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Quantocracy’s Daily Wrap for 12/15/2019

This is a summary of links featured on Quantocracy on Sunday, 12/15/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Tale of an Edgy Panda and some Python Reviews [QuantStrat TradeR]

    This post will be a quickie detailing a rather annoyingfinding about the pandas package in Python. For those not in the know, Ive been taking some Python courses, trying to port my R finance skills into Python, because R seems to have fallen out of favor in the world of finance. (If you know of an opportunity, heres my resume.) So, Im trying to get my Python skills going, hopefully
  • Rebalancing and market price distortions [SR SV]

    Price distortions are an important source of short-term trading profits, particularly in turbulent markets. Here price distortions mean apparent price-value gaps that arise from large inefficient flows. An inefficient flow is a transaction that is not motivated by rational risk-return optimization. One source of such inefficient flows is rebalancing, large-scale institutional transactions

Quantocracy’s Daily Wrap for 12/13/2019

This is a summary of links featured on Quantocracy on Friday, 12/13/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quant’s Look on ESG Investing Strategies [Quantpedia]

    ESG Investing (sometimes called Socially Responsible Investing) is becoming a current trend, and its proponents characterize it as a modern, sustainable, and responsible way of investing. Some people love it, others see it as just another fad that will soon be forgotten. We at Quantpedia have decided to immerse in academic research related to this trend to understand it better. How are ESG scores
  • Trend-Following Plus Momentum in ETFs [Alvarez Quant Trading]

    In a previous post, Trend-following vs. Momentum in ETFs, I compared trend-following and momentum to see which produced better results on a basket of ETFs. In the post, I mentioned combining trend-following and momentum into one strategy to see if combined they can beat buy and hold more often. Testing Info Dates: 1/1/2006 to 10/31/2019 The basket of EYFs is AGG, DBX, EEM, EFA, GLD, HYG, IWM, LQD,
  • Improving the Performance of Deep Value Strategies [Alpha Architect]

    A large body of evidence demonstrates that investment strategies focused on buying stocks that are cheap relative to measures of fundamental value have achieved higher long-term returns than the broad market. Motivated by such legendary investors as Benjamin Graham, David Dodd, and Walter Schloss, deep value investors look to build portfolios of stocks with the cheapest valuations relative to

Quantocracy’s Daily Wrap for 12/11/2019

This is a summary of links featured on Quantocracy on Wednesday, 12/11/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Dimensionality reduction method through autoencoders [Quant Dare]

    Weve already talked about dimensionality reduction long and hard in this blog, usually focusing on PCA. Besides, in my latest post I introduced another way to reduce dimensions based on autoencoders. However, in that time I focused on how to use autoencoders as predictor, while now Id like to consider them as a dimensionality reduction technique. Just a reminder about how autoencoders work.
  • Historical Fed Day Performance By Chairperson [Quantifiable Edges]

    I have written about Fed Day edges for years. Much of the research can be found in the Fed Study category blog posts. Today I decided to share a chart showing historical performance on Fed Days over the course of the last 5 Fed chairpeople. 2019-12-10 Have a happy Fed Day tomorrow!

Quantocracy’s Daily Wrap for 12/10/2019

This is a summary of links featured on Quantocracy on Tuesday, 12/10/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New and Improved Sharpe Ratio adjustment in the handcrafting method [Investment Idiocy]

    In my recent posts on skew and kurtosis I've put together a large number of ideas for possible trading strategies. The next step will be to create and test these ideas out. However I already know from my initial analysis that many of these ideas will probably have poor performance. This leaves me in something of a conundrum. In fact this is just one example of the conundrum that strategy
  • Monte Carlo Simulation: Definition, Example, Code [Quant Insti]

    Years ago, I had made it to the nal round in an interview for a Senior Delta One/Quantitative Futures position at an HFT rm (unnamed for privacy). Things were going well, I had answered two out of three of those ridiculous questions that are only applicable in Subsaharan Africa or Finance interviews (Like how to get 5 gallons from a 6 and 4-gallon jug); I was feeling good. They asked me

Quantocracy’s Daily Wrap for 12/09/2019

This is a summary of links featured on Quantocracy on Monday, 12/09/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • US nonfarm employment prediction using RIWI Corp alternative data [EP Chan]

    The monthly US nonfarm payroll (NFP) announcement by the United States Bureau of Labor Statistics (BLS) is one of the most closely watched economic indicators, for economists and investors alike. (When I was teaching a class at a well-known proprietary trading firm, the traders suddenly ran out of the classroom to their desks on a Friday morning just before 8:30am EST.) Naturally, there were many
  • How to Evaluate Smart Beta ETFs [Factor Research]

    Smart beta ETFs can be compared via a factor score, which relates fees to the factor exposure Value-focused ETFs in the US show a wide range of factor scores Large firms offer more attractive factor scores, but largely due to lower fees INTRODUCTION Beta is like ice cream and comes in many flavors. Broadly we can categorize it into the following four types: Plain beta: Market
  • A Conversation on Rebalance Timing Luck [Flirting with Models]

    My guest today is me. But rather than interview myself, my co-portfolio manager Nathan Faber joins the podcast to take the reigns. In this episode, we talk all things rebalance timing luck. Its been an obsession of mine for years and something we believe to be a dramatically misunderstood and outright ignored source of risk in portfolios. We discuss how we first came across the topic, some
  • Protecting the Downside of Trend When It Is Not Your Friend : Part 1 [Alpha Architect]

    Weve done a poor job hiding our interest in Trend Following (see Trend, Trend, Trend, is your friend. And swing over to Corey Hoffsteins site for even more!). So this paper hits on a subject we know and love. The authors of this study (part 1) have one basic objective: determine if the downside performance of a simple trend-following strategy can be improved by either adding complexity to

Quantocracy’s Daily Wrap for 12/08/2019

This is a summary of links featured on Quantocracy on Sunday, 12/08/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Lessons learned building ML trading system that turned $5k into $200k (h/t @PyQuantNews) [Tradient]

    One of my recent side projects was building an automated trading system for the crypto markets. To be fair, I probably spent more time on this than on my full-time job, so calling it a side project may not be completely accurate. The internet is full of people ready to teach you about trading. Most are trying to sell you something, and many are mistaking random chance for skill. Coming from a
  • Marcos @LopezDePrado testifies before U.S. Congress [Mathematical Investor]

    Famed quantitative financial mathematician Marcos Lopez de Prado, who was recently featured as Master of the Robots by Bloomberg, testified today (6 December 2019) before the U.S. Congress, together with four other panelists. The topic for the panel, organized by the U.S. House Committee on Financial Services, was Robots on Wall Street: The Impact of AI on Capital Markets and Jobs in the Financial
  • Equity return anomalies and their causes [SR SV]

    The vast range of academically researched equity return anomalies can be condensed into five categories: [1] return momentum, [2] outperformance of high valuation, [3] underperformance of high investment growth, [4] outperformance of high profitability, and [5] outperformance of stocks subject to trading frictions. A new empirical analysis suggests that these return anomalies are related to market

Quantocracy’s Daily Wrap for 12/06/2019

This is a summary of links featured on Quantocracy on Friday, 12/06/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Yield Curve Empirics [Scalable Capital]

    Interest rates measure the level of compensation that financial market participants get for lending money. The level of compensation for lending varies over time and is related to several other economic factors. Most important ones are rates set by central banks, inflation rates and underlying credit risks. Bonds with non-negligible credit risks trade at a spread compared to risk-free bonds, but
  • Using Kalman filters to derive predictive factors from limit order book data [Alex Botsula]

    This post is based on the experience I have got while taking part in a very interesting forecasting competition hosted by XTX. Participants were challenged by the task to forecast the future return of a (presumably) Forex asset based on the limit order book (LOB) data. No details of the asset or limit order book dates were disclosed as part of the competition. As part of the competition, XTX
  • Tactical Asset Allocation in November [Allocate Smartly]

    This is a summary of the recent performance of a wide range of excellent Tactical Asset Allocation (TAA) strategies, net of transaction costs. These strategies are sourced from books, academic papers, and other publications. While we dont (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Learn more about what we do or let AllocateSmartly help
  • Global Impact of Investor Home Country Bias [Alpha Architect]

    A large body of research demonstrates that familiarity breeds investment. For example, a study by Gur Huberman found that shortly after AT&T was broken up and shareholders were given shares in each of what were called the Baby Bells, the residents of each region held a disproportionate number of shares of their regional Bell. Each group of regional investors was confident their regional

Quantocracy’s Daily Wrap for 12/05/2019

This is a summary of links featured on Quantocracy on Thursday, 12/05/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Experiments with GANs for Simulating Returns [EP Chan]

    Simulating returns using either the traditional closed-form equations or probabilistic models like Monte Carlo has been the standard practice to match them against empirical observations from stock, bond and other financial time-series data. (See Chan and Ng, 2017 and Lopez de Prado, 2018.) Some of the stylised facts of return distributions are as follows: The tails of an empirical return
  • Leveraged Trading [Following the Trend]

    I tend to be a little skeptical when I see books aimed at retail traders with low amount of trading capital, focusing on leveraged trading on FX, CFDs and the like. The very mention of retail forex trading means that theres a near certainty that whatever comes next is misinformed at best and a scam at worst. But seeing my favorite investment author release a book on the topic got my attention.
  • The 60/40 Benchmark Portfolio [Quant Start]

    In a recent article we introduced systematic tactical asset allocation (TAA) as a low-frequency example of quantitative trading strategy. For those who are taking their first steps in systematic trading, are wanting to consider systematic trading in the context of their retirement planning or are simply wishing to minimise the day-to-day work of running a strategy TAA can be a great choice.

Quantocracy’s Daily Wrap for 12/04/2019

This is a summary of links featured on Quantocracy on Wednesday, 12/04/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Adaptive VIX Moving Average with Ehlers Alpha Formula [CSS Analytics]

    In the last post I described a relatively simply method to incorporate the VIX into the well-known AMA or Adaptive Moving Average framework. The alpha formula requires two separate parameters- a short and a long-term constant which requires greater specification by the user. Ideally the fewer parameters you have to specify the better (although it is important to note that logical requirements for
  • Mitigating overfitting on Trading Strategies [Quant Dare]

    According to Wikipedia in finance, a trading strategy is a fixed plan that is designed to achieve a profitable return by going long or short in markets. The main reasons that a properly researched trading strategy helps are its verifiability, quantifiability, consistency, and objectivity. For every trading strategy, one needs to define assets to trade, entry/exit points, and money management

Quantocracy’s Daily Wrap for 12/03/2019

This is a summary of links featured on Quantocracy on Tuesday, 12/03/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Jim Simons: The man who solved the market [Mathematical Investor]

    Gregory Zuckerman, author of The Greatest Trade Ever, has published a new book highlighting the life and work of Jim Simons, who, at the age of 40, walked away from a very successful career as a research mathematician and cryptologist to try his hand at the financial markets, and ultimately revolutionized the field. Zuckermans new book is titled The Man Who Solved the Market: How Jim Simons
  • How to Choose the Best Period for Indicators [Quantpedia]

    Academic literature recognizes a large set of indicators or factors that are connected with the various assets. These indicators can be utilized in a variety of trading strategies, which means that such indicators are popular among practitioners who seek to invest their funds. Usually, the indicators are connected with some evaluation period. For example, we have a trend following strategy using a