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Quantocracy’s Daily Wrap for 04/27/2020

This is a summary of links featured on Quantocracy on Monday, 04/27/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Introducing Online Portfolio Selection [Hudson and Thames]

    Online Portfolio Selection is an algorithmic trading strategy that sequentially allocates capital among a group of assets to maximize the final returns of the investment. Traditional theories for portfolio selection, such as Markowitzs Modern Portfolio Theory, optimize the balance between the portfolios risks and returns. However, OLPS is founded on the capital growth theory, which solely
  • VIX – Simple and Intuitive Explanation of Volatility Index [Only VIX]

    Few years ago I published two post trying to give simple explanations and intuition behind complicated formulas used for calculating vol indexes. However few of you emailed that some charts are missing from these older posts, and for technical reasons since I could not restore them, I decided to re-created new charts from scratch, and re-write the posts. In this post I will make many
  • Tranching, Trend, and Mean Reversion [Flirting with Models]

    In past research we have explored the potential benefits of how-based diversification through the lens of pay-off functions. Specifically, we explored how strategic rebalancing created a concave payoff while momentum / trend-following created a convex payoff. By combining these two approaches, total portfolio payoff became more neutral to the dispersion in return of underlying assets. We have also
  • Tail Risk Hedge Funds [Factor Research]

    Tail risk funds tend to be most in demand when they are least attractive Short-term bonds provided similar benefits to tail risk funds The TAIL ETF closely replicates the performance of tail risk funds INTRODUCTION In a year where the S&P 500 lost more than 30% in a few weeks, there are few headlines that draw as much attention as these: This Black Swan Trade Saw 1,000% Profits This Week

Quantocracy’s Daily Wrap for 04/26/2020

This is a summary of links featured on Quantocracy on Sunday, 04/26/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Efficiently Simulating Geometric Brownian Motion in R [Robot Wealth]

    For simulating stock prices, Geometric Brownian Motion (GBM) is the de-facto go-to model. It has some nice properties which are generally consistent with stock prices, such as being log-normally distributed (and hence bounded to the downside by zero), and that expected returns dont depend on the magnitude of price. Of course, GBM is just a model and no model is a perfect representation of

Quantocracy’s Daily Wrap for 04/25/2020

This is a summary of links featured on Quantocracy on Saturday, 04/25/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Podcast with @MebFaber: Why an investment plan is a must and how to behave in a market crash [System Trader Show]

    Meb Faber is a co-founder and the Chief Investment Officer of Cambria Investment Management. His speciality is quant investing. Meb is the host of The Meb Faber Show podcast and has authored numerous white papers and books. He is a frequent speaker and writer on investment strategies and has been featured in Barrons, The Wall Street Journal, The New York Times, and The New Yorker. In this
  • Risk premia [OSM]

    Our last post discussed using the discounted cash flow model (DCF) as a method to set return expectations that one would ultimately employ in building a satisfactory portfolio. We noted that if one were able to have a reasonably good estimate of the cash flow growth rate of an asset, then it would be relatively straightforward to calculate the required return. The problem, of course, is figuring
  • Research Review | 24 April 2020 | Covid-19 Blowback [Capital Spectator]

    Howell E. Jackson (Harvard Law School) and Steven L. Schwarcz (Duke U.) April 19, 2020 The coronavirus has produced a public health debacle of the first-order. But the virus is also propagating the kind of exogenous shock that can precipitate and to a considerable degree is already precipitating a systemic event for our financial system. This currently unfolding systemic shock comes a
  • Visualising Oanda’s Orderbook [Dekalog Blog]

    My earlier post of 26th March shows code to visualise the most recent instantaneous snapshot of Oanda's order book, realised as a horizontal bar chart superimposed over a price chart. Below is a screen shot of a different type of chart designed to show the historical order book, which is similar to the proprietary Bookmap software. The background of the chart is a heatmap of the 20 order book

Quantocracy’s Daily Wrap for 04/23/2020

This is a summary of links featured on Quantocracy on Thursday, 04/23/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Paul Novell’s Flagship Strategy SPY-COMP [Allocate Smartly]

    This is a test of the flagship proprietary strategy from Paul Novells Investing for a Living. Paul has been kind enough to share his strategy rules to allow for independent verification of his results. SPY-COMP is like Growth-Trend Timing and a handful of other tactical strategies we track, in that it considers trends in both prices and key economic indicators to switch between risk and
  • Trend Following is Everywhere [Alpha Architect]

