A local non-parametric model for trade sign inference

We investigate a regularity in market order submission strategies for twelve stocks with large market capitalization on the Australian Stock Exchange. The regularity is evidenced by a predictable relationship between the trade sign (trade initiator), size of the trade, and the contents of the limit order book before the trade. We demonstrate this predictability by developing an empirical inference model to classify trades into buyer-initiated and seller-initiated. The model employs a local non-parametric method, k-nearest-neighbor, which in the past was used successfully for chaotic time series prediction. The k-nearest- neighbor with three predictor variables achieves an average out-of- sample classification accuracy of 71.40%, compared to 63.32% for the linear logistic regression with seven predictor variables. The result suggests that a non-linear approach may produce a more parsimonious trade sign inference model with a higher out-of-sample classification accuracy. Furthermore, for most of our stocks the observed regularity in market order submissions seems to have a memory of at least 30 trading days.Order submission, Trade classification, K-nearest-neighbor, Non-linear, Memory

Three Essays on the Empirical Market Microstructure of Money Market Derivatives

This thesis is the first directly to study the entire limit order book of a large market. Herein, I conduct a population study on the microstructure of the Eurodollar future market, to my knowledge this is a) the first study of its type and b) the largest microstructure study ever conducted. I will build a data-drive model that incorporates information from the entire population of quotes updates and transactions on this type of future market. This thesis aims to provide a comprehensive understanding of the market microstructure on money market derivatives and the impact of high-speed algorithmic trading activity on the market characteristics and quality. I apply a broad battery of market volatility and liquidity measurements, and gauge the proportion of high-frequency algorithmic traders in the market. This thesis provides a standard asymmetric information based theoretical model to predict the relation on the term structure of Eurodollar future contracts. The prediction is a non-linear relation between the saturation of algorithmic traders (ATs) versus the impacts on the quality of the market. Therefore, I develop a novel semi-parametric estimator and model the non-linear relation between the impact of the fraction of algorithmic trading and a large set of different market quality indicators including volatility, liquidity and price informativeness. Finally, I consider the efficiency and the speed of high-frequency prices formation by implementing the return autocorrelations and vector autoregression, and also make a contribution to the trade classification algorithm using the order book data. My findings are fourfold. First, the impact of high-frequency trading (HFT) on market quality is a non-linear by implementing the semi-parametric model. This may partially explain why prior studies have found contradictory results regarding the impact of high-frequency traders (HFTs) on market characteristics. Second, prior studies only including the inside quotes or best bid best ask are limited to reflect all the information in the market. My findings suggest that the second level quoting in the limit order book is by far the most rapidly quoted element of the order book. Furthermore, I find that wavelet variance covariance of the bid and the ask side changes substantially over the term structure; providing further supporting evidence of the non-linear impact of HFTs. Finally, the adjustment time of the trade prices formation process is within one second, and the quote prices are even faster within 200 milliseconds (ms). The mid-quoted return autocorrelation is positive and gradually increase from the shortest time interval to the longest time interval. The trade prices are less sensitive to new information as the contract approaches its maturity

Modelling and forecasting liquidity supply using semiparametric factor dynamics

We model the dynamics of ask and bid curves in a limit order book market using a dynamic semiparametric factor model. The shape of the curves is captured by a factor structure which is estimated nonparametrically. Corresponding factor loadings are assumed to follow multivariate dynamics and are modelled using a vector autoregressive model. Applying the framework to four stocks traded at the Australian Stock Exchange (ASX) in 2002, we show that the suggested model captures the spatial and temporal dependencies of the limit order book. Relating the shape of the curves to variables reflecting the current state of the market, we show that the recent liquidity demand has the strongest impact. In an extensive forecasting analysis we show that the model is successful in forecasting the liquidity supply over various time horizons during a trading day. Moreover, it is shown that the model’s forecasting power can be used to improve optimal order execution strategies

DeepLOB: Deep Convolutional Neural Networks for Limit Order Books

We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilises convolutional filters to capture the spatial structure of the limit order books as well as LSTM modules to capture longer time dependencies. The proposed network outperforms all existing state-of-the-art algorithms on the benchmark LOB dataset [1]. In a more realistic setting, we test our model by using one year market quotes from the London Stock Exchange and the model delivers a remarkably stable out-of-sample prediction accuracy for a variety of instruments. Importantly, our model translates well to instruments which were not part of the training set, indicating the model's ability to extract universal features. In order to better understand these features and to go beyond a "black box" model, we perform a sensitivity analysis to understand the rationale behind the model predictions and reveal the components of LOBs that are most relevant. The ability to extract robust features which translate well to other instruments is an important property of our model which has many other applications.Comment: 12 pages, 9 figure