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Quantocracy’s Daily Wrap for 01/12/2024

This is a summary of links featured on Quantocracy on Friday, 01/12/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pragmatic Asset Allocation Model for Semi-Active Investors [Quantpedia]

    The primary motivation behind our study stems from an observation of the Global Tactical Asset Allocation (GTAA) strategies throughout the existing papers the majority of them require relatively frequent rebalancing from the point of view of the ordinary investor. Portfolio rebalancing is usually done on a weekly or monthly basis, and while this period may seem overly boring and slow for the
  • A Short Take on Real-World Pairs Trading [Robot Wealth]

    In textbooks, one often sees pairs trading algorithms start by regressing prices of Asset A on Asset B to calculate a hedge ratio. Ive rarely seen anyone actually do this in the real world. Thats because it is a very unstable thing especially for a pair of volatile assets, and especially over a large amount of data. The basic pairs trading algorithm which you see out in the real world
  • Peer-Reviewed Theory and Expected Stock Returns [Alpha Architect]

    As professor John Cochrane observed, the literature on investment factors now fills a veritable factor zoo, with hundreds of options. How do investors select from among this huge array of possibilities? In order to minimize the risk that outcomes result from data mining, in our book Your Complete Guide to Factor-based Investing, Andrew Berkin and I established six criteria for a factor
  • Research Review | 11 January 2024 | Fat Tail Distributions [Capital Spectator]

    Optimal Portfolio Choice with Fat Tails and Parameter Uncertainty Raymond Kan (U. of Toronto) and Nathan Lassance (LFIN/LIDAM) December 2023 Existing portfolio combination rules that optimize the out-of-sample performance under estimation risk are calibrated assuming multivariate normally distributed returns. In this paper, we show that this assumption is not innocuous because fat tails in returns

Quantocracy’s Daily Wrap for 01/10/2024

This is a summary of links featured on Quantocracy on Wednesday, 01/10/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Skew preferences for crypto degens [Investment Idiocy]

    An old friend asking for help… how can I resist? Here is the perplexing paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4042239 And here is the (not that senstional) abstract: Bitcoin (BTC) returns exhibit pronounced positive skewness with a third central moment of approximately 150% per year. They are well characterized by a mixture of Normals distribution with one normal
  • How Do You Take Your Commodities? [Return Sources]

    Most portfolios are centered around stocks. Stocks are thought of as the primary return driver, while other additions to the portfolio are thought of less as return drivers, and more as diversifiers. The popular 60 / 40 portfolio is a prime example of this. The vast majority of the returns to this portfolio come from stocks, which are much more volatile than bonds. Bonds do contribute returns, of

Quantocracy’s Daily Wrap for 01/09/2024

This is a summary of links featured on Quantocracy on Tuesday, 01/09/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Choi’s Dividend & Growth Allocation [Allocate Smartly]

    This is a test of Paul Chois paper Balance Between Growth and Dividend: Dividend & Growth Allocation (DGA). This strategy would have delivered exceptional performance over the last 50 years, but we would temper future expectations for several reasons we discuss below. Backtested results from 1974 follow. Results are net of transaction costs see backtest assumptions. Learn about what we
  • Sparse Index Tracking: Limiting the Number of Assets in an Index Tracking Portfolio [Portfolio Optimizer]

    In the previous post, I introduced the index tracking problem1, which consists in finding a portfolio that tracks as closely as possible2 a given financial market index. Because such a portfolio might contain any number of assets, with for example an S&P 500 tracking portfolio possibly containing ~500 stocks, it is [sometimes desirable] that the tracking portfolio consists of a small number of
  • Defensive factor strategy – how do you build one? [Alpha Architect]

    Is there a defensive equity factor? Can one be built? Although it seems like an easy question, the answer is not straightforward. The authors of this piece argue for a careful assessment of factor strategies to deliver a defensive profile convincing enough to attract investors. A defensive return-to-risk posture may or may not be achieved by obvious candidates like quality, low volatility, or
  • Duration of U.S. Equities [Finominal]

