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Quantocracy’s Daily Wrap for 05/22/2019

This is a summary of links featured on Quantocracy on Wednesday, 05/22/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quantopian Review and Comparison to AmiBroker [Alvarez Quant Trading]

    In my last post, Avoiding Trades Before Earnings, I mentioned that I used Quantopian to do the research. Several readers asked about my thoughts about Quantopian and how it compares to AmiBroker. Some asked if I had left AmiBroker for Quantopian. What follows are my impressions after using Quantopian for several months and how it compares to AmiBroker. The big question is will I be switching from
  • Volatility Targeting Improves Risk-Adjusted Returns [Alpha Architect]

    Theres a large body of research, including the 2017 study Tail Risk Mitigation with Managed Volatility Strategies by Anna Dreyer and Stefan Hubrich, that demonstrates that, while past returns do not predict future returns, past volatility largely predicts future near-term volatilityvolatility is persistent (it clusters). High (low) volatility over the recent past tends to be followed
  • Technical analysis in major brokerages and financial media [Mathematical Investor]

    Suppose, in the weather forecast part of a local newscast, the person handling the weather displays a chart of recent temperatures in the local area, pointed out trends and waves, then mentions a breakout pattern from a recent temperature range. Most of us would not have much confidence in such a dubious and unorthodox forecast, and, if followed (e.g., for a major storm), could

Quantocracy’s Daily Wrap for 05/21/2019

This is a summary of links featured on Quantocracy on Tuesday, 05/21/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • QuantMinds 2019 Vienna [Cuemacro]

    Vienna is one of those places which has somehow largely eluded my travels. The last time I visited it was 25 years ago. However, it has very much stuck in my memory. History is one of those things which you can never escape from in Vienna. The echos of musical history are everywhere, whether it is Mozart or Beethoven, or the Viennese waltz. There are also the very well known difficult periods of
  • Volatility Anomalies: IVOL and Vol-of-Vol [Alpha Architect]

    Two of the more interesting puzzles in finance are related to volatilitystocks with greater idiosyncratic volatility (IVOL) have produced lower returns and stocks with high uncertainty about risk, as measured by the volatility of expected volatility (vol-of-vol), underperform stocks with low uncertainty. These are anomalies because greater risk should be compensated with higher expected

Quantocracy’s Daily Wrap for 05/20/2019

This is a summary of links featured on Quantocracy on Monday, 05/20/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Dead Simple 2-Asset Portfolio that Crushes the S&P500 (Part 4) [Black Arbs]

    In Part 3 of the series we reviewed the relationship between returns and correlation of the 2-asset portfolio UPRO and TMF. The basic equal weight strategy was very compelling in terms of total return and CAGR. However, the strategy is susceptible to large drawdowns, especially in situations where US equities and long term bonds are out favor, for example in the 2015 and 2018 periods. We also went
  • Disproving a Signal [Flirting with Models]

    Last week we introduced a signal that appeared to generate statistically significant performance results for performing country rotation. This week, we walk through the steps taken to explore the robustness of the signal. We first explore out-of-sample data with sector and emerging market country indices. Unfortunately, definitional differences and limited data impact our ability to pass
  • What is better: Factor Zoo or Factor Museum? [Two Centuries Investments]

    Here are my 8-thoughts and 1 solution idea about Campbell Harvey and Yan Liu recently released paper on their influential concept of the factor zoo. To sum it up, it says that there are too many data-mined factors out there and that we should be using much higher t-statistics to accept factors. Ironically, which is perhaps subtlety intentional, it feels like the mega-list of factors in the paper
  • Improving the Momentum Factor [Factor Research]

    The performance of the Momentum factor in the US has been poor since 2000 Fundamental valuation spreads were ineffective for improving the performance Combinations with other factors and factor volatility filters would have yielded better results INTRODUCTION John H. Cochrane of the Hoover Institution at Stanford University described the ever-growing number of factors in the investment industry as
  • Exploring Stock Price Movements After Major Events (h/t @PyQuantNews) [Steven Wang]

    FDA drug approvals, legal verdicts, mergers, share buybacks, and the occasional CEO podcast appearance, are all examples of events that impact stock prices. Though not as quantifiable as technical indicators, real life events clearly affect prices. In an attempt to further explore the relationship between events and stock prices, I gathered historical price data from the IEX API and scraped events

Quantocracy’s Daily Wrap for 05/19/2019

This is a summary of links featured on Quantocracy on Sunday, 05/19/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Adaptive Huber Regression [Eran Raviv]

    Many years ago, when I was still trying to beat the market, I used to pair-trade. In principle it is quite straightforward to estimate the correlation between two stocks. The estimator for beta is very important since it determines how much you should long the one and how much you should short the other, in order to remain market-neutral. In practice it is indeed very easy to estimate, but I

