Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 01/28/2024

This is a summary of links featured on Quantocracy on Sunday, 01/28/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Join the Race: Quantpedia Awards 2024 Await You [Quantpedia]

    Hello everyone, Two weeks ago, we promised you a surprise, and now its finally time to unveil what we have prepared for you :). Our Quantpedia Awards 2024 aims to be the premier competition for all quantitative trading researchers. If you have an idea in your head about systematic/quantitative trading or investment strategy, and you would like to gain visibility on the professional scene, then
  • Ideas for Crypto Stat Arb Features [Robot Wealth]

    This article continues our recent articles on stat arb: A short take on stat arb trading in the real world A general approach for exploiting stat arb alphas In this article, Ill brainstorm some ideas for predictive features that you could potentially use in a crypto stat arb model. The ideas draw insights from recent discussions and market observations, but of course, you should do your own
  • Equity market timing: the value of consumption data [SR SV]

    The dividend discount model suggests that stock prices are negatively related to expected real interest rates and positively to earnings growth. The economic position of households or consumers influences both. Consumer strength spurs demand and exerts price pressure, thus pushing up real policy rate expectations. Meanwhile, tight labor markets and high wage growth shift national income from
  • Moving Average Distance and Time-Series Momentum [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
  • Quickly compute Value at Risk with Monte Carlo [PyQuant News]

    Value at risk (VaR) is a tool professional traders use to manage risk. It estimates how much a portfolio might lose, given normal market conditions, over a set time period. There are three ways to compute VaR: the parametric method, the historical method, and the Monte Carlo method. In contrast to the parametric and historical methods which are backward looking, Monte Carlo is forward looking. In

Quantocracy’s Daily Wrap for 01/24/2024

This is a summary of links featured on Quantocracy on Wednesday, 01/24/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean Reversion vs Trend Following Through the Years [Alvarez Quant Trading]

    Something I am always thinking about is how the markets are behaving now vs the past few years vs several years ago. My edge on the strategies I trade depends on two main ideas. One, current market behavior is similar to what I tested on which is normally the last 5-10 years. Two, not too many others have found the same edge. Unfortunately for (2), more and more people are trading quant style and

Quantocracy’s Daily Wrap for 01/22/2024

This is a summary of links featured on Quantocracy on Monday, 01/22/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A General Approach for Exploiting Statistical Arbitrage Alphas [Robot Wealth]

    Last week, I wrote a short article about statistical arbitrage trading in the real world. Statistical arbitrage is a well-understood concept: find pairs or baskets of assets you expect to move together, wait for them to diverge, and bet on them converging again. Simple enough. But making it work, especially at scale, is a little more complicated. A somewhat old-school approach takes pairs of
  • Easily compare investment strategies [PyQuant News]

    Portfolio optimization is a balance between maximizing returns and minimizing risk. While it might sound easy, its actually very difficult compare investment strategies. First, we have to accurately forecast future returns and risk. Then, we have to use tricky optimization models to build the portfolios subject to our constraints. Not to mention come up with a strategy that works! Most
  • Outperforming Cap- (Value-) Weighted and Equal-Weighted Portfolios [Alpha Architect]

    Popular benchmarks in academic research studies to evaluate the performance of investment strategies are cap-weighted (market-, or value-weighted), and equal-weighted portfolios. Capitalization-weighted portfolios are used because they are the simplest and cheapest to implement, representing the total market with little to no rebalancing costs. Equal-weighted portfolios have produced higher
  • Trend Following in Bear Markets [Finominal]

    Short-only trend following in stocks generated consistent losses across markets However, combining the strategy with an equities portfolio generated diversification benefits Like other hedging strategies it would be difficult to execute this strategy over the long-term INTRODUCTION Trend following is likely the most researched investment strategy. The folks at AQR have backtested the framework

Quantocracy’s Daily Wrap for 01/19/2024

This is a summary of links featured on Quantocracy on Friday, 01/19/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Dr. Keller & Keuning s Simple Variation of Hybrid Asset Allocation [Allocate Smartly]

    This is a test of the simple variation of Dr. Keller and Keunings strategy from their paper Dual and Canary Momentum with Rising Yields/Inflation: Hybrid Asset Allocation (HAA). Weve covered the balanced version of HAA previously. It has become one of the more popular strategies on our platform, and members have asked us to add this simpler-to-execute variation as well.

Quantocracy’s Daily Wrap for 01/18/2024

This is a summary of links featured on Quantocracy on Thursday, 01/18/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Exploration of CTA Momentum Strategies Using ETFs [Quantpedia]

    Commodity Trading Advisor (CTA) funds are commonly associated with managed futures investing in futures and options, and are a subset of the broader hedge fund universe[1]. Beyond commodities, they have the flexibility to venture into other assets, including interest rates, currencies, fixed income and equity indices. Most of the CTA strategies are trend-following in nature. Trend-following

Quantocracy’s Daily Wrap for 01/17/2024

This is a summary of links featured on Quantocracy on Wednesday, 01/17/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Can You Trade Only The “Best” Trend Signals? [Return Sources]

    Trend following is a relatively simple strategy, at least at the concept level: buy when prices go up, and sell when they go down. The main way that trend followers differentiate themselves is the timeframe over which they measure whether the price has gone up or down. For example, one manager might follow short term trends, like one month price moves. Another might use long-term trends, like
  • Adaptive Asset Allocation Extended [Foss Trading]

    This post extends the replication from the Adaptive Asset Allocation Replication post by running the analysis on OOS (out-of-sample) data from 2015 through 2023. Thanks to Dale Rosenthal for helpful comments. The paper uses the 5 portfolios below. Each section of this post will give a short description of the portfolio construction and then focus on comparing the OOS results with the replicated

Quantocracy’s Daily Wrap for 01/16/2024

This is a summary of links featured on Quantocracy on Tuesday, 01/16/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quant_rv_MV5_big, and a milestone [Babbage9010]

    The same multi-vol quant strategy we all love, but now with 2000+ vol signals to choose from. quant_rv is a daily SPY strategy that uses realized volatility measures of SPY to predict days of lower volatility ahead, that in turn predict positive returns. In the net, quant_rv wins by very modest improvements to the SPY win ratio and by modest improvements to the average win size relative to the
  • Advanced FX carry strategies with valuation adjustment [SR SV]

    FX forward-implied carry is a popular ingredient in currency trading strategies because it is related to risk premia and implicit policy subsidies. Its signal value can often be increased by considering inflation differentials, hedging costs, data outliers, and market restrictions. However, even then, FX carry is an imprecise and noisy signal, and previous research has shown the benefits of
  • 46 awesome books for quant finance, algo trading, and market data analysis [PyQuant News]

    One of the most common questions I get: What books should I read for quant finance, algorithmic trading, and market data analysis? And one of my favorite hobbies is collecting books on the subject. 46 awesome books for quant finance, algo trading, and market data analysis 46 books for quant finance, algo trading, and market data analysis. Books for quant finance, algorithmic trading, and market
  • Getting Value Exposure from Non-Value Funds [Finominal]

    The factor betas of value-focused ETFs range dramatically Non-value-focused funds can have high betas to the value factor However, these often come with large unintended bets INTRODUCTION While some investors are die-hard believers in value investing, others regard this more opportunistically and only occasionally seek exposure to this investing style. Perhaps cheap stocks have been outperforming,

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