The pace of aggregate demand in the macroeconomy exerts pressure on interest rates. In credible inflation targeting regimes, excess demand should be negatively related to duration returns and positively to curve-flattening returns. Indeed, point-in-time market information states of various macro

Today’s newsletter is based on a reader’s suggestion. We look at k-medoids which is a villain in the popular Nintendo game Metroid. No it’s not. But if you know Metroid, you have to agree: It sounds like one! It’s actually a powerful method used in data science to cluster similar data

One of the simplest and most pragmatic approach to volatility forecasting is to model the volatility of an asset as a weighted moving average of its past squared returns1. Two weighting schemes widely used by practitioners23 are the constant weighting scheme and the exponentially decreasing

You may remember our blog post from the end of March, where we tested the current state-of-the-art LLM chatbot: Time flies fast. More than six months have passed since our last article, and half a year in a fast-developing field like Artificial intelligence feels like ten times more. So, we are here

Whenever you want to estimate a model for multiple time series, the Vector Autoregression (VAR) model will serve you well. This model is suitable for handling multiple time series in a single model. You will learn here the theory, the intricacies, the issues and the implementation in Python and R.

The Jegadeesh and Titman (1993) paper on momentum established that an equity trading strategy consisting of buying past winners and selling past losers, reliably produced risk-adjusted excess returns. The Jegadeesh results have been replicated in international markets and across asset classes. As

In today’s newsletter, you’ll use the OpenBB SDK to download news for a topic. Then, you’ll use OpenAI and build a prompt to predict the sentiment of a news headline. You’ll bring it all together with LangChain. The result is a pandas DataFrame with a column of news headlines and a column

While out hiking, I found this rock. Evidently it was created by aliens, as can be shown by a probability argument. The following table gives measurements made on the rock. The first two rows give the overall length and width of the rock. Each of the next six rows, after the first two, gives

Financial time series that are structured as data sampled at a uniform time interval, e.g., hourly, daily, weekly, or monthly, are called discrete-time time series and referred to, from a digital signal processing (DSP) perspective, as being in the “time domain.” Technical market analysts

Usually, in algorithmic trading, we talk about AutoRegressive Integrated Moving Average (ARIMA) models. They’re very popular in the industry. You might remember that the “d” component in the model can be 0, 1, 2, etc. What if 'd' could take fractional values? We’ll learn about such

An ETF-Based Measure of Stock Price Fragility Renato Lazo-Paz (University of Ottawa) July 2023 A growing literature employs equity mutual fund flows to measure a stock’s exposure to non-fundamental demand risk – stock price fragility. However, this approach may be biased by confounding

I’ve previously discussed the Volatility Risk Premium (VRP) and how it differs from the Equity Risk Premium (ERP). Probably the most interesting difference, from the perspective of the trader, is that the VRP may be somewhat amenable to timing – more than the ERP at any rate. In this article,

In this article we are going to continue the discussion on portfolio insurance strategies. An exhaustive description of this methodology was already presented in the article Introduction to CPPI (https://quantpedia.com/introduction-to-cppi-constant-proportion-portfolio-insurance). This article will

Exploring Mean Reversion and Momentum Strategies in Arbitrage Trading Our recent reading group examined mean reversion and momentum strategies, drawing insights from the article, “Dynamically combining mean reversion and momentum investment strategies” by James Velissaris. The aim of the paper

The quality of a trading signal depends on its ability to predict future target returns and to generate material economic value when applied to positioning. Statistical metrics of these two properties are related but not identical. Empirical evidence must support both. Moreover, there are

In the previous week, we looked at the first part of a paper by AQR’s Lasse H Pedersen, Shaun Fitzgibbons and Lukasz Pomorski. This remains an influential paper in that it contains some really useful ideas on how to incorporate ESG in a portfolio optimization problem. You can read the first part

There is an inverse relationship between yield and total return The ideal yield strategy has a high yield, high Sharpe, and low correlation to stocks The yield-to-downside beta ratio enhances the strategy selection process INTRODUCTION @ChatGPT: “What is a rhyme that includes ‘high dividends are

Long-term returns for US Treasury bond funds with a constant maturity (like IEF, TLT and SHY) have been quite predictable simply using initial Treasury yields as an estimate. This observation was popularized by John Bogle. Here’s an excellent recent look from Portfolio Optimizer. How predictable?

