We are delighted to announce the launch of the long-anticipated MLFinlab v2.0.0! This new release brings a host of enhancements and fixes, all of which significantly improve the package’s functionality and usability. A key feature of this release is that all examples have been thoroughly updated and modified across-the-board. This ensures that users can now […]
As we navigate the fast-paced world of quantitative finance, we often find ourselves balancing on a tightrope, juggling complex mathematical models, evolving market dynamics, and a continuous influx of data. Both for newcomers and seasoned quants, trading strategy development presents an intricate puzzle. Yet, the very challenges that make it daunting also make it a […]
by Michael Meyer and Masimba Gonah Introduction Welcome back, fellow traders and machine learning enthusiasts! We hope you’ve been enjoying our journey towards building a successful machine learning trading strategy. If you missed Part 1 of our series, don’t fret – you can always catch up on our exploration of various financial data structures, such […]
by Michael Meyer and Masimba Gonah Introduction Trading in financial markets can be a challenging and complex endeavour, with ever-changing conditions and numerous factors to consider. With markets becoming increasingly competitive all the time, it is a never ending struggle to stay ahead of the curve. Machine learning (ML) has made several advances in recent […]
A group of strategies, named statistical arbitrage or pairs trading strategies are well-known for being market-neutral gained their popularity among institutional and individual investors. In general, to develop a pairs trading strategy, one needs to figure out two aspects, the first is how to select assets to form a process with mean-reverting properties, and the second is how to decide when and how to trade such process. In recent years, many methods have been proposed to answer these two questions. Fitting the spread to an O-U process, cointegration tests, and stochastic control methods are commonly used but are theoretically complicated. For the most part, the trading strategies constructed using these approaches aim to exploit the mean-reverting nature of the constructed spread.
Traditional pairs trading strategies are prone to failures when fundamental or economic reasons cause a structural break and the pair of assets that were expected to move together are no longer having a strong relationship. Such a break may result in asset price spread having abnormally high deviations failing to revert to its historical mean values. Under these circumstances, betting on the spread to revert to its historical mean would result in a loss. To overcome the problem of detecting whether the deviations are temporary or longer-lasting, Bock, M. and Mestel, R. (2009) bridge the literature on Markov regime-switching models and the scientific work on statistical arbitrage to develop a set of useful trading rules for pairs trading.
Pairs trading or statistical arbitrage is a famous strategy among institutional and individual investors since the 1990s. The concept behind this kind of strategy is straightforward. If the prices of assets move together historically, this tendency is likely to continue in the future. When the spread of the prices diverges from its long-term mean, one can short sell the over-priced stock, buy the under-priced one, and wait for the spread to converge to take the profit.
In general, to develop a pairs trading strategy, we need to solve two major issues, the first is how to select assets to form a process with mean reversion properties, and the second is how to decide when to trade…
The hedge ratio estimation problem is one of the most important issues for portfolio managers.
The hedge ratio estimation methods can be divided into two:
– Single Period Method
– Multi-Period Method
In this blog post, we’ll simply go through the main concepts of each method and closely follow a paper by Lopez de Prado, M.M. and Leinweber, D. (2012). Advances in Cointegration and Subset Correlation Hedging Methods. Therefore, for further details and implementation, we would highly recommend you to read individual papers for each of the methods provided.
This is a series where we aim to cover in detail various aspects of the classic Ornstein-Uhlenbeck (OU) model and the Ornstein-Uhlenbeck Jump (OUJ) model, with applications focusing on mean-reverting spread modeling under the context of pairs trading or statistical arbitrage. Given the universality and popularity of those models, the techniques discussed can easily be applied to other areas where the OU or OUJ model seems fit.
In this article, we aim to dive into the classic OU model, and illustrate the most common tasks encountered in applications:
1. How to generate an OU process.
2. Caveats in fitting an OU process.
In our previous article, we’ve discussed a couple of trading strategies exploiting arbitrage between similar stocks using stochastic optimal control methods. A major shortcoming of those approaches is that we restricted ourselves to constructing delta-neutral portfolios. Along with this, the ratio between the stocks in the portfolio is fixed at the start of the investment timeline. These assumptions make the problem simpler, as we only need to calculate the portfolio weights for the spread process as a whole. But, this approach, as [Liu and Timmermann (2013)] discusses, is suboptimal. In this article, we will be discussing a generalized approach that allows the weights corresponding to the stocks in the portfolio to move freely, along with looking at the shortcomings of the previous approaches.