Real-time Market Research Articles

AI-Driven Optimization of Financial Quantitative Trading Algorithms and Enhancement of Market Forecasting Capabilities

Abstract: The application of artificial intelligence (AI) into financial markets has revolutionised quantitative trading and market forecasting by increasing the efficiency of algorithmic trading, improving the accuracy of market predictions and facilitating real-time market decisions. This paper will provide an overview of the application of Al in the financial markets focusing on the use of machine learning (ML), deep learning (DL) and reinforcement learning (RL) in optimizing the trading algorithms, specifically the capability of Al to process very high data points and complex relationships that other quantitative models are unable to capture. We will discuss trading algorithms such as XGBoost, deep neural networks such as long short-term memory (LSTM) networks and convolutional neural networks (CNNs), how they can outperform traditional quantitative trading models and real-time decision making in stock price prediction, pattern recognition and trading strategy optimisation. We will also look at Al-enhanced predictive models that utilise deep learning and layered models, such as Natural language processing (NLP) sentiment analysis to capture the public sentiment in the market to forecast employing diverse datasets such as historical prices, market volatility, macroeconomic factors and social media sentiment to improve the forecasting accuracy. By going through several experiments and case studies, this paper will shed light on the impact of entrusting quantitative trading and market forecasting decisions to AI for improved performance and reduced errors. There are many challenges ahead but AI plays a constructive role in improving the trading strategies and forecasting market outcomes accurately.

Flexible electricity market clearing zones

Zonal markets and nodal pricing are the dominant designs for liberalized electricity markets. We propose an alternative design that changes zones in each bidding period according to the estimated most efficient dispatch. These flexible electricity market clearing zones consider the grid’s physical constraints to a larger degree than zonal markets but maintain their bidding simplicity and few price areas. We propose a proof-of-concept framework for flexible electricity market clearing zones, including a method to enumerate all zonal configurations. We illustrate the performance of this framework on a case study in the Nordic countries using flow-based market clearing (FBMC), considering a model for the day-ahead market and a real-time balancing market. Our results suggest that flexible electricity market clearing zones on sequential day-ahead and real-time balancing markets achieve costs slightly above nodal stochastic clearing. But, contrary to stochastic clearing, it can guarantee short-term revenue adequacy and cost recovery. Moreover, the flexible market design increases day-ahead market price levels and price variability at the nodal level, particularly in scenarios with high renewable generation, demonstrating its capacity to align price signals with network congestion and real-time supply conditions. Flexible electricity market clearing zones can thus facilitate the integration of renewables by enhancing system adaptability and promoting more efficient resource allocation.

Measurement-Based Locational Marginal Prices for Real-Time Markets in Distribution Systems

Measurement-Based Locational Marginal Prices for Real-Time Markets in Distribution Systems

Revolutionizing Procurement: How AI & Data Analytics Driving Cost Effective Solutions in EPC

This paper, therefore, discusses the revolution brought by AI and Data Analytics in the procurement processes of EPC (Engineering, Procurement and Construction) and how each of the challenges, including cost control, enhanced efficiency and procurement downtime, could be solved using the two technologies. When the use of the procurement models is applied, the following benefits will be realized: supplier selection is facilitated using artificial intelligence, thereby automating the entire process; there is increased accuracy of costs compared to manual estimation, therefore reducing bias. The use of AI will allow better historical and real-time data analysis for procurement needs forecast, supplier evaluation and material cost prediction. Furthermore, machine learning algorithms can automate adjustments depending on real-time market variability, such for instance, a change in the price of the materials or a disruption in the supply chain, as well as execution flexibility regarding the project needs. Data Analytics also adds more weightage to procurement efficiency because it gives detailed analysis of the past and present procurement data. By utilizing state-of-the-art analytical frameworks, top managements are allowed to make powerful supplier analyses in relation to reliability, lead time, and cost of contracts prior to negotiations on contract supplier contracts and before contracting risks. What this study aims to do is to highlight real-life implementations of these technologies and how EPC firms can capitalize on these technologies to drive procurement efficiency, cost reduction, project delivery, and supplier relationship all for efficient project delivery. This paper thus provides a detailed discussion on the MI and TI of implementing AI and DA within procurement in the EPC sector via case studies and examples.

