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Quantocracy’s Daily Wrap for 11/16/2019

This is a summary of links featured on Quantocracy on Saturday, 11/16/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • IPO Exploration: Part 1 [Reproducible Finance]

    Inspired by recent headlines like Fear Overtakes Greed in IPO Market after WeWork Debacle and This Years IPO Class is Least Profitable since the Tech Bubble, today well explore historical IPO data and next time well look at the the performance of IPO driven-portfolios constructed during the ten-year period from 2004 – 2014. Ill admit Ive often wondered how a portfolio that allocated
  • Endogenous market risk: updated primer [SR SV]

    Endogenous risk arises from the interaction of financial market participants, as opposed to traded assets fundamental value. It often manifests as feedback loops after some exogenous shock. An important type of endogenous market risk is setback risk, which refers to the asymmetry of the upside and downside potential of a trade that arises from market positioning. Setback risk is a proclivity to
  • State of Trend Following in October [Au Tra Sy]

    A negative October takes the State of Trend Following index in the red for the year. Please check below for more details. Detailed Results The figures for the month are: October return: -3.92% YTD return: -3.23% Below is the chart displaying individual system results throughout October: StateTF October And in tabular format: System October Return YTD Return BBO-20 -4.67% 7.06% Donchian-20 -6.32%

Quantocracy’s Daily Wrap for 11/14/2019

This is a summary of links featured on Quantocracy on Thursday, 11/14/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Podcast: Jim Simons – The pinnacle of trading greatness w/ author @GZuckerman [Chat With Traders]

    Gregory Zuckerman is a writer at the Wall Street Journal and author of The Man Who Solved the Market: How Jim Simons Launched The Quant Revolution. For anyone unfamiliar, Jim Simons is the brilliant-minded mathematician who founded hedge fund Renaissance Technologies. Using quantitative models and with billions in AUM, Renaissance has averaged annualized returns of net 39% since 1988. And these
  • How to avoid unwanted curve fitting during backtest [Philipp Kahler]

    Whenever you develop an algorithmic trading strategy, curve fitting is one of the most dangerous hazards. It will lead to severe losses in real time trading. This article will show you some ways to detect if the performance of your algorithmic trading strategy is based on curve fitting. Curve fitting what is it? Every algorithmic trading strategy will have some parameters. There is no way
  • The Investment Factor and Expected Returns [Alpha Architect]

    It is well documented in the literature that over the long term, low-investment firms have outperformed high-investment firms.(1) This finding has led to the investment factor (CMA, or conservative minus aggressive) being incorporated into the leading asset pricing modelsthe four-factor Q model (market beta, size, investment and profitability), the Fama-French five-factor model that adds value,
  • Hiring a Software Developer to Code Up a Trading Strategy [Quant Start]

    At QuantStart we place an emphasis on fully automated systematic trading and the processes that surround it. However we should be careful to distinguish between the separate concepts of systemisation and automation. The former involves a trading strategy that can be codified into a set of rules, which canand often iscalculated and traded in a manual fashion. The latter encompasses the case

Quantocracy’s Daily Wrap for 11/13/2019

This is a summary of links featured on Quantocracy on Wednesday, 11/13/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trend-following vs. Momentum in ETFs [Alvarez Quant Trading]

    In Tactical Asset Allocation (TAA) or Dual Momentum (DM) strategies, they often will use trend-following or momentum to decide whether to invest in asset or not. I have two questions. One, how often does either trend-following or momentum they beat buy and hold? Two, of the two which one beats the other more often? Trend-Following Rules Buy Last day of month Close is greater than the 10-month
  • Investment, Expected Investment, and Expected Stock Returns [Alpha Architect]

    A new DFA article by Rizova and Saito (2019, Investment and Expected Stock Returns) (1) rehashes previous arguments in Fama and French (2006, 2015) on the investment factor. The core arguments are as follows: Valuation theory predicts that expected investment is negatively correlated with expected return, all else equal; and Current asset growth is a good proxy for expected investment.

Quantocracy’s Daily Wrap for 11/12/2019

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

  • Kurtosis and expected returns [Investment Idiocy]

    In my last post, I stated my intention to write a series of posts about skew. Slight change of plan, since one loyal reader suggested that I write about kurtosis. I thought that might be fun, since I haven't thought about kurtosis much, and the literature on kurtosis isn't as well developed. It turns out that considering both together leads to some very interesting results. The plan is
  • The Man Who Solved the Market Notes [Systematic Edge]

    When it comes to the worlds most secretive hedge fund any content is worthwhile to read. I finished the book is 3 days and had to re-read a couple more chapters to ensure I fully absorbed the couple nuggets in there. I would recommend this book to everyone! The mystery behind how Simons discovered the truth is shrouded in mystery. Even googling about what they traded doesnt yield many
  • Investor IQ Website is Live (In Beta) [CSS Analytics]

    For readers interested in getting signals and analytics on hundreds of ETFs and individual stocks our Investor IQ website is currently live and free during our beta-testing phase. We will be adding new data and analytics gradually over time as well as improving website functionality. The Economic Model is currently hosted on the site and predictions are updated every 2-3 days in real-time.
  • Are Early Stage Investors Biased Against Women? [Alpha Architect]

