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Quantocracy’s Daily Wrap for 01/26/2020

This is a summary of links featured on Quantocracy on Sunday, 01/26/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Portfolio starter kit [OSM]

    Say youve built a little nest egg thanks to some discipline and frugality. And now you realize that you should probably invest that money so that youve got something to live off of in retirement. Or perhaps you simply want to earn a better return than stashing your cash underneath your bed, I mean your savings account. How do you choose the assets? What amount of money should you put into
  • How to Turn Cross-Sectional into Time-Series Momentum [Alpha Architect]

    A point of confusion for many new quant momentum investors is the difference between Time- Series Momentum and Cross-Sectional Momentum: Time-series (TS) looks at each individual stocks momentum and owns assets with positive momentum while shorting those with negative momentum; Cross-sectional (CS) observes a universe of stocks and chooses those with the best momentum relative to the universe
  • The q-factor model for equity returns [SR SV]

    Investment-based capital asset pricing looks at equity returns from the angle of issuers, rather than investors. It is based on the cost of capital and the net present value rule of corporate finance. The q-factor model is an implementation of investment capital asset pricing that explains many empirical features of relative equity returns. In particular, the model proposes that the following

Quantocracy’s Daily Wrap for 01/23/2020

This is a summary of links featured on Quantocracy on Thursday, 01/23/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Hidden Risk FIRE Investors Miss [Movement Capital]

    The financial independence, retire early (FIRE) movement has gained a lot of traction. We retired at 30 headlines get clicks and have made people question the typical retirement timetable: A main goal for those pursuing FIRE is to reach a portfolio balance that can reasonably fund their retirement. This is found by dividing estimated expenses by an initial withdrawal rate that (hopefully)
  • Visualization Sector Trends with R Code [Alpha Architect]

    Welcome to a year-end installment of Reproducible Finance with R, a series posts that will be a little bit different from the norm on Alpha Architect (see here for my last post). We will search for and hopefully unearth some interesting market conditions, but well primarily focus on the code that we use for telling stories with data. Todays project is to visualize market sector
  • Pre-Election Drift in the Stock Market [Quantpedia]

    There are many calendar / seasonal anomalies by which we can enhance our overall investment strategy. One of the least frequent but still very interesting anomalies is for sure the Pre-Election Drift in the stock market in the United States. This year is the election year, and public discussion is getting more heated. The current president of the United States and candidate for re-election, Donald
  • Correlations Profile | Major Asset Classes | 23 January 2020 [Capital Spectator]

    Return correlations for the major asset classes have edged down in recent years, which implies that diversification opportunities have increased, if only marginally. The correlation readings are only modestly softer overall and for several asset class pairings its fair to say that nothing much has changed. But reviewing all the key slices of global markets by way of pairwise return correlations

Quantocracy’s Daily Wrap for 01/22/2020

This is a summary of links featured on Quantocracy on Wednesday, 01/22/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Persistency Beyond Almost All Other Rallies [Quantifiable Edges]

    Last week I noted the current rally was reaching historical extremes for persistency. Here I will look at another study from the subscriber letter, and then update last weeks study. In last nights letter I looked at all times back to the inception of the NASDAQ in 1971 in which both SPX and the NASDAQ Composite closed above their 10ma for at least 30 days in a row. The short list is below.
  • Calculating a VIX3M Style Index Back to 1990 Reveals Surprising Trends [Six Figure Investing]

    The Cboes VIX (30-day) and VIX3M (93-day) indexes enable us to quantify volatility term structures but until now, historical analyses between VIX style indexes have been limited to dates after December 2001. This post introduces the results of VIX3M style calculations back to 1990, and reviews issues and trends that were revealed. In November 2007, the Cboe introduced VIX3M, a volatility

Quantocracy’s Daily Wrap for 01/21/2020

This is a summary of links featured on Quantocracy on Tuesday, 01/21/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Skew who? [OSM]

    In our last post on the SKEW index we looked at how good the index was in pricing two standard deviation (2SD) down moves. The answer: not very. But, we conjectured that this poor performance may be due to the fact that it is more accurate at pricing larger moves, which occur with greater frequency relative to the normal distribution in the S&P. In fact, we showed that on a monthly basis, two
  • Quant Summit Europe, March 11-12, 2020 in London

