Factor seasonality always seemed to be an idea that was too close to factor timing to help build factor strategies. Surprisingly, the authors find a substantial factor seasonality effect across global markets, suggesting that the assumption is unwarranted. This is the first study I have encountered

Portfolio Selection via Topological Data Analysis – Petr Sokerin, Kristian Kuznetsov, Elizaveta Makhneva, Alexey Zaytsev Portfolio management is an essential part of investment decision-making. However, traditional methods often fail to deliver reasonable performance. This problem stems from the

In March, we posted a series of three articles where our goal was to construct a market timing strategy that would reliably sidestep the equity market during bear markets. Each article focused on trading signals based on a specific group of indicators, namely, price-based indicators, macroeconomic

Expanding the Fama-French Factor Model with the Industry Beta Anatoly B. Schmidt (NYU Tandon School of Engineering) August 2023 Recently it was shown that the news-based stock pricing model (NBSPM) outperforms the momentum-enhanced five-factor Fama-French model (FF5M) for a representative list of

We cannot start without a cheap quip: Technical analysis is an astrology for men. Market technicians believe that prices currently contain all information about any asset. It is undoubtedly an oversimplified assumption, as the market is much more complex than that. But suppose you try to use

A big issue for me with this project is: how do we validate this whole approach? We started out with a super simple vol-based timing strategy (long/flat market exposure). I rather glibly state that realized (or perhaps more properly, historical) volatility is a “sensible, logical, statistically

We are often asked about stock market valuation models such as Shiller’s CAPE Ratio and the Buffet Indicator. These models predict long-term returns, usually forecasting the next 10 years. Our recent analysis of one such valuation model, the Aggregate Investor Allocation to Equities, motivated us

Picture this: you're about to board a 10-hour flight. As you board the plane (or maybe some time waiting at the gate), a notification pops up in your pocket. You're busy with other things, so you ignore it and forget about it (in fact, you're used to ignoring notifications because you

One particularly notable indicator change that occurred at the close on Friday is that out NASDAQ/SPX Relative Leadership indicator flipped so that it is now showing the SPX as leading and the NASDAQ as lagging. This can be seen in the chart below. NASDAQ/SPX Relative Strength shows NASDAQ faltering

Buying cheap growth stocks is intuitively appealing to investors Almost 50% of the US stocks are trading below a PEG ratio of 1 currently However, GARP stocks have not generated positive excess returns since 2005 INTRODUCTION In 2019, we published a research note on growth-at-reasonable-price (GARP)

AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting – Oleksandr Shchur, Caner Turkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, Yuyang Wang We introduce AutoGluon-TimeSeries – an open-source AutoML library for probabilistic time series forecasting. Focused on

The business cycle is a pattern that captures changes in economic activity over time. The changes in the business cycle occur in a sequential or serial manner, moving through a predictable sequence of phases. These cycles are consistent but vary in both duration and intensity. The phases of the

This is the last of the advanced quant dev series post - next week, we will go back to the basics, and cover the details in how we arrive at the advanced quant backtesting library, which evolved from a rudimentary system consisting of a single signal, single model strategy to a multi signal, multi

In today’s newsletter, we’ll cover the up-market capture ratio, a framework for evaluating investment performance in rising markets. Even though the ratio is used by professional money managers, you can use it to better gauge your own investment performance. Let’s dive in! How to use capture

Nowcasting economic trends can make use of a broad range of machine learning methods. This not only serves the purpose of optimization but also allows replication of past information states of the market and supports realistic backtesting. A practical framework for modern nowcasting is the

In today’s newsletter, you’ll use Polars, a high-speed data-handling tool that’s becoming essential in quantitative finance and algorithmic trading. You’ll see how to compare its performance to pandas for many common data manipulation techniques. By the end of this post, you’ll understand

In his 2012 paper “The Other Side of Value: The Gross Profitability Premium,” Robert Novy-Marx demonstrated that profitability, as measured by gross profits-to-assets, had roughly the same power as book-to-market (value factor) in predicting the cross-section of average returns – profitable