    Similar to some better-known factors, such as size and value, time-series momentum (TSMOM) historically has demonstrated abnormal excess returns. For the less familiar with trend following its worth your time to review Alpha Architects white paper on trend following here. TSMOM is measured by a portfolio that is long assets that have had recent positive returns and short assets that have had
  • Trend Analysis using Open Interest, Rollover and FII/DII Activity in Python [Quant Insti]

    The first quarter of 2020 has been one of the most challenging times in the post World War II era. The crash in oil prices due to geopolitical reasons and the COVID-19 global pandemic were the dominant themes. Financial markets act as bellwethers and give us a reflection of the overall sentiment for the world economy. These sentiments are reflected not only in the price but also in other metrics
  • Brent Oil Price Time-Series in Python with 1-Minute Data Sampling [Quant at Risk]

    Recent actions in WTI Futures pricing on Apr 20, 2020 caused my curiosity to have a deep look at intraday crude oil price time-series. With no surprise, I couldnt find any free and effortlessly available dataset on the Internet. This is a common problem for lots of quants and data analysts: conducting your own research over financial markets you are doomed to download and use daily data. The
  • The Pandemic Portfolio – Risk Parity, Convexity, and Multi-Asset Factors in Extreme Markets [Invest Resolve]

    How long will the recession last? How deep will it be? What are the long-term implications for the economy, markets, and society? The global pandemic has ushered in a period of extreme uncertainty and investors are left with too many unanswered questions and afraid for their portfolios. Where do we go from here? In this episode, the ReSolve team begins with an examination of some of the

Quantocracy’s Daily Wrap for 04/22/2020

This is a summary of links featured on Quantocracy on Wednesday, 04/22/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Market Cap vs. Crash Severity [Alvarez Quant Trading]

    Has the market sell-off and subsequent bounce treated all stocks the same? A good portion of the bull market move from 2009 to 2019 has been led by the big-cap stocks. Did they hold up better during the March sell-off? What about with the bounce? Did the smaller-cap stocks have a bigger bounce? The Setup February 19 At the S&P 500 high on February 19, I took all 503 stocks in the index and
  • Hedging an Option through the Black-Scholes model in discrete time [Quant Dare]

    The Black-Scholes formula can be used to create a hedge for an option. However, this model is derived in continuous time. What happens when we use it to hedge an option in discrete-time? European options are financial securities which give their holder the right (but not the obligation) to buy or sell an asset (called the underlying asset) at a predetermined price (called the strike price) at a
  • Geek Note: How to Properly Lag Monthly Economic Data [Allocate Smartly]

    Well be talking about Paul Novells flagship SPY-COMP strategy on the blog tomorrow. The strategy uses monthly economic data, like the kind available from the FRED database. Weve covered a handful of strategies like this in the past (think Philosophical Economics Growth Trend-Timing). Whenever we do, we invariably get a ton of questions, because it can be confusing to determine how to
  • How to Compute Active Share [Alpha Architect]

    In the short video below, I show how to compute Active Share. The accompanying excel file with the formulas can be found here. I start by computing the active share for two hypothetical funds, and then examine the active share of a few live ETFs.

Quantocracy’s Daily Wrap for 04/21/2020

This is a summary of links featured on Quantocracy on Tuesday, 04/21/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Momentum vs Factor Valuation [Falkenblog]

    I am not a fan of most equity factors, but if any equity factor exists, it is the value factor. Graham and Dodd, Warren Buffet, Fama and French have all highlighted value as an investment strategy. Its essence is the ratio of a backward-looking accounting value vs. a forward-looking discounting of future dividends. As we are not venture capitalists, but rather, stock investors, all future

Quantocracy’s Daily Wrap for 04/20/2020

This is a summary of links featured on Quantocracy on Monday, 04/20/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • One Factor World [Two Centuries Investments]

    For the past decade, asset managers have been educating clients about factor investing as it became the new norm. And yet after all these years, portfolios are still composed of one factor: Equity Beta. Among many questionable assertions and assumptions behind factor investing (our thoughts here, here and here), there is one that remains true: Equity Beta is the most significant Risk Factor in
  • Estimating Pandemic Economic Costs for “Face-to-Face” Businesses [Alpha Architect]

    To describe the impact of social distancing, a theory of communication is developed and described comprehensively in this article. The focus is on the relative importance of worker interactions, the cost of those interactions and their impact on the size of wage subsidies intended as compensation for the disruption due to social distancing. The authors develop a model of communication whereby the
  • Smart Beta Fixed Income ETFs [Factor Research]

