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With the rapid growth of social media (forums, blogs, social networks, etc.) entities like polling organizations, advertising companies, as well as democratic organizations are seeking to discover the opinions of their audiences more than ever. Analyzing such information helps them to perform operations like production monitoring, market forecasting, and civil society demands with high accuracy. Besides these entities, individuals/customers also choose from other users' feedback. The most basic processing that takes place in the field of sentiment analysis is to classify a text into a positive or negative class. Yet, given to wide usage of image-based social media, there have been limited attempts to deduce emotions from images or videos. In this paper, a bimodal sentiment analysis is presented by combining visual and textual information. In the proposed method, the image is sentimentally captioned by an encoder-decoder network, first. To achieve this goal, a sentiment image captioning method has been used to generate positive, negative, or neutral captions for each input image. Then, the generated captions and the user's published text are sent to the relation network and then the final sentiment label is predicted. In this one-shot learning approach, only one image is used to predict a sentiment, without any training. Results show the proposed method can compete with the State-of-the-Art methods in the field of visual sentiment analysis. In this respect, the proposed method achieves 70% accuracy on TWITTER and 75% accuracy on MVSA datasets, which can be regarded as a champion considering that this method is a one-shot approach. [ABSTRACT FROM AUTHOR]

Quantum technologies (QTs) hold the promise to transform a wide range of industries, such as computing, communications, finance, healthcare, defense, space, and beyond. Of the various QTs, the most relevant is presently quantum computing (QC), of significant projected market potential, with some estimates forecasting it to reach many billion dollars over the next few years. There are, however, risks to factor, as highlighted in this perspective, the most relevant technical, economic, and societal risks. Those financial and societal are projected to become increasingly relevant phased with the progressing solution of the technical issues. The synergy with artificial intelligence comes with further opportunities as development can be faster and more effective, but also increases risks. AI presents numerous opportunities, but it also comes with several risks and challenges. Some of the major risks associated with AI include bias and fairness, transparency and explainability, job displacement, security concerns, ethical concerns, privacy issues, lack of regulation and standards, and exponential growth and unintended consequences. Balancing the advantages of AI-driven quantum technologies with associated risks presents a significant challenge. It necessitates careful consideration of ethical, economic, and technical aspects to ensure that these technologies are developed and deployed in a manner that is beneficial and equitable for everyone. [ABSTRACT FROM AUTHOR]

The global interest in market prediction has driven the adoption of advanced technologies beyond traditional statistical models. This paper explores the use of machine learning and deep learning techniques for stock market forecasting. We propose a comprehensive approach that includes efficient feature selection, data preprocessing, and classification methodologies. The wavelet transform method is employed for data cleaning and noise reduction. Feature selection is optimized using the Dandelion Optimization Algorithm (DOA), identifying the most relevant input features. A novel hybrid model, 3D-CNN-GRU, integrating a 3D convolutional neural network with a gated recurrent unit, is developed for stock market data analysis. Hyperparameter tuning is facilitated by the Blood Coagulation Algorithm (BCA), enhancing model performance. Our methodology achieves a remarkable prediction accuracy of 99.14%, demonstrating robustness and efficacy in stock market forecasting applications. While our model shows significant promise, it is limited by the scope of the dataset, which includes only the Nifty 50 index. Broader implications of this work suggest that incorporating additional datasets and exploring different market scenarios could further validate and enhance the model's applicability. Future research could focus on implementing this approach in varied financial contexts to ensure robustness and generalizability. [ABSTRACT FROM AUTHOR]

In this paper, the self‐monitoring learning model FinBERT is used to identify text emotions, and the sliding time window time‐lagged cross‐correlation (WTLCC) method is utilized to screen Baidu Index keywords for the Shanghai Stock Exchange Index and 18 A‐share listed companies. There are five different types of indicators constructed: news media sentiment, public attention, investor sentiment, investor sentiment disagreement, and media sentiment disagreement. To accurately describe the structure of sentimental contagion, this paper combines graph neural network to learn and output the sentimental contagion graph, and then constructs multivariable time series forecasting with graph neural networks (MTGNN) volatility forecasting model, which can extract the spatial–temporal dependence of variables in pairs. The results show that MTGNN model possesses the highest forecasting accuracy, which performs 30.30% lower on average across four evaluation indicators for Shanghai Stock Exchange Index than temporal pattern attention–long short‐term memory model, which ranks second. For all of the models considered in this paper, adding sentimental contagion mechanism can significantly improve the volatility forecasting accuracy. The error of MTGNN is reduced the most, with a 15.21% average reduction for the Shanghai Stock Exchange Index. The contagion relationship among media reports, investor sentiment, and attention can help provide new ideas for enhancing the precision of volatility forecasting from the public opinion environment in the financial market. [ABSTRACT FROM AUTHOR]

This study systematically reviewed the literature on using the Google Search Volume Index (GSVI) as a proxy variable for investor attention and stock market movements. We analyzed 56 academic studies published between 2010 and 2021 using the Web of Sciences and ScienceDirect databases. The articles were classified and synthesized based on the selection criteria for building the GSVI: keywords of the search term, market region, and frequency of the data sample. Next, we analyze the effect of returns, volatility, and trading volume on the financial variables. The main results can be summarized as follows. (1) The GSVI is positively related to volatility and trading volume regardless of the keyword, market region, or frequency used for the sample. Hence, increasing investor attention toward a specific financial term will increase volatility and trading volume. (2) The GSVI can improve forecasting models for stock market movements. To conclude, this study consolidates, for the first time, the research literature on GSVI, which is highly valuable for academic practitioners in the area. [ABSTRACT FROM AUTHOR]

Stock market forecasting remains a difficult problem in the economics industry due to its incredible stochastic nature. The creation of such an expert system aids investors in making investment decisions about a certain company. Due to the complexity of the stock market, using a single data source is insufficient to accurately reflect all of the variables that influence stock fluctuations. However, predicting stock market movement is a challenging undertaking that requires extensive data analysis, particularly from a big data perspective. In order to address these problems and produce a feasible solution, appropriate statistical models and artificially intelligent algorithms are needed. This paper aims to propose a novel stock market prediction by the following four stages; they are, preprocessing, feature extraction, improved feature level fusion and prediction. The input data is first put through a preparation step in which stock, news, and Twitter data (related to the COVID-19 epidemic) are processed. Under the big data perspective, the input data is taken into account. These pre-processed data are then put through the feature extraction, The improved aspect-based lexicon generation, PMI, and n-gram-based features in this case are derived from the news and Twitter data, while technical indicator-based features are derived from the stock data. The improved feature-level fusion phase is then applied to the extracted features. The ensemble classifiers, which include DBN, CNN, and DRN, were proposed during the prediction phase. Additionally, a SI-MRFO model is suggested to enhance the efficiency of the prediction model by adjusting the best classifier weights. Finally, SI-MRFO model's effectiveness compared to the existing models with regard to MAE, MAPE, MSE and MSLE. The SI-MRFO accomplished the minimal MAE rate for the 90th learning percentage is approximately 0.015 while other models acquire maximum ratings. [ABSTRACT FROM AUTHOR]

In this paper, we present a data-driven approach to forecasting stock prices in the Moroccan Stock Exchange. Our study tests three predictive models: ARIMA, LSTM, and transformers, applied to the historical stock price data of three prominent credit companies (EQD, LES, and SLF) listed on the Casablanca Stock Exchange. We carefully selected and optimized hyperparameters for each model to achieve optimal performance. Our results showed that the LSTM model achieved high accuracy, with R-squared values exceeding 0.99 for EQD and LES and surpassing 0.95 for SLF. These findings highlighted the effectiveness of LSTM in stock price forecasting. Our study offers practical insights for traders and investors in the Moroccan Stock Exchange, demonstrating how predictive modeling can aid in making informed decisions. This research contributes to advancing stock market forecasting in Morocco, providing valuable tools for navigating the Casablanca Stock Exchange. [ABSTRACT FROM AUTHOR]

The use of both machine learning and deep learning in forecasting the stock market has attracted the interest of finance, as it holds the promise of better and more efficient prediction of stock market trends. With the markets becoming more intricate and fluid with every passing moment, relying on sophisticated predictive models becomes indispensable. This work is intended to investigate how good machine learning versus deep learning can be for stock market forecasts — based on their ability to predict stock market trends — and analyze techniques' effectiveness inside each methodological field. We compared the exploration of advances in long short-term memory with the traditional methods that demonstrate improved accuracies in predicting stock markets. Therefore, this study attempts to place itself within the framework of the discussion about using artificial intelligence for financial decisions by contributing new knowledge. Indeed, the complexity and unpredictability of stock market dynamics necessitate a diverse range of data sources for accurate prediction, and this research aims to shed light on the potential of integrating advanced technologies to enhance forecasting capabilities within the finance sector. [ABSTRACT FROM AUTHOR]