Quantocracy’s Daily Wrap for 05/17/2019

This is a summary of links featured on Quantocracy on Friday, 05/17/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Future of QTPyLib [Ran Aroussi]

    I released the first version of QTPyLib, my Python library for algo traders, in 2016. If you had told me then that I would still be working on it three years later, I probably wouldn't have believed you. But guess what? That's precisely where I'm doing 🙂 The first release of QTPyLib was a basic engine for live trading using Interactive Brokers. That's it. Nothing more. Nothing
  • Financial Experts Ignoring Better Statistical Methods? [CXO Advisory]

    Why are expert economic and financial (econometric) forecasters so inaccurate? In his April 2019 presentation package for a graduate course at Cornell entitled The 7 Reasons Most Econometric Investments Fail, Marcos Lopez de Prado enumerates shortcomings of standard econometric statistical methods, which concentrate on multivariate linear regressions. In contrast, advanced computational

Quantocracy’s Daily Wrap for 05/15/2019

This is a summary of links featured on Quantocracy on Wednesday, 05/15/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Backtesting Bias: Feels Good, Until You Blow Up [Robot Wealth]

    In an ideal trading universe, wed all have a big golden causation magnifying glass. Through the lens of this fictional tool, youd zoom in and understand the fleeting, enigmatic nature of the financial markets, stripping bare all its causes and effects. Knowing exactly what causes exploitable inefficiencies would make predicting market behaviour and building profitable trading

Quantocracy’s Daily Wrap for 05/14/2019

This is a summary of links featured on Quantocracy on Tuesday, 05/14/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How Inflation Makes the ‘Value’ Factor a Sector Bet [Fortune Financial]

    There have been numerous attempts to explain the lackluster performance of value investing so far this decade, which is currently on pace for its worst annualized performance for a decade since the 1930s: Without getting into the arguments made by others, which have been debated elsewhere, I will take a deeper look into what I think is the likeliest explanation for this phenomenon, although it is
  • A Laboratory for Machine Learning in Finance [Quants Portal]

    In the summer of 2018 we attended a conference organized by Quantopian in which we heard Dr. Marcos Lopez de Prado outlined the challenges of building successful quantitative investment platforms. His book, Advances in Financial Machine Learning provides solutions to many of the problems faced by the quantitative finance community. We, however, could not find a cogent implementation of these ideas
  • Shiny New Toys [CSS Analytics]

    Its been a long time folks, but we have some shiny new toys in the works. Current trends in the industry and working with data scientists has made me a believer in the benefits of using a machine learning approach. I have always been a proponent of theory-free approaches on this blog as long as they are designed with robust architecture. In contrast, strict adherence to simplistic theories
  • Why The Failed Bounce Is Not A Signal To Sell [Quantifiable Edges]

    After closing at a 20-day low on Thursday, the market put in a bounce attempt on Friday. Mondays decline to a new low meant that initial bounce attempt failed. But in last nights subscriber letter we saw several studies that showed the failed bounce was more likely to see another bounce attempt than it was to sell off further. The study below triggered in yesterdays Quantifinder,

Quantocracy’s Daily Wrap for 05/13/2019

This is a summary of links featured on Quantocracy on Monday, 05/13/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Fractional Differentiation [Quants Portal]

    In this article we delve into the challenge of making an asset price series stationary (for reasons discussed below) and preserving as much memory/signal from the original series. We take inspiration from Chapter 5 of the Advances in Financial Machine Learning (AFML) by Dr. Marcos Lopez de Prado therein he discusses fractionally differencing the time series (as opposed to integer differencing). A
  • Member Analysis: The Effect of Combining Strategies on Timing Luck [Allocate Smartly]

    We enjoy hearing from members about their experiences using our platform to analyze and combine tactical asset allocation strategies. We do a bad job of sharing that feedback with other members, and thats a shame, because theres often a lot of wisdom in it. So lets change that. What follows is an email from member Mark demonstrating the benefit of combining strategies not just on
  • Country Rotation with Growth/Value Sentiment [Flirting with Models]

    Value investing has not only underperformed with regard to security selection, but also country selection over the last decade. In an effort to avoid country value traps, we set out to design two signals that might better confirm when a country is likely to exhibit positive re-valuation. We find that one of the signals exhibits curious results, leading us to develop an entirely new metric for
  • 10 Large Scale Factor Anomaly Studies with Definitions [Two Centuries Investments]

    A Taxonomy of Anomalies and their Trading Costs 2015, Robert Novy-Marx and Mihail Velikov (with data) and the Cross-Section of Expected Returns, 2013, Campbell Harvey, Yan Liu, Caroline Zhu (factor list) A Comparison of New Factor Models, 2014, Kewei Hou, Chen Xue, Lu Zhang The Supraview of Return Predictive Signals, 2012, Jeremiah Green, John Hand, Frank Zhang Does Academic Research Destroy
  • Short Selling + Insider Selling = Profitable Strategy? [Alpha Architect]

