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Quantocracy’s Daily Wrap for 04/03/2019

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

  • Global Equity Momentum: A Craftsman’s Perspective – Executive Summary [Invest ReSolve]

    Quantitative investment researchers often seek uniquely optimal parameterizations of their strategies amongst a broad robust region of parameter choices. However, this ignores a critically important feature of investing Diversification. By diversifying across many equally legitimate parameter choices an ensemble investors may be able to preserve expected performance with a higher
  • The 50/50 SPY Strategy [Alvarez Quant Trading]

    I was talking to my trading buddy about the annoying part of trend following strategies. They may get you out of the major sell off but then you miss part of the run up. Using a 200-day moving average on the SPY would have got you out in late 2018. This would have been within 10% from the top and you would not had the pain of the additional 10% drop in December. But one would not have gotten back
  • Understanding the shape of data (II) [Quant Dare]

    Topology could be used to gain insight on the shape of our data, as we explained in our last post. Today, we will put this theory into practice by analyzing the 2008 financial crisis. Persistence diagrams We will start by giving an equivalent representation of the persistence barcode that we saw previously. We are talking about the persistence diagram. First, lets recall how the persistence

Quantocracy’s Daily Wrap for 04/02/2019

This is a summary of links featured on Quantocracy on Tuesday, 04/02/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Over 90 Years of Golden Crosses (and a look at past drawdowns) [Quantifiable Edges]

    The SPX made a Golden Cross formation on Monday. A Golden Cross occurs when the 50ma crosses over the 200ma. Having the 50ma above the 200ma is commonly considered a bullish market condition and generally it is. I used my Norgate data and Amibroker software to look back as far as 12/31/1928. Below is a list of all Golden Crosses since then. (Note that prior to 1957, S&P 90 data was used.
  • Intro to Hidden Markov Chains [Quant Insti]

    In a situation where you wish to determine the returns on investment, one may have all the expertise to do this but without certain information (missing pieces) it would not be possible to derive to a conclusive figure. In practical terms assume you have the value of all returns of all assets in your portfolio; without the rate at which each asset produces the returns we will not have a true

Quantocracy’s Daily Wrap for 04/01/2019

This is a summary of links featured on Quantocracy on Monday, 04/01/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in March [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
  • Short Selling + Insider Selling = Bad News [Alpha Architect]

    What are the research questions? Is there a relationship between short selling activity and insider selling? What is the impact of short selling trading strategies that are conditioned on insider trading signals? Does the price impact of short selling subsume that of insider trading? Is the price impact of short selling trades permanent or are do they eventually reverse? What are the Academic
  • Inverted Yield Curve: Danger or Noise? [Two Centuries Investments]

    In addition to market valuation ratios like CAPE, the slope of the yield curve is one of the most talked about signals used to estimate future recessions and market returns. During the second half of last month (March 2019), the yield curve has inverted by about 5 basis points with the 10-year rate reaching 2.37% and 3-month 2.42%. Although, by the end of the month, the curve flipped slightly
  • Taxes and Trend Equity [Flirting with Models]

    Due to their highly active nature, trend following strategies are generally assumed to be tax inefficient. Through the lens of a simple trend equity strategy, we explore this assertion to see what the actual profile of capital gains has looked like historically. While a strategic allocation may only realize small capital gains at each rebalance, a trend equity strategy has a combination of large
  • Test of Constant Velocity Model Kalman Filter [Dekalog Blog]

    Following on from my previous post, this post is a more detailed description of the testing methodology to test kinematic motion models on financial time series. The rationale behind the test(s) which are described below is different from the usual backtesting in that the test(s) are to determine whether the Kalman filter model is mismatched or not, i.e. whether the model innovations match the
  • Factor Olympics Q1 2019 [Factor Research]

    2019 has started favorable for factor investors, compared to 2018 Low Volatility generated the best and Value the worst performance Factor performance is comparable in the US & Europe, but different in Japan INTRODUCTION We present the performance of five well-known factors on an annual basis for the last 10 years. We only present factors where academic research highlights positive excess

Quantocracy’s Daily Wrap for 03/30/2019

This is a summary of links featured on Quantocracy on Saturday, 03/30/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Noisy Data strategy testing [Philipp Kahler]

    Algorithmic trading adds noise to the markets we have known. So why not add some noise to your historic market data? This way you can check if your algorithmic trading strategies are fit for the future. Learn how to generate noisy data and how to test your strategies for stability in a noisy market. Synthetic market data? Generating synthetic market data to test algorithmic strategies is a
  • Survival in the trading factor zoo [SR SV]

    The algorithmic strategy business likes quoting academic research to support specific trading factors, particularly in the equity space. Unfortunately, the rules of conventional academic success provide strong incentives for data mining and presenting significant results. This greases the wheels of a trial-and-error machinery that discover factors merely by the force of large numbers and

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