In the research reviewed here, the authors analyze the relationship of aggregate market liquidity to the time-series performance of reversal strategies. The strength and persistence of reversals and reversal driven strategies appear to be different depending on specific risk features of those

The ability to predict stock returns and the equity risk premium (ERP) is of great interest to academics, financial practitioners, and investors, as future estimated returns have implications for asset allocations. To date, the best metric we have for forecasting future equity returns and the ERP is

Most recently, I announced some coming, exciting upcoming things for quantpylib: Exciting Additions to Quantpylib Exciting Additions to Quantpylib HangukQuant · May 6 Read full story as well as a demontration of its powers: Dual Momentum in Cryptocurrencies? Dual Momentum in Cryptocurrencies?

The higher the yield, the lower the total return on average Combining the highest yielding strategies leads to risky portfolios Combining high yielding but uncorrelated strategies is more sensible INTRODUCTION We previously highlighted an almost linear inverse relationship between an investment

Bootstrapping is a statistical method which consists in sampling with replacement from an original data set to compute the distribution of a desired statistic, with plenty of possible variations depending on the exact context (non-dependent data, dependent data…). Because bootstrap methods are

Over the last few weeks, we talked about funding arbitrage, Crypto Arbitrage (1 Week Setup) Crypto Arbitrage (1 Week Setup) HangukQuant · Apr 10 Read full story extended the quant library to incorporate crypto backtesting Announcing Crypto and Other Backtesting Logic ; Quantpylib Announcing Crypto

Inventory scores are quantamental (point-in-time) indicators of the inventory states and dynamics of economies or commodity sectors. Inventory scores plausibly predict base metal futures returns due to two effects. First, they influence the convenience yield of a metal and the discount at which

Momentum continues to receive much attention from researchers because of the strong empirical evidence. Out of the hundreds of exhibits in the factor zoo, momentum (both cross-sectional [long-short] and absolute [trend]) was one of just five equity factors that met all the criteria (persistent,

Here are some things we know about Tactical Asset Allocation: (Learn more: What is TAA?) We shouldn’t go 100% “all in” on just one TAA strategy. That introduces “specification risk”, or the risk that we’ve bet on an underperforming horse. Instead, we should combine multiple strategies

As an equity quantitative analyst, you have recurring positioning analysis tasks. Your most effective approach is to model your object of study (usually stocks, portfolios or indexes) and decompose its behavior into common risk factors. You can create your own “factor model” or use existing

My first test of trading forex news announcements is to test the efficacy of breakouts immediately following a news announcement related to the US dollar, specifically, only the high impact news as shown on the forexfactory calendar in red. The intention would be to capture some of the profit

Embeddings are used in neural networks to transform large, sparse data into manageable, dense formats. In other words, they simplify complex data, making it easier to analyze. We can use embeddings to capture dense information about drivers of stock returns. This approach is a great way to select

Market-neutral multi-factor products have reached all-time highs However, long-only multi-factor products have consistently underperformed Portfolio construction and implementation matters INTRODUCTION Investors have been style investing since the inception of stock markets. Some chase trends, while

Can AI models improve on the failures in predicting returns strictly from a practical point of view? In this paper, the possibilities are tested with a battery of AI models including linear regression, dimensional reduction methods, regression trees and neural networks. These machine learning models

The equal risk contribution (ERC) portfolio, introduced in Maillard et al.1, is a portfolio aiming to equalize the risk contributions from [its] different components1. Empirically, the ERC portfolio has been found to be a middle-ground alternative1 to an equally weighted portfolio and a minimum

Last week, we looked at simple data analysis techniques to test for persistence. But we only looked at a feature that is measured over a single day – the absolute range. Such a feature makes it easy to test persistence because you don’t have the problem of overlapping data. Each data point is

Diversification is the cornerstone of investing. This principle, fundamentally understood as not concentrating all resources into a single investment, inherently seeks to minimise risk while potentially smoothing out returns over the long term. By spreading investments across uncorrelated assets,

It’s been almost 11 years now since I posted my introduction to quantitative investing. It’s kind of hard to believe as I’m typing these words. A lot has happened since that summer day in 2013. And a lot has stayed the same. Learning about, implementing, and allocating to this type of

While economic information undeniably wields a significant and widespread influence on financial markets, the systematic incorporation of macroeconomic data into trading strategies has thus far been limited. This reflects skepticism towards economic theory and serious data problems, such as

Quality and low volatility stocks provide substantially different exposures Neither has outperformed the stock market, but both were less volatile Low volatility stocks have done poorly in recent years INTRODUCTION If you believe that the economy is entering a recession and can only invest in

This blog is more a note to self for experimenting further with DSPy (arXiv, GitHub) than a pedagogical or original intro to the framework. It essentially follows this weaviate tutorial with small adaptations, notably removing the weaviate part of it, and replacing their retrieval module by a very

In the fast-paced world of cryptocurrencies, understanding market sentiment can provide a crucial edge. As investors and traders seek to anticipate the volatile movements of Bitcoin, innovative approaches are continuously explored. One such method involves leveraging Google Trends data to gauge

The justification for neutralizing sectors in factor strategies is a work in progress. To date, academic researchers haven’t had an empirical model to mimic the impact of removing sector “effects” on the measurement and performance of factor strategies. The authors develop and test a

It’s rumored that Renaissance Technologies uses hidden Markov models in their trading. In fact, the Baum-Welch algorithm used with Markov models was partially invented by Leonard Baum who would help found RenTec. In today’s newsletter, we’ll look an example of using a Markov model to detect