Strategic behaviors of renewable energy generation companies participating in the electricity and carbon coupled markets based on non-cooperative game theory

Renewable energy (RE) can participate in electricity and carbon market trading in China, which can help stimulate the investment of RE. Thus, we propose a bi-level model for non-cooperative game interactions among power generation companies (GenCos), considering the electricity and carbon coupled markets. The upper-level model considers strategic behaviors driven by maximizing profits for GenCos. The lower-level model includes a two-stage electricity market: the day-ahead market and the real-time market; it implements joint clearing of energy and reserves, and continuous trading with competitive auctions in the carbon market based on the clearing results of the electricity market. Simulations are performed based on the actual data of the power system in a certain region of Guangdong Province in China. The results show that electricity and carbon coupled markets can effectively reduce market costs and carbon emissions, improve the society welfare and RE benefits. The uncertain increase in RE output and higher installed capacity reduce the total revenue of the RE in the coupled markets. The initial carbon quota decomposition optimization decision of the coal-fired GenCos and gradual reduction in the initial carbon quota will increase the revenue of the RE GenCos.

Integrating ChatGPT with Salesforce for Real-Time Market Insights on Accounts

This paper explores the integration of ChatGPT, an AI-driven natural language processing model, with Salesforce to enhance sales teams' access to real-time market information about specific accounts. By integrating ChatGPT with Salesforce through API calls, users can fetch the latest market trends, news articles, stock prices, financial data, legal filings, and acquisitions related to specific accounts directly on the Account detail page. This integration not only improves decision-making and customer engagement but also offers a scalable, automated solution that saves time and enhances data accuracy. The paper includes coding examples, architecture diagrams, and use cases to demonstrate the implementation, and it discusses key performance indicators (KPIs) for assessing its effectiveness in real-world applications. Keywords: Salesforce, ChatGPT, AI Integration, Real-Time Data, Account Insights, API, Market Trends, CRM, Natural Language Processing, Automation

Marketing affordance of social media for product innovation: the role of organizational structures

PurposeDespite substantial research attention on the business use of social media in innovation literature, the action-based mechanisms through which social media affect firms' product innovation remain unclear. We address this gap by drawing on the affordance theory and examining the effects of social media applications on product innovations.Design/methodology/approachThe study was based on analyzing data collected through a questionnaire survey of Chinese firms. Partial least squares structural equation modeling was applied to test the hypotheses.FindingsThe results show that social media applications have a positive effect on marketing affordances (including customer behavior pattern discovery, real-time market response and marketing ambidexterity), in turn positively influencing product innovation performance. Organizational structures shape the actualization of the marketing affordance of social media; while organizational formalization positively moderates the relationships between social media application and marketing affordances, organizational centrality weakens these relationships.Practical implicationsManagers will benefit from understanding how social media can be applied to help their firms achieve product innovation. When manufacturing firms engage in social media for product innovation, they may find it worthwhile to focus on marketing actions such as customer behavior pattern discovery, real-time market response and marketing ambidexterity.Originality/valueThis study elucidates the action-based mechanism of marketing affordance by which social media usage improves product innovation performance. This study also highlights the design of organizational structure as an important approach to realizing the potential benefits of social media in product innovation.

