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Quantocracy’s Daily Wrap for 12/10/2023

This is a summary of links featured on Quantocracy on Sunday, 12/10/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Brownian Motion Simulation with Python [Quant Start]

    In this article we will explore simulation of Brownian Motions, one of the most fundamental concepts in derivatives pricing. Brownian Motion is a mathematical model used to simulate the behaviour of asset prices for the purposes of pricing options contracts. A typical means of pricing such options on an asset, is to simulate a large number of stochastic asset paths throughout the lifetime of the
  • Simulation of Gary Antonacci s Dual Momentum Sector Rotation Strategy [NLX Finance]

    Heres a backtest of Gary Antonaccis DMSR (Dual Momentum Sector Rotation) strategy. The author is best known for his GEM (Global Equity Momentum) strategy, which he popularised in 2014, in his book Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk , McGraw-Hill Education. As a reminder, GEM (Global Equity Momentum) is a strategy that is well known to

Quantocracy’s Daily Wrap for 12/09/2023

This is a summary of links featured on Quantocracy on Saturday, 12/09/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Adaptive Asset Allocation Replication [Foss Trading]

    The paper, Adaptive Asset Allocation: A Primer by Adam Butler, Mike Philbrick, Rodrigo Gordillo, and David Varadi addresses flaws in the traditional application of Modern Portfolio Theory related to Strategic Asset Allocation. It shows that estimating return and (co)variance parameters over shorter time horizons are superior to estimates over long-term horizons because parameter estimates
  • The Art and Science of Trading Carry [Robot Wealth]

    Lets talk about carry trades. First, what exactly is a carry trade? A carry trade is a trade that pays you to hold it. A position where, if nothing changes except the passing of time, you expect to make money. Lets go through some examples. FX carry The classic example is the FX carry trade, where you borrow a low-yielding currency to buy a high-yielding one and profit from the interest
  • Diseconomies of Scale in Investing [Alpha Architect]

    Abstract: One of the problems for investment funds is that success contains the seeds of destruction as cash inflows follow outperformance. In his seminal 2005 paper, Five Myths of Active Portfolio Management, Jonathan Berk suggested asking, Who gets money to manage? He answered that since investors have access to databases that provide returns histories, and everyone wants to have

Quantocracy’s Daily Wrap for 12/06/2023

This is a summary of links featured on Quantocracy on Wednesday, 12/06/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The “Strike Price” of Long-Only Trend Following [Return Sources]

    Long-only trend following is a popular way to protect equity portfolios from huge drawdowns, and for several good reasons: 1) It has the advantage of behaving somewhat like insurance, or put options, in that youre exposed to much of the upside and not much of the downside. 2) It doesnt damage your returns as much as buying put options does. (Notice how I said that it doesnt damage your
  • How to stream real-time options data [PyQuant News]

    Ive been trading options contracts for more than 23 years. When I started out, I had to rely on expensive broker data feeds for real-time options data for trading and low-quality free data I scraped from websites for analysis. I spent countless hours reverse engineering the CBOE website for real-time options data, only to have my IP address blocked. I spent $1,125 on 5 years of historic options
  • Forecasting time series with decomposition [PyQuant News]

    In todays newsletter, Im going to show you how to forecast a time series of US unemployment data using decomposition. Time series decomposition is breaking down a single time series into different parts. Each part represents a pattern that you can try to model and predict. The patterns usually fall into three categories: trend, seasonality, and noise. Time series decomposition models are
  • After-Tax Performance of Actively Managed Funds [Alpha Architect]

    Market efficiency, higher trading costs and higher expense ratios are not the only hurdles to successful active management (market timing and individual security selection). For taxable investors, the burden of higher taxes raises the hurdle.(1) From 2002 until now, S&P Dow Jones Indices has published its S&P Indices Versus Active (SPIVA) Scorecard, comparing the performance of actively

Quantocracy’s Daily Wrap for 12/01/2023

This is a summary of links featured on Quantocracy on Friday, 12/01/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Cloud or Local: Where to Run Your Quant Trading? [Quant Rocket]