    What are the research questions? This study uses a long and comprehensive time series covering 1977-2014, with just under 180,000 quarterly observations for trades of short sellers and demand for shares by corporate insiders. (see here for a related paper we covered recently that talks about informational advantages). The data is used to construct practical trading strategies utilizing in
  • SPX Iron Condor – 2018 Review [DTR Trading]

    In this post we'll look at how the SPX iron condor has been performing since I last analyzed its results back in 2016 (here). For this article, we'll just look at the following variations and how they performed from January 2007 through December 2018: 66 DTE – 25 pt wings, 12 Delta (200:50) / 2 DTE – exit if the trade has a loss of 200% of its initial credit OR if the trade has a profit
  • Hedge Fund ETFs [Factor Research]

    Core hedge fund strategies are available as low-cost and transparent ETFs The performance of hedge fund ETFs has been comparable to that of their benchmarks ETFs have only captured 1% of hedge fund assets INTRODUCTION As Amazon has been decimating large parts of the retail industry over the last two decades, ETFs have done the equivalent to the mutual fund industry in the financial world. Today
  • Welcome to Investor IQ [CSS Analytics]

    There is some interesting new content on the CSSA blog that will be very useful for readers. Investor IQ is currently a free tool that shows basic trend signals (Buy, Hold or Sell) for a wide range of US and Canadian ETFs as well as a relative strength ranking. The signals will be updated as of the close of Friday and posted on Monday morning. This feature is currently in Beta and will be expanded

Quantocracy’s Daily Wrap for 05/12/2019

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

  • The Edge of Technical Indicators [Philipp Kahler]

    Classical technical indicators like RSI and Stochastic are commonly used to build algorithmic trading strategies. But do these indicators really give you an edge in your market? Are they able to define the times when you want to be invested? This article will show you a way to quantify and compare the edge of technical indicators. Knowing the edge of the indicator makes it an easy task to select
  • Systematic trading strategies: fooled by live records [SR SV]

    Allocators to systematic strategies usually trust live records far more than backtests. Given the moral hazard issues of backtesting in the financial industry, this is understandable (view post here). Unfortunately, for many systematic strategies live records can be even more misleading. First, the survivor bias in published live records is worsening as the business has entered the age of mass
  • Alternative data for FX [Cuemacro]

    I recently visited the USA. I managed to visit a number of different cities. Whilst I was there primarily for work, I managed to squeeze in a few days to look around the various cities I visited. I visited Philadelphia for the first time, and I saw Independence Hall and the Liberty Bell, both of which I strongly recommend you see. Im a big foodie (as Im sure you may have guessed by reading

Quantocracy’s Daily Wrap for 05/10/2019

This is a summary of links featured on Quantocracy on Friday, 05/10/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Don’t Be A Value Hero [Quiet Quant]

    Lets imagine a world where value stocks actually provide some outperformance at some point In a world like that, are there any simple rules one could implement into their value system to help avoid falling knives? The optimist in me says yes, but the realist says nope. Turns out, it is probably somewhere in between. The test is pretty simple: U.S. Common Stocks (Top 50% of Market)
  • Going with the FX flow [Cuemacro]

    In this paper, we discuss using CLS intraday hourly flow data to understand FX markets. Using two different trading strategies (daily and hourly) against a generic trend model, we find that CLSs flow data relating to funds and non-bank financial firms (NBFIs) tends to have a positive contribution to spot returns when viewed on an aggregate contemporaneous basis. By contrast, flows from buy-side
  • Research Review | 10 May 2019 | Tail Risk [Capital Spectator]

    Tail Risk Management for Multi-Asset Multi-Factor Strategies David Chambers (University of Cambridge), et al. January 8, 2019 Multi-asset multi-factor portfolio allocation is typically centred around a risk-based allocation paradigm, often striving for maintaining equal volatility risk budgets. Given that the common factor ingredients can be highly skewed, we specifically incorporate the notion of
  • CBI Hits 10+ While $SPX is in a Long-Term Uptrend [Quantifiable Edges]

    It is notable that the Quantifiable Edges Capitulative Breadth Indicator (CBI) closed at 10 on Thursday. Below is a study that shows other times the CBI reached 10 while the SPX was above its 200ma. 2019-05-10 A very high percentage of instances closed higher when looking out 4 or more days. The numbers certainly seem to point to a bullish edge. Below is a profit curve that assumes a 5-day holding
  • State of Trend Following in April [Au Tra Sy]

    Positive month for the State of Trend Following index. YTD fighting back to the zero line Please check below for more details. Detailed Results The figures for the month are: April return: 2.89% YTD return: -1.6% Below is the chart displaying individual system results throughout April: