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Quantocracy’s Daily Wrap for 05/31/2021

This is a summary of links featured on Quantocracy on Monday, 05/31/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Explanatory Power of Factor Momentum [Alpha Architect]

    Momentum is the tendency for assets that have performed well (poorly) in the recent past to continue to perform well (poorly) in the future, at least for a short period of time. 1 In 1997, Mark Carhart, in his study On Persistence in Mutual Fund Performance, was the first to use a momentum factor, together with the three FamaFrench factors (market beta, size, and value), to explain mutual
  • Factor momentum: a brief introduction [SR SV]

    Standard equity factors are autocorrelated. Hence, it is not surprising that factor strategies have also displayed momentum: past returns have historically predicted future returns. Indeed, factor momentum seems to explain all return momentum in individual stocks and across industries. Momentum has been concentrated on a subset of factors, most notably those related to betting against beta,

Quantocracy’s Daily Wrap for 05/27/2021

This is a summary of links featured on Quantocracy on Thursday, 05/27/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Fit forecast weights by instrument, by group or fit across all markets? Or all three? [Investment Idiocy]

    I've long been a critic of the sort of people who think that one should run a different trading system for each instrument that you trade. It is the sort of thing that makes intuitive sense; surely the S&P 500 is a completely different animal to the Corn future? And that's probably true for high frequency traders, but not at the sort of timescales that I tend to trade over (holding
  • Different methods for mitigating overfitting on Neural Networks [Quant Dare]

    Using Machine Learning and Deep Learning models to solve scientific problems of greater or lesser complexity is a challenge. Referring to neural networks, on the one hand, simple networks with too little capacity will not learn the problem well producing a model that underfits the data. On the other hand, complex networks with too much capacity will learn it too well leading to a model that
  • Update on Recent Matrix Profile Work [Dekalog Blog]

    Since my previous post, on Matrix Profile (MP), I have been doing a lot of online reading about MP and going back to various source papers and code that are available at the UCR Matrix Profile page. I have been doing this because, despite my initial enthusiasm, the R tsmp package didn't turn out to be suitable for what I wanted to do, or perhaps more correctly I couldn't hack it to get

Quantocracy’s Daily Wrap for 05/25/2021

This is a summary of links featured on Quantocracy on Tuesday, 05/25/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Value and Momentum Investing: Combine or Separate? [Alpha Architect]

    When it comes to Value and Momentum investing we often get asked the following set of questions: Should I use value and momentum, in one screen, to form a single portfolio of stocks? ("Blended", "combined", or "integrated") Or should I focus on the value and momentum factor separately, and then combine the factor portfolios? ("Pure", "Separated",
  • Estimating Fair Value For The 10-Year Treasury Yield, Part II [Capital Spectator]

    Earlier this month, I reviewed a model that estimates a theoretical level for the worlds most-important interest rate: the 10-year Treasury yield. In todays follow-up, lets consider a second model for additional context. The goal in this series is to select several models with an eye on combining the estimates. A long line of literature demonstrates, rather convincingly, that one of the
  • Portfolio Construction in Venture Capital [Factor Research]

    A few winners generate most of the venture capital returns Given this asymmetrical return distribution, portfolios should be constructed equally Missing the winners is simply too risky INTRODUCTION 2020 turned out to be a record year for the venture capital industry, despite the global pandemic. More than 12,000 investments were made into early to late-stage start-ups at a combined value of $166

Quantocracy’s Daily Wrap for 05/24/2021

This is a summary of links featured on Quantocracy on Monday, 05/24/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Machine Learning Based Statistical Arbitrage [Jonathan Kinlay]

    Applying Machine Learning in Statistical Arbitrage In this series of posts I want to focus on applications of machine learning in stat arb and pairs trading, including genetic algorithms, deep neural networks and reinforcement learning. Pair Selection Lets begin with the subject of pairs selection, to set the scene. The way this is typically handled is by looking at historical correlations and

Quantocracy’s Daily Wrap for 05/21/2021

This is a summary of links featured on Quantocracy on Friday, 05/21/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Free ticket to Algo Trading Summit Online Conference July 15th – Includes many Quantocracy contributors!