    Is it better to run your quant trading in the cloud or locally? In this article, I outline the pros and cons of each approach and explain why running locally is often better for research while running in the cloud is better for live trading. Don't assume the cloud is better It's common to imagine that every serious workload should run in the cloud. We associate the cloud with modern,

Quantocracy’s Daily Wrap for 11/29/2023

This is a summary of links featured on Quantocracy on Wednesday, 11/29/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What is a robust stochastic volatility model research paper [Artur Sepp]

    I would like to share my research and thoughts about stochastic volatility models and, in particular, about the log-normal stochastic volatility model that I have been developing in a series of papers (see introductory paper with Piotr Karasinski in 2012, the extension to include quadratic drift with Parviz Rakhmonov in 2022, and application of the model to Cheyette interest rate model and to
  • Commodity carry as a trading signal part 2 [SR SV]

    Carry on commodity futures contains information on implicit subsidies, such as convenience yields and hedging premia. Its precision as a trading signal improves when incorporating adjustments for inflation, seasonal effects, and volatility. There is strong evidence for the predictive power of various metrics of real carry with respect to subsequent future returns for a broad panel of 23
  • A New Book Takes A Deep Dive At Solving The Portfolio Problem [Capital Spectator]

    Financial wisdom is said to be cyclical rather than cumulative, but thats unfair. At least in the dominion of portfolio management and design, academics and money managers have made great strides in decoding Mr. Markets cryptic signals over the past half century. The challenge, having led the proverbial horse to water, is making him drink. The stakes are high. History, in fact,
  • Statistical Shrinkage (4) – Covariance estimation [Eran Raviv]

    A common issue encountered in modern statistics involves the inversion of a matrix. For example, when your data is sick with multicollinearity your estimates for the regression coefficient can bounce all over the place. In finance we use the covariance matrix as an input for portfolio construction. Analogous to the fact that variance must be positive, covariance matrix must be positive definite to

Quantocracy’s Daily Wrap for 11/28/2023

This is a summary of links featured on Quantocracy on Tuesday, 11/28/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Guide to Forecast Scalars [Return Sources]

    In my last post about the overnight anomaly, I created a trading signal based on the difference between recent overnight returns and recent intraday returns. I calculated the signal for various time frames (ranging from about a week to about a year), and I mentioned that I applied different forecast scalars to each time frame. I didnt really elaborate what a forecast scalar is, and I
  • Overlapping Momentum Stocks – do they cause outperformance? [Alpha Architect]

    Momentum investors utilize different timeframes to identify high momentum equities: past 6, 9, 12 months as an example. Obviously, there is a significant degree of overlap in momentum stocks identified across various past time frames. However, there has been little research focused on understanding the characteristics of momentum stocks formed on six and 12 months that overlap one another. The

Quantocracy’s Daily Wrap for 11/26/2023

This is a summary of links featured on Quantocracy on Sunday, 11/26/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Improving the default plot timescale for backtesting in R [Babbage9010]

    Default plots often include a few or many bars of misleading data where a strategy may have zeros or NAs compared with the benchmark, for example where the strategy uses a moving average lookback period before generating a trade signal. Theres a simple way to start the plot after the strategy is generating signals, and it improves quality of the stats generated too. I dont see this tip often
  • Covered calls: are investors making a devil’s bargain? [Alpha Architect]

    Many retail investors focus on generating what they consider to be income, leading to the popularity of dividend-focused strategies. To take advantage of this demand, investment firms have marketed covered call strategies that are purported to not only generate income but also reduce volatility. Covered calls involve selling call options on a security owned by a fund in exchange for a premium. The

Quantocracy’s Daily Wrap for 11/21/2023

This is a summary of links featured on Quantocracy on Tuesday, 11/21/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Overnight Anomaly: Alive and Well [Return Sources]

