Order Book Research Articles

LOB modeling using Hawkes processes with a state-dependent factor

A point process model for order flows in limit order books is proposed, in which the conditional intensity is the product of a Hawkes component and a state-dependent factor. In the LOB context, state observations may include the observed imbalance or the observed spread. Full technical details for the computationally-efficient estimation of such a process are provided, using either direct likelihood maximization or EM-type estimation. Applications include models for bid and ask market orders, or for upwards and downwards price movements. Empirical results on multiple stocks traded in Euronext Paris underline the benefits of state-dependent formulations for LOB modeling, e.g. in terms of goodness-of-fit to financial data.

Augmented bilinear network for incremental multi-stock time-series classification

Deep Learning models have become dominant in tackling financial time-series analysis problems, overturning conventional machine learning and statistical methods. Most often, a model trained for one market or security cannot be directly applied to another market or security due to differences inherent in the market conditions. In addition, as the market evolves over time, it is necessary to update the existing models or train new ones when new data is made available. This scenario, which is inherent in most financial forecasting applications, naturally raises the following research question: How to efficiently adapt a pre-trained model to a new set of data while retaining performance on the old data, especially when the old data is not accessible? In this paper, we propose a method to efficiently retain the knowledge available in a neural network pre-trained on a set of securities and adapt it to achieve high performance in new ones. In our method, the prior knowledge encoded in a pre-trained neural network is maintained by keeping existing connections fixed, and this knowledge is adjusted for the new securities by a set of augmented connections, which are optimized using the new data. The auxiliary connections are constrained to be of low rank. This not only allows us to rapidly optimize for the new task but also reduces the storage and run-time complexity during the deployment phase. The efficiency of our approach is empirically validated in the stock mid-price movement prediction problem using a large-scale limit order book dataset. Experimental results show that our approach enhances prediction performance as well as reduces the overall number of network parameters.

Electricity price spike formation and LNG prices effect under gross bidding scheme in JEPX

This study empirically analyzes the price spikes that occurred in the Japan Electric Power Exchange (JEPX) market in January 2021 and draws energy policy implications from the results. The contributions of this research are as follows. First, we propose a novel structural model to capture electricity price spikes based on the buy and sell bid curves of the JEPX order book and incorporate the effect of LNG spot price fluctuations on the price of electricity. Second, the results of our empirical analysis show that the model can adequately capture the price spike in the JEPX market that occurred in January 2021. Third, the estimation of the model parameters shows that the natural gas spot price immediately shifts the price curve upward. As an important energy policy implication from the results, we propose that to manage the risk of electricity price spikes, the LNG effect on the market players’ bidding behavior, rather than the marginal cost of the supply curve, be considered; from this perspective, the LNG spot price, rather than the trend of the LNG price a few months earlier, must be monitored, and the institutional design of the JEPX market should take this into account.

Gender Representations in Greek Children’s Literature: Activities of Male and Female Characters

In recent decades, extensive research has examined gender depiction in children’s literature as well as its impacts on young readers’ minds and attitudes. Children’s picture books are considered an important socializing factor as they provide their audience with a wide variety of information about life. Due to their young age, children have limited experience and knowledge of the world and therefore they depend on books in order to draw information about socially acceptable behaviours. As a cultural product, literature usually expresses the dominant ideology of the society in which it is produced and consumed. Therefore, intentionally or not, books frequently reproduce established attitudes, regardless of their validity, such as gender bias. This happens due to the fact that ideologies penetrate the language itself and by extension the way we think, speak or write. For this reason it is possible for books of the twenty-first century to still depict outdated gender portrayals. Naturally this is not a conscious, out loud statement of the writer or the illustrator, but it appears implicitly, usually as a given. Such gender portrayals can be outlined in a story by a character’s interests and activities. Leisure activities, sports and other hobbies can provide hints about the character’s personality, physical condition and talents. In the past, research has shown disparities in children’s literature, with male characters monopolizing ‘energetic’ activities and female characters spending their free time more statically. Stereotyped gender representations in picture books reproduce gender biases by implanting them to the next generation’s minds. Using content analysis methodology, this qualitative research examines if gender is depicted stereotypically in children’s picture books published in Greece from 2009 to 2019. According to findings, both male and female characters of the sample engage in activities which are in agreement with traditional gender-stereotyped traits. Implicit gender categorization of interests and hobbies operates restrictively for young children. This new data can constitute a useful tool for science, publishers and the state, as gender inequalities are considered to be the source of various social problems globally.

Business Model Innovation in the German Industry: Case Studies from the Railway, Manufacturing and Construction Sectors

For a long time, the business activities of industrial companies in mechanical engineering, plant construction or the automotive industry focused on products and product-related services. Digital pioneers have refined these products such as machinery, equipment, and devices with data-driven services, which they make available worldwide via digital platforms. This emerging trend influences existing and future business models of all companies. This transition from product-related, single-sided markets to platform markets is characteristic of the digital age. The speed at which business models must change continues to be underestimated by many market participants, especially when order books are well filled and the pressure to change appears to be low. Industrial and service companies need to adapt to the changes induced by new market players to secure future business success and remain competitive in the digital age. The aim of this article is to intensify the debate on digital business models in the industry by providing practical examples of business model innovations in three industries.

The Resurrection of Jesus: Apologetics, Polemics, History by Dale C. Allison Jr.

Reviewed by: The Resurrection of Jesus: Apologetics, Polemics, History by Dale C. Allison Jr. Glenn B. Siniscalchi dale c. allison jr., The Resurrection of Jesus: Apologetics, Polemics, History (New York: T&T Clark, 2021). Pp. xi + 403. Paper $47.95. Dale Allison's published works on the historical Jesus have always been rigorous, thought-provoking, and accessible. In his latest installment, A. clarifies, expands, and strengthens most of his earlier thoughts on the historical approach to the resurrection of Jesus in much greater detail. NT critics and other inquirers who are dedicated to understanding the academic study of Christian origins and the rise of resurrection faith must engage the intricacies of A.'s latest book in order to push forward any meaningful developments in this controversial area of NT studies. The book covers a dizzying array of arguments and reflections related to the standard discussion points that are dedicated to the credibility of Jesus's resurrection (i.e., the burial of the crucified Jesus, the discovery of his empty grave, the series of after-death communications, and the disciples' willingness to die for their newfound faith). A.'s expansive pedagogy is on full display throughout the book, showing a variety of creative and defensible ways to think about the origins of the Jesus movement. For instance, he accepts most aspects of the traditional evidence but also includes the historicity of Jesus's prophetic predictions of his own resurrection from the dead. There is also great emphasis placed on the apocalyptic expectations that characterized Jesus's ministry and how they affected the original meanings that were assigned to the after-death experiences. "Resurrection," then, was the best category that was used by the first percipients to explain and describe the metanormal experiences of the Easter Jesus. These provocative contentions and conclusions notwithstanding, a major theme in A.'s book is that historical investigations into earliest Christianity cannot be of service to the ambitions of Christian apologists and their primary interlocutors, the secular skeptics. In A.'s consistently held view, both camps unjustifiably stretch the shape of historical reason in support of their predetermined convictions. Instead, historical methods and conclusions are very limited in what they can accomplish. According to A., the first strata of evidence for the resurrection may include, for instance, the sequence of postmortem appearances to the disciples and other unbelievers (cf. 1 Cor 15:3–8), but one can easily invoke certain controversial studies in religious studies, historiography, the philosophy of religion, biblical studies, and parapsychology, in order to understand or even modify common understandings of what the first percipients saw and experienced. In effect, believing in the bodily resurrection does not necessarily entail the supernatural reanimation of Jesus's cadaver (the latter view has been endorsed by William Lane Craig, Gary Habermas, Michael Licona, and N. T. Wright, among a host of other well-known supporters). I can decipher at least two important reasons why A. is hesitant to accept the common verdicts that are upheld by Christian apologists. The first reason is presented toward the beginning of the book and is candidly discussed in lucid detail: his background beliefs are multifaceted, fluid, and even inconsistent in a critical approach to the biblical texts. Worldviews are indeed difficult to pinpoint within a person's mind and psychological outlooks, but we should agree with A. in acknowledging that people frequently observe and interpret texts and other artifacts from the past in radically different ways. Worldviews simply cannot be reduced to belief in an omnipotent, personal God who will probably act in human history within a religiously charged context. We hold to many unrecognized beliefs about psychology [End Page 349] and philosophy that often shade our interpretations and intuitions related to historical investigations. The second reason is due to the information overload, hyper-pluralism, and fragmented nature of contemporary biblical studies as it relates to the historical quests for Jesus. A. is thoroughly immersed in this world, and he emphasizes the difficulty of arriving at certain conclusions in the attempt to decide whether the evidence points decidedly in favor of Jesus's physical resurrection. Because I found myself agreeing with most of A.'s newest book, I can no...

Mobility and Identity in Chaucer's Canterbury Tales

Mobility and Identity in Chaucer's Canterbury Tales

A FAST Hardware Decoder Optimized for Template Features to Obtain Order Book Data in Low Latency

High-Frequency Trading (HFT) systems require high computational performance for real-time trading and data analysis. FAST protocol, an extension of FIX protocol, is one of the main communication pattern adopted by these systems. This work presents an open source hardware component, implemented in Field-Programmable Gate Array (FPGA), to decode market data messages to produce the necessary commands to construct order books in low latency for the Brasil Bolsa Balcão B3 stock exchange. The proposed hardware component optimized for a B3 template is able to decode messages at average latency of 0.72us and throughput of 1.4M FAST messages per second. The results are from logs of real messages with average size of 85 bytes each.

An Efficient Data-Independent Priority Queue and its Application to Dark Pools

We introduce a secure data-independent priority queue which supports polylogarithmic-time insertion operations and constant-time deletions and read-front (aka peek) operations as opposed to the originally introduced queue by Toft (PODC '11). Moreover, we minimize the number of comparisons required to perform different operations on Toft's priority queue. Data-independent data structures—first identified explicitly by Toft, and further elaborated by Mitchell and Zimmerman (STACS '14)—serve the purpose of computing on encrypted data without executing branching code which can be used to avoid prohibitively expensive operations in secure computation applications. Focusing on the costly sorting operations, we show significant asymptotic improvements over prior privacy preserving dark pool applications. Dark pools are securities-trading venues which attain ad-hoc order privacy, by matching orders outside of publicly visible exchanges via the so-called dark pool operators. In this paper, we describe an efficient and secure dark pool (implementing a full continuous double auction) based on our new priority queue. Our construction's security guarantees are cryptographic based on secure multiparty computation (MPC), and do not require that the dark pool operators are trusted. Our construction improves upon the asymptotic efficiency attained by previous efforts. Existing cryptographic dark pools process new orders in time which grows linearly in the size of the standing order book; ours does so in polylogarithmic time. We describe a concrete implementation of our MPC protocol with malicious security in the honest majority setting. We also report benchmarks of our implementation and compare them to prior works. Our protocol reduces the total running time by several orders of magnitude over prior secure dark pool solutions.

From zero-intelligence to queue-reactive: limit-order-book modeling for high-frequency volatility estimation and optimal execution

Estimators of integrated and spot variance are tested using the queue-reactive model of the limit order book

Annotated Listing of New Books

Editor's Note Our policy is to annotate all English-language books on economics and related subjects that are sent to us. A very small number of foreign-language books are called to our attention and annotated by our consulting editors or others. Our staff does not monitor and order books published; therefore, if an annotation of a book does not appear six months after the publication date, please write to us or the publisher concerning the book.

Statistical arbitrage trading across electricity markets using advantage actor–critic methods

In this paper, risk-constrained arbitrage trading strategies that exploit price differences arising across short-term electricity markets, namely day-ahead (DAM), continuous intraday (CID) and balancing (BAL) markets, are developed and evaluated. To open initial DAM positions, a rule-based trading policy using DAM and CID price forecasts is proposed. DAM prices are predicted using both technical indicator features and data augmentation methods, such as autoencoders and generative adversarial networks. Meanwhile, CID prices are predicted using novel features that are engineered from the limit order book. Using the forecasts, the direction of price movements is correctly predicted the majority of the time. To manage open DAM positions while optimising the risk-reward ratio, deep reinforcement learning agents trained using the advantage actor–critic algorithm (A2C) are employed. Evaluated across Dutch short-term markets, A2C yields profits surpassing those obtained using A3C and other benchmarks. We expect our study to benefit electricity traders and researchers who seek to develop state-of-art intelligent trading strategies.

Impact of High-Frequency Trading with an Order Book Imbalance Strategy on Agent-Based Stock Markets

It is known that there is a positive correlation between order book imbalance and future returns. Although some previous studies using actual trading data have suggested that high-frequency trading (HFT) may take this characteristic into account, HFT firms have not disclosed their specific strategies. Furthermore, there has been a long-standing debate in the empirical research field as to whether HFT is the cause of flash crashes, but no final conclusion has been reached. In the present study, we analysed the impacts of HFT taking into account the correlation between order book imbalance and future returns on a stable market and on a market with a flash crash, using agent-based simulations, which are said to be capable of analysing events in their essence. We also analysed how HFT investment performance differs between those two market conditions. The results showed that HFT has the effect of further stabilizing the market when the market is stable but does not take place during flash crashes and so is unable to affect the market either for the good or the bad. The results also suggest that the proposed HFT’s performance is more sensitive to market price fluctuations than conventional HFT (i.e., HFT following a position market-making strategy) and tends to have high risk and high returns.