Fractional Brownian motion is defined as a stochastic Gaussian process XtXt​ that starts at zero X0=0X0​=0 has an expectation of zero E[Xt]=0E[Xt​]=0 and has the following covariance1: E[XtXs]=σ212(∣t∣2H+∣s∣2H−∣t−s∣2H)(1)

Sum up: by combining all the vols into one strategy and randomizing key parameters, we can generate useful signals that yield a decent return with some consistency. We’re not meeting all the quant_rv goals yet, but we’re making progress on all the fronts. ~ Links to earlier parts ~ Part 1:

Portfolio Management: A Deep Distributional RL Approach – David Pacheco Aznar This thesis presents the development and implementation of a novel Deep Distributional Reinforcement Learning (DDRL) approach in the field of quantitative finance: the Distributional Soft Actor-Critic (DSAC) with an LSTM

During 2017 I blogged about Statistical Shrinkage. At the end of that post I mentioned the important role signal-to-noise ratio (SNR) plays when it comes to the need for shrinkage. This post shares some recent related empirical results published in the Journal of Machine Learning Research from the

The traditional financial theory attributes security returns to market- or factor-based risk, with no role ascribed to other influences. In this research, the authors argue for including investor demand as an additional variable in explaining returns. Can changes in investor demand generate

We started off with the conceptualisation of trading alpha in different abstract representations, such as mathematical formulas, graphs and visual representations: Alpha-Encoding Data Structures Alpha-Encoding Data Structures HangukQuant · Jun 30 Read full story For machine trading this would

Backtest speed can significantly affect research friction. The ability to form a hypothesis and quickly get an answer from a backtest allows you to investigate more hypotheses. In this article, I explore several factors that affect backtest speed and compare the performance of 3 open-source

The low-beta anomaly for the capital asset pricing model (CAPM)—low-beta stocks outperform high-beta stocks—was first documented more than 50 years ago by Fischer Black, Michael Jensen, and Myron Scholes in their 1972 paper, “The Capital Asset Pricing Model: Some Empirical Tests.” In our

Introduction Simulator/backtester architecture Preparing the data Simulating a single market Simulating market orders Part 2: Simulating trading costs Simulating funding Simulating many markets Finish Subscriber materials (source code) Introduction In the last article, we looked at how markets work

Pure macro(economic) strategies are trading rules that are informed by macroeconomic indicators alone. They are rarer and require greater analytical resources than standard price-based strategies. However, they are also more suitable for pure alpha generation. This post investigates a pure macro

Quantocracy: This is a curated mashup of quantitative trading links. Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling – Masanori Hirano, Kentaro Minami, Kentaro Imajo Deep hedging is a deep-learning-based framework for derivative hedging in incomplete markets. The

The square root of your portfolio’s covariance matrix gives you a powerful way of understanding where your portfolio variance is coming from. Here I show how to calculate the square root and provide an interactive example to explore how it works. Author Adrian Letchford Published 27 July 2023

Abstract: We find that by using a novel measure of investor attention, generated from InvestingChannel’s clickstream data on online financial news consumption, we can identify broad groups of stocks which are less efficiently priced and therefore where anomalies such as Value and Momentum are

Crypto-market price actions often revolves around the news. Good or bad? It does not matter. However, the recent long-term battle between the SEC and Ripple seemed to reignite the markets. On July 13, 2023, XRP/USDT suddenly shoot up, dragging a number of not so obvious cryptos up along. This was

In recent years, the Transformer architecture has experienced extensive adoption in the fields of Natural Language Processing (NLP) and Natural Language Understanding (NLU). Google AI Research’s introduction of Bidirectional Encoder Representations from Transformers (BERT) in 2018 set remarkable

We started off with the conceptualisation of trading alpha in different abstract representations, such as mathematical formulas, graphs and visual representations: Alpha-Encoding Data Structures Alpha-Encoding Data Structures HangukQuant · Jun 30 Read full story For machine trading this would

