Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 09/30/2019

This is a summary of links featured on Quantocracy on Monday, 09/30/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Building a garden “trading” office (off topic but fun) [Investment Idiocy]

    (Both) regular followers of this blog will have been on tenterhooks for many months now, waiting for my next post. I have been busy! First of all, I've been finishing my third book. More detail on that later, in the next post. I've also had a fair bit of holiday time. But mainly over the summer I've been building my own garden office. Now a post about constructing a garden office
  • Quality: Independent attributes or a real factor? [Alpha Architect]

    The authors do a very nice survey on measures of quality found in the academic literature and in commercially available quality indexes. They examine seven quality categories including: profitability, earnings stability, capital structure, growth, accounting quality, payout/dilution and investment. In order to mitigate datamining biases the authors employ 3 criteria (Hsu, Kalesnik and Viswanathan,
  • Macro and Momentum Factor Rotation [Flirting with Models]

    While many investors have adopted a multi-factor approach to style investing, some have pushed these boundaries by advocating for an active, rotational approach to factor allocation. In a recent white paper, MSCI suggests several methods that might be conducive for performing style rotation, including macro-, momentum-, and value-based signals. In this commentary, we attempt to test the macro- and

Quantocracy’s Daily Wrap for 09/26/2019

This is a summary of links featured on Quantocracy on Thursday, 09/26/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • An Approach to Time Series Data when Data is Limited (ARIMA / VAR) [Auquan]

    Investors are slowly becoming more and more interested in ethical investing. Part of the reason is the industry is starting to care more, but the other reason is that there is a lot of evidence to show that it can produce better or at least equivalent returns. One subset of this type of investing is known as ESG investing. In short, this uses company filings about their environmental, social and
  • The Short Duration Premium [Alpha Architect]

    In my June 4, 2019 article The Re-Death of Value, or Dj Vu All Over? I noted that one possible explanation for at least part of the poor performance of value stocks over the past decade has been the sharp fall in both the real interest rate (due to weak global growth) and unexpected inflation. As supporting evidence, I cited a study which found that those two outcomes favor longer
  • Trading Using Machine Learning In Python [Quant Insti]

    In recent years, machine learning, more specifically machine learning in Python has become the buzz-word for many quant firms. In their quest to seek the elusive alpha, a number of funds and trading firms have adopted to machine learning. While the algorithms deployed by quant hedge funds are never made public, we know that top funds employ machine learning algorithms to a large extent. Take, for

Quantocracy’s Daily Wrap for 09/25/2019

This is a summary of links featured on Quantocracy on Wednesday, 09/25/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Intraday Futures Calendar Spreads and the Impact of Transaction Costs [Quant Rocket]

    Intraday trading strategies offer great promise as well as great peril. This post explores an intraday trading strategy for crude oil calendar spreads and highlights the impact of transaction costs on its profitability. Background In a previous post, I explored an end-of-day pairs trading strategy in which the chief difficulty was to find suitable pairs. Pairs that cointegrate in-sample often
  • The Simplest Momentum Indicator [Alvarez Quant Trading]

    We all have our favorite momentum indicators. One of mine is percent off 1 year high. This requires 252 data points and comparisons, plus a division. Another one is the 200-day moving average. This requires 200 closing prices, 199 additions and a division. A simple momentum indicator is Rate of Change which is the return of the asset of the last N days. This requires two prices and a division to
  • Volatility Clustering: Are large price moves followed by large price moves? [Oxford Capital]

    Concept: Volatility clustering: Large price moves tend to be followed by large price moves, and small price moves tend to be followed by small price moves. Research Question: Is there a tendency of large price moves in one direction to be followed by large price moves in the opposite direction? Specification: Table 1. Results: Figure 1-4. Trade Setup: We identify large price moves via Wide Range
  • Pairs Trading in Zorro [Robot Wealth]

    In our previous post, we looked into implementing a Kalman filter in R for calculating the hedge ratio in a pairs trading strategy. You know, light reading We saw that while R makes it easy to implement a relatively advanced algorithm like the Kalman filter, there are drawbacks to using it as a backtesting tool. Setting up anything more advanced than the simplest possible vectorised backtesting

Quantocracy’s Daily Wrap for 09/24/2019

This is a summary of links featured on Quantocracy on Tuesday, 09/24/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Deep Trading with TensorFlow: Recapitulating [Todo Trader]

    e have already traveled a good part of the trip, but there is still an important part. In this post, I tell you where we are and how much we have left. Courage, we sure got it! The Machine Learning Workflow The following diagram provides a high-level overview of the stages in a machine learning workflow. It is made in the IDEF0 style because I am looking for simplicity (BPMN is not pleasant for
  • The Volatility Effect Revisited [Alpha Architect]

    One dirty little secret that has been hiding behind the curtains of finance for a long time, is that high-risk stocks do not have higher returns than low-risk stocks. Back in 1975 Haugen and Heins first recognized the low-risk anomaly: Our emperical efforts do not support the conventional hypothesis that risk systemic or otherwise generates a special reward. Indeed, our results indicate