    Recent studies of startup activity in the U.S. find that only roughly 1015% of startups are founded by women. There are a number of potential explanations including gender differences in technical training or risk preferences. However, many have also speculated that part of the gender gap may, in fact, be due to a lower propensity for investors to fund female entrepreneurs seeking capital. This

Quantocracy’s Daily Wrap for 11/11/2019

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

  • Robot Wealth 6 Week Bootcamp – Trading with Machine Learning – Enrollment Ends Friday [Robot Wealth]

    Use machine learning to find profitable trades in big data sets, boost the performance of existing strategies, and build a winning trading portfolio Use machine learning and distributed computing to solve the universe selection problem in an equity pairs trading operation Build a framework for trade filtering that can be applied to any strategy to improve performance Work with a team of retail
  • The Limit of Factor Timing [Flirting with Models]

    We have shown previously that it is possible to time factors using value and momentum but that the benefit is not large. By constructing a simple model for factor timing, we examine what accuracy would be required to do better than a momentum-based timing strategy. While the accuracy required is not high, finding the system that achieves that accuracy may be difficult. For investors focused on
  • Two Centuries of Creativity Quantified [Two Centuries Investments]

    In the past, I shared thoughts about the need to combine creative and organized thinking in order to generate investment alpha. A robust investment process and a strict disciplined application are concepts that one often hears in typical quant and fundamental investment teams. These are important but not sufficient to generate out-performance. In addition, readers know that I believe
  • The Case Against REITs [Factor Research]

    Real estate stocks featured moderate correlations to stock markets over the last 30 years However, diversification benefits for equity portfolios were only marginal Other strategies provide similar yield and downside protection characteristics INTRODUCTION Surveys often reveal investor behaviour that is challenging to understand. For example, Preqins Alternative Investor Outlook for H2 2019
  • Combine Market Trend and Economic Trend Signals? [CXO Advisory]

    A subscriber requested review of an analysis concluding that combining economic trend and market trend signals enhances market timing performance. Specifically, per the example in the referenced analysis, we look at combining: The 10-month simple moving average (SMA10) for the broad U.S. stock market. The trend is positive (negative) when the market is above (below) its SMA10. The 12-month simple

Quantocracy’s Daily Wrap for 11/08/2019

This is a summary of links featured on Quantocracy on Friday, 11/08/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What s The Best Methodology For Measuring Drawdown Risk? [Capital Spectator]

    The possibilities for quantifying risk in portfolio analytics seems to be limited only by the imagination of researchers. Indeed, you can find dictionaries that wade through an ever-lengthening list of indicators. But any short list of robust metrics surely deserves to include drawdown, which offers a powerful combination of relevance and simplicity. A new research paper reminds, however, that

Quantocracy’s Daily Wrap for 11/05/2019

This is a summary of links featured on Quantocracy on Tuesday, 11/05/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Buying Global Stocks at All-Time Highs [Allocate Smartly]

    This analysis was inspired by EconomPic and Meb Faber. Here we test a simple strategy that goes long Global Stocks (ACWI) when they make a new all-time month-end high, otherwise US bonds. The takeaway: Dont fear buying stock (indices) when they close at all-time highs. A new high shouldnt be your only reason for buying stocks, but its not in and of itself a reason to shy away. Test
  • Combinatorial Purged Cross-Validation Explained [Quantoisseur]

    In this tutorial I explain how to adapt the traditional k-fold CV to financial applications with purging, embargoing, and combinatorial backtest paths.
  • Engineering To Quant Finance – How To Make The Transition [Quant Start]

    At QuantStart we often receive email queries about the possibility of making a career transition to quantitative finance, particularly for individuals who currently consider themselves mid-career. In a more general sense we have previously discussed whether it is possible to become a quant during your thirties. However, for those with more specific technical expertiseespecially those with a
  • Is Buying Stocks at an All-Time High a Good Idea? [Meb Faber]

    No, its not a good idea, which should surprise no one. The fact that it is a GREAT idea, well, that should surprise everyone. Most investors fret when markets hit new highs, but should they? The below is inspired by our friend Jake @ Econompic, who examined the following query: What if you bought stocks at all-time highs, otherwise you sat in the safety of government bonds? Jake found
  • Introduction to Support Vector Machines [Quant Insti]

    Support Vector Machines were widely used a decade back, but now they have fallen out of favour. The below data from google trends can establish this more clearly. (Source: Google Trends) Why did this happen? As more and more advanced models were developed, support vector machines fell out of favour. It takes a lot of time to train a non-linear kernel, say RBF (Radial Basis Function), of a support
  • 2 Unfilled Up Gaps And A 50-Day High [Quantifiable Edges]

    Monday not only saw SPY make a 50-day high, but it was also the 2nd day in a row with an unfilled gap up. The study below is from last nights letter and was previously discussed several other times in the subscriber letter (click here for free trial). It examined other times SPY left at least 2 unfilled up gaps and closed at a 50-day high. 2019-11-05-1 The size of the follow-through isnt