    Machine learning, quantum computing and beyond: cutting-edge quant solutions to finance problems Quant Summit Europe gives you the opportunity to meet with, learn and exchange ideas with over 130 renowned industry quants and data scientists from the worlds leading banks, buy-side institutions and universities. Attend this unrivalled summit and join the quant elite in Europe in order to access
  • Enterprise Multiples and Expected Stock Returns [Alpha Architect]

    One of the foundation concepts of the Alpha Architect investment philosophy is the utilization of Enterprise Multiples in the value discovery process. Enterprise multiples are often referred to as the business buyer metric and are a key valuation tool used by investment bankers and business buyers (see here). In addition, the empirical support for the metric is strong: Loughran and Wellman
  • Should I Stay or Should I Growth Now? [Flirting with Models]

    Nave value factor portfolios have been in a drawdown since 2007. More thoughtful implementations performed well after 2008, with many continuing to generate excess returns versus the market through 2016. Since 2017, however, most value portfolios have experienced a steep drawdown in their relative performance, significantly underperforming glamour stocks and the market as a whole. Many investors

Quantocracy’s Daily Wrap for 01/20/2020

This is a summary of links featured on Quantocracy on Monday, 01/20/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Scholz Brake: Fixing Germany s New 1000% Trader Tax [Financial Hacker]

    Would you like to read a 18-page pounderous law draft titled Law for introducing a duty to report cross-border tax structuring? The members of the German Bundestag apparently didnt. Nothing can be said against reporting cum-ex or similar constructs, so the new law, proposed by finance minister Olaf Scholz, passed legislation on December 12, 2019. Only afterwards its real content became
  • Diversification [Falkenblog]

    I was interested in calculating what the portfolio volatility would be for a portfolio given various correlation assumptions, and also the number of assets. So I took two portfolio of the S&P500 in two very different years: 2008 and 2017. The VIX had one of its highest average levels in 2008, at 31.5, while its lowest in 2017, at 11.0. Because I'm interested in low vol portfolios, I took
  • Private Equity: Fooling Some People All the Time? [Factor Research]

    Private equity return data should be viewed with caution Returns are likely overstated while volatility is understated Private equity returns are highly correlated to public equities TWO MAGIC WORDS This time is different might be the four most dangerous words in investing. Uncorrelated returns may just be the two most lucrative. These seven syllables have been applied across the span

Quantocracy’s Daily Wrap for 01/18/2020

This is a summary of links featured on Quantocracy on Saturday, 01/18/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Breaking Down 50 Years of Industry Data [Fortune Financial]

    It has long been a belief of mine that the industry in which a company operates has a huge impact on its performance, and that most industries simply are not worthwhile for long-term investment consideration. To further this discussion, I took the detailed industry data found in Professor Ken Frenchs data library, and analyzed the performance of each over the fifty-year period ending in
  • Research Review | 17 January 2020 | Volatility [Capital Spectator]

    Macro News and Long-Run Volatility Expectations Anders Vilhelmsson (Lund University) December 10, 2019 I propose a new model-free method for estimating long-run changes in expected volatility using VIX futures contracts. The method is applied to measure the effect on stock market volatility of scheduled macroeconomic news announcements. I find that looking at long-run changes gives qualitatively

Quantocracy’s Daily Wrap for 01/16/2020

This is a summary of links featured on Quantocracy on Thursday, 01/16/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Timing Low Volatility with Factor Valuations [Alpha Architect]

    Funds flows are frequently analyzed by investors to gauge the demand for investment strategies, but it represents a challenging exercise. Key issues are data availability as few market participants disclose their holdings as well as reporting frequency as limited data is published in real-time. The resulting headlines in media, therefore, are often confusing, although they naturally also reflect
  • Predicting Bank Nifty Open Price Using Deep Learning [Quant Insti]

    With the advent of several machine / deep learning models, there have been several theories emerging in applying these techniques for stock market prediction because of the difficulty and complexity it involves. In this project, were trying to solve the problem using a classifier to predict whether the Bank Nifty index listed in NSE will go up or down, on the next day open using two Deep