The Role of Edge Computing in Driving Real-time Personalized Marketing: a Data-driven Business Perspective

The fast pace of marketing innovation in the digital age has sparked a growing need for instant, personalized conversations with customers. This study investigates the vital significance of edge computing in elevating real-time personalized marketing by reducing latency, improving speed of data processing, and allowing companies to quickly, data-driven decisions. Through a rigorous investigation, the research reports on the ways edge computing solves problems linked to cloud solutions, like bandwidth issues and privacy matters, providing a more effective framework for localized data management. Conducting a mixed-methods study using case studies and quantitative performance metrics, this research showcases important enhancements in consumer engagement and conversion rates amongst enterprises that have implemented edge computing for their personalized marketing efforts. Findings indicate a reduction of 30% in data transfer latency and a 25% higher level of interactions with customers in real time as a result of marketing activities made possible by the edge. The paper wraps up with a look at the innovation of this method in the face of technological innovations and presents recommendations for firms attempting to raise consumer engagement via personalized marketing approaches facilitated by edge computing.

An Optimal Multi-Modal Approach for Stock Market Price Forecasting with Fused Sentiment Analysis for Real Time Data

Abstract: This study presents an innovative fusion-based methodology that integrates real-time stock market technical indicators with news sentiment analysis from financial news feeds to enhance stock selection decisions. The proposed framework employs a Bidirectional Long Short-Term Memory (Bi-LSTM) model for forecasting stock prices and a Deep Neural Network (DNN) used in conjunction with transformer-based model for sentiment classification, both optimized through the incorporation of real-time datasets. To further refine feature selection, Artificial Bee Colony (ABC) and Firefly algorithms are utilized, significantly improving the accuracy of price trend predictions and sentiment analysis. Technical indicators from stock market data are processed using the Bi-LSTM model to predict future stock prices. Concurrently, sentiment data from the Economic Times is pre-processed through Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and pre-trained transformer models to extract key sentiment scores. The ABC algorithm enhances textual feature selection, while the Firefly algorithm reduces skewness in the error distribution of technical indicators, thereby improving the alignment between forecasted and actual stock prices. The fusion mechanism integrates predicted price movements with sentiment analysis to provide actionable insights for stock selection. The decision-making framework classifies stocks based on the consistency between price movements and sentiment: stocks predicted to rise with positive sentiment are labelled as "Sure Selection"; rising prices with negative sentiment are categorized as "Dicey Selection"; declining prices with positive sentiment result in a "Risky Selection"; and negative sentiment with falling prices trigger an "Avoid Selection". The proposed approach achieved a prediction accuracy of 67.00% for the Bi-LSTM model, with a precision of 0.714, recall of 0.695, and an F1 score of 0.677, demonstrating the model's robustness. The mean absolute error (MAE) for the Bi-LSTM model was 4.30, indicating strong predictive performance. The combined use of ABC and Firefly algorithms optimizes both technical and sentiment features, significantly contributing to the overall performance of the prediction model. By fusing multiple data sources and employing advanced optimization techniques, this methodology offers a unique and effective solution for stock market prediction and decision-making

Machine Learning Driven Options Pricing Model for Changing Market Conditions

This paper presents the design and implementation of an Adaptive Options Pricing Model utilizing deep learning techniques to enhance the accuracy of options pricing in volatile financial markets. Traditional models such as the Black-Scholes and Binomial frameworks have served as foundational tools in options pricing; however, they are constrained by assumptions of constant volatility and market efficiency, which often fail to reflect real-world dynamics. To address these limitations, the proposed model incorporates historical pricing data alongside real-time market sentiment derived from news articles and social media. By leveraging advanced deep learning architectures, particularly Long Short-Term Memory (LSTM) networks, the model dynamically adapts to changing market conditions, capturing complex, nonlinear relationships in the data. The performance of the model is evaluated against traditional pricing methods, demonstrating significant improvements in pricing accuracy and responsiveness to market fluctuations. This research contributes valuable insights for traders and risk managers seeking to optimize their strategies in the derivatives market.