A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy. [ABSTRACT FROM AUTHOR]

Software-based technical indicators have been widely used for the stock market forecasting, aiming to predict market direction. Even though many algorithms for the software based technical indicators are presented, there are almost no hardware implementations reported in the literature. In this paper, the hardware implementation is presented for three commonly used technical indicators: Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), and Aroon. Latency evaluation is conducted for Bitcoin and Ethereum within a single-day timeframe, utilizing the Xilinx Zynq-7000 programmable SoC XC7Z020-CLG484-1 platform. Additionally, various hardware/software (HW/SW) partitioning strategies are explored to leverage the flexibility of software alongside the performance advantages of hardware via the Zynq SoC platform. The results show that the best performing technical indicator is MACD with a speedup of 30 times over its software only counterpart. Furthermore, a hybrid design integrating multiple technical indicators is proposed, pairing MACD with RSI due to their competitive throughput values, differing by only 0.38 microseconds. This hybrid approach capitalizes on the parallel processing capabilities of hardware, enabling multiple systems to operate simultaneously. • Proposed SoC framework boosts HFT system efficiency. • HW/SW partitioning on Zynq-7000 SoC enhances flexibility. • MACD, RSI, Aroon indicators implemented for forecasting. • MACD outperforms others on ZYNQ SoC, 30x faster than software. • MACD and RSI combination optimal for hybrid designs. [ABSTRACT FROM AUTHOR]

Stock market forecasting is done by analyzing multivariate financial time series generated through technical analysis. However, high-dimensional data deteriorates the prediction performance due to irrelevant features that lead to higher computational costs. Feature selection is used to reduce data dimensionality and select the most informative features. A two-stage hybrid feature selection method is proposed to improve the performance of the forecasting model. In the first stage, a way to aggregate multiple filter methods is introduced as Multi-Filter Feature Selection (MFFS). Three filter methods are used for MFFS to scan the dataset from different aspects. In the second stage, Levy's Flight-based Chicken Swarm Optimization (LFCSO) is proposed. Levy's flight is introduced to update the position of Chickens to handle local optima and early convergence. The proposed MFFS reduces the computational cost by filtering the ambiguous features with reduced computational load for the second stage. Deep learning models are used for forecasting using a reduced feature set. Extensive experiments have been performed with three stock indices. The proposed model is assessed against the feature subsets obtained under different scenarios. Performance validation is done by comparing the proposed model and the existing work based on various performance metrics. The experimental result shows that the proposed model outperforms the existing models. [ABSTRACT FROM AUTHOR]

Owing to the dynamic nature of the financial industry, determining accurate stock market forecasts remains a significant challenge. Traditional forecasting methods often struggle to capture the intricate and volatile dynamics of stock price movements. Similarly numerous strategies for stock market prediction have been presented, precise prediction in this field still requires attention. Based on this insight, a novel sequential training model is proposed by adopting the optimal feature selection procedure. In order to determine stock price predictions, primarily financial Nifty data is obtained from the corresponding source. After acquiring financial data, the feature extraction phase is used to extract features from fundamental analysis, such as the Relative Strength Index, Rate of Change, Average True Range, and Exponential Moving Average. Additionally, statistical characteristics such as mean, standard deviation, variance, skewness, and kurtosis are derived from the stock market data. In order to select parameters, the fitness dependent randomised tunicate swarm optimization technique is utilized after the features have been retrieved. Feature selection improves the deep learning process and increases prediction capability by selecting the most important variables and eliminating irrelevant features. A novel sequential training technique is introduced aimed at forecasting stock market trends by leveraging the chosen features. The suggested approach undergoes comprehensive testing, evaluating its predictive capability using accuracy, precision, and recall metrics, implemented towards enhancing future stock price forecasts. [ABSTRACT FROM AUTHOR]

