Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 03/28/2019

This is a summary of links featured on Quantocracy on Thursday, 03/28/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Differences Between the VIX Index And At-the-Money Implied Volatility [Relative Value Arbitrage]

    When trading options, we often use the VIX index as a measure of volatility to help enter and manage positions. This works most of the time. However, there exist some differences between the VIX index and at-the-money implied volatility (ATM IV). In this post, we are going to show such a difference through an example. Specifically, we study the relationship between the implied volatility and
  • An End of Quarter Edge [Quantifiable Edges]

    It is worth noting that Friday is the last trading day of the quarter. And the last day of the quarter has some interesting characteristics. I often hear the term window dressing mentioned by the media when referring to end of quarter activity. The suggestion is that fund managers will make late adjustments to their portfolios, in order to make them appear more attractive. So their list of

Quantocracy’s Daily Wrap for 03/27/2019

This is a summary of links featured on Quantocracy on Wednesday, 03/27/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pitfalls When Assessing Market-Timing Strategies [Alpha Architect]

    Consider a market-timing strategy which supposedly predicts the direction of the stock market trend. Such a strategy generates Buy and Sell signals. A Buy signal is the signal to buy stocks, whereas a Sell signal is the signal to sell. Simple enough, but how does one evaluate the forecast accuracy of the market timing strategy? Quant traders often evaluate the forecast accuracy of a market-timing
  • Risk targeting and dynamic asset allocation: absolute or relative momentum? [Investment Idiocy]

    Quite a few of my recent blog pieces have been picked up by the lovely folk at allocate smartly. So I thought I'd write an asset allocation piece, as the readers of my second book "Smart Portfolios" probably feel neglected with the lack of articles on investment rather than trading. Absolute or relative momentum? The motivation for this comes from a table in my second book, which
  • Fundamental Manifoldness [Quant Dare]

    One of the hardest and most frequent tasks for anyone in the quantitative finance world is to summarize or visualize in a simple way a vast amount of data to represent a company. In this blog, we have covered different Machine Learning techniques that allow us to summarize information through dimensionality reduction. These techniques let us improve the performance of our models and the display of

Quantocracy’s Daily Wrap for 03/25/2019

This is a summary of links featured on Quantocracy on Monday, 03/25/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • AI and Alternative Data in Investing – Hype or Reality? [Two Centuries Investments]

    Having just attended a great AI conference in New York, here are some observations: First about AI Most quants prefer the term Machine Learning (ML) instead of AI. Questions still remain of where AI (ML) adds value in a quant investment process. For example, Mans CIO Sandy Rattray said that it works well for the execution of trades but is not that helpful in forecasting returns (see here). He
  • Time Dilation [Flirting with Models]

    Information does not flow into the market at a constant frequency or with constant magnitude. By sampling data using a constant time horizon (e.g. 200-day simple moving average), we may over-sample during calm market environments and under-sample in chaotic ones. As an example, we introduce a highly simplified price model and demonstrate that trend following lookback periods should be a
  • Seasonality May Again Flip This Week To Bullish [Quantifiable Edges]

    With regards to seasonality, we are in an interesting period right now. The last couple of weeks the market played out well according to seasonal patterns. We saw March opex week put in nice gains as it often does. And then we saw the week after Quad-witching suffer losses this past week. Interestingly, the week after the 4th Friday in March has been a strong one over the last 21 years. (Not as
  • Black Swans, Major Events and Factor Returns [Factor Research]

    It is questionable if investors should prepare for catastrophic events Factor returns are almost random after black swan and major events Simple diversification is likely the best option for the expected and unexpected INTRODUCTION Investors fear black swan events, although it can be argued that this fear is irrational. The black swan theory is a metaphor that describes a surprise event that has a
  • Signaling systemic risk [SR SV]

    Systemic financial crises arise when vulnerable financial systems meet adverse shocks. A systemic risk indicator tracks the vulnerability rather than the shocks (which are the subject of stress indicators). A systemic risk indicator is by nature slow-moving and should signal elevated probability of financial system crises long before they manifest. A recent ECB paper proposed a practical

Quantocracy’s Daily Wrap for 03/21/2019

This is a summary of links featured on Quantocracy on Thursday, 03/21/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Does Meta Labeling Add to Signal Efficacy? [Quants Portal]

    This weeks research was consumed by the concept of Meta-Labeling, how it works, and does it work out-of-sample? We have published a research report as well as an accompanying slide show. There was quite a bit of discussion this on the topic, the following is a link to a Github issue where a few friends and I discuss it at length: Meta-Labeling Q&A. Maksim Ivanov wrote a good blog post on it
  • Why the Size Premium Should Persist w/ @LarrySwedroe [Alpha Architect]

    As the chief research officer for Buckingham Strategic Wealth and The BAM Alliance, Im often asked, after any asset class or factor experiences a period of poor performance, if the historical outperformance of stocks with that characteristic has disappeared because the premium has become well known and arbitraged away. The size premiums relatively poor performance in U.S. stocks over the
  • Revisiting the Kalman Filter [Dekalog Blog]

    Some time ago ( here, here and here ) I posted about the Kalman filter and recently I have been looking at Kalman filters again because of this Trend Without Hiccups paper hosted at SSRN. I also came across this Estimation Lecture paper which provides MATLAB code for the testing of Kalman filters and my Octave suitable version of this code is shown in the code box below.

Quantocracy’s Daily Wrap for 03/20/2019

This is a summary of links featured on Quantocracy on Wednesday, 03/20/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Asset Allocation Roundup [Allocate Smartly]

    Five recent asset allocation articles (tactical or otherwise) that you might have missed: 1. ETF Bond Rotation (Alvarez Quant Trading) Cesar looks at different flavors of a simple momentum-based bond rotation strategy. Using momentum to time bond asset classes has not worked nearly as well as it has with other assets. And unlike most asset classes, very short-term momentum has been more effective
  • Generating Financial Series with Generative Adversarial Networks [Quant Dare]

    The scarcity of historical financial data has been a huge hindrance for the development of algorithmic trading models ever since the first models were devised. In the ever-changing economic reality we live in, countless models are tried and evaluated. Most of these models seek to extract information from the market by measuring a set of reasonable variables. Through backtesting, an overwhelming
  • Hedging Long-Term Risk with an Intraday Strategy [Quant Rocket]

    Do intraday strategies have a place in the portfolios of long-term investors and fund managers? This post explores an intraday strategy that works best in high volatility regimes and thus makes an attractive candidate for hedging long-term portfolio risk. Trading hypothesis: first half hour predicts last half hour Source paper: Gao, Lei and Han, Yufeng and Li, Sophia Zhengzi and Zhou, Guofu,

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.