    The online conference for quants and algo-traders. Hear from the best and brightest minds in algo-trading. Join like-minded pros and gain hands-on, actionable information from the best and brightest minds in algo-trading
  • Trees and networks [OSM]

    Its been over a month since our last post and for that we must apologize. We endeavor to be more prolific, but sometimes work and life get in the way. On the work front, lets just say we wont have to spend as much time selling encyclopedias door-to-door, which should free up more time to dedicate to writing value-added blog posts. On the life front, we had the chance to hike several
  • Pairs Trading – A Real-World Profitable Strategy [Milton FMR]

    Pairs trading is popular due to its simple approach and effectiveness. At the heart of the strategy is how the prices of two assets diverge and converge over time. Pairs trading algorithms profit from betting on the fact that spread deviations return to their mean. One of the more notable hedge funds that implemented a pairs trading strategy was Long Term Capital Management. The company was
  • Bitcoin Elasticity and Volatility [Mark Best]

    So the crypto markets on May 19th were fun!? If you have been a part of these markets for any time, this volatility is not that surprising. That said it is still pretty amazing that the price can drop 30% in a single day. This is maybe more surprising since it is to the backdrop of an increase in institutional investors and a narrative of this time its different. I was asked a question
  • Predictability of the Value Premium Across Asset Classes [Alpha Architect]

    The value spread is the difference between the value signal in the long versus the short portfolio. This isnt the first time we have hit on this topic. Wes and I have done several posts on the subject: Timing Value and Momentum with Valuation-Spreads The Returns to Value Strategies When Valuation Spreads Are Wide (Deep Value) The Forecasting Power of Value, Profitability, and Investment Spreads

Quantocracy’s Daily Wrap for 05/20/2021

This is a summary of links featured on Quantocracy on Thursday, 05/20/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Are Ben Graham s Disciples Value and Quality Factor Investors? [Alpha Architect]

    I examine the performance records of performance of Ben Graham's well-known disciples: Walter Schloss, Tom Knapp, Warren Buffett, Bill Ruane, Charlie Munger, Rick Guerin, and Stan Perlmeter. The research question I seek to address is the following: Do the academic "value" and "quality" factors explain the performance of these legendary investors? Surprisingly, the evidence

Quantocracy’s Daily Wrap for 05/18/2021

This is a summary of links featured on Quantocracy on Tuesday, 05/18/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • More Robust Strategies [Financial Hacker]

    The previous article dealt with John Ehlers AM and FM demodulating technology for separating signal and noise in price curves. In the S&C June issue he described a practical example. Applying his FM demodulator makes a strategy noticeably more robust at least with parameter optimization. The simple example strategy is basically a short-term trend follower. The price curve is
  • ESG Performance Breakdown by E, S, and G [Alpha Architect]

    The relationship among ESG ratings from third-party providers has historically produced conflicting results. Differences in sourced information and weighting schemes have produced low correlations between ratings and as a result, have handicapped the efforts to understand the relationship between ESG ratings and performance. Consequently, the credibility and willingness to use and invest utilizing

Quantocracy’s Daily Wrap for 05/17/2021

This is a summary of links featured on Quantocracy on Monday, 05/17/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Idea Streams #3 Seeking Diversification Amidst Global Market Correlations [Quant Connect]

    The CSI 300 is a capitalization-weighted stock market index that tracks the top 300 stocks listed on the two main stock exchanges in mainland China. In April 2020, South China Morning Post reported that the 120-day correlation between the CSI 300 Index and the S&P 500 index recently rose to its highest level since Bloomberg began compiling the data in 2002. The rise in correlation can be
  • Max Sortino Added to the Portfolio Optimizer (And Whether That Matters) [Allocate Smartly]

    We track more than 60 Tactical Asset Allocation strategies, which members can combine together into custom portfolios. To make creating those portfolios easier, we provide an optimizer showing the best performing combinations of strategies based on the members investment objective, such as maximizing the Sharpe Ratio (risk-adjusted return) or minimizing volatility. By popular demand, weve
  • $SPX Loves Tax Day [Quantifiable Edges]

    In the 4/12/19 blog I showed a study about US tax day (normally April 15th). The reason tax day may be important is that it is the last day that people can make IRA contributions to count for the previous tax year. This can create a last-minute rush and you will often have an inflow of funds heading into the market right around and on the day taxes are due. Fund managers will often put this money
  • Managed Futures: Fast & Furious vs Slow & Steady [Factor Research]

    Managed futures strategies aim to exploit short- or long-term trends Short-term trend followers are often seen as offering better stock market crash protection characteristics Our analysis highlights that the differences are marginal INTRODUCTION Aesops famous story of the race between the tortoise and the hare was put up to a test in 2016 when researchers made them compete with each other in
  • Research Review | 14 May 2021 | Stock Returns [Capital Spectator]