The electricity market in Spain holds significant importance in the nation's economy and sustainability efforts due to its diverse energy mix that encompasses renewables, fossil fuels, and nuclear power. Accurate energy price prediction is crucial in Spain, influencing the country's ability to meet its climate goals and ensure energy security and affecting economic stakeholders. We have explored how leveraging advanced GPT tools like OpenAI's ChatGPT to analyze energy news and expert reports can extract valuable insights and generate additional variables for electricity price trend prediction in the Spanish market. Our research proposes two different training and modelling approaches of generative pre-trained transformers (GPT) with specialized news feeds specific to the Spanish market: in-context example prompts and fine-tuned GPT models. We aim to shed light on the capabilities of GPT solutions and demonstrate how they can augment prediction models by introducing additional variables. Our findings suggest that insights derived from GPT analysis of electricity news and specialized reports align closely with price fluctuations post-publication, indicating their potential to improve predictions and offer deeper insights into market dynamics. This endeavor can support informed decision-making for stakeholders in the Spanish electricity market and companies reliant on electricity costs and price volatility for their margins. [ABSTRACT FROM AUTHOR]

This study presents an innovative financial time series analysis approach by integrating Complete Ensemble Empirical Mode Decomposition (CEEMDAN) with the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model. The primary contribution of the research lies in significantly enhancing the predictive accuracy and understanding of the dynamics governing major stock indices. CEEMDAN adaptively decomposes complex financial time series into intrinsic mode functions (IMFs), a technique that has yet to be extensively utilized in this domain. IMFs are combined with ARIMAX's predictive proficiency, which accounts for the influence of historical trends and external factors. Our study showcases an R² of 0,93, aligning with some of the high-performing models in the literature. However, the unique strength of our model lies in its lag-free predicting of the DJIA, effectively mirroring its volatility and major movements with high fidelity, making it highly practical for financial applications. [ABSTRACT FROM AUTHOR]

The development of machine learning based models to predict the movement of a financial market has been a challenging problem due to the low signal-to-noise ratio under the effect of an efficient market. Although, researchers have developed neural network based predictive models to address this issue, the selection of an appropriate neural architecture is seldom addressed. This study, therefore, proposes a new framework to evolve efficacious yet parsimonious neural architectures for the movement prediction of stock market indices using technical indicators as inputs. The proposed approach formulates the neural architecture search as a multi-criteria optimization problem to balance the efficacy with the complexity of architectures. In addition, the implications of different underlying trading tendencies, which may be present in the pre-COVID and peri-COVID time periods, are investigated. An ϵ -constraint framework is proposed as a remedy to extract remaining concordant information from the possibly partially conflicting pre-COVID data. Further, a new search paradigm, two-dimensional swarms (2DS) is proposed for the multi-criteria neural architecture search, which explicitly integrates sparsity as an additional search dimension in particle swarms. A detailed comparative evaluation of the proposed approach is carried out by considering genetic algorithm and several combinations of empirical neural design rules with a filter-based feature selection method (mRMR) as baseline approaches. The results of this study convincingly demonstrate that the proposed approach can evolve comparatively efficient and parsimonious networks. [ABSTRACT FROM AUTHOR]

This article employs an Empirical Mode Decomposition (EMD)-based multiresolution causality approach to explore the scale-by-scale interconnectedness between Bitcoin and the stock markets of Gulf Cooperation Council (GCC) countries. EMD is utilized to decompose signals into intrinsic mode functions (IMFs), which delineate variations across different frequency scales, thus facilitating the identification of distinct oscillations and trends within the signals. The study reveals a significant positive connectedness between Bitcoin and most GCC stock markets, albeit with some variations observed across countries and time periods. Employing multiresolutional causal analysis through EMD provides a valuable framework for examining the nonlinear relationships among financial assets, offering insights into Bitcoin's potential as a diversification tool in certain periods within GCC stock markets. • A novel EMD-based multiresolutional causality paradigm is introduced. • This tool is employed to study the multiscale connectedness bitcoin-GCC stock markets. • We identify significant links between most GCC stock markets and Bitcoin • We highlight Bitcoin's role as an expanding variable in GCC stock market forecasting. [ABSTRACT FROM AUTHOR]

Stock market prediction is a subject of great interest within the finance industry and beyond. In this context, our research investigates the use of reinforcement learning through implementing the double deep Q-network (DDQN) alongside technical indicators and sentiment analysis, utilizing data from Yahoo Finance and StockTwits to forecast NVIDIA's short-term stock movements over the dynamic and volatile period from 2 January 2020, to 21 September 2023. By incorporating financial data, the model's effectiveness is assessed in three stages: initial reliance on closing prices, the introduction of technical indicators, and the integration of sentiment analysis. Early findings showed a dominant buy tendency (63.8%) in a basic model. Subsequent phases used technical indicators for balanced decisions and sentiment analysis to refine strategies and moderate rewards. Comparative analysis underscores a progressive increase in profitability, with average profits ranging from 57.41 to 119.98 with full data integration and greater outcome variability. These results reveal the significant impact of combining diverse data sources on the model's predictive accuracy and profitability, suggesting that integrating sentiment analysis alongside traditional financial metrics can significantly enhance the sophistication and effectiveness of algorithmic trading strategies in fluctuating market environments. [ABSTRACT FROM AUTHOR]

