Modelling of stock market security price Dynamics Using market microstructure Data

This study provides a comprehensive benchmarking of traditionalsystem identification and modern machine learning (ML) models forthe data-driven modeling of dynamical systems, with a focus on processsystems engineering (PSE) applications. To achieve this, we deployAutoSID, an automated end-to-end framework inspired by Machine LearningOperations (MLOps) principles. While AutoSID facilitates model selection,training, validation, and evaluation, its purpose here is to serveas a platform to investigate how well MLOps-inspired tools can beadapted for system identification tasks in PSE. Our investigationincludes a comparison of 12 diverse model architectures from the systemidentification, machine learning, and deep learning literature, evaluatedacross 11 PSE case studies under varying data regimes. We employ fourmodel search or hyperparameter optimization algorithms and three modelselection criteria to ensure a thorough assessment. Our findings highlightthe importance of model selection as the crucial step in system identification.Specifically, our results demonstrate the effectiveness of Bayesianoptimization with tree-structured parzen estimators (TPE) for balancedmodel selection, while k-fold cross-validation proves to be a robustmetric for performance evaluation during the selection process. Inlarge-scale data scenarios, where performance differences between k-fold cross-validation and information criteria are small,information criteria emerge as a computationally efficient alternative.Once the “best” model structure is decided, in termsof model performance, we find that ML models with balanced complexity,such as tree ensemble models, consistently achieve superior predictiveaccuracy and computational efficiency, outperforming both simplisticand overly complex models. These findings provide actionable insightsinto model selection and performance evaluation for PSE practitionersand demonstrate the potential of incorporating MLOps-inspired workflowsinto the system identification process.

Modern machine learning (ML) models are being used heavily in business domains to build effective decision support systems. As a primary requirement, supervised ML models need large labeled datasets. However, obtaining a high volume of labeled training data is both expensive and time-consuming. Researchers have proposed several labeling approaches to avoid manual labeling efforts. Active learning (AL) and Data Programming (DP) are two state-of-the-art techniques used to label datasets. Nevertheless, both approaches have their strengths and weaknesses. For example, AL is computationally expensive to apply on large industrial datasets; and labels generated by DP are often inaccurate and difficult to interpret. To address these challenges, in this paper, we propose a novel hybrid method that integrates the scalability of DP with the user engagement and accuracy of AL. The proposed approach aims at optimizing the labeling process by applying DP to generate initial noisy training data and then use AL to query the user to label only those points that maximize the accuracy of the final labels with a minimum annotation cost. To evaluate the proposed approach, we have used five open source datasets and a real-world business dataset of 1.5 million records. We use traditional active learning and data programming techniques as baselines to compare the performance and annotation cost of our proposed approach. The results show that the proposed method can achieve higher labeling accuracy than data programming. It also can minimize the labeling cost in real-world business scenarios, while delivering a comparable level of performance (accuracy) with active learning.

Improving autocoding performance of rare categories in injury classification: Is more training data or filtering the solution?

Machine Learning Can Accurately Predict Overnight Stay, Readmission, and 30-Day Complications Following Anterior Cruciate Ligament Reconstruction

The global stock market has experienced a profound transformation over the past few decades due to rapid advancements in Information Technology (IT). Traditional stock market systems, once dominated by manual trading floors, paper-based documentation, and delayed information dissemination, have been replaced by highly automated, technology-driven platforms. Information Technology now forms the backbone of modern stock market operations, enabling real-time trading, high-speed data processing, electronic clearing and settlement, and enhanced market surveillance. This research paper examines the role of Information Technology in shaping modern stock market practices, with particular emphasis on trading efficiency, transparency, investor participation, decision-making processes, and associated technological risks. The study adopts a descriptive and empirical research design to analyze how IT-enabled systems—such as electronic trading platforms, online brokerage services, mobile trading applications, algorithmic trading systems, and digital data analytics tools—have redefined stock market functioning. Primary data were collected through a structured questionnaire administered to 104 respondents, including individual investors, students, salaried employees, and businesspersons with exposure to stock market activities. The questionnaire was designed to assess awareness levels, usage patterns, perceived benefits, and perceived risks related to IT-based stock trading systems. Secondary data were sourced from academic journals, textbooks, stock exchange publications, and authoritative financial websites to establish a strong theoretical and conceptual foundation. The findings of the study indicate that awareness of Information Technology in the stock market is generally high among respondents; however, the depth of understanding varies considerably. Online trading platforms and mobile trading applications are perceived as the most important technological systems, highlighting the growing importance of accessibility and convenience in modern trading practices. A significant proportion of respondents acknowledge that IT has led to substantial improvements in trading speed and operational efficiency, allowing transactions to be executed within seconds. At the same time, a notable segment of investors perceives electronic trading systems as complex, indicating challenges related to digital literacy and platform usability. The study further reveals mixed perceptions regarding the role of real-time data and digital analytics in investment decision-making. While many respondents believe that technology-based tools support better and faster investment decisions, others associate continuous data flow with confusion, impulsive behavior, or increased risk. Awareness of algorithmic and automated trading exists at a basic level, but detailed understanding remains limited, suggesting a gap between technological advancement and investor knowledge. Cybersecurity threats, system failures, and technical errors emerge as the most significant risks associated with IT-driven stock market systems. The increasing dependence on digital infrastructure has heightened concerns regarding data security, system reliability, and market stability. Nevertheless, technological advancements have also strengthened regulatory oversight through automated surveillance systems, improved transparency, and faster settlement cycles. The study concludes that Information Technology has fundamentally reshaped modern stock market practices by enhancing efficiency, transparency, and inclusivity. However, the effective utilization of technology depends on continuous investor education, simplified system design, robust cyber security frameworks, and adaptive regulatory mechanisms. The paper contributes to existing literature by offering an integrated analysis of technological, behavioral, and operational dimensions of modern stock markets, particularly from the perspective of an emerging economy. It provides practical insights for policymakers, stock exchanges, brokerage firms, and investors seeking to balance technological innovation with market stability and investor protection.

Learning What Makes Catalysts Good

Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.

During hydraulic fracturing jobs, engineers must monitor the wellhead pressure and adjust the pumping schedule in real time to avoid screenout, optimize the proppant and fluid amounts, and minimize cost. In this paper, we use machine learning to predict wellhead pressure in real time during hydraulic fracturing. The new algorithm can assist engineers in monitoring and optimizing the pumping schedule. We explored several neural network models. For each hydraulic fracturing stage, we train a machine learning (ML) model with the data from the first several minutes and predict the wellhead pressure for the next several minutes; we then add the data for the next several minutes, train a second ML model and predict the pressure for the next couple of minutes; and so on. We used several performance metrics to compare different models and select the best model for deployment to the Cloud, where a real-time completions platform is developed and hosted. We selected more than 100 hydraulic fracturing stages from several wells completed in the Delaware Basin and tested several ML methods on the historical data. The wellhead pressure can be predicted with an acceptable accuracy by a slightly nonlinear machine learning model. We tested the ML model on the Cloud, where real-time streaming data such as slurry rate and proppant concentration are gathered. The computation is fast enough that a real-time wellhead pressure can be predicted.

The paper investigates the incorporation of emotional AI into Shark Algorithms to enhance trading performance through affective computing. By integrating sentiment analysis, these advanced algorithms are equipped with emotional intelligence to improve the accuracy of price predictions, devise sentiment-driven trading strategies, and optimize risk management techniques. The research highlights that embedding sentiment scores into conventional trading models provides a more nuanced understanding of market behavior, leading to enhanced decision-making processes.Key findings demonstrate that sentiment-enhanced price prediction models outperform traditional methods in capturing market trends, offering a significant edge in forecasting accuracy. The study further reveals the profitability of trading strategies driven by sentiment analysis, as they capitalize on emotional market responses, which are often overlooked by purely quantitative approaches. Additionally, the research emphasizes the effectiveness of sentiment-based position sizing as a tool for risk mitigation, allowing traders to adjust their exposure based on the emotional state of the market, thereby reducing potential losses during periods of heightened volatility.However, the integration of emotional AI into trading algorithms is not without challenges. The paper identifies issues related to the quality and reliability of sentiment data, which can vary widely depending on the source and the methodology used for analysis. Ethical considerations also arise, particularly concerning the potential for market manipulation and the broader impact of emotionally driven trading on financial markets. Technical complexities, such as the integration of real-time sentiment analysis with high-frequency trading systems, present additional hurdles.The study concludes by proposing future research directions, including the development of more sophisticated sentiment analysis techniques that can better capture the nuances of market sentiment. It also suggests advancements in real-time data processing to enhance the responsiveness of trading algorithms. Improved risk management strategies are recommended to address the specific risks associated with sentiment-driven trading. Furthermore, the paper advocates for the establishment of ethical frameworks to guide the use of emotional AI in financial markets, along with long-term studies to assess its impact on market efficiency and stability. The integration of emotional AI into trading algorithms represents a transformative step in financial technology, with the potential to influence market dynamics and trading practices significantly.

In recent years, machine learning has become prevalent in numerous tasks, including algorithmic trading. Stock market traders utilize machine learning models to predict the market’s behavior and execute an investment strategy accordingly. However, machine learning models have been shown to be susceptible to input manipulations called adversarial examples. Despite this risk, the trading domain remains largely unexplored in the context of adversarial learning. In this study, we present a realistic scenario in which an attacker influences algorithmic trading systems by using adversarial learning techniques to manipulate the input data stream in real time. The attacker creates a universal adversarial perturbation that is agnostic to the target model and time of use, which remains imperceptible when added to the input stream. We evaluate our attack on a real-world market data stream and target three different trading algorithms. We show that when added to the input stream, our perturbation can fool the trading algorithms at future unseen data points, in both white-box and black-box settings. Finally, we present various mitigation methods and discuss their limitations, which stem from the algorithmic trading domain. We believe that these findings should serve as a warning to the finance community regarding the threats in this area and promote further research on the risks associated with using automated learning models in the trading domain.

The clinical necessity of lymph node dissection in papillary thyroid carcinoma (PTC) surgery remains contentious. This study compared four logistic regression (LR) models (with distinct feature selection strategies) and four machine learning (ML) models to preoperatively predict lymph node metastasis (LNM) risk in PTC patients, with emphasis on multidimensional evaluation and cross-populational generalizability. Data from 3,175 PTC patients (2021 cohort) were randomly split into training (70%) and testing (30%) subsets, with external validation performed using a Chinese (2024, n = 104) and a Canadian (2019–2022, n = 412) cohort. Twelve predictors were screened, and models were evaluated using metrics of discrimination (AUC), calibration (Brier Score), classification accuracy, and clinical utility. The prevalence of LNM was 34.48%, 36.54%, and 30.10% in the internal, Chinese, and Canadian cohorts, respectively. Among ML models, Random Forest achieved the highest internal AUC (0.767), whereas XGBoost demonstrated superior generalization (external AUCs: 0.785 and 0.725). LR models, particularly BestSubset-GLM, outperformed these ML models with an internal AUC of 0.770 and external AUCs of 0.831 and 0.785. Notably, BestSubset-GLM exhibited high specificity (0.86), precision (0.59), favorable calibration (Brier Score < 0.20), and robust clinical utility across the approximately 15–90% threshold probability range. Extrathyroidal extension, tumor size above 1.00 cm, younger age, and male gender were identified as key LNM risk factors. Bethesda classification and molecular aberrations were integrated into models. BestSubset-GLM balanced parsimony, interpretability, and generalizability, thereby supporting clinical decision-making through dynamic nomograms. Comprehensive evaluation beyond AUC is crucial.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-19345-4.

In the stock market , stock price movements depend on more number of parameters here we are focusing on how to optimize the multiple parameters based on the running result in algorithm trading on historical data. It is a time consuming task in such a large search space for parameters as well as the huge volume of historical data. In this paper a new technique is realized how to optimize multiple parameters using Hadoop MapReduce, this technique utilizes the parallel processing capability. Here we provide complete procedure on how the method is realized and the configurations needed to be considered in the method.

Background/Objective: Subarachnoid hemorrhage (SAH) is associated with high morbidity and mortality rates, necessitating prognostic algorithms to guide decisions. Our study evaluates the use of machine learning (ML) models for predicting 1-month and 1-year mortality among SAH patients using national electronic health records (EHR) system. Methods: Retrospective cohort of 29,274 SAH patients, identified through national EHR system from January 2017 to December 2022, was analyzed, with mortality data obtained from central civil registration system in Türkiye. Variables included (n = 102) pre- (n = 65) and post-admission (n = 37) data, such as patient demographics, clinical presentation, comorbidities, laboratory results, and complications. We employed logistic regression (LR), decision trees (DTs), random forests (RFs), and artificial neural networks (ANN). Model performance was evaluated using area under the curve (AUC), average precision, and accuracy. Feature significance analysis was conducted using LR. Results: The average age was 56.23 ± 16.45 years (47.8% female). The overall mortality rate was 22.8% at 1 month and 33.3% at 1 year. One-month mortality increased from 20.9% to 24.57% (p < 0.001), and 1-year mortality rose from 30.85% to 35.55% (p < 0.001) in the post-COVID period compared to the pre-COVID period. For 1-month mortality prediction, the ANN, LR, RF, and DT models achieved AUCs of 0.946, 0.942, 0.931, and 0.916, with accuracies of 0.905, 0.901, 0.893, and 0.885, respectively. For 1-year mortality, the AUCs were 0.941, 0.927, 0.926, and 0.907, with accuracies of 0.884, 0.875, 0.861, and 0.851, respectively. Key predictors of mortality included age, cardiopulmonary arrest, abnormal laboratory results (such as abnormal glucose and lactate levels) at presentation, and pre-existing comorbidities. Incorporating post-admission features (n = 37) alongside pre-admission features (n = 65) improved model performance for both 1-month and 1-year mortality predictions, with average AUC improvements of 0.093 ± 0.011 and 0.089 ± 0.012, respectively. Conclusions: Our study demonstrates the effectiveness of ML models in predicting mortality in SAH patients using big data. LR models’ robustness, interpretability, and feature significance analysis validate its importance. Including post-admission data significantly improved all models’ performances. Our results demonstrate the utility of big data analytics in population-level health outcomes studies.

