Research articles for the Hudson and Thames home page.

Experimental Design and Common Pitfalls of Machine Learning in Finance

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The first lecture from the Experimental Design and Common Pitfalls of Machine Learning in Finance series addresses the four horsemen that present a barrier to adopting the scientific approach to machine learning in finance.

The second lecture focuses on a protocol for backtesting and how to avoid the seven sins of backtesting. By implementing the research protocol outlined in these articles, an investment manager can avoid making the seven common mistakes when backtesting and building quant models.

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QuantConnect Integration with MlFinLab

Announcing that MlFinLab is fully integrated into the powerful backtesting and execution platform of QuantConnect!

At the start of 2022, we set out to improve the user experience across all of our products and to improve the accessibility of our libraries. This meant integrations into platforms that have a strong community, historical simulations, data feeds, and live execution. QuantConnect was a natural choice!

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How to Build a World-Class Quant Team

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What is the best environment and culture for a quant team? This question may have different answers depending on who you ask. Fortunately, there are some glimpses and statements from the top quantitative research groups that afford us a window into their work environment behind the scenes. For this short article, we have scraped the internet for some fascinating insights into the structure and culture of some of the best performing quant hedge funds in the world.

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Best Research Practices for Your Quantitative Finance Research Group

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How do you perform research? And what are some of the best-recommended practices that you should follow?

In this article, I will describe some of the main aspects of the scientific research process and also recommend some best research practices. The topics that I will cover are literature reviews, writing a research proposal, performing research, writing a paper for publication, useful tools, and, finally, hosting reading groups.

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Pairs Trading Based on Renko and Kagi Models

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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.

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Pairs Trading with Markov Regime-Switching Model

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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.

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Optimal Trading Thresholds for the O-U Process

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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…

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Introduction to Hedge Ratio Estimation Methods

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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.

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Caveats in Calibrating the OU Process

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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.

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Extended Optimal Arbitrage Strategies

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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.

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