Over the very long term, while value stocks have been less profitable and have had slower growth in earnings than growth stocks, they have provided higher returns. Among the reasons are that value stocks have traded at substantial valuation discounts compared to growth stocks, and reversion to the

Lately, I’ve been thinking a lot about the Volatility Risk Premium (VRP). The VRP makes much more sense (to me, at least) when I have the Equity Risk Premium (ERP) for context and comparison. So, in this article, I want to discuss the ERP and the VRP, their similarities and differences, and how I

In the previous lecture, we did a line by line walk through and intuitive explanation of the need for volatility targeting: Code Walkthrough for the Alpha Simulator: Simple Trend Rule with Volatility Targeting HangukQuant · Sep 23 Code Walkthrough for the Alpha Simulator: Simple Trend Rule with

I'm sometimes asked where I get my ideas for new trading strategies from. The boring truth is I rarely test new trading strategies, and I mostly steal ideas when I feel in the mood. Today for example I saw this tweet post on twitter X: The original paper is here (requires subscription or

In the AutoRegressive Moving Average (ARMA) models: A Comprehensive Guide of my ARMA article series, I covered the theoretical aspects of Autoregressive Moving Average models (ARMA). In the AutoRegressive Moving Average (ARMA) models: Using Python, I simulated different ARMA models, their

I summarise key insights from a few papers studying the limit order book. You’l learn how to measure volume imblanace in the limit order book and how well it predicts price moves. Author Adrian Letchford Published 1 October 2023 Length 5 minutes Like what you see? Follow Adrian on Twitter to be

VectorBT PRO (VBT) is a proprietary Python package designed for backtesting and analyzing quantitative trading strategies. In today’s guest post, you’ll use VectorBT PRO to algorithmically detect chart patterns from 230 million unique pattern and window combinations. All in about 2 minutes.

In today’s post we will be using AI to improve a module of the Alternative Data-Driven Investment (ADDI) strategy developed by ETS Asset Management Factory, which is an automatic Long – Short investment strategy that aims to obtain stable performance de-correlated from the market and with a

This past week was the 4th week in a row that the SPX declined. It is quite unusual to see SPX close down for 4 weeks in a row, but still remain above its 40-week moving average. Below is a look at other times since 1975 that this action has occurred. SPX down 4 weeks in a row but above 40-week

The first ETFs emerged in 1993 and closely tracked broad-based indexes for a low fee. Since then, the competitive situation in the ETF industry today has differentiated itself by adding a new breed of ETFs that reflected specialization into popular investment themes. When the evolution of the ETF

Environmental, social, and governance (ESG) investing continues to increase in popularity, with many institutional and individual investors incorporating ESG criteria into their investment decision-making process. Three main themes have driven this massive shift of assets: 1) Many investors are

Buying high-quality stocks and avoiding low-quality ones can improve investment returns. In this post, I use Alphalens and Zipline to analyze the Piotroski F-Score, a composite measure of a firm's financial health and quality. This post is part of the fundamental factors series, which explores

In the first part of my ARMA article series, I covered the background theory of lag operators, the stationarity and invertibility of Autoregressive Moving Average models (ARMA) and the different types of versions you can create from it. Here, we’ll explore theoretically these models using Python.