    Sectors and factors were not very sensitive to changes in interest rates on average However, the averages are misleading as the sensitivity varies significantly over time The duration of factors was more dispersed than that of sectors INTRODUCTION If equity investors are from Mars, then fixed-income investors are from Venus. Despite nearly all investors holding various combinations of stocks and

Quantocracy’s Daily Wrap for 01/05/2024

This is a summary of links featured on Quantocracy on Friday, 01/05/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why Do US Stocks Outperform EM and EAFE Regions? [Quantpedia]

    Investing in emerging markets (EM) or developed markets (DM) outside of the United States tends to follow cyclical trends. At times, it becomes popular and crowded to focus solely on U.S. stocks, while in other periods, the trend shifts to favor everything except U.S. equities. This inclination often relies on historical and past performance data, although it doesnt guarantee identical outcomes
  • Crowded Trades Increase Crash Risks [Alpha Architect]

    Arbitrageurs keep markets efficient by moving prices to reflect their fundamental values. However, anomalies can persist because of limits to arbitragethe costs and risks of shorting. The costs and risks of shorting, however, are not the only risks that arbitrageurs face. The publication of research on anomalies in asset pricing models has led to a dramatic increase in factor-based investment

Quantocracy’s Daily Wrap for 01/04/2024

This is a summary of links featured on Quantocracy on Thursday, 01/04/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Sketching the Option Backtester v2 (with Code downloadable for ALL readers) [Hanguk Quant]

    In the last post, we wrote code to test for the pnl of a system that continuously rebalances and shorts the atm straddle on index options. Sketching the Option Backtester (with Code downloadable for ALL readers) HangukQuant December 21, 2023 Sketching the Option Backtester (with Code downloadable for ALL readers) Read full story We just made a few notes – the code is rather inflexible in that

Quantocracy’s Daily Wrap for 01/02/2024

This is a summary of links featured on Quantocracy on Tuesday, 01/02/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Deep Dive into Volatility Targeting [Return Sources]

    In the world of trend following, the biggest, most longstanding debate is about whether or not to target a certain level of volatility on an ongoing basis. Listeners of the podcast Top Traders Unplugged will be very familiar with this debate. Unfortunately, some of the language surrounding this argument has become confusing and vague. When we say, volatility targeting, we need to be clear
  • Most popular posts 2023 [Eran Raviv]

    This blog is just a personal hobby. When Im extra busy as I was this year the blog is a front-line casualty. This is why 2023 saw a weaker posting stream. Nonetheless I am pleased with just over 30K visits this year, with an average of roughly one minute per visit (engagement time, whatever google-analytics means by that). This year I only provide the top two posts (rather than the usual 3).
  • Factor Olympics 2023 [Finominal]

    The performance of factors was unexciting and poor in 2023 Quality performed the best, low volatility the worst Low-risk and cheap stocks are currently highly correlated INTRODUCTION We present the performance of five well-known factors on an annual basis for the last 10 years. Specifically, we only present factors where academic research supports the existence of positive excess returns across

Quantocracy’s Daily Wrap for 01/01/2024

This is a summary of links featured on Quantocracy on Monday, 01/01/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Weekend Effect in the Market Indices [Relative Value Arbitrage]

    The weekend (or Monday) effect in the stock market refers to the phenomenon where stock returns exhibit different patterns on Mondays compared to the rest of the week. Historically, there has been a tendency for stock prices to be lower on Mondays. Various theories attempt to explain the weekend effect, including investor behaviour, news over the weekend, and the impact of events occurring during

Quantocracy’s Daily Wrap for 12/30/2023

This is a summary of links featured on Quantocracy on Saturday, 12/30/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quickly store 2,370,886 rows of historic options data with ArcticDB [PyQuant News]

