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Quantocracy’s Daily Wrap for 06/13/2020

This is a summary of links featured on Quantocracy on Saturday, 06/13/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Portfolio simulations [OSM]

    In our last post, we compared the three most common methods used to set return expectations prior to building a portfolio. Of the threehistorical averages, discounted cash flow models, and risk premia modelsno single method dominated the others on average annual returns over one, three, and five-year periods. Accuracy improved as the time frame increased. Additionally, aggregating all three

Quantocracy’s Daily Wrap for 06/12/2020

This is a summary of links featured on Quantocracy on Friday, 06/12/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Monster Factor Correlation Chart [Alpha Architect]

    We recently added a monster correlation matrix to our factor library data sheet that maps out the correlation of all the factors against every other factor. Here is a sample output that highlights the difference in the correlations between value factors and different quality factors. Note the big difference between book to market, which has a negative correlation to quality, and the other metrics
  • Research Review | 12 June 2020 | Forecasting [Capital Spectator]

    Breaking Bad Trends Ashish Garg (Research Affiliates), et al. May 7, 2020 We document and quantify the negative impact of trend breaks (i.e., turning points in the trajectory of asset prices) on the performance of standard trend-following strategies across several assets and asset classes. The frequency of trend breaks has increased in recent years, which can help explain the lower performance of

Quantocracy’s Daily Wrap for 06/11/2020

This is a summary of links featured on Quantocracy on Thursday, 06/11/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 5 Surprising Things We Learned from a Factor Investing Expert [Alpha Architect]

    Lu Zhang and his colleagues made some waves with their new paper, Replicating Anomalies. (now published in the RFS congrats!). We have a summary of the paper here. Lu Zhang, and his colleagues, Kewei Hou and Chen Xue, spent nearly 3 years carefully compiling and replicating 447 anomalies identified in the academic literature. The paper is so dense Ryan even created a blog post that
  • How to extend ETF prices with mutual fund data using SQL [Robot Wealth]

    On Zero to Robot Master Bootcamp, we teach how to build a portfolio of three automated systematic trading strategies. One of them is a long term Risk Premia Harvesting strategy which trades asset class ETFs. ETFs are useful instruments for analysing long term (tradeable) performance of various asset classes but many have been introduced only relatively recently and have limited data available.
  • Counterpoint: ETF Activity May Make the Stock Market MORE Efficient [Alpha Architect]

    The Securities and Exchange Commission (SEC) has called for more research and discussion on the impacts of ETFs. In a previous post, we covered why ETFs have not screwed up correlations, liquidity, and alpha opportunities. However, here is another post from Wes that outlines arguments that ETFs may screw up stock market efficiency. In short, how ETFs affect stock market efficiency is an ongoing

Quantocracy’s Daily Wrap for 06/08/2020

This is a summary of links featured on Quantocracy on Monday, 06/08/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 8 week algo trading course taught by full-time pro traders [Robot Wealth]

    Learn to research, build & trade three systematic trading strategies, from scratch, in 8 weeks. Join Bootcamp Today as featured by: The Financial Hacker In 8 weeks on the Zero to Robot Master Bootcamp, you will learn how to run a portfolio of three automated systematic trading strategies. These are the strategies that you will learn to build and trade on Zero to Robot Master bootcamp. Teach me
  • Tail Hedging [Flirting with Models]

    The March 2020 equity market sell-off has caused many investors to re-investigate the potential benefits of tail risk hedging programs. Academic support for these programs is quite limited, and many research papers conclude that the cost of implementation for nave put strategies out-weighs the potential payoff benefits. However, many of these studies only consider strategies that hold options to
  • Musings on Low Volatility [Factor Research]

    The Low Volatility strategy failed to protect investors in March and April 2020 Industrials & materials generated positive and technology & real estate negative relative performance Low Vol strategies do not deliver ESG benefits INTRODUCTION Low volatility (Low Vol) strategies have gained popularity over the past decade with retail and institutional investors alike. Although initially,

Quantocracy’s Daily Wrap for 06/07/2020

This is a summary of links featured on Quantocracy on Sunday, 06/07/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Curated list of libraries, packages and resources for Quants [Milton FMR]

    Numerical Libraries & Data Structures numpy NumPy is the fundamental package for scientific computing with Python. scipy SciPy (pronounced Sigh Pie) is a Python-based ecosystem of open-source software for mathematics, science, and engineering. pandas pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools
  • Downloading FX Pairs via Oanda API to Calculate Currency Strength Indicator [Dekalog Blog]

    In the past I have posted a series of blog posts about a Currency Strength Indicator (here, here, here and here). This blog post gives an Octave function to use Oanda's API to download all the 10 minute OHLC data required to calculate the above strength indicators on the 10 minute time frame. ## Copyright (C) 2020 dekalog ## ## This program is free software: you can redistribute it and/or

Quantocracy’s Daily Wrap for 06/05/2020

This is a summary of links featured on Quantocracy on Friday, 06/05/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Fat Tails Everywhere? Profiling Extreme Returns: Part II [Capital Spectator]

    Its long been established that stock market returns arent normally distributed and that fat tails (extreme returns that are unexpected for a normal distribution) apply. This has implications, of course, for portfolio design and management. The first question: What are the choices for managing tail risk for equity exposures? There are many answers, each with a different set of pros and cons.
  • Excess Returns Podcast: Systematic Value Investing [Alpha Architect]