Following the recent release of the popular large language model ChatGPT, the topic of machine learning and AI seems to have skyrocketed in popularity. The concept of machine learning is, however, a much older one and has been the topic of various research and technology projects over the last

I was working on testing a market timing indicator that I read about it. It was showing some promise and the next step was to compare it to my benchmark. My benchmark is using the 200-day moving average. But an additional rule removes a lot of the whipsaws that can happen. After doing the

Note: This is a rare non-geeky, non-quantitative, stream of thought blog post. Because we’re so deep into this world of Tactical Asset Allocation (TAA), we’re sometimes asked for our thoughts on such-and-such black box TAA strategy. By “black box” we mean a strategy for which the trading

The article explores the limitations of traditional country-level stock market indexes constructed based on issuing firms’ domicile. Additionally, it introduces a new type of national stock market index called the EMindex, which is based on companies’ business activities rather than their

In the last post we did a line-by-line walkthrough of the alpha simulator, using a uniform random variable to represent the signal generating component of the alpha backtest. We also raised a few questions: How can we manage the varying levels of risk profiles of different stocks in our asset

Real government bond yields are indicators of standard market risk premia and implicit subsidies. They can be estimated by subtracting an estimate of inflation expectations from standard yields. And for credible monetary policy regimes, inflation expectations can be estimated based on concurrent

Since the development of the CAPM, which explains about two-thirds of the variation of returns among diversified portfolios, academic research has attempted to find models that increase the explanatory power of the cross-section of stock returns. Models are not like cameras that provide an exact

Trading technology continues to make trading of stocks easier, cheaper, and faster However, despite this and other financial innovations like ETFs, the US stock market structure hasn’t changed Likely explained by the fact that its core participants are unable to change INTRODUCTION Consider the

As a whole, Tactical Asset Allocation (TAA) did not manage losses during the 2022 bear market as well as it has during previous downturns. Individual strategies varied and some did well, but a primary function of TAA is loss management, and any failure to do so is worth analyzing further. In this

I want to discuss a couple of simple trade-off considerations around quant trading signals that may not be obvious. Here’s the price of some asset: Our main job is to predict how it’s likely to move. To do this, you use information about it that you think is predictive. And at any point in time:

Volatility estimation and forecasting plays a crucial role in many areas of finance. For example, standard risk-based portfolio allocation methods (minimum variance, equal risk contributions, hierarchical risk parity…) critically depend on the ability to build accurate volatility forecasts1.

Value investing originated as an investment strategy in which investors try to beat the stock market by looking for stocks that trade at a price below their intrinsic value or book value. Value investors do not subscribe to the efficient-market hypothesis, which suggests that stock prices always

Pump-and-Dump (P&D) schemes to manipulate the prices of cryptocurrencies are unlike the P&D schemes found in the equity market. They produce very large price distortions on the order of 65%, very large trading volumes of 13.5x the average, and generate very large profits to cryptocurrency

Convertible bonds are typically viewed as debt rather than equity instruments However, these are highly correlated to equities The diversification benefits are limited as these just represent diluted equity proxies INTRODUCTION Let’s say you’re the CEO of a small listed company that isn’t

Seasonality effects, one of the most fascinating phenomena in the world of finance, have captured the attention of investors and researchers worldwide. Since these anomalies are often driven by factors other than general market trends, they usually don’t correlate strongly with market movements,

TL;DR – There really is a low volatility anomaly in the SPY data; low volatility today predicts low volatility tomorrow and risk-adjusted returns are higher investing daily in the lower vol half of predicted market days. Same data, new analysis, better graphs and you’ll see it too. First up, a

The article investigates time-series (TS) momentum strategies and their performance in financial markets based on various speeds or lookback horizons. The study aims to understand the connections between different speeds of TS momentum, unobservable variables like trend, turning points, and noise

Machine Learning (ML) has emerged as a powerful tool in the field of Artificial Intelligence, revolutionising various aspects of our lives. Whether it's recognising human handwriting or enabling self-driving cars, ML has become an integral part of our daily routines. With the exponential growth

There are style factors like value and traditional fixed income factors like term premium The correlations of these factors has been low However, it is not clear which are better suited for a factor exposure analysis INTRODUCTION In our last research article, we compared fixed income factors from