    Over 1,200,000 options contracts trade daily. Storing options data for analysis has become something only professionals can do using sophisticated tools. One of the professionals recently open sourced their tools for lightening fast data storage and retrieval. ArcticDB is a DataFrame database that is used in production by the systematic trading company, Man Group. Its used for storage,
  • Tracking systematic default risk [SR SV]

    Systematic default risk is the probability of a critical share of the corporate sector defaulting simultaneously. It can be analyzed through a corporate default model that accounts for both firm-level and communal macro shocks. Point-in-time estimation of such a risk metric requires accounting data and market returns. Systematic default risk arises from the capital structures vulnerability and
  • The Financial Distress Puzzle [Alpha Architect]

    That riskier assets should command higher expected returns is the most basic of asset pricing theories. Clearly, financial distress is a risk characteristic, but it presents a puzzle, as there has not been a linear relationship between it and stock returns. For example, John Birge and Yi Zhang, authors of the April 2017 study Risk Factors That Explain Stock Returns: A Non-Linear Factor Pricing

Quantocracy’s Daily Wrap for 12/26/2023

This is a summary of links featured on Quantocracy on Tuesday, 12/26/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Differentiated Trend Following [Return Sources]

    Trend following boils down to one basic idea: buy when the price goes up, and sell when it goes down. Its implementation, though, could be much more complicated. There are a myriad methods and timeframes to choose from, and these methods and timeframes are by and large the dials that CTAs can turn in constructing their trend programs. One manager can focus on long term trend, another one medium
  • Easily cross-validate parameters to boost your trading strategy [PyQuant News]

    Trading strategies often rely on parameters. To enhance and effectively cross-validate these parameters can provide a competitive advantage in the market. However, reliable cross-validation strategies can lead to look-ahead bias and other pitfalls that can lead to overestimating a strategys performance. In todays newsletter, well use VectorBT PRO to easily implement a variety of
  • Are stock returns predictable at different points in time? [Alpha Architect]

    The question of whether stock returns are predictable is of long-standing interest to both academics and investment practitioners. Commonly accepted investment strategies, for example, will behave quite differently in the presence of stock return predictability. The research literature is unclear on the answer and suggests that return predictability, if it exists, will be difficult to exploit on
  • Momentum Everywhere, Including Equity Options [Alpha Architect]

    Because of the strong evidence, momentum continues to receive much attention from researchers. Out of the hundreds of exhibits in the factor zoo, one of just five equity factors that met all the criteria (persistent, pervasive, robust, implementable, and intuitive) Andrew Berkin and I established in our book Your Complete Guide to Factor-Based Investing was momentum (both cross-sectional

Quantocracy’s Daily Wrap for 12/21/2023

This is a summary of links featured on Quantocracy on Thursday, 12/21/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 2023 Rally – How Strong Is It? [Alvarez Quant Trading]

    This end of year rally which started on October 2023 has been strong. My trading buddy and I started wondering how this compares to the past. Is this a normal strong rally or an abnormally strong one? Determining this is always tough because it depends on the indicators you use. Because of that, I tried lots of them. This will be a post short on words but with lots of tables. Where are
  • Judging the Quality of Indicators [Dekalog Blog]

    In my previous post I said I was trying to develop new indicators from the results of my new PositionBook optimisation routine. In doing so, I need to have a methodology for judging the quality of the indicator(s). In the past I created a Data-Snooping-Tests-GitHub which contains some tests for statistical significance testing and which, of course, can be used on these new indicators.
  • Research Review | 21 DEC 2023 | Portfolio Design & Risk Factors [Capital Spectator]

    Factor Zoo (.zip) Alexander Swade (Lancaster University) et al. October 2023 The number of factors allegedly driving the cross-section of stock returns has grown steadily over time. We explore how much this factor zoo can be compressed, focusing on explaining the available alpha rather than the covariance matrix of factor returns. Our findings indicate that about 15 factors are enough to