Harnessing the Power of AI in Salesforce: An Empirical Study on Enhanced Data Insights and Organizational Efficiency

Abstract: This article examines the integration of Artificial Intelligence (AI) in Salesforce platforms to enhance business analytics and decision-making processes. We explore how AI-powered solutions are revolutionizing data analysis, providing organizations with actionable insights, and improving market responsiveness. The article investigates key AI techniques implemented in Salesforce, including machine learning algorithms for pattern recognition and natural language processing for unstructured data analysis. We analyze the impact of these technologies on identifying trends, forecasting, threat detection, and opportunity identification for sustainable growth. Through case studies and empirical analysis, we demonstrate the effectiveness of AI-driven Salesforce solutions in streamlining data processing, enabling real-time market adaptation, and enhancing organizational efficiency. The article also addresses the technological infrastructure supporting these AI solutions, discussing cloud-based computing resources, integration challenges, and data security considerations. Our findings suggest that AIpowered Salesforce solutions significantly improve business analytics capabilities, allowing companies to make more informed decisions and maintain a competitive edge in rapidly evolving markets. This article contributes to the growing body of knowledge on AI applications in customer relationship management and business intelligence systems

Dynamic Pricing and Energy Management of Virtual Power Plant Based on Source-Charge Uncertainty

With the intermittent, random and volatile distributed renewable energy mass influx into the grid and power market, the power system will face greater challenges. As a small energy autonomous system that integrates distributed generation, energy storage and load, virtual power plant connects the power market and end users, and it is also an important subject of power market reform. Its operating efficiency directly determines the effectiveness of market reform. However, virtual power plant faces the market risk of double uncertainty of internal renewable energy output and load demand during operation, so it needs to optimize the market behavior to protect its own interests. Therefore, the paper takes virtual power plant as the research object, and discusses the dynamic pricing strategy of virtual power plant considering the double uncertainty of source and charge and real-time market linkage from the aspects of theoretical analysis, modeling construction and simulation analysis, to provide reference for decision pricing of virtual power plant and formulation of electricity market reform policy.

Has Real Time Spot Electricity Market in India Impacted Day-Ahead Spot Electricity Market?

Introduction of Real-Time Market (RTM) on June 1st, 2020, marked pivotal advancement in electricity trading in India featuring bi-hourly auctions and power transmission based on temporal blocks. Adherence to operational protocols outlined in Procedure for Scheduling Collective Transactions in RTM along with statutory directives like CERC Power Market Regulations 2010 ensures efficient market operation. Notable features include 15-minute contract trading, clandestine bidding and authorization from SLDC. Day-Ahead Market (DAM) facilitates electricity exchange through double-sided closed auctions ensuring compliance with regulatory protocols. In this study we explore whether Real Time Spot Electricity Market in India has impacted Day-Ahead Spot Electricity Market. Findings of the study provide crucial insights for market participants, stakeholders, open access consumers and electric utilities is India.

An efficient hybrid approach for forecasting real-time stock market indices

The stock market’s volatility, noise, and information overload necessitate efficient prediction methods. Forecasting index prices in this environment is complex due to the non-linear and non-stationary nature of time series data generated from the stock market. Machine learning and deep learning have emerged as powerful tools for identifying financial data patterns and generating predictions based on historical trends. However, updating these models in real-time is crucial for accurate predictions. Deep learning models require extensive computational resources and careful hyperparameter optimization, while incremental learning models struggle to balance stability and adaptability. This paper proposes a novel hybrid bidirectional-LSTM (H.BLSTM) model that combines incremental learning and deep learning techniques for real-time index price prediction, addressing these scalability and memory challenges. The method utilizes both univariate time series derived from historical index prices and multivariate time series incorporating technical indicators. Implementation within a real-time trading system demonstrates the method’s effectiveness in achieving more accurate price forecasts for major stock indices globally through extensive experimentation. The proposed model achieved an average mean absolute percentage error of 0.001 across nine stock indices, significantly outperforming traditional models. It has an average forecasting delay of 2 s, making it suitable for real-time trading applications.

