Marked point processes and intensity ratios for limit order book modeling

This paper extends the analysis of Muni Toke and Yoshida (2020) to the case of marked point processes. We consider multiple marked point processes with intensities defined by three multiplicative components, namely a common baseline intensity, a state-dependent component specific to each process, and a state-dependent component specific to each mark within each process. We show that for specific mark distributions, this model is a combination of the ratio models defined in Muni Toke and Yoshida (2020). We prove convergence results for the quasi-maximum and quasi-Bayesian likelihood estimators of this model and provide numerical illustrations of the asymptotic variances. We use these ratio processes to model transactions occurring in a limit order book. Model flexibility allows us to investigate both state-dependency (emphasizing the role of imbalance and spread as significant signals) and clustering. Calibration, model selection and prediction results are reported for high-frequency trading data on multiple stocks traded on Euronext Paris. We show that the marked ratio model outperforms other intensity-based methods (such as “pure” Hawkes-based methods) in predicting the sign and aggressiveness of market orders on financial markets.

The adaptive market hypothesis and high frequency trading

This paper uses NASDAQ order book data for the S&P 500 exchange traded fund (SPY) to examine the relationship between one-minute, informational market efficiency and high frequency trading (HFT). We find that the level of efficiency varies widely over time and appears to cluster. Periods of high efficiency are followed by periods of low efficiency and vice versa. Further, we find that HFT activity is higher during periods of low efficiency. This supports the argument that HFTs seek profits and risk reduction by actively processing information, through limit order additions and cancellations, during periods of lower efficiency and revert to more passive market-making and rebate-generation during periods of higher efficiency. These findings support the argument that the adaptive market hypothesis (AMH) is an appropriate description of how prices evolve to incorporate information.

Reinforcement-learning-based optimal trading in a simulated futures market with heterogeneous agents

This paper simulates a futures market with multiple agents and sequential auctions, where agents receive long-lived heterogeneous signals on the true value of an asset and with a known deadline. The evolution of the amount of differential information and its impact on the distribution of overall gains and the pace of truth discovery is examined for various depth levels of the limit order book (LOB). The paper also formulates a dynamic programming model for the problem and presents an associated reinforcement learning (RL) algorithm for finding optimal strategy in exploiting informational disparity. This is done from the perspective of an agent whose information is superior to the collective information of the rest of the market. Finally, a numerical analysis is presented based on a futures market example to validate the proposed methodology for finding the optimal strategy. We find evidence in favor of a waiting strategy where agent does not reveal her signal until the last auction before the deadline. This result may help bring more insight into the micro-structural dynamics that work against market efficiency.

Tick Size and Informed Trading: Evidence from the Taiwanese Stock Market

We adopt the Sandås model for order-book equilibrium to examine informed trading on the Taiwanese stock market, a purely order-driven call-auction market. We find that adverse-selection cost is low for well-known stocks with high liquidity and low volatility, but cost is high for monitoring the order books of those stocks. Our empirical results show that the impact of adverse selection is greatest at the beginning of each trading day and that informed traders engage in stealth trading, supporting the stealth trading hypothesis. Finally, with the special tick size rules on the market, both adverse-selection cost and monitoring cost decline as tick size decreases.

Volatility and Depth in Commodity and FX Futures Markets

Prior theory suggests a positive relation between volatility and market depth, while past empirical research finds contrasting results. This paper examines the relation between the volatility and the limit order book depth in commodity and foreign exchange futures markets during a turbulent time using the generalized method of moments (GMM). Results indicate a negative relation between volatility and depth and suggest that the depth in the limit order book decreases as volatility increases. Findings help to understand how market participants provide liquidity in response to shifts in prices.

The Relation between Intraday Limit Order Book Depth and Spread

Prior studies that examine the relation between market depth and bid–ask spread are often limited to the first level of the limit order book. However, the full limit order book provides important information beyond the first level about the depth and spread, which affects the trading decisions of market participants. This paper examines the intraday behavior of depth and spread in the five-deep limit order book and the relation between depth and spread in a futures market setting. A dummy-variables regression framework is employed and is estimated using the generalized method of moments (GMM). Results indicate an inverse U-shaped pattern for depth and an increasing pattern for spread. After controlling for known explanatory factors, an inverse relation between the limit order book depth and spread is documented. The inverse relation holds for depth and spread at individual levels in the limit order book as well. Results indicate that market participants actively manage both the price (spread) and quantity (depth) dimensions of liquidity along the five-deep limit order book.

Investigation synchronous planning methods efficiency in small-batch make-to-order production

Lack of effective planning methods is one of the reasons for uneven use of production capacity, the occurrence of unplanned downtime and various types of losses. Small-scale make-to-order productions are characterized by weak workplaces specialization, by the versatility of the equipment used with a wide range of used components. They differ in the presence of repeated processing of part at the same workplaces and in occurrence of “a return flow” in a production system. The article discusses the features of planning due to technological specifics in such enterprises and a significant number of applicable incoming nomenclature items. Existing systems of production planning such Manufacturing Execution System and Advanced Planning & Scheduling System are compared, variants of their algorithmic implementation are proposed. The authors investigated the effectiveness of the application of the developed algorithms for different planning levels - interdepartmental and shop, depending of the size of order book and number of technological operations. The analysis of the conducted research allows us to assert that to provide synchronous scheduling in make-to-order small-scale productions it is necessary to apply combined planning approach using APS-and MES-technologies together.