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Quantocracy’s Daily Wrap for 06/03/2019

This is a summary of links featured on Quantocracy on Monday, 06/03/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in May [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
  • Tactical Credit [Flirting with Models]

    In this commentary we explore tactical credit strategies that switch between high yield bonds and core fixed income exposures. We find that short-term momentum signals generate statistically significant annualized excess returns. We use a cross-section of statistically significant strategy parameterizations to generate an ensemble strategy.Consistent with past research, we find that this ensemble
  • Quantamental Investing – A Century of Inventions [Two Centuries Investments]

    Last weeks talk by Edward Altman at the 50-year anniversary of Altmans Z-score event at the CFA New York inspired me to compile an expanded list of memorable inventions in equity analysis. Each one is a successful blend of quantitative and fundamental thinking – which is increasingly being called quantamental investing, for example see here and here. I am inspired by this list,
  • How to Allocate Smartly to Smart Beta [Factor Research]

    This research note was originally published in the Beyond Beta magazine from ETF Stream. Here is the link. SUMMARY Single factor excess returns are attractive over the long-term, less in the short-term Comparing popular asset allocation models does not highlight one superior methodology Multi-factor portfolios generated excess returns in two out of three regions since 2008 INTRODUCTION Obesity
  • Is factor momentum really everywhere? [Alpha Architect]

    The research presented here covers the largest number of factors (65) tested in the academic literature. The most robust and well-cited factors appear in the list of data items, available since the 1960s. A notable exclusion is the IBES dataset, which is available only in the 1980s. Is there persistence in factor returns? If so, can timing models based on autoregressive patterns work successfully

Quantocracy’s Daily Wrap for 05/31/2019

This is a summary of links featured on Quantocracy on Friday, 05/31/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Optimising MetaTrader for Algorithmic Trading [Robot Wealth]

    If youve ever delved into the world of retail foreign exchange trading, youll have come across the MetaTrader platform. Lets be clear. The platform has its drawbacks. If youve traded grown-up markets, some of the features will leave you scratching your head. But one things for sure MetaTrader provides fast, convenient access to pretty much every retail forex broker on the
  • Downloading option chain and fundamental from Yahoo! Finance with Python [Ran Aroussi]

    The recently updated yfinance added a lot more capabilities to this already popular library. You can now download fundamental data, including company financials, balance sheet and cashflow, as well as option chain data. Here's how… First, import yfinance and create a ticker object: 1 2 import yfinance as yf aapl = yf.Ticker("AAPL") Next, let's get information about the stock
  • Extended Kalman Filter, Alternative Version [Dekalog Blog]

    Below is alternative code for an Extended Kalman filter for a sine wave, which has 4 states: the sine wave value, the phase, the angular frequency and amplitude and measurements thereof. I have found it necessary to implement this version because I couldn't adjust my earlier version code to accept and measure the additional states without the Cholesky decomposition function chol() exiting and

Quantocracy’s Daily Wrap for 05/30/2019

This is a summary of links featured on Quantocracy on Thursday, 05/30/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Python Monte Carlo vs Bootstrapping [Python For Finance]

    In this article I thought I would take a look at and compare the concepts of Monte Carlo analysis and Bootstrapping in relation to simulating returns series and generating corresponding confidence intervals as to a portfolios potential risks and rewards. Both methods are used to generate simulated price paths for a given asset, or portfolio of assets but they use slightly differing
  • Skewness Effect in Commodities [Alpha Architect]

    Nothing lasts forever and this definitely stands true for equity markets where volatility can explode and investors can lose a lot of money very quickly. Because of equity market volatility investors often seek so-called crisis alpha instruments, or assets that tend to go up when equity markets are in crisis. (Here, here, and here are some prior write-ups on the topic). Unfortunately,

Quantocracy’s Daily Wrap for 05/29/2019

This is a summary of links featured on Quantocracy on Wednesday, 05/29/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trade Cost Optimisation II: Tracking Error and the Cutting Plane Algorithm [Scalable Capital]

    This blog article builds on our first blog article about trade cost optimisation approaches. We discuss some weaknesses of the simple approach presented in the first article and make suggestions for extending and improving the trade cost optimisation towards a more sophisticated and powerful algorithm. Distances between portfolios can be measured with different metrics: We compare turnover and
  • Our Systematic Value Philosophy [Flirting with Models]

