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Quantocracy’s Daily Wrap for 02/09/2019

This is a summary of links featured on Quantocracy on Saturday, 02/09/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Towards Better Keras Modeling [Alpha Scientist]

    The field of deep learning is frequently described as a mix of art and science. One of the most "art-sy" parts of the field, in my experience, is the subject of network topology design – i.e., choosing the right geometry, size, depth, and type of the network. Machine learning practitioners develop rules of thumb and instincts for reasonable starting points, and heuristics exist for how
  • Portfolio construction through handcrafting: Empirical tests [Investment Idiocy]

    This post is all about handcrafting; a method for doing portfolio construction which human beings can do without computing power, or at least with a spreadsheet. The method aims to achieve the following goals: Humans can trust it: intuitive and transparent method which produces robust weights Can be easily implemented by a human in a spreadsheet Can be back tested Grounded in solid theoretical
  • Understanding dollar cross-currency basis [SR SV]

    Covered interest parity is an arbitrage condition that equalizes costs of direct USD funding and of synthetic USD funding through FX swaps. Deviations are called dollar cross-currency basis and have become a common occurrence since the great financial crisis. A negative dollar basis means direct funding in USD if accessible is cheaper than synthetic funding via swaps. An apparent

Quantocracy’s Daily Wrap for 02/08/2019

This is a summary of links featured on Quantocracy on Friday, 02/08/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Smart Money Indicator: A New Risk Management Tool [Alpha Architect]

    We have all heard the mantra, You cant time the market! But in reality, investors attempt to do just that every day as part of their tactical asset allocation strategies, which are less extreme variants of the classic trend-following risk-on/risk-off approach, which many associate with market timing.(1) Moreover, numerous studies have shown that institutional investors routinely
  • Portfolio weightlifting (II) [Quant Dare]

    In a previous post, we took a look at the computation of a portfolios exposure to its allocations. Then, to show the effects of active management, we compared the return made by two portfolios. But there is so much more to look inside the financial time series. Since we left a couple of cliffhangers, lets jump into them now. Risk metrics First of all, lets begin with those hidden dangers

Quantocracy’s Daily Wrap for 02/04/2019

This is a summary of links featured on Quantocracy on Monday, 02/04/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Two Risks That Ruin Long-Run Investing [Two Centuries Investments]

    The first risk of investing is the Drawdown Risk – the loss from the peak. The second risk of investing is the Low Return Risk – the under-performance vs. expectations over a stretched period of time. First, a few words about drawdown. Quants measure risk in many ways like Volatility, Skew, Variance, Beta, Tracking Error etc – but, in my experience, clients care the most about the drawdown. Not
  • What Caused the Volatility Tsunami on 5-Feb-2018? [Six Figure Investing]

    In the afternoon of February 5th, 2018, what looked like a bad day for a group of high flying volatility-based products turned into a devastating decline. Four factors combined to ruin their day: A Flawed Architecture Relying on the Past to Predict the Future Billions Under Management A Record-Breaking VIX spike Twenty-five minutes before the close of the New York Stock exchange on February 5th,
  • Manager Sentiment and Stock Returns [Alpha Architect]

    What are the Research Questions? The authors investigate the asset pricing implications of corporate manager sentiment, focusing on its predictability for future U.S. stock market returns. Specifically, they ask the following research questions: Does high corporate manager sentiment lead to speculative market overvaluation? Is the predictive power of such an indicator stronger compared to other
  • No Pain, No Premium [Flirting with Models]

    In this commentary, we discuss what we mean by the phrase, no pain, no premium. We re-frame the discussion of portfolio construction from one about returns to one about risk and argue that without risk, there should be no expectation of return. With a risk-based framework, we argue that investors inherently act as insurance companies, earning a premium for bearing risk. This risk often
  • Over Two Centuries of Global Factor Premiums [Invest ReSolve]

    Hot off the press, a new paper by Guido Baltussen, Laurens Swinkels and Pim van Vliet at Dutch quant powerhouse, Robeco, covers global multi-asset factor premiums over an unprecedented sample of 217 years. We thought the topics and findings were important and timely enough to warrant a summary. The new paper, titled Global Factor Premiums examines global equity indexes, 10-year government
  • Storing time series data [Cuemacro]

    As regular readers of my posts may have realised, I kind of like burgers. Its a simple meal, but somehow very satisfying. Theres obviously vast differences between the burgers on sale, in terms of quality. It isnt necessarily the case that the most expensive burger will be best. In practice, excessively expensive burgers, end up having too many ingredients, which result in a burger which
  • The Basic Recipe For Rationalizing Errors In Belief [Alex Chinco]

    Behavioral-finance models are often written down so that, although each individual trader holds incorrect beliefs, market events nevertheless unfold in such a way that traders can rationalize their own errors. e.g., consider the model in Scheinkman and Xiong (2003). In this model, each individual trader knows that every other trader is over-confident, and he knows that every other trader thinks