Comparison between Blockchain P2P Energy Trading and Conventional Incentive Mechanisms for Distributed Energy Resources—A Rural Microgrid Use Case Study

Peer-to-Peer (P2P) energy trading is a new financial mechanism that can be adopted to incentivize the development of distributed energy resources (DERs), by promoting the selling of excess energy to other peers on the network at a negotiated rate. Current incentive programs, such as net metering (NEM) and Feed-in-Tariff (FiT), operate according to a centralized policy framework, where energy is only traded with the utility, the state-owned grid authority, the service provider, or the power generation/distribution company, who also have the upper hand in deciding on the rates for buying the excess energy. This study presents a comparative analysis of three energy trading mechanisms, P2P energy trading, NEM, and FiT, within a rural microgrid consisting of two prosumers and four consumers. The microgrid serves as a practical testbed for evaluating the economic impacts of these mechanisms, through simulations considering various factors such as energy demand, production variability, and energy rates, and using key metrics such as economic savings, annual energy bill, and wasted excess energy. Results indicate that while net metering and FiT offer stable financial returns for prosumers, P2P trading demonstrates superior flexibility and potentially higher economic benefits for both prosumers and consumers by aligning energy trading with real-time market conditions. The findings offer valuable insights for policymakers and stakeholders seeking to optimize rural energy systems through innovative trading mechanisms.

Game Theory for Predicting Stocks’ Closing Prices

We model the financial markets as a game and make predictions using Markov chain estimators. We extract the possible patterns displayed by the financial markets, define a game where one of the players is the speculator, whose strategies depend on his/her risk-to-reward preferences, and the market is the other player, whose strategies are the previously observed patterns. Then, we estimate the market’s mixed probabilities by defining Markov chains and utilizing its transition matrices. Afterwards, we use these probabilities to determine which is the optimal strategy for the speculator. Finally, we apply these models to real-time market data to determine its feasibility. From this, we obtained a model for the financial markets that has a good performance in terms of accuracy and profitability.

Electric vehicle aggregator as demand dispatch resources: Exploring the impact of real-time market performance on day-ahead market

As the integration of variable renewable energy sources (VREs) into power grids escalates, effectively managing the unpredictability of VRE generation becomes crucial. Electric vehicles, utilized as distributed energy storage, emerge as a potential solution. However, it remains unclear whether electric vehicle aggregators (EVAs), acting as demand dispatch resources, have a favorable impact on the overall electricity market, especially when considering their performance in real-time markets during day-ahead energy allocations. Therefore, this paper proposes a decentralized clearing mechanism (DCM) relying on an multi-attribute evaluation model of EVAs for optimal bidding strategies in the day-ahead market. The EVAs multi-attribute evaluation model employs an integration of the Entropy-Analytic Hierarchy Process, allowing for the simultaneous consideration of real-time performance and day-ahead bidding prices of EVAs. Consequently, the decentralized clearing mechanism supports strategic electricity planning in the day-ahead market by incorporating the actual market performance of EVAs. Furthermore, a real-time market bidding model is developed to balance dynamic mismatches between VRE generation and demand, aiming to maximize profits through optimized real-time commitment and real-time dispatch. System simulations on the IEEE39 bus system demonstrate that the DCM enhances overall profits by 18.69% compared to the centralized clearing mechanism.

