The emergence of high-frequency trading has led to improvements in numerous algorithmic trading strategies. Consequently, there is a growing demand for quantitative analysis and optimization techniques to develop these strategies. We present a paper by Aydoğan et al. (2022), which discusses the derivation of the optimal prices for HFT to execute the limit buy and sell orders where a stochastic volatility model generates the mid prices of the assets in the market.

Order book information is crucial for traders, but it can be complex. With the numbers of stocks listed in stock exchanges, it is impossible to track all the available information for the human mind. Therefore, the order flows could be an interesting dataset for machine learning models. The novel research of Kolm, Turiel and Westray (2021) utilizes deep-learning for high-frequency return forecasts for 115 NASDAQ stocks based on order book information at the most granular level.

The paper has several key contributions. Firstly, it does not forecast one single return but rather a whole vector of returns – a term structure consisting of mid-price return forecasts at a specified horizon. The forecasted term structure provides essential information about the most optimal execution algorithms (or a trading strategy). According to the authors, forecasts have an „accuracy peak“ at two price changes, after which the accuracy declines. Secondly, the paper compares several methods: autoregressive model with exogenous inputs, MLP, LSTM, LSTM-MLP, stacked LSTM, and CNN-LSTM. Therefore, the article could also serve as a horse race across several possible forecasting methods. Lastly, using more traditional statistical approaches, the authors have identified a better forecasting performance in more information-rich stocks. As a result, this novel research could benefit many areas such as high-frequency trading (but trading costs must be considered), optimal execution strategy, or market-making.

Often, this blog provides novel research that may not include the straightforward trading strategy, yet it is an interesting insight for portfolio managers, risk managers, investors or traders. Novel research of Brogaard et al. (2020) examines the crucial role of market makers during extreme price movements. According to the authors and the past literature, there are two competing theories of how the extreme price movements end, and both are related to the market makers. It is the constrained liquidity provision theory and the strategic liquidity provision. This research tests and explains these competing theories, with findings that are in line with the strategic liquidity provision. The results can be found particularly interesting during extreme price movements because the paper has shown that firstly, liquidity providers scale back and only interfere later. Market makers utilize price pressures in stressful times in a profitable way, since they profit from subsequent reversals.

Authors: Jonathan Brogaard, Konstantin Sokolov and Jiang Zhang

Title: How do Extreme Price Movements End?

Tick data is the most granular high-frequency data available, and so is the most useful in market microstructure analysis. Unfortunately, tick data is also the most susceptible to data corruption and so must be cleaned and conditioned prior to being used for any type of analysis.  

This article, written by Ryan Maxwell, examines how to handle and identify corrupt tick data (for analysts unfamiliar with tick data, please try an intro to tick data first).