Enhancing Financial Market Efficiency Through Data Science: Mitigating Information Asymmetry

Purpose This study examines how improvements in auditor partner quality affect the stock price dynamics of client firms by focusing on synchronicity and crash risks. Synchronicity reflects how positively a firm’s stocks are consistent with market volatility, whereas crash risk captures the potential for sudden and severe declines. This study contributes to research on audit quality, market efficiency and financial stability by providing insights for regulators and investors on the role of auditor quality in enhancing market efficiency and reducing crash risks. Design/methodology/approach Using panel data from Taiwan’s publicly listed firms (2005–2021), we applied the fixed-effects methodology of Bertrand and Schoar (2003) and Aobdia et al. (2015) to assess auditor partner quality. Audit quality is measured using the standardized ranks of firm and partner fixed effects, with higher ranks indicating higher quality. Findings The findings show that although individual audit quality has a minimal impact on stock price synchronicity, it significantly reduces crash risk. High-quality audits enhance financial reporting and investor confidence and lower the chances of panic selling during adverse events, thus helping the market absorb negative news. These results underscore the critical role of auditor quality in promoting financial stability and trust despite its limited effect on synchronicity. Research limitations/implications Our findings of studies utilizing fixed effect methodology may not be generalizable beyond the specific context in which the data were collected. This limitation is particularly relevant if the data used is from a specific industry, region or time period. Practical implications The findings of this study offer several practical insights for regulators, investors and policymakers. Regulators can promote financial stability by encouraging higher audit partner quality through stricter standards and rotation policies. Investors may use auditor quality as a signal of reliable reporting to make informed decisions and mitigate crash risks. Firms engaging high-quality auditors can enhance reporting credibility, attract long-term investors and reduce volatility during negative events. Strengthening audit quality supports market efficiency, lowers crash risks and boosts investor confidence. Social implications Improved audit partner quality promotes transparency and accountability, fostering public trust in financial markets. It reduces the likelihood of sudden market crashes that can harm investors and destabilize economies, protecting individual wealth and pension funds. Additionally, by enhancing market efficiency, high audit quality contributes to sustainable economic growth and financial stability, benefiting society at large. These improvements support responsible corporate governance, align firms with stakeholder interests and reduce the social costs associated with financial misconduct or market disruptions. Originality/value Audit quality plays a crucial role in stock price dynamics, influencing both stock price synchronicity and crash risk. Stock price synchronicity measures the extent to which a company’s stock moves in line with broader market or industry trends, with high synchronicity indicating that market-wide factors dominate and low synchronicity suggesting greater influence from firm-specific information. On the other hand, stock price crash risk refers to the likelihood of a sudden, severe price drop, often triggered by financial mismanagement, fraud or unexpected negative events. High-quality audits can reduce crash risk by enhancing financial transparency and mitigating information asymmetry.

Big data analytics (BDA) is an advanced analytic technique used with very large and diverse sets of data from different sources. Natural language processing (NLP) is a technology that interfaces with different fields such as computer science, linguistics, and human-computer interactions. Over the past few years, there is a growing number of firms, which are using different BDA and NLP applications in their businesses. Only a few of the research have investigated different dimensions of NLP and BDA and their impacts on the overall organizational performance. There is a growing interest among researchers and practitioners in understanding the consequences for firms that adopt BDA and NLP applications. In this context, the aim of this article is to determine the factors for the usage of BDA and NLP applications in business. With the help of dynamic capability view theory and existing literature, a theoretical model was developed conceptually. Later, the model was validated using structural equation modeling approach considering 1287 samples from 23 firms, primarily based in Asia and Europe, which use NLP and BDA applications. The article finds that NLP and BDA applications help the firms to improve their operational efficiency, which in turn improves the overall firm performance.