Quantocracy’s Daily Wrap for 01/15/2020

This is a summary of links featured on Quantocracy on Wednesday, 01/15/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Petra on Programming: A New Zero-Lag Indicator [Financial Hacker]

    I have been recently hired to code a series of indicators based on monthly articles in the Stocks & Commodities magazine, and to write here about the details of indicator programming. Looking through the magazine, I found many articles useful, some a bit weird, some a bit on the esoteric side. So I hope I wont have to code Elliott waves or harmonic figures one day. But this first one is a
  • Autoencoder based outlier detection in Forex [Quant Dare]

    In FOREX, both the EURCHF and USDCHF series have outliers that can be a problem when applying Machine Learning techniques to them. So, in this post, the performance of an autoencoder detecting these anomalies is going to be studied. Analyzing the EURCHF and USDCHF returns, it can be seen that there are days in which there was a very abrupt change in the price. The reasons for this are as follows:
  • Top 5 Most Interesting Papers from the Annual Finance Geek Fest [Alpha Architect]

    The American Finance Association Annual Meetings have now come and gone (here is information on the broader conference). The conference was in sunny San Diego this year and Im told it did not disappoint! 1 This 3-day conference collects the brightest minds in academia to discuss hundreds of new research papers a gold mine for new and exciting ideas! We always look at the mounds of papers

Quantocracy’s Daily Wrap for 01/14/2020

This is a summary of links featured on Quantocracy on Tuesday, 01/14/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Skew and Kurtosis as trading rules [Investment Idiocy]

    This is part X of my series of blog posts on skew and kurtosis, where 2 A post on skew: measuring, and it's impact on future returns A post on kurtosis: measuring, it's impact on future returns, and it's interaction with skew. A post on trend following and skew (which I actually wrote first, hence the confusion!) This post: on using skew and kurtosis as trading rules This series
  • The Hierarchical Risk Parity Algorithm: An Introduction [Hudson and Thames]

    Portfolio Optimisation has always been a hot topic of research in financial modelling and rightly so a lot of people and companies want to create and manage an optimal portfolio which gives them good returns. There is an abundance of mathematical literature dealing with this topic such as the classical Markowitz mean variance optimisation, Black-Litterman models and many more. Specifically,
  • Bitcoin plus Harry Brown s Permanent Portfolio A mix in heaven? [Sanz Prophet]

    What would happen if you took $5,000 out of your $100,000 permanent portfolio and allocated it to Bitcoin? From 3.6% annual to 15% annual returns? Got to love the Permanent Portfolio I have been somewhat obsessed with the simplicity and fundamental thinking behind the permanent portfolio. I have written and analyzed it various times (here and here ) a well as created variations from it that are
  • How ESG Affects Valuation, Risk, and Performance [Alpha Architect]

    We have done a fair amount on the investment merits of ESG investing, but the question of how ESG affects the fundamental performance of a firm (in a causal fashion) is addressed in this study. For example, this paper askes questions such as, Are high ESG scoring firms more adept at managing their risks, thus leading to higher valuations? Or is it the reverse: are firms with higher valuations

Quantocracy’s Daily Wrap for 01/13/2020

This is a summary of links featured on Quantocracy on Monday, 01/13/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Beware Strategies That Fall Down on Good Data [Allocate Smartly]

    Sources of long-term historical data are few and far between. Because its been generously provided for free, one of the most often used is data from Professor French (of Fama-French fame). Others include Shiller and Ibbotson. These data sets are fine for a first pass at testing out ideas, but they often dont remotely match up to something that can actually be traded in todays market.
  • How Expensive Are ESG Stocks? [Factor Research]

    Highly ranked ESG stocks trade at higher valuation multiples than the stock market However, the difference in multiples is minor and far less than extreme than for Growth stocks ESG ETFs generated lower returns than the stock market, but were also less volatile INTRODUCTION Europeans seem far more focused on the environment than Americans, which might be considered unusual given that both share an
  • Principal Component Analysis in Trading [Quant Insti]

    As trading becomes automated, we have seen that traders seek to use as much data as they can for their analyses. But we all know that adding more variables leads to more complications and that in turn might make it harder to come to solid conclusions. Think about it, we have more than 3000 companies in the New York Stock Exchange. A simple exercise to find pairs between them will be really