    Long-Horizon Stock Returns Are Positively Skewed Adam Farago and Erik Hjalmarsson (University of Gothenburg) April 28, 2021 At long horizons, multiplicative compounding induces strong-to-extreme positive skewness into stock returns; the magnitude of the effect is primarily determined by single-period volatility. Consequently, at horizons greater than five years, returns individual or
  • The macro forces behind equity-bond price correlation [SR SV]

    Since the late 1990s, the negative price correlation of equity and high-grade bonds has reduced the volatility of balanced portfolios and boosted Sharpe ratios of leveraged long-long equity-bond strategies. However, this correlation is not structurally stable. Over the past 150 years, equity-bond correlation has changed repeatedly. A structural economic model helps to explain and predict

Quantocracy’s Daily Wrap for 05/13/2021

This is a summary of links featured on Quantocracy on Thursday, 05/13/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Learning Candlestick Patterns [Tr8dr]

    In the previous posts I described an Reinforcement Learning approach to Learning the Exit part 1, part 2. My initial conclusions there have been: reward smoothing (with the labeler) leads to more robust results than a reward on position exit without smoothing the learning process struggled and had more volatility from epoch to epoch obtained the best results with smoothed reward obtained
  • The Rust Programming Language [Mark Best]

    I love programming! There is something really satisfying about solving a complicated problem concisely. That said I see programming languages as a tool to solve a problem rather than purely coding for coding sake. I have used a lot of programming languages over the last 20 years namely Java, R, Matlab, Python, C++ and now Rust. It is pretty common to read articles about language wars and which one
  • Fixed income when you re between a rock and a hard place – Part 2 [Alpha Architect]

    In Part 1, we defined fixed income factors. But factors alone will not solve each investors problem. Below, we extend the discussion by walking through a case study that shows how an asset allocator might use factors to solve a common problem: how to invest in a low yield environment given the practical constraints faced by many investors. Or, how can factors help investors stuck between a rock

Quantocracy’s Daily Wrap for 05/12/2021

This is a summary of links featured on Quantocracy on Wednesday, 05/12/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Getting historical data from MetaTrader [Thiago Marzagao]

    Getting historical intraday financial data can be a pain, especially for non-US markets. If you have deep pockets you can simply buy the data you need, but for retail investors the cost is prohibitive. If you want historical transaction-level data for the Brazilian stock market, for instance, TickData will sell it to you for about US$ 65000. Hard pass. What to do? I recently learned about an app
  • Strategy Backtesting in Mathematica [Jonathan Kinlay]

    This is a snippet from a strategy backtesting system that I am currently building in Mathematica. One of the challenges when building systems in WL is to avoid looping wherever possible. This can usually be accomplished with some thought, and the efficiency gains can be significant. But it can be challenging to get ones head around the appropriate construct using functions like FoldList, etc,
  • Different ranking methods for a monthly S&P500 Stock Rotation Strategy [Alvarez Quant Trading]

    Recently for my own trading, I have been researching rotational strategies on both the weekly and monthly timeframes. The most common indicator that I use for ranking stocks is Rate of Change (ROC) of the closing price. I read about using Rate of Change on the EMA to rank stocks. I liked a small twist on the idea and wanted to know how it compared to what I am using. Then this led me down another
  • A Decade of Cryptocurrencies [Grzegorz Link]

    It has been almost 11 years since the first official Bitcoin trades in July of 2010. It's price has experienced quite a run. Although controversial, cryptocurrencies have firmly taken hold of the current investing landscape, won hearts and minds of groups of investors, suggesting they are here to stay for longer than many have anticipated. As more data becomes available, it is interesting to
  • Value Investing Still Beats Growth Investing, Historically [Alpha Architect]

    A few weeks ago I saw comments on Twitter regarding the Russell 3,000 Value and Growth indices having approximately the same returns since inception. For example, here is Ben Johnson from Morningstar 1 As viewed from this tweet, and is born out in the data for the Russell indices, it appears that Value investing has no edge relative to growth investing over the past 40+ years! 2 So once again its
  • Estimating Fair Value For The 10-Year Treasury Yield [Capital Spectator]

    The world is awash in efforts to model a theoretical value for the stock market the CAPE ratio, for example. But while the equities hog much of the attention on this front, similar analytics for the worlds most important interest rate are no less valuable. How to begin? Not surprisingly, there are countless possibilities. Alas, time is short. Enter a model that generates a baseline estimate