during an uptrend were easily sold especially when volume came in very light. But that has not held true in recent years. There were several studies I examined last night that noted the low volume, but they have all lost their edge over the last several years. An example can be seen in the chart below, which is representative of the results I was seeing.
  • Tests of Constant and Variable Acceleration Model Kalman Filters [Dekalog Blog]

    In my last post I said that this next post would report the results of tests on a Constant Acceleration model Kalman filter, and the results are: fail, just like the Constant Velocity model, so I won't bore readers with reporting the details of the failed tests. However, tests of a Variable Acceleration model have been more successful, and so this post is about the results of tests on this
  • State of Trend Following in March [Au Tra Sy]

    Slightly negative result for the State of Trend Following last month, leaving the YTD number in slight positive territory. Please check below for more details. Detailed Results The figures for the month are: March return: -0.22% YTD return: 2.55% Below is the chart displaying individual system results throughout March: StateTF March And in tabular format: System March Return YTD Return BBO-20

Quantocracy’s Daily Wrap for 04/04/2019

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

  • The Momentum of News [Alpha Architect]

    Since the development of the capital asset pricing model (CAPM) in the 1960s, hundreds of anomalies (what John Cochrane famously called a zoo of new factors) have been uncovered in the cross-section of stock returns. While some of the anomalies (such as the size and value factors) have risk-based explanations, others (such as momentum) have behavioral-based explanations and thus demonstrate
  • Wisdom State of Trend Following – March 2019 [Wisdom Trading]

    Please find this months report of the Wisdom State of Trend Following. Performance is hypothetical. Chart for March: The chart for the first quarter: And the 12-month chart: Below are the summary stats: Horizon Return Ann. Vol. Last month 1.49% 12.99% Year To Date -6.21% 12.56% Last 12 months -10.53% 14.61% Last calendar year (2018) -10.27% 15.83% Since Index Launch (08-13) -12.37% 13.92%
  • Mutual Fund Investors Irrationally Naive? [CXO Advisory]

    Do retail investors rationally account for risks as modeled in academic research when choosing actively managed equity mutual funds? In their March 2019 paper entitled What Do Mutual Fund Investors Really Care About?, Itzhak Ben-David, Jiacui Li, Andrea Rossi and Yang Song investigate whether simple, well-known signals explain active mutual fund investor behavior better than academic asset