Applications for the stock market that can be relied upon to provide the information regular and professional investors need to make better-informed purchases are a boon to both. A well-thought-out sales approach may help buyers mitigate risk, zero in on the companies most likely to provide the highest returns, and increase their chances of making a purchase. Due to numerous interrelationships between various company values, executing stock market research utilizing batch processing methods provides an exceptionally tough task. Technology advancements, such as the licensing of global processes, usher in a new age for stock market forecasting. The present market climate has increased the significance of data applications. An interesting and new contribution made by this work is the proposal of a deep learning-based resilience time series model for forecasting future market values. By analyzing financial time series, this study aimed to build a sophisticated strategy for forecasting stock market prices. The use of artificial intelligence (AI) to forecast future market behavior is one of the most intriguing developments in this generation of technological breakthroughs. Particularly promising results have been found when applying deep learning techniques to the task of predicting the actions of market players. In this paper, we propose a method for forecasting the final prices of publicly traded firms like Tesla, Inc. and Apple, Inc. by merging a convolutional neural network with long short-term memory (LSTM). These two methods were both developed with the help of deep learning techniques. The time frame covered by statistics used in these projections is two years. To evaluate the performance of our deep learning models, we compared their market predictions using several different metrics, including mean squared error (MSE), root mean squared error (RMSE), normalized root mean squared error (NRMSE), and Pearson's R. When compared to the options, the CNN-LSTM deep learning algorithm did the best. When comparing convolution neural networks CNN-LSTM model to the normal LSTM and a simpler deep learning variant, the latter showed to be more effective at forecasting stock market values. This was the case when examining all three variants together. [ABSTRACT FROM AUTHOR]

The intricacies and dynamism of financial markets pose challenges to models seeking to comprehensively capture the multitude of factors influencing stock price movements. As such, there remains room for improvement in forecasting accuracy. In response, we introduce a novel approach that unifies the Root Mean Square Error (RMSE), loss functions of Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). By concurrently optimizing their RMSE loss functions, our novel approach takes use of the capabilities of LSTM for learning long-term time series relationships and CNN for extracting deep features from data. To maximize the efficacy of each model branch within this unified framework, we split the training set into two different representations, one consisting of standard time series data and the other of standard picture data. We compare our proposed model to others in the field to demonstrate its viability, particularly Backpropagation (BP), LSTM, CNN, and a fusion LSTM-CNN model. Experimental evaluations conducted on three diverse datasets—Development Bank, Stock Connect Index (SCI), and Composite Index (CI)—validate the robust predictive performance and applicability of our joint RMSE loss LSTM-CNN model, thus showcasing its potential in financial forecasting. [ABSTRACT FROM AUTHOR]

In light of the existing challenges in capturing short-term fluctuations and achieving accurate predictions in stock market time series data, this research presents the "Dual-Attention MDWT-CVT-LSTM," a revolutionary financial time series forecasting model. This model makes use of a dual-attention mechanism in conjunction with a Variant Transformation-Gated Long Short-Term Memory (LSTM) network. The approach relies on the Multilevel Discrete Wavelet Transform (MDWT) to separate stock market index sequences into high-frequency and low-frequency components. Furthermore, a transformative gating mechanism is incorporated into the LSTM, featuring fusion gated units to create the VT-LSTM, effectively addressing abrupt short-term information changes. Within the dual-attention network, VT-LSTM is combined with one-dimensional temporal convolution (Conv1D) to extract spatial and temporal features of data at different frequencies. This facilitates the prediction of various sub-sequences, enabling multilevel and multi-path forecasting research. The experimental evaluation uses diverse models on stock market index datasets and individual stock datasets. The results show the superior prediction accuracy of the proposed model compared to alternative methods, underscoring its practical viability in stock market forecasting applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel integration of Machine Learning (ML) models with Monte Carlo simulations to enhance financial forecasting and risk assessments in dynamic market environments. Traditional financial forecasting methods, which primarily rely on linear statistical and econometric models, face limitations in addressing the complexities of modern financial datasets. To overcome these challenges, we explore the evolution of financial forecasting, transitioning from time-series analyses to sophisticated ML techniques such as Random Forest, Support Vector Machines, and Long Short-Term Memory (LSTM) networks. Our methodology combines an ensemble of these ML models, each providing unique insights into market dynamics, with the probabilistic scenario analysis of Monte Carlo simulations. This integration aims to improve the predictive accuracy and risk evaluation in financial markets. We apply this integrated approach to a quantitative analysis of the SPY Exchange-Traded Fund (ETF) and selected major stocks, focusing on various risk-reward ratios including Sharpe, Sortino, and Treynor. The results demonstrate the potential of our approach in providing a comprehensive view of risks and rewards, highlighting the advantages of combining traditional risk assessment methods with advanced predictive models. This research contributes to the field of applied mathematical finance by offering a more nuanced, adaptive tool for financial market analyses and decision-making. [ABSTRACT FROM AUTHOR]

