"Information Content of Book and Trade Order Flow at Different Time Scales" (joint work with Alessio Sancetta and Yuri Taranenko)
Abstract:
This paper studies information spillovers from the NASDAQ Limit Order Book (LOB) and assesses their impact on price predictability. Using LOB data for 35 large cap US stocks from March 2019 to February 2023, we aggregate data at different trading volume time scales and train various machine learning algorithms: Linear Discriminant Analysis, Ridge Classifiers, Random Forests and a Deep Neural Network. Our empirical findings suggest that trade order flow information is the most persistent and prices are predictable with respect to it. We document that machine learning models are able to predict mid price directions accurately, yet this informational advantage dissipates within the first 10 milliseconds. Moreover, our findings suggest that model complexity does not necessarily ensure higher financial returns. Using information on quoting activity from other exchanges, we also conclude that market participants may choose to quote more heavily on the NASDAQ, but they do so without leaking more information. Additionally, employing both panel and cross sectional analysis, we examine how stock-specific and market determinants affect intra-day predictability across different days. Overall, more liquid stocks with higher market beta exhibit higher intraday returns. We show that there is persistency in high frequency performance and the dynamic adjustment towards the long-run average lasts up to three trading days. Finally, we document that over time, the value of order flow has decreased. Then, it is plausible to infer that the growing use of algorithmic trading has increased market competition and consequently enhanced market efficiency.
"Bayesian Inference in Dynamic Panel Stochastic Frontier Models" (joint work with Mike Tsionas and Marwan Izzeldin)
Abstract:
The paper adopts a dynamic panel stochastic frontier model that incorporates firms' intertemporal decision behaviour and short-run stagnant adjustments to the production process. Its dynamic specification recognises short-run output adjustment costs, where final output may be only partially adjusted to the optimum level. In nesting previous panel stochastic frontier models, our new approach delivers a flexible framework that accommodates heterogeneous technologies and latent time-varying inefficiency effects. Model inference is based on Bayesian Markov Chain Monte Carlo (MCMC) techniques with data augmentation. Using simulated data, we illustrate that our model performs very well in small and moderate samples. Last, we present our model in an empirical example, analysing all publicly listed UK companies operating in the manufacturing and construction sector over the period 2004-2022. A general finding is that most firms exhibit stagnant production processes, with the half-life for adjusting supply to be as high as 5-quarters. The estimated technical efficiencies range from 43% to 96% across the years.
"A simple method for modelling the energy efficiency rebound effects with an application to energy demand frontiers"
Abstract:
This paper proposes a new simple approach to model the macroeconomic energy efficiency rebound effects. The method follows recent developments in stochastic frontier models and assumes that efficiency improvements do not necessarily reduce energy demand proportionally. Instead, we allow country-specific rebound effects to mitigate or intensify the efficiency effects on the aggregate energy demand. The method incorporates a reduced-form stochastic frontier model with country-specific heteroscedasticity, and the method of moments approach for estimating the country-specific rebound effects. The estimation can be implemented relatively easily in any standard statistical package. Last, we illustrate the model in an empirical application, where we estimate the energy efficiency scores and the corresponding rebound effects for 20 OECD member countries from 1980 to 2018. Our results reveal that for most countries, we find modest to considerable partial rebound effects ranging from 28% to 92%. In addition, we show that for 2018 the average energy efficiency score is approximately 84%, indicating that there is potential for further energy savings.
"Assessing the post-LASPO effect on Legal Aid and the number of Orders: The case of the Children Act" (joint work with Konstantinos Kalliris and Theodore Alysandratos)
Abstract:
This article explores the effects of cuts to legal aid on access to justice. The Legal Aid, Sentencing and Punishment of Offenders Act 2012 (LASPO) introduced extensive cuts, including the exclusion of almost the entire area of private family law its scope. We empirically assess the effects of this reform on access to justice, using formal statistical techniques. We find that the cuts had an adverse impact on the availability of legal aid and, consequently, access to justice. This adverse impact was mostly linked to areas where people need legal aid the most (due to high unemployment and low growth), thus undermining both equal access to justice and, arguably, equality of arms in the courtroom. We also find that the average of cost of legal aid per case is approximately £8,000, and, therefore, some cases could be funded at a relatively low cost for the taxpayer. Given the failure of mediation to cover the lack of access to family courts and the emergence of legal aid deserts, we raise the question of whether the blanket removal of almost an entire area of law from LASPO's scope is a justifiable policy.
"Endogenous Technical Efficiency: An application to UK manufacturing sector"