A Time-Dependent Markovian Model of a Limit Order Book

This paper considers a Markovian model of a limit order book where time-dependent rates are allowed. With the objective of understanding the mechanisms through which a microscopic model of an orderbook can converge to more general diffusion than a Brownian motion with constant coefficient, a simple time-dependent model is proposed. The model considered here starts by describing the processes that govern the arrival of the different orders such as limit orders, market orders and cancellations. In this sense, this is a microscopic model rather than a ``mesoscopic'' model where the starting point is usually the point processes describing the times at which the price changes occur and aggregate in these all the information pertaining to the arrival of individual orders. Furthermore, several empirical studies are performed to shed some light into the validity of the modeling assumptions and to verify whether certain stocks satisfy the conditions for their price process to converge to a more complex diffusion.

Dynamic programming principle and computable prices in financial market models with transaction costs

How to compute (super) hedging costs in rather general financial market models with transaction costs in discrete-time? Despite the huge literature on this topic, most of results are characterizations of the super-hedging prices while it remains difficult to deduce numerical procedure to estimate them. We establish here a dynamic programming principle and we prove that it is possible to implement it under some conditions on the conditional supports of the price and volume processes for a large class of market models including convex costs such as order books but also non convex costs, e.g. fixed cost models.

Spoilt for choice: Determinants of market shares in fragmented equity markets

We analyze the determinants of the trading volumes of different trading mechanisms in equity markets using an extensive panel data set from European markets comprising public limit order books, call auctions, dark pools, internalization platforms, and the over-the-counter market. Market shares, resulting from investors’ order routing decisions, are driven by the degree of immediacy and anonymity offered by the venues, their ability to offer off-tick executions, as well as the informational environment and conditions in the market. Findings for small and large trades are distinctly different, likely because traders jointly choose trade size and venue type.

Jump Diffusion Approximation for the Price Dynamics of a Fully State Dependent Limit Order Book Model

We study a microscopic limit order book model, in which the order dynamics depend on the current best bid and ask price and the current volume density functions, simultaneously, and derive its macroscopic high-frequency dynamics. As opposed to the existing literature on scaling limits for limit order book models, we include price changes which do not scale with the tick size in our model to account for large price movement, for example, being triggered by highly unforeseen events. Our main result states that, when the size of an individual limit order and the tick size tend to zero, while the order arrival rate tends to infinity, the microscopic limit order book model dynamics converge to two one dimensional jump diffusion processes describing the prices coupled with two infinite dimensional fluid processes describing the standing volumes at the buy and sell side.

Trader Competition in Fragmented Markets: Liquidity Supply Versus Picking-Off Risk

AbstractBy employing a dynamic model with two limit order books, we show that fragmentation is associated with reduced competition among liquidity suppliers and lower picking-off risk of limit orders. Due to these countervailing channels, the impact of fragmentation on liquidity and welfare differs with asset volatility: When volatility is high (low), liquidity and aggregate welfare in a fragmented market are higher (lower) than in a single market. However, fragmentation always shifts welfare away from agents with exogenous trading motives and toward intermediaries. We empirically corroborate our model’s predictions about liquidity. Our model reconciles the mixed results in the empirical literature.

Turkish Shipyards During COVID-19 Pandemic

It might be unimaginable to anticipate the pandemic but taking the initiative in measures when Covid-19 occurred was critical to success. However, the impact of this worldwide pandemic, ever known in World History, was predictable. Covid-19, the largest known pandemic in world history, will cause social, economic, and political changes. Country governments, non-governmental organizations, and companies have developed new skills and competencies to overcome the economic and social crisis caused by the pandemic with minimum damage. This paper aims to investigate the effects of the pandemic and the measures taken in Turkish shipyards in the first phase of the Covid-19 pandemic with the cooperation of GISBIR and Istanbul Technical University Academic staff. Firstly, current academic studies were reviewed by conducting a literature search. Then, GISBIR data sheets, Baltic Dry Cargo Index, shipyard order book statistics, and employment statistics were evaluated as primary data. The shipyards' websites and the sector managers' reports are secondary data. Despite the pessimistic news, academic studies, and surveys published during the outbreak's initial phase, the authors could not find any evidence to indicate the medium-term negative impact of Covid-19 on Turkish shipyards. Although the Baltic Dry Index showed a decline in the first quarter of 2020, it rose right after the second quarter, and the shipyard order book statistics increased. Employment statistics, on the other hand, continued to increase gradually. Recovery depends on the shipyards overcoming the harmful effects of the crisis by investing in continuous improvement efforts, green technology, and systems, with the support of GISBIR, giving importance to effective crisis management and distinctive ship production.

UNISWAP - A CASE STUDY OF DECENTRALIZED EXCHANGES ON THE BLOCKCHAIN

The paper takes a close look at an emerging industry - decentralized finance on the blockchain. The goal of this paper is to compare centralized order book (CLOB)-based exchanges to automated market maker (AMM)-based decentralized exchanges (DEX) and analyze the challenges that AMM-based DEXes are facing today using Uniswap as an example.