The stock market serves as a macroeconomic indicator, and stock price forecasting aids investors in analysing market trends and industry dynamics. Several deep learning network models have been proposed and extensively applied for stock price prediction and trading scenarios in recent times. Although numerous studies have indicated a significant correlation between market sentiment and stock prices, the majority of stock price predictions rely solely on historical indicator data, with minimal effort to incorporate sentiment analysis into stock price forecasting. Additionally, many deep learning models struggle with handling the long-distance dependencies of large datasets. This can cause them to overlook unexpected stock price fluctuations that may arise from long-term market sentiment, making it challenging to effectively utilise long-term market sentiment information. To address the aforementioned issues, this investigation suggests implementing a new technique called Long-term Sentiment Change Enhanced Temporal Analysis (LEET) which effectively incorporates long-term market sentiment and enhances the precision of stock price forecasts. The LEET method proposes two market sentiment index estimation methods: Exponential Weighted Sentiment Analysis (EWSA) and Weighted Average Sentiment Analysis (WASA). These methods are utilized to extract the market sentiment index. Additionally, the study proposes a Transformer architecture based on ProbAttention with rotational position encoding for enhanced positional information capture of long-term emotions. The LEET methodology underwent validation using the Standard & Poor's 500 (SP500) and FTSE 100 indices. These indices accurately reflect the state of the US and UK equity markets, respectively. The experimental results obtained from a genuine dataset demonstrate that this method is superior to the majority of deep learning network architectures when it comes to predicting stock prices. [ABSTRACT FROM AUTHOR]

This research aims at applying the Artificial Organic Network (AON), a nature-inspired, supervised, metaheuristic machine learning framework, to develop a new algorithm based on this machine learning class. The focus of the new algorithm is to model and predict stock markets based on the Index Tracking Problem (ITP). In this work, we present a new algorithm, based on the AON framework, that we call Artificial Halocarbon Compounds, or the AHC algorithm for short. In this study, we compare the AHC algorithm against genetic algorithms (GAs), by forecasting eight stock market indices. Additionally, we performed a cross-reference comparison against results regarding the forecast of other stock market indices based on state-of-the-art machine learning methods. The efficacy of the AHC model is evaluated by modeling each index, producing highly promising results. For instance, in the case of the IPC Mexico index, the R-square is 0.9806, with a mean relative error of 7 × 10 − 4 . Several new features characterize our new model, mainly adaptability, dynamism and topology reconfiguration. This model can be applied to systems requiring simulation analysis using time series data, providing a versatile solution to complex problems like financial forecasting. [ABSTRACT FROM AUTHOR]

Stock market forecasting is a significant task, and investing in the stock marketplace is a significant part of monetary research due to its high risk. Therefore, accurate forecasting of stock market analysis is still a challenge. Due to stable and volatile data, stock market forecasting remains a major challenge for investors. Recent machine learning (ML) models have been able to reduce the risk of stock market forecasting. However, diversity remains a key challenge in developing better erudition models and extracts more intellectually priceless qualities to auxiliary advanced predictability. In this paper, we propose an efficient strategy optimization using a hybrid ML model for stock market prediction (HM-SMP). The first contribution of the proposed HM-SMP model is to introduce chaos-enhanced firefly bowerbird optimization (CEFBO) algorithm for optimal feature selection among multiple features which reduce the data dimensionality. Second, we develop a hybrid multi-objective capuchin with a recurrent neural network (HC-RNN) for the prediction of the stock market which enhances the prediction accuracy. We use supervised RNN to predict the closing price. Finally, to estimate the presence of the proposed HM-SMP model through the benchmark, stock market datasets and the performance can be compared with the existing state-of-the-art models in terms of accuracy, precision, recall, and F-measure. [ABSTRACT FROM AUTHOR]