    Recently I was invited to talk with Justin and Jack on the Excess Returns Podcast. I thank them for the opportunity and enjoyed the conversation! Below are some of the topics we discussed: Struggles in value and its long-term potential going forward. What would it take to convince you that value investing doesnt work anymore? Impact of the shutdown on earnings and how to look at analyzing

Quantocracy’s Daily Wrap for 06/04/2020

This is a summary of links featured on Quantocracy on Thursday, 06/04/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why Aren’t Call Options More Expensive Than Put Options? [Robot Wealth]

    Why arent calls more expensive than puts for an asset which is more likely to go up than down? We have an asset trading at $100 for which the distribution of future returns is a known fact. It has annual returns described by a normal distribution with mean 5% and standard deviation 10%. This is, therefore, an asset with positive drift. It is more likely to go up than down. Because we are
  • Do Interest Rates Explain Value s Underperformance? [Alpha Architect]

    From January 2017 through March 2020, the value premium, defined by HML (the return of high book-to-market stocks minus the return of low book-to-market stocks experienced a drawdown of 42 percent. 1 If we extend the period back to January 2007, the drawdown of about 51 percent is the largest ever. There have been many attempts to explain the reason for the dramatic underperformance; weve even

Quantocracy’s Daily Wrap for 06/03/2020

This is a summary of links featured on Quantocracy on Wednesday, 06/03/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why We Use Apache Beam For Our Systematic Trading Data Pipeline [Robot Wealth]

    In the world of Big Data, there are lots of tools and technologies to choose from. Choosing the right one depends on the things that you are building and the problems you are trying to solve. Trading firms have skilled teams that monitor and deploy data pipelines for their organisation and the technical overhead that comes with that. Firms invest in data infrastructure and research because
  • Variational autoencoder as a method of data augmentation [Quant Dare]

    In this blog weve talked about autoencoders several times, both as outliers detection and as dimensionality reduction. Now, we present another variation of them, variational autoencoder, which makes possible data augmentation. If you have ever faced Machine Learning problems, you will have dealt with the lack of data to train models. Well, this method will give you an interesting way of getting

Quantocracy’s Daily Wrap for 06/02/2020

This is a summary of links featured on Quantocracy on Tuesday, 06/02/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Petra on Programming: The Correlation Cycle Indicator [Financial Hacker]

    The previous article dealt with indicators based on correlation with a trend line. This time well look into another correlation-based indicator by John Ehlers. The new Correlation Cycle indicator (CCY) measures the price curve correlation with a sine wave. This works surprisingly well not for generating trade signals, but for a different purpose. Ehlers published the indicator together with
  • How I Explain Crappy Returns [Alpha Architect]

    Diversification. It looks great on paper, but for the past ten years, being globally and Factor diversified has been anything but great. Understandably, many diversified investors are looking at their returns and wondering why they should follow such a strategy when the S&P 500 which costs almost nothing has performed so well. To answer this question, lets come back to my
  • Machine learning is simply statistics – part 2 [Eran Raviv]

    Another opinion piece. If you cant explain it simply you dont understand it well enough. (Albert Einstein) Rant in progress A bit on Deep Learning What is so deep about deep learning? Nothing. There is nothing deep about it. If you read through the excellent Deep Learning book you can see (p. 167 in my copy) that a deep learning model with say three layers, omitting dependency on parameters,
  • Working with Tidy Financial Data in tidyr [Robot Wealth]

    Holding data in a tidy format works wonders for ones productivity. Here we will explore the tidyr package, which is all about creating tidy data. In particular, lets develop an understanding of the tidyr::pivot_longer and tidyr::pivot_wider functions for switching between different formats of tidy data. In this video, youll learn: What tidy data looks like Why its a sensible approach
  • How to Get (Almost) Free Tick Data [Black Arbs]

    Access to high quality, cost effective market data is a continuing problem for retail traders. I was recently told about the ongoing efforts of the startup brokerage Alpaca. The gentleman I spoke with said the API gave access to the tick data of thousands of stocks everyday and without cost. I thought it was too good to be true but recently I took a little bit of time to investigate. In this
  • Is value dead? Has the story changed? No. [Alpha Architect]

    Although there is widespread agreement that systematic value strategies have turned in at least a decade of underperformance, there is little agreement as to the underlying cause or cause(s). However, a number of rationalizations and critiques have emerged that question the long term viability of value strategies. The authors address in detail the relevant research or empirically test each of the
  • Mean Reversion Strategies in Python (Course Review) [Black Arbs]

    In this post I will be reviewing the course Mean Reversion Strategies by Dr. E.P. Chan ( His research and publications have garnered widespread appreciation, over the years. Unfortunately for Python programmers most of his past research was done in Matlab. Matlab was a very popular tool for researchers at one point but has been overtaken by the ubiquity of the Python programming language. So

Quantocracy’s Daily Wrap for 06/01/2020

This is a summary of links featured on Quantocracy on Monday, 06/01/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Exploiting The Non-Farm Payrolls Drift [Robot Wealth]

    Anyone thats been around the markets knows that the monthly release of the United States Department of Labors Non-Farm Payrolls (NFP) data can have a tremendous impact, especially in the short term. NFP is a snapshot of the state of the employment situation in the US, representing the total number of paid workers, excluding farm employees and public servants. We know your barn is hiding a
  • Bonds & The Invisible Thief [Factor Research]

    US bonds generated positive total returns in most inflation regimes Returns were mixed when inflation was above 4% Real returns were strongly negative when inflation was high INTRODUCTION Inflation is like cancer. It largely happens out of plain sight and requires focus to be noticed. The data is not easily understood and the perspective changes frequently. The outlook is difficult to predict. And