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Quantocracy’s Daily Wrap for 03/19/2019

This is a summary of links featured on Quantocracy on Tuesday, 03/19/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Fractional Differencing Implementation (FD Part 3) [Kid Quant]

    Well…That took a lot longer than I expected it too. 6 weeks later and I finally have the last installation in these series of posts. It's also the longest one so you could say it was worth the wait. I recently found out that Python 2.7 (the python I've used for EVERY project) will soon be deprecated. In other words, any support or bug-fixes will cease to exist. In an effort not to
  • Using Dynamic Mode Decomposition (DMD) to Rotate Long-Short Exposure Between Market Sectors [Quantoisseur]

    Part 1 Theoretical Background The Dynamic Mode Decomposition (DMD) was originally developed for its application in fluid dynamics where it could decompose complex flows into simpler low-rank spatio-temporal features. The power of this method lies in the fact that it does not depend on any principle equations of the dynamic system it is analyzing and is thus equation-free [1]. Also, unlike
  • Monte Carlo Simulation of strategy returns [Philipp Kahler]

    Monte Carlo Simulation uses the historic returns of your trading strategy to generate scenarios for future strategy returns. It provides a visual approach to volatility and can overcome limitations of other statistical methods. Monte Carlo Simulation Monte Carlo is the synonymous for a random process like the numbers picked by a roulette wheel. The Monte Carlo Simulation does the same to your

Quantocracy’s Daily Wrap for 03/18/2019

This is a summary of links featured on Quantocracy on Monday, 03/18/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trend Following in Cash Balance Plans [Flirting with Models]

    Cash balance plans are retirement plans that allow participants to save higher amounts than in traditional 401(k)s and IRAs and are quickly becoming more prevalent as an attractive alternative to defined benefit retirement plans. The unique goals of these plans (specified contributions and growth credits) often dictate modest returns with a very low volatility, which often results in conservative
  • Smart Beta Asset Allocation Models [Factor Research]

    Most smart beta strategies outperformed the market since 1990, but few have in recent years Diversifying across strategies mitigates the risk of underperformance Various asset allocation models for creating multi-factor portfolios highlight similar results INTRODUCTION The appearance of smart beta ETFs has simplified the life of investors as they no longer need to suffice themselves with plain
  • 10 Ways to Combine Quant and Fundamental Approaches that Work (and 10 that don’t) [Two Centuries Investments]

    Can quantitative and fundamental approaches be successfully combined? In my estimate, this has been a top 5 industry question for a long time, including this conference at which Ill be speaking at tomorrow The short answer is: Yes More-so, I believe quantitative approaches cannot work without being guided by fundamental principles and insightful questions. Even if the model is fully technical
  • How to collect market tick data [Cuemacro]

    A lunch break is probably more of a necessity from a break perspective than anything else. You could make the break aspect as short as possible, by getting a ready made takeaway. However, that kind of negates the whole break aspect of it all. I end up going through various cycles of what I have for lunch at work, largely due to the plethora of food choices available near my office in London.
  • How to estimate risk in extreme market situations [SR SV]

    Estimating portfolio risk in extreme situations means answering two questions: First, has the market entered an extreme state? Second, how are returns likely to be distributed in such an extreme state? There are three different types of models to address these questions statistically. Conventional extreme value theory really only answers the second question, by fitting an appropriate

Quantocracy’s Daily Wrap for 03/15/2019

This is a summary of links featured on Quantocracy on Friday, 03/15/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Day of the week and the cross-section of returns [Eran Raviv]

    I just finished reading an interesting paper by Justin Birru titled: Day of the week and the cross-section of returns (reference below). The story is much too simple to be true, but it looks to be so. In fact, I would probably altogether skip it without the highly ranked Journal of Financial Economics stamp of approval. However, by the end of the paper I was as convinced as one can be
  • The Bearish Aftermath Of Quad Witching [Quantifiable Edges]

    A Twitter follower ( @SonnyRico ) asked me about weeks following Quad-witching, which occurs in March, June, September, and December. As I have shown in the past, the 2nd half of December has shown bullish tendencies historically (ignore 2018), but those other 3 have NOT been good weeks for the market. In fact, back in September I discussed the Weakest Week, which is the week after September
  • Research Review | 15 March 2019 | Nowcasting [Capital Spectator]

    Factor Timing Revisited: Alternative Risk Premia Allocation Based on Nowcasting and Valuation Signals Olivier Blin (Unigestion), et al. 10 September 2018 Alternative risk premia are encountering growing interest from investors. The vast majority of the academic literature has been focusing on describing the alternative risk premia (typically, momentum, carry and value strategies) individually. In
  • 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 literature that