Quantocracy’s Daily Wrap for 11/04/2019

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

  • 16 Articles on Quantamental Investing [Two Centuries Investments]

    As the book about the most successful quant, Jim Simons, comes out tomorrow (The Man Who Solved the Market), I felt inspired to review the recent press on quant investing, but with a focus on the quantamental theme – where quantitative and qualitative ideas can collaborate well to produce alpha. Sorted by date: 1. Human Insight, Computer Power: What is Quantamental Investing?
  • Tactical Asset Allocation in October [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 Growth-Trend Timing [Flirting with Models]

    While trend following may help investors avoid prolonged drawdowns, it is susceptible to whipsaw where false signals cause investors to either buy high and sell low (realizing losses) or sell low and buy high (a missed opportunity). Empirical evidence suggests that using economic data in the United States as a filter of when to employ trend-following a growth-trend timing model has
  • Factor Investing in Emerging Markets [Factor Research]

    The trends in factor performance are similar in emerging and developed markets Factor returns were higher in emerging than in developed markets However, higher transaction costs need to be considered carefully INTRODUCTION Capital markets of developed countries like the US are highly efficient and mutual fund managers have struggled to generate any alpha, at least after fees. Theoretically, fund
  • A method for de-trending asset prices [SR SV]

    Financial market prices and return indices are non-stationary time series, even in logarithmic form. This means not only that they are drifting, but also that their distribution changes overtime. The main purpose of de-trending is to mitigate the effects of non-stationarity on estimated price or return distribution. De-trending can also support the design of trading strategies. The simplest basis
  • Preliminary Results from Weight Agnostic Training [Dekalog Blog]

    Following on from my last post, below is a selection of the typical resultant output from the Bayesopt Library minimisation 3 3 2 2 2 8 99 22 30 1 3 3 2 3 2 39 9 25 25 1 2 2 3 2 2 60 43 83 54 3 2 1 2 2 2 2 0 90 96 43 3 2 3 2 2 2 2 43 33 1 2 3 2 3 2 2 0 62 98 21 2 2 2 2 2 18 43 49 2 2 2 3 2 4 1 2 0 23 0 0 2 2 1 2 3 2 0 24 63 65 3 2 2 2 3 5 92 49 1 0 2 3 2 1 1 7 84 22 17 1 3 2 4 1 1 46 1 0 99 7 2 2

Quantocracy’s Daily Wrap for 11/01/2019

This is a summary of links featured on Quantocracy on Friday, 11/01/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Jim Cramer Using the S&P Oscillator [CXO Advisory]

    A reader asked about the usefulness of the S&P Short-range Oscillator as sometimes used by Jim Cramer to forecast U.S. stock market returns. The self-reported Performance of the oscillator, relying on in-sample visual inspection with snooped thresholds, is of small use. Since continuous historical values of the indicator are not publicly available, we conduct an out-of-sample test by:

Quantocracy’s Daily Wrap for 10/30/2019

This is a summary of links featured on Quantocracy on Wednesday, 10/30/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Ubiquitous “Sell in May” [Allocate Smartly]

    As a site that tracks all things asset allocation, it seems like a miss not to include the most well known of asset allocation strategies: Sell in May and go away (aka the Halloween Indicator). This is a fitting time to add it to the lineup: The strategy is killing it this year and a change in allocation is upon us. The Sell in May strategy advocates holding stocks during the wintery
  • Weight Agnostic Neural Net Training [Dekalog Blog]

    I have recently come across the idea of weight agnostic neural net training and have implemented a crude version of this combined with the recent work I have been doing on Taken's Theorem ( see my posts here, here and here ) and using the statistical mechanics approach to creating synthetic data. Using the simple Octave function below with the Akaike Information Criterion as the minimisation
  • Can Anomalies Survive Insider Disagreements [Alpha Architect]

    Anomalies such as Value and Momentum have been exploited for years, yet the source of these premiums emerged as a major unresolved puzzle. Potential explanations can be grouped into two broad categories: compensation for risk or mispricing. This paper studies this puzzle by investigating the relationship between insider trades and stock anomalies. Heres a post about Insider trading
  • Towards the Risk-Free Curve: Logarithmic vs. Arithmetic Returns [Quant Dare]

    As Nassim Taleb states, ideas come and go, stories stay. So today Maximiliano and myself are going to build for you a story which hopefully will carve in your mind the importance of doing things right; or put differently, of using logarithmic returns instead of arithmetic returns when you should. To do so, we will use one again a common process carried out in Finance: return annualization. We will
  • Tracking Macro Factors In Portfolio Strategies [Capital Spectator]

    Earlier this month I briefly reviewed a recent BlackRock report that highlighted that macroeconomic factors are typically driving investment strategy results. As a follow-up, lets take a quick look at a basic real-world example of analyzing portfolios through a macroeconomic lens. First, lets recap why macroeconomic analytics are useful. The basic takeaway: macro influences (economic growth,