    In finance, the overnight anomaly is the name for the unusual phenomenon that overnight stock market returns are much higher than intraday returns. In a 2008 paper, Return Differences between Trading and Non-trading Hours: Like Night and Day, the authors break down the U.S. equity premium into its night-time and day-time components, and find that the entire premium is due to night
  • A Long-Term Look at the Wednesday Before Thanksgiving [Quantifiable Edges]

    Thanksgiving week has shown some strong seasonal tendencies over the years. Ive documented this in years past on the blog. From a seasonal standpoint, Wednesday before Thanksgiving is one of the most bullish trading days of the year. The chart below shows performance from Tuesdays close to Wednesdays close on the day before Thanksgiving. SPX performance on Wed before Thanksgiving It

Quantocracy’s Daily Wrap for 11/20/2023

This is a summary of links featured on Quantocracy on Monday, 11/20/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Exponentially weighted covariance in an Equal Risk Contribution portfolio optimisation problem [Robot Wealth]

    The Equal Risk Contribution (ERC) portfolio seeks to maximally diversify portfolio risk by equalising the risk contribution of each component. The intuition is as follows: Imagine we have a 3-asset portfolio Assets 1 and 2 are perfectly correlated (correlation of 1.0) Asset 3 is uncorrelated with the other two (correlation of 0.0) Lets say we equal-weighted the three assets. Wed have 33% in
  • Takeaways from QuantMinds 2023 [Cuemacro]

    Its been exactly a decade since I attended my first QuantMinds event. At the time, it was called Global Derivatives, and as the name suggests, it was very much focused towards option pricing and its associated areas. It was held for a number of years in Amsterdam, at the Okura Hotel (whilst I cant remember what the burgers were like, I can safely say the sushi was excellent year), and then
  • Statistical Shrinkage [Eran Raviv]

    Imagine youre picking from 1,000 money managers. If you test just one, theres a 5% chance you might wrongly think theyre great. But test 10, and your error chance jumps to 40%. To keep your error rate at 5%, you need to control the family-wise error rate. One method is to set higher standards for judging a managers talent, using a tougher t-statistic cut-off. Instead of the usual
  • Commodity carry as a trading signal part 1 [SR SV]

    Commodity futures carry is the annualized return that would arise if all prices remained unchanged. It reflects storage and funding costs, supply and demand imbalances, convenience yield, and hedging pressure. Convenience and hedging can give rise to an implicit subsidy, i.e., a non-standard risk premium, and make commodity carry a valid basis for a trading signal. An empirical analysis of carry
  • Research Review | 17 November 2023 | Return Expectations [Capital Spectator]

    Causes of Deviations from a Real Earnings Yield Model of the Equity Premium Austin Murphy and Zeina N. Alsalman (Oakland University) October 2023 A market-based forecast of inflation added to equity earnings yields explains much of the variation in stock market returns over multi-year horizons. Return deviations from the prediction are found to be negatively related to the current inflation rate

Quantocracy’s Daily Wrap for 11/15/2023

This is a summary of links featured on Quantocracy on Wednesday, 11/15/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Military Expenditures and Performance of the Stock Markets [Quantpedia]

    Si vis pacem, para bellum, is an old Roman proverb translated to English as If you want peace, prepare for war, and it is the main idea behind the military policy of a lot of modern national states. In the current globally interconnected world, waging a real hot war has very often really negative trade and business repercussions (as the Russian Federation realized in 2022).
  • Using time series lag() in R finance [Babbage9010]

    Backtesting quant strategies in R requires paying attention to how we lag() our time series. Here be dragons. Lagging a time series relative to another is important in many areas, but we use it a lot in backtesting financial strategies. Ive struggled with the logic of lag() several times, and gotten it wrong more than once. And different packages within the R universe apparently use lag()
  • Sector Rotation Strategy: Should Trading Rules Make Sense? [Alvarez Quant Trading]

    I was doing my usual reading when I came across a sector rotation strategy. I have seen lots of these strategies but this one had a different twist. The strategy was a momentum strategy but instead of buying the top three, it was buying the middle three. The article gave no reason other than it works and gives the best results. In general, people fall into two camps about trading rules. The