    Factor investing in fixed income has been heralded as the next frontier in asset management Smart beta fixed income ETFs in the US manage only slightly more than $2 billion of assets Defensive strategies reduced drawdowns during the ongoing coronavirus crisis INTRODUCTION Investing is becoming more scientific over time as technology continues to advance, but it will never be a hard science like
  • Well, you No, you gotta do more than that. [Flirting with Models]

    Since 2009, any decision to de-risk in a trend equity portfolio has largely been the wrong decision. At the time of writing, we implement a 1-month tranching process in most of our trend mandates, which has the effect of dollar-cost averaging signal changes over a 1-month period. We adopted this approach for a number of reasons, including: (1) to align our rebalance frequency with what we believe
  • Dual Momentum & Rate of Change: Trading Strategy Review [Oxford Capital]

    Concept: Dual momentum trading strategy based on Rate of Change (ROC). Research Goal: Performance verification of dual momentum signals. Specification: Table 1. Results: Figure 1-2. Trade Filter: Long Filter: Slow Rate of Change (ROC1) is above zero. Short Filter: Slow Rate of Change (ROC1) is below zero. Trade Setup: Long Setup: Fast Rate of Change (ROC2) is above zero. Short Setup: Fast Rate of

Quantocracy’s Daily Wrap for 04/19/2020

This is a summary of links featured on Quantocracy on Sunday, 04/19/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Parameter Optimisation for Systematic Trading [Robot Wealth]

    Optimisation tools have a knack for seducing systematic traders. And whats not to love? Find me the unique set of parameters that delivered the greatest return in my ten-year backtest. And do it in under five seconds. Thats certainly attractive. But do you want to hear something controversial? When it comes to the parameters of a systematic trading strategy, in the majority of cases

Quantocracy’s Daily Wrap for 04/17/2020

This is a summary of links featured on Quantocracy on Friday, 04/17/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Petra on Programming: A Unique Trend Indicator [Financial Hacker]

    This months project is a new indicator by John Ehlers, first published in the S&C May 2020 issue. Ehlers had a unique idea for early detecting trend in a price curve. No smoothing, no moving average, but something entirely different. Lets see if this new indicator can rule them all. The basic idea of the Correlation Trend Indicator (CTI) is quite simple. The ideal trend curve is a straight
  • Dividends, Stock Prices, and Inflation [Alpha Architect]

    Building on the concepts presented in my Dividends Are Different article, here we present data and observations highlighting the relationship between inflation and 1) company fundamentals, 2) dividends, and 3) stock market movements. 1 We look at empirical data to investigate how inflation relates to market prices, earnings, and dividends. We measure results over 25-year time periods fairly
  • Attention Data Geeks: Our Factor Investing Data Library is Open [Alpha Architect]

    Are you doing independent factor research? Have you spent countless hours on Ken Frenchs website? Do you run factor regressions for fun? Congrats you are officially a finance geek and you will probably benefit from our new factor investing library. Our library has over 300 factors to choose from and has data available from 92 to the most recent period. The factors are built across the
  • Working with High-Frequency Tick Data – Cleaning the Data [Quantpedia]

    Tick data is the most granular high-frequency data available, and so is the most useful in market microstructure analysis. Unfortunately, tick data is also the most susceptible to data corruption and so must be cleaned and conditioned prior to being used for any type of analysis. This article, written by Ryan Maxwell, examines how to handle and identify corrupt tick data (for analysts unfamiliar

Quantocracy’s Daily Wrap for 04/16/2020

This is a summary of links featured on Quantocracy on Thursday, 04/16/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation: Mid-April Checkup [Allocate Smartly]

    Tactical Asset Allocation (TAA) weathered the storm in February and March, significantly paring down losses vs conventional buy & hold. So far it has trailed the bounce in April, but these are early days. We track 50+ TAA strategies sourced from books, papers, etc., allowing us to draw some broad conclusions about TAA as a style. In the table below we show the MTD and YTD returns of these 50+
  • A Review of Zorro for Systematic Trading [Robot Wealth]

    One of the keys to running a successful systematic trading business is a relentless focus on high return-on-investment activities. High ROI activities include: Implementing new trading strategies within a proven framework. An example might be to implement a portfolio of pairs trades in the equity market. Scaling existing strategies to new instruments or markets. For example, porting the pair
  • Is There a Tail Risk Premium in Stocks? [Alpha Architect]

    It has been well documented both that stock returns have much fatter tails than a normal distribution would generate, and that tail events occur much more frequently than a normal curve would predict. 1 For example, Benoit Mandelbroit and Richard Hudson examined the daily index movements of the Dow Jones Industrial Index from 1916 to 2003. They noted that if stock returns were normally