Quantocracy’s Daily Wrap for 03/13/2019

This is a summary of links featured on Quantocracy on Wednesday, 03/13/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Advances in Financial Machine Learning Package (Update) [Quants Portal]

    First of all we want to thank everyone who has reached out to us with ideas and contributions to our package. Without all of your help, none of this would be possible. We have done a lot of work this week and hope that this update provides you with more insight into both the package for Advances in Financial Machine Learning, as well as the research notebooks which answer the questions at the back
  • How is mean reversion doing? Dead, Shrinking or Doing Just Fine [Alvarez Quant Trading]

    A common question I get from readers is does mean reversion still work? The last time I wrote about this topic was in 2015, a long time ago, in the post The Health of Stock Mean Reversion: Dead, Dying or Doing Just Fine I did not realize it had been so long. Time to look at it again. The Test Date Range: 1/1/2001 to 12/31/2018 Entry: Stock is member of the Russell 3000 Two period RSI
  • Why Taleb’s Antifragile Book is a Fraud [Falkenblog]

    In Nassim Taleb book Antifragile he emphasizes that if you see a fraud and do not say fraud, you are a fraud, I am thus compelled to note that Antifragile is a fraud because its theme is based on intentional misdirection. The most conspicuous and popular examples he presents are also explicitly mentioned as not the essence of antifragility. Indeed, incoherence is Talebs explicit
  • Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis [Alpha Architect]

    R is a programming language that owes its lineage to S, a language designed in its own developers words, to turn ideas into software, quickly and faithfully.(1) Shiny is an interactive web technology that makes it easy to take R models and publish them to the web. Jonathan L. Regenstein, Jr., the director of financial services at RStudio (an integrated development environment for
  • Ranking Quality [Quant Dare]

    The application of Machine Learning for ranking is widely spread. This application of Machine Learning is a little different from the classical ones of classification and regression. In the case of ranking, the interest is not in the accuracy of an estimated value (regression) or the guess about the membership of an element in a cluster or other (classification); rankings care about proper
  • State of Trend Following in February [Au Tra Sy]

    A fairly late and flat report for our State of Trend Following Index. Not the greatest start of the year. Please check below for more details. Detailed Results The figures for the month are: February return: 0.71% YTD return: -6.26% Below is the chart displaying individual system results throughout February: StateTF February And in tabular format: System February Return YTD Return BBO-20 -0.79%

Quantocracy’s Daily Wrap for 03/12/2019

This is a summary of links featured on Quantocracy on Tuesday, 03/12/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Random Forest Algorithm In Trading Using Python [Quant Insti]

    In this blog, well discuss what are Random Forests, how do they work, how they help in overcoming the limitations of decision trees. With the boom of Machine Learning and its techniques in the current environment, more and more of its algorithms find applications in various domains. The functions and working of machine learning algorithms differ from each other wherein one algorithm may be
  • GARP Investing: Golden or Garbage? [Factor Research]

    GARP aims to combine Growth and Value investing GARP stocks have outperformed the market since 1989 It is somewhat perplexing how well the strategy worked VALUE VERSUS GROWTH With their thousands of employees, suites of products, international reach, and legendary histories, General Electric (GE) and Amazon are true corporate empires. Of course, GEs fortunes have lately been in decline while
  • Low Volatility Turnover with Value and Momentum [Alpha Architect]

    What are the research questions? What is the relationship between turnover and returns from a low volatility portfolio that integrates value and momentum exposures with low volatility? Does the relationship change if a only one factor is integrated with a low volatility strategy? Note: This is a follow up piece to this blog piece. What are the Academic Insights? CONCAVE WITH DIMINISHING RETURNS.

Quantocracy’s Daily Wrap for 03/11/2019

This is a summary of links featured on Quantocracy on Monday, 03/11/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Monsters of Investing: Fast and Slow Failure [Flirting with Models]

    Successful investing requires that investors navigate around a large number of risks throughout their lifecycle. We believe that the two most daunting risks investors face are the risk of failing fast and the risk of failing slow. Slow failure occurs when an investor does not grow their investment capital sufficiently over time to meet future real liabilities. This often occurs because they fail
  • The Largest Cost Facing Investors Today [Two Centuries Investments]

    Alternative Title: The Gap Everywhere There exist many flavors of market timing. Some are obvious: In 1929, an influential businessman states that US Equities will return 24% per year for the next 20 years; or in 1999, a stock market forecaster predicts Dow Jones to double On dollar-weighted basis, equity mutual fund investors under-perform buy-and-hold by 6.2% per year during the past 30 years.