Quantocracy’s Daily Wrap for 02/02/2019

This is a summary of links featured on Quantocracy on Saturday, 02/02/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in January [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
  • Why herding is the death of momentum [SR SV]

    Momentum trading, buying winning assets and selling losing assets, is a most popular trading strategy. It relies on sluggish market adjustment, allowing the trader to follow best-informed investors before the more inert part of the market does. Herding simply means that market participants imitate each others actions. Herding accelerates and potentially exaggerates market adjustments. The more

Quantocracy’s Daily Wrap for 02/01/2019

This is a summary of links featured on Quantocracy on Friday, 02/01/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Size and Value in China [Alpha Architect]

    What are the research questions? China represents the worlds second largest stock market and a growing component of the worlds GDP. China also operates under peculiar political and economic environments relative to the market economies of the Western world. Because China is so unique, a plausible research hypothesis is that traditional asset pricing models, such as the Fama-French 3-factor
  • Classic Cars as an Alternative Investment [CXO Advisory]

    Are some types of cars attractive alternative investments? In their September 2018 paper entitled My Kingdom for a Horse (or a Classic Car), Dries Laurs and Luc Renneboog investigate price determinants and investment performance of classic cars from veteran cars (built 1888-1907) through modern classics (1975-1990). They estimate returns and risks for several classic car price indexes via a

Quantocracy’s Daily Wrap for 01/30/2019

This is a summary of links featured on Quantocracy on Wednesday, 01/30/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • SPY TLT Rotation [Alvarez Quant Trading]

    For my retirement accounts, I like to trade ETF strategies that require little work. One strategy we have all seen is the SPY/TLT strategy. There are many flavors of this concept. Some pick the best one over the last N months. Then there are different ways of allocating a portion of the portfolio to each. I currently dont trade any SPY/TLT strategy and wanted to see if there was something
  • Can a Machine Learning Model Predict the SP500 by Looking at Candlesticks? [Mario Filho]

    Candlestick chart patterns are one of the most widely known techniques that claim to predict the market direction inside technical analysis circles. The development of this technique goes back to 18th century Japan, and its attributed to a Japanese rice trader. It consists of finding patterns based on charts made of the above figure with prices over a period of time. There are many
  • Marcos Lopez de Prado named 2019 Quant of the Year by Journal of Portfolio Management [Mathematical Investor]

    Marcos Lopez de Prado, a member of Mathematicians Against Fraudulent Financial and Investment Advice (MAFFIA), has been named 2019 Quant of the Year by Journal of Portfolio Management. Here are some excerpts from their press release: The Journal of Portfolio Management (JPM) has named Marcos Lopez de Prado 2019 Quant of the Year. JPM has instituted the annual Quant of the Year
  • Where is the Value? [Factor Investor]

    Investors always want to know whats cheapcheap relative to the opportunity set and relative to history. Cheapness could refer to any number of thingsprice relative to trailing twelve months earnings, to trailing earnings over multiple years, to analyst earnings estimates, to long-run projections, or a dozen other variations based on sales, cash flows, book value, etc. Because analyst

Quantocracy’s Daily Wrap for 01/29/2019

This is a summary of links featured on Quantocracy on Tuesday, 01/29/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Growing List of Long-Run Factor Studies [Two Centuries Investments]

    While there exists a well-established (at least a century-old) academic interest in the long-run properties of asset class returns like the U.S. Equity, Fixed Income, Commodity and Real Estate Markets, only during the past decade, has there emerged a branch of literature studying the cross-sectional factors like price momentum and value, as well as other effects like trend and volatility over the
  • Why Waiting Until The Announcement Is A Tough Way To Trade The Fed [Quantifiable Edges]

    Wednesday is a Fed Day a day in which the Federal Reserve concludes their scheduled meeting and releases a policy statement. Fed Days have historically shown a bullish inclination (up until Powell took over last year, as I showed on Sunday). One interesting aspect of Fed Days that I covered in the book is that the bullish inclinations have basically played out prior to the actual Fed

Quantocracy’s Daily Wrap for 01/28/2019

This is a summary of links featured on Quantocracy on Monday, 01/28/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tightening the Uncertain Payout of Trend-Following [Flirting with Models]

    Long/flat trend-following strategies have historically delivered payout profiles similar to those of call options, with positive payouts for larger positive underlying asset returns and slightly negative payouts for near-zero or negative underlying returns. However, this functional relationship contains a fair amount of uncertainty for any given trend-following model and lookback period. In
  • HFT-like Trading Algorithm in 300 Lines of Code You Can Run Now [Alpaca]

    Commission Free Trading API Trading with commission free API opened up many interesting ideas. Lots of people liked the idea of trading stocks using Google Spreadsheet, and some people have been building their own Slack integrations. You can even build a robo advisor that automates longer-term investment strategies. Manage Your Stocks from Google Spreadsheet Using API You might think API trading
  • The Failure of Factor Investing was Predictable [Alpha Architect]

    In a recent ETF column, Allan Roth listed five investment lessons. While I agreed with much of what he wrote, one claimfactor investing has failed miserably called for examination of the facts. But first, a little background. William Sharpe, Jack Treynor and John Linter are typically given most of the credit for introducing the capital asset pricing model (CAPM). The CAPM was the first
  • Cross Validation in Machine Learning Trading Models [Quant Insti]

    The application of the machine learning models is to learn from the existing data and use that knowledge to predict the future unseen events. The model needs to be thoroughly tested and cross-validated to profitably trade in live trading. After reading this, you will be able to: Cross validate whether your model is good in predicting buy signal and/or sell signal Demonstrate the performance of
  • Value, Momentum and Carry Across Asset Classes [Factor Research]

    Cross-asset multi-factor exposure might be an attractive diversifier for an equity portfolio Factors share trends across asset classes, indicating common drivers However, relationships are time-varying, increasing complexity and risks INTRODUCTION There is a 72% probability of the San Franciso Bay Area getting hit by at least one earthquake of a magnitude of 6.7 or stronger between today and 2043
  • Last Chance for Early Bird Pricing: AI and Data Science in Trading Conference, NYC March 2019

    There is so much hype and confusion surrounding AI and alt data at the moment. The AI & Data Science in Trading conference separates the hype from the reality Professor David Hand, Imperial College, London Finding alpha has always required asset managers to raise the bar in terms of technology. Today, the combination of endless new data sources, cheap computing and new AI techniques

Quantocracy’s Daily Wrap for 01/27/2019

This is a summary of links featured on Quantocracy on Sunday, 01/27/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Value, Momentum & Carry Across Asset Classes [Alpha Architect]

    There is a 72% probability of the San Franciso Bay Area getting hit by at least one earthquake of a magnitude of 6.7 or stronger between today and 2043 according to the United States Geological Survey, which is a scientific agency of the U.S. government. An earthquake of that magnitude is likely to cause major damage in populated areas, especially if accompanied by a tsunami. Given high
  • R tips and tricks higher-order functions [Eran Raviv]

    A higher-order function is a function that takes one or more functions as arguments, and\or returns a function as its result. This can be super handy in programming when you want to tilt your code towards readability and still keep it concise. Consider the following code: # Generate some fake data > eps > x > y # Load libraries required > library(quantreg) > library(magrittr) >
  • Is Jerome Powell The Most Hated Fed Chairperson Ever? [Quantifiable Edges]

    Fed Days have a long history of showing a bullish tendency, and we have a large number of Fed Day studies to refer. For those that are unaware, a Fed Day is simply a day where the Federal Reserve completes a scheduled meeting and provides a policy announcement. Meetings typically take place 8 times per year, and in recent years the meetings have all been 2 days in length, with the 2nd day being
  • Compound Your Knowledge: Episode 2-ESOPs, Factors, Incentives [Alpha Architect]

    In todays video, we examine three posts. First, we examine ESOPs and 1042 QRP (qualified replacement property) with Doug Pugliese. Second, we examine a guest post by Nicolas Rabener examining Value, Momentum and Carry over the past 10 years. Last, we examine a guest post by Elisabetta discussing a new paper on rankings/incentives.

Quantocracy’s Daily Wrap for 01/24/2019

This is a summary of links featured on Quantocracy on Thursday, 01/24/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Right Now It s KDA Asset Allocation [QuantStrat TradeR]

    This post will introduce KDA Asset Allocation. KDA I.E. Kipnis Defensive Adaptive Asset Allocation is a combination of Wouter Kellers and TrendXplorers Defensive Asset Allocation, along with ReSolve Asset Managements Adaptive Asset Allocation. This is an asset allocation strategy with a profile unlike most tactical asset allocation strategies Ive seen before (namely, it barely
  • Algorithmically Detecting (and Trading) Technical Chart Patterns with Python [Alpaca]

    Defining Technical Chart Patterns Programmatically Ever wondered how to programmatically define technical patterns in price data? At the fundamental level, technical patterns come from local minimum and maximum points in price. From there, the technical patterns may be defined by relative comparisons in these min/max points. Lets see if we could have played this by algorithmically identifying
  • The Stay Rich Portfolio (or, How to Add 2% Yield to Your Savings Account) [Meb Faber]

    In 2012, Eike Batista had an estimated worth of more than $35 billion. The self-made Brazilian billionaire created an empire that stretched from mining to oil to public works. Many considered him the pride of Brazil. Barely two years later, he had lost all $35 billionand owed another $1.2 billion to creditors. How does this happen? How does a $35 billion portfolio evaporate practically