The National Academies recommend academic institutions foster a basic understanding of data science in all undergraduates. However, data science education is not currently a graduation requirement at most colleges and universities. As a result, many graduates lack even basic knowledge of data science. To address the shortfall, academic institutions should incorporate introductory data science into general education courses. A general education IT course provides a unique opportunity to integrate data science education. Modules covering databases, spreadsheets, and presentation software, already present in many survey IT courses, teach concepts and skills needed for data science. As a result, a survey IT course can provide comprehensive introductory data science education by adding a data science module focused on modeling and evaluation, two key steps in the data science process. The module should use data science software for application, avoiding the complexities of programming and advanced math, while enabling an emphasis on conceptual understanding. We implemented a course built around these ideas and found that the course helps develop data savvy in students.

Data Scientist - The New ProfessionData scientist is probably the most trendy job in Information Technology (IT) nowadays. This new profession emerged with the Big Data wave. Even though there is no such thing like an exact job profile, we expect that the data scientist can handle all the Big Data challenges that are novel to us. Without being a magician she or he shall help to deliver us all the magic Big Data promises. The data scientist capitalizes on unstructured data without taking the roles of a programmer, database expert, statistician, or content manager. All these professions are around for decades. So, why invent a new one?More than anything, what data scientists do is make discoveries while swimming in data [...] they are able to bring structure to large quantities of formless data and make analysis possible. [3] They sketch, orchestrate, and control the discovery process. The leading paradigm in this process it to find information that meets a certain need or provides an answer for a certain problem. need to avoid the temptation of following a data-driven approach instead of a problem-driven one. [5] The data scientist has to develop an idea about the required information that meets our information need. We can expect that data scientists have a deep understanding of the foundations of both, the nature of information and the domain. They follow a mental model on the information demand that abstractly reflects the facts they expect to encounter in data and the way to detect and present them to us. We expect the data scientist to discover novel information that may provide us with new insights. And we want these insights to be true. It is thus part of the data scientist's responsibility to make sure that the discovered information is not only novel but also trustworthy. The data scientists cannot prove data analysis models. That exceeds their capabilities. We cannot hold them liable for information that eventually turns out to be wrong. Nevertheless their skills should include a sound sensation of plausibility that helps them to raise doubts and to prompt a closer look when the results of data analysis seem too questionable to them. However, separating questionable results from plausible ones is a task that is far from being trivial.Separating Data Science from Data FictionWhen driving a car we often encounter these nice roadside signs that sometimes make us realize that we're driving too fast. Furthermore, on some occasions there is official personnel not far from these signs measuring our speed, explaining to us our traffic infraction face-to-face and documenting it on a speeding ticket. Doesn't this sound a bit outdated? There are enough sensors in a car that measure location, speed, time, and more. Even if the car's sensors don't measure those, the driver's cellphone can do it. Combining this sensor information with the cartographic data about roads and speed limits we can easily imagine that, by the end of day, the car or the phone exactly knows every infraction we committed and can automatically trigger the issuing of speeding tickets. Aren't there just legal aspects that hamper this technically feasible scenario making its way into reality?One trait of Big Data is the availability of sensor data and their combination with already available data generating new information, like the detection of traffic infraction. We can extrapolate this scenario in many directions, including more types of infraction, driving for an irresponsibly long time without a substantial break, or detecting patterns of aggressive driving. We can extend it also towards future possibilities if we think about sensor information from smart watches indicating a possibly problematic pulse rate or from the car's air control sensor that the driver may drive under the influence of alcohol.Much like in Data Mining, the strength of Big Data originates from the combination of facts producing new information. …

Unlike the US’s Regulation National Market System (RNMS), the EU’s Markets in Financial Instruments Directive (MiFID) does not impose a formal exchange trading linkage or guarantee a best execution price. This raises concerns about consolidated market quality in increasingly fragmented European markets. We investigate the impact of visible trading fragmentation on the quality of the London equity market and find a non-linear relationship between fragmentation and adverse selection risk. At moderate levels of fragmentation, order flow competition reduces adverse selection risk and enhances market efficiency by reducing arbitrage opportunities. Contrarily, high levels of fragmentation heighten adverse selection issues.

Data Science is associated with new discoveries, the discovery of value from the data. It is a practice of deriving insights and developing business strategies through transformation of data in to useful information. It has been evaluated as a scientific field and research evolution in disciplines like statistics, computing science, intelligence science, and practical transformation in the domains like science, engineering, public sector, business and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. In this paper we entitled epicycles of analysis, formal modeling, from data analysis to data science, data analytics -A keystone of data science, The Big data is not a single technology but an amalgamation of old and new technologies that assistance companies gain actionable awareness. The big data is vital because it manages, store and manipulates large amount of data at the desirable speed and time. Big data addresses detached requirements, in other words the amalgamate of multiple un-associated datasets, processing of large amounts of amorphous data and harvesting of unseen information in a time-sensitive generation.
 As businesses struggle to stay up with changing market requirements, some companies are finding creative ways to use Big Data to their growing business needs and increasingly complex problems. As organizations evolve their processes and see the opportunities that Big Data can provide, they struggle to beyond traditional Business Intelligence activities, like using data to populate reports and dashboards, and move toward Data Science- driven projects that plan to answer more open-ended and sophisticated questions.
 Although some organizations are fortunate to have data scientists, most are not, because there is a growing talent gap that makes finding and hiring data scientists in a timely manner is difficult. This paper, aimed to demonstrate a close view about Data science, big data, including big data concepts like data storage, data processing, and data analysis of these technological developments, we also provide brief description about big data analytics and its characteristics , data structures, data analytics life cycle, emphasizes critical points on these issues.

This paper discusses the potential of machine learning, data science, and natural language processing (NLP) in mitigating the incidence of spoofing and financial risks hinged on cyber threats. Another one is spoofing; it is the act of impersonating legitimate entities to gain unauthorized information, and it is indeed a threat to the public and companies to some extent. The research introduces two primary methodologies to combat spoofing: an email filtering system using a machine learning algorithm and an encryption and decryption system using a Caesar Cipher and Python programming language. It distinguishes between approved domains and unapproved domains by using machine learning and successfully filters out phishing emails from reaching the intended clients. This study also illustrates how to conduct email domain verification using MongoDB Atlas, which a database is containing approved vendors’ domains, to reduce spoofing. Specifically, incorporating NLP helps the system analyze raw data to categorize it and identify patterns potentially leading to a spoofing attempt, enhancing the spoofing detection and prevention of the system. The paper also presents arguments that require awareness and integration of new technologies in the security frameworks. Hence, incorporating machine learning, data science, and NLP presents robust, versatile, and cost-effective solutions to enhance cybersecurity and ultimately protect vital information and organizations’ monetary loss due to cybercrimes. The paper was first completed in 2021 and later I modified the article with latest updates till date 2024.

This thesis examines the intersection of citizen science, serious games, and artificial intelligence, focusing on the methodological challenges that arise when participatory projects generate large and complex datasets. Citizen science has broadened public involvement in research, but recurring issues such as missing data, inconsistencies, observer variation, and limited reproducibility continue to affect data quality. Serious games also offer opportunities for engagement and learning, yet their behavioural data is often underused due to analytical constraints. By applying data science, machine learning, and explainable AI, this thesis shows how these techniques can improve the reliability and interpretation of citizen science data.<br/><br/>The research was carried out in collaboration with JGM Serious eXperiences, a serious-gaming organisation in Leeuwarden that develops escape-room-based games for professional training. These settings provided a controlled environment for observing real-time team behaviour under pressure. The collaboration reflects a form of participatory citizen science: JGM provided data and practical questions, while academic analysis refined behavioural indicators and strengthened scientific validity.<br/><br/>The thesis consists of three interconnected studies. The first, a systematic literature review, synthesises 90 publications on the use of AI in citizen science and highlights challenges such as data imbalance, bias, and limited transparency. The second study develops a data science framework to clean and validate JGM’s behavioural dataset, addressing missing and inconsistent entries. The third study applies machine learning and explainable AI to identify behavioural predictors of team success.<br/><br/>Together, these studies demonstrate how data science can enhance methodological rigour in citizen science by improving data preprocessing, analysis, and interpretation. The findings offer practical guidance on integrating data science and AI techniques into participatory projects to support more reliable, transparent, and impactful research.

In today’s data-driven world, adding data science to educational programs is essential for preparing students for the changing needs of the 21st-century job market. This chapter discusses the reasons, methods, challenges, and potential benefits of including data science concepts across various educational levels and fields. As businesses increasingly depend on data-driven decision-making, students need foundational data skills not just in STEM (science, technology, engineering, and mathematics) areas but also in business, humanities, social sciences, and the arts. The chapter starts by looking at the development of data-focused education, starting with basic computer skills in the early 2000s and moving to the current focus on machine learning, statistics, and predictive analytics. It lays out the main parts of a solid data science curriculum, including data collection, cleaning, analysis, visualization, and ethical issues. It also explains how these parts can blend into existing subjects to create interdisciplinary learning chances.

What Exactly is Market Integrity? An Analysis of One of the Core Objectives of Securities Regulation

In the era of Big Data and Data Science, Text analysis within, Natural Language Processing (NLP), suffers from the curse of high dimensionality. The use of NLP in applications such as speech processing, semantic webs, and word processing has become a main element in today’s Artificial Intelligence and Big Data Applications. A natural language parsing system must incorporate three components of natural language, namely, lexicon, morphology, and syntax. As Arabic is highly derivational, each component requires extensive exploitation of the associated linguistic characteristics. Parsing Arabic sentences still has open challenges due to several reasons including the relatively free word order of Arabic, the length of sentences, and the omission of diacritics (vowels) in written Arabic and the frequency of pro-drop phenomena. This research exploits Visual Prolog to provide a scalable platform for Arabic parser and explains the details of the used lexicon and parser and shows the scalability of the system to address more functions.

The Relationship between Operational and Market Efficiency Improvement Towards Financial Performance

After a disaster such as earthquakes, debris flows, forest fires, or landslides, etc., a lot of people have to be away from their home and gather in a shelter. In addition, the refugees suffer from the shortage of necessary resources due to impaired life infrastructure, such as damaged roads and communication networks. The degree of reducing damage depends on the amount of food, water, daily necessities and communication resources required by each shelter. How to effectively and efficiently allocate resources according to grasp the exact need of a disaster situation will be an important issue. We estimate the degree of the disaster by collecting and analyzing big data from the SNS, and building a platform for the communication resources to be efficiently and effectively allocated. In order to achieve this goal, we are challenging the following issues A) Understanding situations (user requirements) after disaster occur The SNS streams large scale semantic information about real time situation in society, especially during and after disaster. It is both domain-specific and computational challenge in processing the heterogeneous large data set to extract the exact situational content with reduced semantic uncertainty. The machine learning (ML) and natural language processing (NPL) tool kits are useful in semantic analysis, but still needs domain-specific implementation and computational improvement for the situation understanding from the SNS big data. B) Understanding distribution patterns of situations/users' requirements The disaster related situation is spatiotemporally correlated, and varies dynamically in space and time. It is also domain-specific and computational challenge in estimating the spatiotemporal distribution patterns of the disaster affect based on the spatial big data from SNS. The scan statistics such as the spatial scan have provided well tested mathematical tools and software for spatial data mining. However, new methodologies are necessary since the assumptions have to be different when it meets the spatial big data in SNS. And the computational complexity in spatial big data is also a bottleneck for real-time processing. C) Solving uncertainty of big crowd data One of the major features in big crowd data, e.g., SNS data, is uncertainty behind the data. Especially in a disaster scenario, the collecting time period cannot be long enough to smooth the data automatically. How to efficiently solve uncertainty problem in the big crowd data in a disaster scenario becomes a new and big challenge for disaster management.

Machine learning (ML) is revolutionizing industries by providing innovative solutions that drive sustainable economic growth and market efficiency. The integration of ML techniques in various sectors, including finance, healthcare, manufacturing, and energy, offers significant potential for enhancing productivity, optimizing resource allocation, and fostering innovation. This review explores the transformative impact of ML on economic growth and market efficiency, emphasizing its role in predictive analytics, decision-making, and automation. In finance, ML algorithms are employed to predict market trends, manage risks, and detect fraudulent activities. By analyzing vast amounts of data in real-time, ML models enable financial institutions to make informed decisions, reduce operational costs, and improve customer experiences. In healthcare, ML applications range from diagnosing diseases and predicting patient outcomes to optimizing treatment plans and managing healthcare resources, thereby improving patient care and reducing costs. The manufacturing sector benefits from ML through predictive maintenance, quality control, and supply chain optimization. ML models analyze data from sensors and production lines to predict equipment failures, ensuring timely maintenance and minimizing downtime. This leads to increased productivity and reduced waste, contributing to economic sustainability. Energy management is another critical area where ML drives efficiency. ML algorithms optimize energy consumption, forecast renewable energy production, and enhance grid management. By integrating ML into energy systems, organizations can reduce their carbon footprint and operational costs while promoting sustainable energy practices. Moreover, ML enhances market efficiency by enabling businesses to understand consumer behavior and preferences. Retailers use ML to personalize marketing strategies, optimize pricing, and manage inventory effectively. This not only boosts sales but also improves customer satisfaction and loyalty. Despite the significant benefits, the adoption of ML presents challenges such as data privacy concerns, algorithmic bias, and the need for skilled professionals. Addressing these challenges requires robust regulatory frameworks, ethical guidelines, and investment in education and training. In conclusion, harnessing ML techniques is pivotal for driving sustainable economic growth and market efficiency. By leveraging ML's capabilities in predictive analytics, decision-making, and automation, industries can achieve higher productivity, optimized resource utilization, and innovative solutions that contribute to long-term economic sustainability and competitiveness. As ML continues to evolve, its potential to transform various sectors and promote sustainable development remains immense.

Recent years have witnessed a revolution in data science and ‘big data’ into which psychiatric research has also being drawn. ‘Big data’ has been defined as data sets which are so large in size, so fast to change, and so complex in structure that traditional data processing techniques are overwhelmed [1]. The mining and exploitation of such big data resources as Electronic Healthcare Records (EHRs) present an exciting challenge to the field of psychiatric epidemiology. The number of big data projects within psychiatric research is growing, and Stewart and Davis’s literature review is, therefore, timely [2]. Technological advances in data processing and storage, computer networking, mobile technology, and data manipulation have rendered huge quantities of healthcarerelated data potentially amenable to analysis. Such studies potentially offer much larger patient numbers, wider parameters of study, and longer timescales of follow-up, than is typical of randomised controlled trials (RCTs) or cohort studies. A further advantage of this data is it often arises from naturalistic clinical settings, in terms of both clinical practice and patient health and comorbidity. Indeed, while often considered the ‘gold standard’ in medical research, RCTs do have important limitations, such as overly-strict exclusion criteria. Routine clinical data sets can, therefore, be complementary to RCT data, while also making research findings more relevant to everyday clinical practice. In addition, the quantity of clinical ‘big data’ potentially allows analysis of rarer clinical conditions, or subject areas that would be unlikely to meet ethical approval for more conventional studies (for example, medication usage in pregnancy). Big data studies provide the scale and breadth of patient numbers required for stratified, predictive, and personalised medicine research. In addition, notwithstanding many challenges of working with big data, large-scale analysis of routinely collected healthcare data sets has already demonstrated effectiveness in fields such as pharmacovigilance [3] and post-marketing clinical trials. As described in detail by Stewart and Davis, the challenging aspects of working with ‘big data’ are captured by the taxonomy of ‘Vs’—volume, velocity, and variety—first described by Laney [4] and extended since [1, 2]. There are additional issues to working with healthcare big data specifically, including that clinical administrative data are generally not collected, curated, or formatted in a manner optimised for research, and the inherent sensitivity of the data in terms of personal privacy. As the possibilities for healthcare big data expand into such areas as text mining and natural language processing of clinical records, near ‘real-time’ updating of repositories, and incorporation of new data streams from mobile and wearable technology, robust systems must be in place to channel the potential data deluge. How great a role ‘cloud’ computing and storage will have in this is an intriguing question. In healthcare big data, the benefits offered by the cloud in increased power and accessibility, and reduced cost, must be weighed against concerns over data ownership, encryption, and unauthorized access. An additional question is whether unique patient identifiers (UPIs) utilised by some national healthcare systems can be extended more This comment refers to the article available at doi:10.1007/s00127016-1266-8.