    As a firm, Newfound Research focuses on tactical allocation strategies. However, we also spend time researching other mandates such as systematic value in an effort to introduce lateral thinking to our process. Three years ago, we built a systematic value portfolio that seeks to create a style pure investment result, attempting to diversify process specification and timing luck risk.
  • News Sentiment and Bonds [Alpha Architect]

    Academic literature has documented a news sentiment effect on equities ( here and here ). The authors investigate the following research question: Does the sentiment derived from media content impact bond market investors? What are the Academic Insights? By studying the sentiment extracted from articles from the entire Thomson Reuters news universe (over 400,000 news sentiment items per month from

Quantocracy’s Daily Wrap for 05/28/2019

This is a summary of links featured on Quantocracy on Tuesday, 05/28/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Song of Value and Growth [Quiet Quant]

    Despite Uncle Warrens understanding of the connection of growth and value, those of us that come to investing through the factor and/or academic world, have always been taught that growth investing is a terrible way to invest. This is simply because we have, in most cases, been taught that growth is the opposite of value, and why wouldnt we believe that? Morningstar style boxes, the French
  • Random Portfolio Generator – Are you Good or Lucky? [Rayner Gobran]

    I am not a fan of benchmarking against widely available indexes. Most anyone you ask will tell you that you should benchmark against an index because it is an objective measure of performance. It provides you with the beta that allows you to figure out if an investment manager delivers alpha. Is this actually true? No. An index is a trading system in disguise. An index is created by

Quantocracy’s Daily Wrap for 05/27/2019

This is a summary of links featured on Quantocracy on Monday, 05/27/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Volatility vs Risk [Two Centuries Investments]

    Much has been written on this topic, but for what its worth, here is my take. Volatility is how much something moves up and down. The stock market is more volatile than the bond market, on average. Yet, a black-box hedge fund might be less volatile than S&P500, but is it less risky? Risk = Unexpected Outcomes + Unrecoverable Consequences In my view, Risk is an unexpected outcome that
  • Cheap versus Expensive Countries [Factor Research]

    A global value portfolio on country level features structural country biases Returns were positive since 1990, but lacked consistency Value on country and single stock level exhibit the same trends, highlighting common performance drivers INTRODUCTION Holding Value stocks is emotionally challenging as cheap valuations are usually due to companies experiencing temporary or structural issues such as
  • Extended Kalman Filter [Dekalog Blog]

    In the code box below I provide code for an Extended Kalman filter to model a sine wave. This is a mashup of code from a couple of toolboxes I have found online, namely learning-the-extended-kalman-filter and EKF/UKF Tollbox for Matlab/Octave. The modelled states are the phase, angular frequency and amplitude of the sine wave and the measurement is the ( noisy ) sine wave value itself.
  • An Updated Look At Memorial Week Historical $SPX Performance [Quantifiable Edges]

    The week of Memorial Day has shown some interesting seasonal tendencies over the years. But it has been less consistent recently. The chart below is one I have shown in the past, and have now updated. It examines SPX performance from the Friday before Memorial Day to the Friday after it. 2019-05-24 There was no substantial edge apparent throughout the 70s, but starting in 1983 through 2009 there
  • Alternatives To Correlation For Quantifying Diversification [Capital Spectator]

    Diversification is famously described as the only free lunch in investing and so its no surprise that modeling, analyzing and otherwise dissecting the concept is a core part of portfolio design and management. The correlation coefficient is often the go-to metric in this corner of finance. But like any one statistical measure, there are pros and cons with correlation and so relying on it
  • Risk-Factor Identification: A Critique [Alex Chinco]

    In standard cross-sectional asset-pricing models, expected returns are governed by exposure to aggregate risk factors in a market populated by fully rational investors. Heres how these models work. Because investors are fully rational, they correctly anticipate which assets are most likely to have low returns in especially inconvenient future states of the worldi.e., returns that are highly
  • U.S. Treasuries: decomposing the yield curve and predicting returns [SR SV]

    A new paper proposes to decompose the U.S. government bond yield curve by applying a bootstrapping method that resamples observed return differences across maturities. The advantage of this method over the classical principal components approach would be greater robustness to misspecification of the underlying factor model. Hence, the method should be suitable for bond return predictions

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