Running regressions on past returns is a great tool for academic researchers who understand this approach’s nuance, assumptions, pitfalls, and limitations. However, when factor regressions become part of a sales effort and/or are put in the hands of investors/advisors/DIYers, “the tool can

In a multi-asset portfolio, it is usual that some assets have shorter return histories than others1. Problem is, the presence of assets whose return histories differ in length makes it nearly impossible to use standard portfolio analysis and optimization methods… Estimating the historical

It’s just too easy to do all the volatility measures, with quantmod (well, with TTR actually). Let’s skip all the preliminaries and have a look. And, a Pearson pairs table: C2C Parkinson Rogers-Satchell Garman-Klass,Yang-Zhang C2C 1.0000000 0.4395541 0.2619220 0.3573710 Parkinson 0.4395541

I first learned about this algorithm in the book Kernel Adaptive Filter: A Comprehensive Introduction1 sometime in 2012 or 2013. This book goes in depth into how to build kernel filters and does a fantastic job of easing you into the mathematics. I highly recommend having a read if you can. In my

Reinforcement Learning for Credit Index Option Hedging – Francesco Mandelli, Marco Pinciroli, Michele Trapletti, Edoardo Vittori In this paper, we focus on finding the optimal hedging strategy of a credit index option using reinforcement learning. We take a practical approach, where the focus is

Betting on War? Oil Prices, Stock Returns and Extreme Geopolitical Events Knut Nygaard (Oslo Metropolitan U.) and L.Q. Sørensen (Storebrand Asset Mgt.) July 2023 We show that the ability of oil price changes to predict stock returns is largely limited to five extreme geopolitical events: the 2022

My August 4, 2022, Alpha Architect article examined the research demonstrating that cross-sectional momentum has provided a premium that has been found to be persistent across time and economic regimes, pervasive around the globe and across sectors and asset classes (stocks, bonds, commodities and

Everybody knows what volatility is. But there’s more than one way to measure it. The last couple of posts I’ve been trying to document a little more about the plain vanilla standard way to measure vol in the context of my efforts toward finding an ETF switching strategy to use realized

This study explores the degree to which fund concentration as measured by high tracking error or active share, affects the magnitude of excess returns and whether or not the likelihood of outperformance or underperformance are distributed similarly. Three methods of analysis were used to examine the

There is grave danger in tying your strategy to one selected set of parameters, particularly if those parameters are cherry picked to give more exciting results than other possible choices. I’m trying to working to avoid that in quant_rv. So far, quant_rv has two main parameters that can vary: the

Financial Machine Learning – Bryan T. Kelly, Dacheng Xiu We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed

While sustainable investing continues to gain in popularity, economic theory suggests that if a large enough proportion of investors choose to favor companies with high sustainability ratings and avoid those with low sustainability ratings (sin businesses), the favored company’s share prices will

An empirical analysis of the U.S. bond market since the 1960s emphasizes occasional abrupt regime changes, as defined by yield levels, curve slopes, and related volatility metrics. An arbitrage-free bond pricing model illustrates that bond risk premia can be decomposed into two types. One is related

Should investors prefer companies with high profit margins or companies with improving profit margins? Is it better to own an unprofitable company that's getting better, or a profitable company that's getting worse? This post explores these questions by analyzing the profitability growth

Using the input data as described in Quantitative And Machine Learning Asset Analysis: Single Moving Average (SMA) – (current price – N day average)/N day average, where N = 21, 42, 63, …, 231, 252, formed into an array. Dual Moving Average (DMA) – Same as SMA with 21 day average substituted

The conventional wisdom suggests that by spreading your investments across a wide range of assets, you can mitigate risk and achieve greater long returns. In this article, we will explore the diversification benefits of conglomerate stocks and why they can be valuable additions to a stock portfolio.

This blog post serves as a summary and exploration of ~100 papers, providing insights into the key trends presented at ICML 2023. The papers can be categorized into several sub-fields, including Graph Neural Networks and Transformers, Large Language Models, Optimal Transport, Time Series Analysis,