Quantocracy’s Daily Wrap for 03/08/2019

This is a summary of links featured on Quantocracy on Friday, 03/08/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Synthetic Data Generation (Part-1) – Block Bootstrapping [Black Arbs]

    Data is at the core of quantitative research. The problem is history only has one path. Thus we are limited in our studies by the single historical path that a particular asset has taken. In order to gather more data, more asset data is collected and at higher and higher resolutions, however the main problem still exists; one historical path for each asset. Derivatives pricing has come up with
  • Options Expiration Week Performance By Month 2019 Update [Quantifiable Edges]

    Next week is monthly options expiration week. Ive noted several times over the years that Op-ex week in general is pretty bullish. March, April, October, and December it has been especially so. S&P 500 options began trading in mid-1983. The table below is one I have showed in March each of the last several years. It goes back to 1984 and shows op-ex week performance broken down by month.

Quantocracy’s Daily Wrap for 03/07/2019

This is a summary of links featured on Quantocracy on Thursday, 03/07/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Sector Business Cycle Analysis [Alpha Architect]

    There are different investment approaches to identify sector winners and losers, such as price momentum strategies, top down approach based on specific macroeconomic indicators or bottom-up approaches to identify sectors with improving fundamentals. One widely used approach is business cycle analysis. Since economic cycles usually exhibit characteristics that impact sectors or industries

Quantocracy’s Daily Wrap for 03/05/2019

This is a summary of links featured on Quantocracy on Tuesday, 03/05/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Intraday Momentum with Leveraged ETFs [Quant Rocket]

    Does forced buying and selling of underlying shares by leveraged ETF sponsors cause predictable intraday price moves? This post explores an intraday momentum strategy based on the premise that it does. Daily rebalancing of leveraged ETFs Source: Ernie Chan, Algorithmic Trading: Winning Strategies and Their Rationale, Wiley, May 28, 2013, chapter 7. Per their fund objectives, leveraged ETFs must

Quantocracy’s Daily Wrap for 03/04/2019

This is a summary of links featured on Quantocracy on Monday, 03/04/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tiingo.com – My Go-To Database For Historical Market Prices [Capital Spectator]

    In the spring of 2017, Yahoo pulled a fast one on the crowd by suddenly changing the technical coding rules for accessing its financial data, leaving countless R users high and dry, including yours truly. Numerous R files that had been meticulously written, revised and maintained over months and years were suddenly broken. Not fun. As frustrating as that day was (not to mention the weeks that
  • Day of the Week Matters for Some Anomalies [Alpha Architect]

    According to psychology literature, mood increases from Thursday to Friday and decreases on Monday. In general, people tend to evaluate future prospects more optimistically when they are in a good mood than when they are in a bad mood. In equity markets, the presence of optimism or pessimism that is unrelated to fundamentals, usually called sentiment, delivers clear, testable cross-sectional
  • The Quant Conference – April 12th in New York City – Learn About The Industry From Those Who Built It

    The rise of big data, application of machine learning and other technological developments in recent years have transformed the quantitative finance industry, and The Quant Conference has been conceived as a unique event to provide an educational forum for delegates to hear about current trends in the cutting-edge field of quantitative finance. The Quant Conferences speaker roster of industry
  • Value, Momentum and Basis in Commodity Futures: 1877-2017 [Two Centuries Investments]

    Commodity Futures contracts were established in 1865, but commercially available data starts in 1959, leaving an 80+ year period of unstudied history. In our latest academic paper Two Centuries of Commodity Futures Premia Chris Geczy and I use hand-collected futures data to extend the well-known cross-sectional Value, Momentum and Basis factors in commodity futures back to 1877. This paper
  • How Much Accuracy Is Enough? [Flirting with Models]

    It can be difficult to disentangle the difference between luck and skill by examining performance on its own. We simulate the returns of investors with different prediction accuracy levels and find that an investor with the skill of a fair coin (i.e. 50%) would likely under-perform a simple buy-and-hold investor, even before costs are considered. It is not until an investor exhibits accuracy in
  • Tactical Asset Allocation in February [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
  • Benchmarking Smart Beta ETFs [Factor Research]

    Long-only factor portfolios can be used for benchmarking smart beta ETFs Results highlight minor tracking errors Likely explained by relatively homogenous factor definitions by ETF issuers INTRODUCTION Investment professionals are not known for their creativity, but that is perhaps only because people outside of the finance industry do not understand the intricacies of finance. Significant amounts