Market making is the process whereby a market participant, called a market maker, simultaneously and repeatedly posts limit orders on both sides of the limit order book of a security in order to both provide liquidity and generate profit. Optimal market making entails dynamic adjustment of bid and ask prices in response to the market maker’s current inventory level and market conditions with the goal of maximizing a risk-adjusted return measure. This problem is naturally framed as a Markov decision process, a discrete-time stochastic (inventory) control process. Reinforcement learning, a class of techniques based on learning from observations and used for solving Markov decision processes, lends itself particularly well to it. Recent years have seen a very strong uptick in the popularity of such techniques in the field, fueled in part by a series of successes of deep reinforcement learning in other domains. The primary goal of this paper is to provide a comprehensive and up-to-date overview of the current state-of-the-art applications of (deep) reinforcement learning focused on optimal market making. The analysis indicated that reinforcement learning techniques provide superior performance in terms of the risk-adjusted return over more standard market making strategies, typically derived from analytical models.

In complex systems, statistical dependencies between individual components are often considered one of the key mechanisms which drive the system dynamics observed on a macroscopic level. In this paper, we study cross‐sectional time‐lagged dependencies in financial markets, quantified by nonparametric measures from information theory, and estimate directed temporal dependency networks in financial markets. We examine the emergence of strongly connected feedback components in the estimated networks, and hypothesize that the existence of information feedback in financial networks induces strong spatiotemporal spillover effects and thus indicates systemic risk. We obtain empirical results by applying our methodology on stock market and real estate data, and demonstrate that the estimated networks exhibit strongly connected components around periods of high volatility in the markets. To further study this phenomenon, we construct a systemic risk indicator based on the proposed approach, and show that it can be used to predict future market distress. Results from both the stock market and real estate data suggest that our approach can be useful in obtaining early‐warning signals for crashes in financial markets.

Predicting asset price trends is often posed as a classification problem, where trends are classified as positive or negative. Since asset price series are noisy and volatile, it is difficult to distinguish true trends from short-term fluctuations. To this end, several trend definitions have been proposed in the literature, but it is yet to be known how these trend definitions affect the performance of classification algorithms designed to learn such labels from historical data. In this paper, we define the robustness of the trend labeling algorithm as a measure of how well a classifier designed to learn such labels can withstand a change in the cumulative return considering the classifier’s generalization error. Moreover, we propose a noise model to simulate the desired accuracy score, which allows us to evaluate the robustness of a trend labeling algorithm without the need to train an actual classifier and consequently choose the optimal algorithm in terms of robustness. Experimental results confirm the adequacy of the proposed noise model and show that classification algorithms perform better when trained with such optimal labels.

Unsupervised learning methods have been increasingly used for detecting latent factors in high-dimensional time series, with many applications, especially in financial risk modelling. Most latent factor models assume that the factors are pervasive and affect all of the time series. However, some factors may affect only certain assets in financial markets, due to their clustering within countries, asset classes, or sector classifications. In this paper we consider high-dimensional financial time series with pervasive and cluster-specific latent factors, and propose a clustering and latent factor estimation method. We also develop a model selection algorithm, based on the spectral properties of asset correlation matrices and asset graphs. A simulation study with known data generating processes demonstrates that the proposed method outperforms other clustering methods and provides estimates with a high degree of accuracy. Moreover, the model selection procedure is also shown to provide stable and accurate estimates for the number of clusters and latent factors. We apply the proposed methods to datasets of asset returns from global financial markets using a backtesting approach. The results demonstrate that the clustering approach and estimated latent factors yield relevant information, improve risk modelling and reduce volatility in optimal minimum variance portfolios.