Macroeconomic Announcement Premium Hengjie Ai (University of Wisconsin-Madison), et al. November 2023 The paper reviews the evidence on the macroeconomic announcement premium and its implications on equilibrium asset pricing models. Empirically, a large fraction of the equity market risk premium is

While empirical research on cross-sectional (long-short) momentum has shown high returns, investors have also experienced huge drawdowns—momentum exhibits both high kurtosis and negative skewness. Since 1926, there have been several momentum crashes that featured short but persistent periods of

After my last post on using trend following in Crypto markets I thought I’d continue on that thread and look at applying trend following on a newish concept (similar concepts have existed at the institutional level for a long time) in creating diversified portfolios, called return stacking. First,

Some factor trends continued from 2023, but there were also two significant rotations Momentum performed the best, size the worst Most long-short multi-factor products have generated positive excess returns INTRODUCTION We present the performance of five well-known factors on an annual basis for the

It has been a while since I’ve updated the blog. These days I spend most of my time writing for subscribers of my newsletter but I’d like to get back to writing publicly again. So, to launch myself back into public blogging I thought I would write about Crypto and how classic trend following

Regression-based statistical learning helps build trading signals from multiple candidate constituents. The method optimizes models and hyperparameters sequentially and produces point-in-time signals for backtesting and live trading. This post applies regression-based learning to macro trading

Market indices are vital in finance, offering investors a quick look at the overall performance of a specific market or sector. They act as benchmarks for assessing the performance of different investment portfolios. Investors can copy the performance of an index for exposure to securities without

An assumption we often make in trading research is that the future will be at least a little like the past. I see a lot of beginners making this assumption implicitly without recognising that they’re making it or thinking about whether it’s reasonable to do so. That’s a mistake. If you are

Macroeconomic trends affect stocks differently, depending on their lines of business and their home markets. Hence, point-in-time macro trend indicators can support two types of investment decisions: allocation across sectors within the same country and allocation across countries within the same

Out of the hundreds of exhibits in the factor zoo, momentum was 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. Because of the strong

Consider a financial asset whose price, Xt​, follows a mean-reverting stochastic process. A common model for mean reversion is the Ornstein-Uhlenbeck (OU) process, defined by the stochastic differential equation (SDE): Objective The trader aims to maximize the expected cumulative profit from

Stocks can have a high, low, or negative sensitivity to interest rates The duration profile of stocks changes frequently Having rates exposure in an equities portfolio is not necessarily a concern INTRODUCTION The goal of conducting research is to provide clarity by answering questions or confirming

In the rapidly evolving landscape of cryptocurrency markets, understanding the underlying dynamics that drive price movements and investor sentiment can be a matter of survival. However, there are myriad facets of trading reality, and the only thing that we can do is to slowly understand them one

Recently a reader sent me a leveraged ETF strategy that he wanted tested for the blog. Over the last couple of months, I have been noticing renewed interest in leveraged ETF trading. More clients are coming to me to test out leverage trading ideas. I have been testing my own ideas. What I liked

Inflation-themed ETFs have heterogeneous portfolios However, commodities and oil have been better inflation hedges And offer higher diversification benefits INTRODUCTION In November 2021 we analyzed inflation-themed ETFs (read Inflation-Themed ETFs: As Complicated as Inflation) and concluded that

Here’s a thing that I suspect will make money, but that I haven’t yet tested (for reasons that I will explain shortly): Every day, at the start of the trading day, get the SPX straddle price and convert it to an expected SPX price move. Then at the end of the trading day, take the SPX price and

Macroeconomic trends affect stocks differently, depending on their lines of business and their home markets. Hence, point-in-time macro trend indicators can support two types of investment decisions: allocation across sectors within the same country and allocation across countries within the same

The benefits of diversification are well known. In fact, it’s been called the only free lunch in investing. Investors who seek to benefit from diversification of the sources of risk and return of their portfolios must accept that adding unique sources of risk means that their portfolio will

This is a test of the 12-Month High Switch Model, a Tactical Asset Allocation (TAA) strategy from Meb Faber. Meb has done more than anyone to popularize TAA as a trading style, including many of the fundamental concepts used today. This is another of his simple but effective ideas. Backtested

Stocks for a low-growth & high-interest rate environment should have high-quality characteristics However, there are many ways to define quality stocks Historically quality portfolios have not generated excess returns INTRODUCTION Ignoring the past is one of the biggest investing mistakes.

The lifeblood of quant finance is data. The problem is that data is sometimes hard to come by. It may be expensive or just not available. What if we had a way to generate synthetic market data? Artificially recreating a dataset is a complex process. The new data needs to mimic the existing data

Jerry Kaufman, known for his technical indicators bible, presented in TASC 1/24 a trading strategy based on upwards and downwards gaps. For his system, he invented the Gap Momentum Indicator (GAPM). Here I’m publishing the C version of his indicator, and a simple trading system based on it. The

Perhaps the most well-documented and researched asset pricing anomaly is momentum—the tendency of past winner stocks to outperform past loser stocks over the next several months. While average time-series momentum (trend following) returns have been high, strategies employing trend following have

Cryptocurrencies are already one of the major asset classes. They fill the top pages of magazines and are a topic of a day to day conversation. There are a lot of ways to buy them through a lot of different channels. But some of the hardcore HODLers like to keep their coin portfolio safe – they

In the previous post of this series on volatility forecasting, I described the simple and the exponentially weighted moving average volatility forecasting models. In particular, I showed that these two models belong to the generic family of weighted moving average volatility forecasting models1,

Financial modeling has long sought to develop frameworks that accurately capture the complex dynamics of asset prices. Traditional models often focus on either momentum or mean reversion effects, struggling to incorporate both simultaneously. In this blog post, we introduce a two-factor model that