Quantocracy’s Daily Wrap for 09/23/2019

This is a summary of links featured on Quantocracy on Monday, 09/23/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • World’s Largest Quant Conference of Its Kind: The Quant Conference | 1st November, 2019 | London, UK

    Enjoy 15% off with the promo code: QUANTOCRACY2019. The Quant Conference has been conceived as an educational setting where attendees can learn about the current trends in the field of quantitative finance. Furthermore, it brings a unique opportunity to network with aspiring students, professional peers, prospective employers, academics and investors. The Quant Conference's speakers roster of
  • Inverted Yield Curve: Belgium 1840 – 2018 [Two Centuries Investments]

    Over the last few months, much of the financial press expressed concerns about the impact of inverted yield curves on financial markets, in particular, the stock returns. Some previous academic literature has shown that there exists a link between yield curves and economic growth (see references below). However, the question remains whether yield curve inversions can be used as a predictive
  • Trend Following Active Returns [Flirting with Models]

    Recent research suggests that equity factors exhibit positive autocorrelation, providing fertile ground for the application of trend-following strategies. In this research note, we ask whether the same techniques can be applied to the active returns of long-only style portfolios. We construct trend-following strategies on the active returns of popular MSCI style indices, including Value, Size,
  • Smart Beta vs Alpha + Beta [Factor Research]

    Investment portfolios can be simplified by separating alpha from beta Alpha + beta portfolios offer higher risk-adjusted returns than smart beta The main hurdle for better portfolios is investor behaviour, not a lack of products INTRODUCTION In Buddhist teaching, the primary obstacles that prevent us from ascending to a higher state are ignorance, greed, and anger. This trio of poisons not only

Quantocracy’s Daily Wrap for 09/21/2019

This is a summary of links featured on Quantocracy on Saturday, 09/21/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The quantitative path to macro information efficiency [SR SV]

    Financial markets are not information efficient with respect to macroeconomic information because data are notoriously dirty, relevant economic research is expensive, and establishing stable relations between macro data and market performance is challenging. However, statistical programming and packages have prepared the ground for great advances in macro information efficiency. The
  • The Weakest Week (2019 update) [Quantifiable Edges]

    As I have shown many times in the past, there isnt a more reliable time of the year to have a selloff than this upcoming week. I have often referred to is as The Weakest Week. Since 1960 the week following the 3rd Friday in September has produced the most bearish results of any week. Below is a graphic to show how this upcoming week has played out over time. 2019-09-20-1 As you can see

Quantocracy’s Daily Wrap for 09/20/2019

This is a summary of links featured on Quantocracy on Friday, 09/20/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean-Reversion in Trend-Following Performance Using a 120-day Lookback [CSS Analytics]

    In the last post we showed that trend-following tends to be mean-reverting in the short-term. Data analysis also shows that trend-following has an even stronger mean-reverting effect using a 6-month or 120-day window using the same methodology. Take a look at the chart below using the BarclayHedge SG Trend Index: In the last post I hypothesized that the mean-reversion effect exists because

Quantocracy’s Daily Wrap for 09/19/2019

This is a summary of links featured on Quantocracy on Thursday, 09/19/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How To Make A Kalman Filter in R for Pairs Trading [Robot Wealth]

    Anyone whos tried pairs trading will tell you that real financial series dont exhibit truly stable, cointegrating relationships. If they did, pairs trading would be the easiest game in town. But the reality is that relationships are constantly evolving and changing. At some point, were forced to make uncertain decisions about how best to capture those changes. One way to incorporate both
  • A simple algorithm to detect complex chart patterns [Philipp Kahler]

    Finding complex chart patterns has never been an easy task. This article will give you a simple indicator for complex chart pattern recognition. You will have the freedom to detect any pattern with any pattern length. Not just 2-bar candlestick formations, but complex stuff like V-Tops spread over 20 bars. Defining a chart pattern I am using a simple string definition of a pattern. See the example

Quantocracy’s Daily Wrap for 09/18/2019

This is a summary of links featured on Quantocracy on Wednesday, 09/18/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean-Reversion in Trend-Following Performance [CSS Analytics]

    In a recent post I showed that the momentum factor has been mean-reverting in the short-term, and that this effect can be used to trade both the factor and momentum strategies effectively. An obvious extension is to see whether trend-following as a factor is also mean-reverting. After all, time-series momentum and momentum have been shown to be related in the research. To represent the
  • Cognitive Trading System Model [Todo Trader]

    Yes, Artificial Intelligence (AI) is here to stay. Previously on this blog, I have written about the Basis of the Scientific Trading System as well as the Artificial Intelligence Trading Systems. Since then, I have designed a trading system model which I believe could satisfy all requirements of the present and future trading systems. In this post, I will describe the model following the
  • Factor Investing from Concept to Implementation [Alpha Architect]

    There is a substantial debate on the topic of factor investing and whether or not the backtested excess returns are actually achievable in practice. Much of the research on the topic suggests that practitioners in the field are unable to capture any of the so-called factor premiums. For example, the debate is covered here, here, and here. Turns out there is another piece of literature