Abstract: Stock market forecasting remains a major concern to investors, financial institutions, and traders. Most classical forecasting techniques such as statistical modelling and technical analysis generally fail to account for the complexity and non-linearity of financial markets. Recently, neural networks, a new-age branch of machine learning, have emerged as a powerful weapon for better forecasting in the stock market. Neural networks, especially deep learning models such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), are most suited to learning complex patterns from the historical data and reveal hidden correlations or relationships between different variables which have not been captured by the traditional models. The article discusses the role of neural networks in predicting stock market trends and the applications of these models in financial forecasting. The fundamentals of neural networks are explored, their special applications in stock market prediction, and the advantages they confer over traditional techniques. The objective of this article is to also provide insights into key obstacles, including data quality, overfitting, and interpretability, which can impinge upon the effective use of neural networks in finance. Case studies and simulated situations help present neural networks' use, where they are positioned to enhance stock market prediction output with accuracy and reliability. It seems that future directions that research in this field will take would involve integration with different alternative data sources, with quantum computing anticipated to change the landscape of financial prediction models. The insights provided... Keywords: Neural Networks, Stock Market Predictions, Machine Learning, Financial Forecasting, Trend Analysis, Deep Learning, Predictive Analytics, Data Sciences, and Algorithmic Trading.

The integration of Artificial Intelligence (AI) into financial forecasting has transformed traditional stock market prediction methods. This research paper explores the effectiveness of AI techniques in forecasting stock trends within the Indian stock market over the last few years. We examine AI methodologies, including machine learning (ML), deep learning (DL), and hybrid models applied to the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). By comparing historical data with AI-predicted trends, this study evaluates prediction accuracy and market relevance (Patel et al., 2015). Furthermore, the research outlines existing study gaps and proposes a future scope of integrating AI with behavioral finance and real-time analytics (Chen et al., 2022). The prediction of stock market trends remains a significant challenge due to the stochastic and non-linear nature of financial time series data (Zhang &amp; Zhou, 2020). With the proliferation of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL) techniques, there has been a paradigm shift in the modeling and forecasting of stock price movements (Fischer &amp; Krauss, 2018). This paper presents a comprehensive study on the effectiveness of AI in predicting stock market trends within the Indian financial ecosystem, focusing on a comparative analysis of AI models implemented over the last five years (2018–2023) on major indices and stocks listed on the NSE and BSE. This research evaluates the performance of multiple AI algorithms—including Support Vector Machines (SVM), Random Forests (RF), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and hybrid ensemble models—in forecasting short- and medium-term price trends using historical stock data (Krauss et al., 2017; Chen &amp; He, 2021). Standard evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and classification accuracy are employed to assess model efficacy. The results indicate that while traditional machine learning algorithms offer moderate predictive power, advanced deep learning and hybrid models significantly outperform them, particularly during periods of high market volatility, such as the COVID-19 pandemic and post-lockdown recovery phases (Weng et al., 2018; Jain &amp; Jain, 2021). The study identifies key research gaps, including limited sectoral diversification in datasets, inadequate integration of sentiment and behavioral data, overfitting issues in complex models, and the lack of real-time prediction systems in the Indian context (Gupta &amp; Pathak, 2022). Furthermore, current models often ignore nonquantitative factors such as investor sentiment, macroeconomic indicators, and global events, which can critically impact prediction accuracy (Nassirtoussi et al., 2014). These limitations suggest the need for a more holistic and interdisciplinary approach to AI-driven financial forecasting. The scope for future research includes the development of real-time, adaptive AI systems using high-frequency trading data, the incorporation of behavioral finance through social media and news analytics, and the exploration of quantum computing-based AI models (Liu et al., 2023). From a practical standpoint, the findings of this study offer valuable insights for institutional investors, financial analysts, regulatory bodies, and developers of AIpowered trading platforms. The study concludes that while AI is not a definitive solution for market prediction, it provides a powerful augmentative tool that, when designed with robustness, transparency, and adaptability, can significantly enhance decision-making in India’s fastevolving financial markets.