A two-step optimization model for virtual power plant participating in spot market based on energy storage power distribution considering comprehensive forecasting error of renewable energy output

As a complement to the medium and long-term market, the spot market plays an important role in maintaining the security and stability of the power grid. However, as spot trading is more proximate to the actual operation of the power system, the virtual power plant (VPP) is exposed to greater volatility in renewable energy output as well as market prices. To enhance its market competitiveness, this paper constructs a two-step optimization model for VPP participation in the spot market. Based on long-term transaction, the energy storage power is distributed based on the comprehensive forecasting error of renewable energy output. This makes the VPP obtain the maximum profit in the day-ahead market. The trading deviation penalty is reduced by adjusting the energy storage operation plan in the real-time market. The conditional value at risk is used to measure the risk of a trading strategy. The profit of VPP participation in the spot market is analyzed under different risk levels, thus providing a basis for decision makers with different risk appetites. The results show that: (1) The sensitivity of the growth rates of EtVaRαand EtCVaRα to risk level shows an opposite trend. And there is an excess of arbitrage power at low risk level. (2) Although the total cost decreases as the risk level increases, the actual profit in the spot market tends to increase and then decrease. The risk level of the VPP should be set at about 0.4 to make full use of energy storage and obtain the maximum market benefit. (3) The energy storage power can be distributed more accurately according to the forecasting error of renewable energy. This not only enhances its arbitrage ability, but also ensures its regulation ability, thus improving the overall benefit of VPP at different risk levels. (4) The sensitivity of the trend in the unit profit of traded quantity in the spot market to the trading proportion in the medium and long-term market is not monotonic.

I genuinely recognize you: how technological engagement and privacy increase the online purchase intent of consumers

Purpose The purpose of the study was to explore the purchase intention of online consumers by proposing and validating a model supported by exhaustive reviews from top-rated journals, where digital technology, consumer privacy, consumer engagement and online advertising were the extracted constructs influencing consumer learning on digital platforms and finally evaluating the purchase intention of online consumers. Design/methodology/approach A questionnaire representing these constructs was then sent to the 470 respondents on LinkedIn, and then designations like business heads, managers and faculty from educational institutions were selected using a stratified sampling technique and, finally, PLS-SEM robust computation standards aided in research model assessment and validation. Findings Results predicted that the variance explained by individual independent constructs defines consumer privacy as a priority for companies, followed by online advertising, consumer engagement and digital technology while measuring the final purchase intent for online consumption. Also, with dynamism in consumer sentiments and a rapidly changing technological environment, the consumer’s digital behaviour may differ in the coming future in relation to their online purchase intent. Research limitations/implications Current research anticipates that the final online purchase intent of consumers has been vividly covered by our independent constructs, but an unexplained R2 of 31% still promotes prospects related to the existing research. Furthermore, India has a huge rural population that, with a lack of money, has a complex behavioural mindset due to religious issues. Practical implications It is important to note that in a real-time market, a better understanding of the duality of persuasive and smart technology and the evaluation of the performance of social media helps in deciding the final online consumer intent. Originality/value The need for digital transformation has become an essential necessity for companies while managing the expectations and needs of the fastest-growing online consumers.

Integrated energy cluster hierarchical regulation technology considering demand response

Integrated Energy Cluster (IEC), the regional aggregation of integrated energy systems (IES), has accumulated plenty of dispatchable resources with the development of energy market. This, while significantly providing system Demand Response (DR) potential, also complicates the interaction of the IEC with the main grid and increases the difficulty of system scheduling. To address this issue, this paper proposes a Reinforcement Learning-driven multi-agent hierarchical regulation framework that makes full use of DR to maximize the benefits of both IEC and main grid. Firstly, in the context of the DR market, a mechanism for IECs to bid in the real-time DR market is proposed. Furthermore, an "IEC-main network" hierarchical regulation model taking account of DR is established to minimize the IEC operation cost and maximize the societal benefit. Moreover, an optimization algorithm utilizing Deep Deterministic Policy Gradient (DDPG) with Multi-process (MP) and Priority Experience Replay (PER) mechanism is proposed to allow adaptability to high-latitude and large-scale applications. In case study, the proposed model and algorithm is tested on an 8-node system and a 24-node system. The result indicates that the hierarchical regulation model considering DR can improve the system economy by 2.59 % than that without DR and the improved DDPG algorithm can enhance training effectiveness in comparison with DDPG and PER-DDPG.