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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

Quantocracy’s Daily Wrap for 09/16/2019

This is a summary of links featured on Quantocracy on Monday, 09/16/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • No, the VIX is Not Broken [Six Figure Investing]

    Hardly a month goes by without some pundit trumpeting that the VIX is broken. But before you worry too much, consider some of the non-obvious characteristics of the Cboes Fear Gauge. First a Summary These charts list possible explanations for perceived VIX brokenness Complaint Market in Low Volatility State (21-day std dev close-to-close log returns VIX too high * VIX has an overhead
  • The Failure of Value Investing explained [Alpha Architect]

    Its no secret that value has had a bad bout of performance in recent memory. This underperformance has been thoroughly examined by multiple research teams and weve done some of our own work on the subject. Weve also done in-depth rebuttals to value investing is dead articles in the past (Alternative Facts about Formulaic Value Investing, The Re-Death of Value, or Dj Vu All
  • Factors and the Glide Path [Flirting with Models]

    Value and momentum equities exhibited significant performance last week raising short-term questions about factor crowding and long-term questions about appropriate factor diversification. We explore the idea of appropriate factor diversification through the lens of a retiring investor, asking the question, are all equity styles appropriate at all points in an investors lifecycle? Using a
  • Risk Parity Part II: The Long-Run View [Two Centuries Investments]

    In Part I Risk Parity, I discussed theChasing Diversifiers problem that harms investors performance. This week, I apply the Relevance of the Long-Run concept to Risk Parity. First, let me acknowledge upfront that deep historical data is messy and is not precise. Depending on the source, you might get different results. Having said that, I still prefer to supplement my analysis with
  • Will investors outlive their savings? [Mathematical Investor]

    As we explained in an earlier Mathematical Investor blog, target-date funds are currently the rage in the finance world. The term refers to a mutual fund that targets a given retirement date, and then steadily shifts the allocation of assets from, say, a 80%/20% mix of stocks and bonds at the start to, say, a 30%/70% or 20%/80% mix as the target date approaches. Vanguard Group, which manages
  • Is Low Vol the New Value? [Factor Research]

    The Low Volatility factor exhibited significant exposure to Value since 1989 The factors were highly correlated in the 1990s, but less after the financial crisis Quantitative easing was positive for Low Volatility, but negative for Value INTRODUCTION Riding the Ferris wheel in an amusement park is the equivalent of investing in a Low Volatility strategy in the stock market. It is all about
  • Reinforcement learning and its potential for trading systems [SR SV]

    In general, machine learning is a form of artificial intelligence that allows computers to improve the performance of a task through data, without being directly programmed. Reinforcing learning is a specialized application of (deep) machine learning that interacts with the environment and seeks to improve on the way it performs a task so as to maximize its reward. The computer employs trial and

Quantocracy’s Daily Wrap for 09/13/2019

This is a summary of links featured on Quantocracy on Friday, 09/13/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pattern Recognition with the Frechet Distance [Robot Wealth]

    Chart patterns have long been a favourite of the technical analysis community. Triangles, flags, pennants, cups, heads and shoulders. Name a shape, someone somewhere is using it to predict market behaviour. But, is there a grain of truth or reliability in these patterns? Can it really give you a hint as to the future direction of the market? ah, I see a blue star pattern on my chart a good
  • Mo Data: Using Mean-Reversion in the Momentum Factor to Time Momentum [CSS Analytics]

    In the last post we used the data available for the momentum factor using an ETF (ticker: MOM) which seeks to replicate The Dow Jones Thematic Market Neutral Momentum Index to time when to be in or out of high momentum stocks. Alpha Architect recently did some interesting analysis of the distribution of returns for the same momentum index in this post. One of the challenges was the lack of data

Quantocracy’s Daily Wrap for 09/12/2019

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

  • When Should You Buy Momentum? Mean-Reversion in The Momentum Factor [CSS Analytics]

    new concepts in quantitative research Home CSSA Investor IQ When Should You Buy Momentum? Mean-Reversion in The Momentum Factor September 12, 2019 by david varadi Recently there was a good post by Bespoke Research highlighting the Momentum Massacre that we recently witnessed in the market. High- flying momentum stocks were decimated and the low momentum/losing stocks made a roaring comeback.
  • Value: Don’t Call it a Comeback, it’s Been Here for Years [Alpha Architect]

    Value and Momentum each had back to back extreme returns (five sigma) days on Monday, September 9th and Tuesday, September 10th. The Dow Jones Thematic Market Neutral Value Index (Value) started the week up 3.45%, its best day since inception on December 31st, 2001. The Value Index followed this up on Tuesday, September 10th with a 2.56% return, its 7th best day since inception. The Dow
  • Exploring Simplicity In Tactically Managed ETF Portfolios [Capital Spectator]

    Risk management has become a high priority for many investors over the past decade. The worst financial crisis and recession since the Great Depression in 2008-2009 clearly has the power to focus minds. Research shops have moved heaven and earth to search for solutions that attempt to limit risk without sacrificing return. The question is whether simplicity is competitive in this quest? As a

Quantocracy’s Daily Wrap for 09/11/2019

This is a summary of links featured on Quantocracy on Wednesday, 09/11/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • K-Means Clustering Algorithm For Pair Selection In Python [Quant Insti]

    From showing related articles at the end of the article you have browsed through to creating a personalised recommendation based on your viewing habits, you would be surprised of the number of times you have been interacting with the K-means algorithm without even realising it. The above examples were simply in the realm of everyday life. When you turn your eye towards the colossal industries
  • Encoding financial texts into dense representations [Quant Dare]

    The market is driven by two emotions: greed and fear. Have you ever heard that quote? It is quite popular in financial circles and there may just be some truth behind it. After all, when people, with short-term investments, think are going to lose a lot of money, many of them sell as fast as they can. When they think they can make money the same happens, they tend to buy as fast as they can.