The article presents a methodology for determining the value of the expansion coefficient of a reconsolidated caving zone in the context of forecasting the rise in underground mine water levels and consequent surface subsidence caused by the process of flooding the closed coal mines. The paper also provides a brief characterisation of analytical predictive models regarding surface subsidence during the process of flooding coal mines. In order to describe the vertical deformation of the reconsolidated porous rock mass in the caving zone, a linear-elastic medium of Biot was utilised. The conducted theoretical calculations demonstrate a high agreement with the results obtained through the identification of the expansion coefficient parameter based on the analysis of in-situ subsidence measurements in Dutch and German mining areas. The proposed methodology was applied to a real case study involving the forecasting of the impact of the flooding process on the underground workings of the German Ibbenbüren mine. The article constitutes a significant contribution to the field of forecasting the rise in underground mine water levels and surface subsidence during the process of flooding closed coal mines. The presented methodology and obtained results can be valuable for researchers, engineers, and decision-makers involved in the planning and management of mining areas. [ABSTRACT FROM AUTHOR]

3D printing enables the creation of complex and sophisticated designs, offering enhanced efficiency, customizability, and cost-effectiveness compared to traditional manufacturing methods. Ceramics, known for their heat resistance, hardness, wear resistance, and electrical insulation properties, are particularly suited for aerospace, automotive, electronics, healthcare, and energy applications. The rise of 3D printing in ceramics has opened new possibilities, allowing the fabrication of complex structures and the use of diverse raw materials, overcoming the limitations of conventional fabrication methods. This review explores the transformative impact of 3D printing, or additive manufacturing, across various sectors, explicitly focusing on ceramics and the different 3D ceramics printing technologies. Furthermore, it presents several active companies in ceramics 3D printing, proving the close relation between academic research and industrial innovation. Moreover, the 3D printed ceramics market forecast shows an annual growth rate (CAGR) of more than 4% in the ceramics 3D printing market, reaching USD 3.6 billion by 2030. [ABSTRACT FROM AUTHOR]

In this paper, recurrent neural networks consisting of GRU and LSTM architectures are used to extract meaningful insights, characteristics, and specific patterns from previously observed, equally spaced, stock market data. The long-term dependency of nonlinear time series data can be learned using GRU and LSTM. In the first phase, the sliding window technique is used to analyse the daywise (i) open, (ii) high, (iii) low, and (iv) closing values of various stocks on the stock market to forecast the future. Performance comparisons show that the proposed GRU and LSTM networks outperform the existing models in terms of prediction accuracy. Multiple datasets were compared and the findings are: (1) the proposed model has a MAPE of 0.630, while the present model's is 1.748; (2) the MAPE of the proposed model is 0.6243, while that of the existing model is 1.92; (3) the recommended model has a MAPE of 0.7924, while the existing model's is 0.8587; (4) the recommended model's MAPE is 1.191, but the current model's MAPE is 2.99. In the second phase of the process, SMA, EMA, RSI, MACD, and ADX are chosen from among the many technical indicators and used in conjunction with OHLC to further optimise the models. [ABSTRACT FROM AUTHOR]

Various researches have been conducted on forecasting stock prices. Several tools ranging from statistical techniques to quantitative methods have been used by researchers to forecast the market. But so far, very little research has been done on forecasting the stock markets of the Gulf countries such as Saudi Arabia, United Arab Emirates, Oman, Kuwait, Bahrain, and Qatar. Our approach is to predict the market indices of the Gulf countries using Long Short‐Term Memory (LSTM) techniques. Thereafter, we optimized the hyperparameters of the LSTM technique using various optimization methods such as Grid Search and Bayesian Optimization with Gaussian Process and found out the best‐suited hyperparameter for the LSTM model. We tried the LSTM method for predicting the indices using data from the last twenty years. [ABSTRACT FROM AUTHOR]

We propose an indicator for detecting anomalous stock market valuation in real time such that market participants receive timely signals so as to be able to take stabilizing action. Unlike existing approaches, our anomaly indicator introduces three methodological novelties. First, we use an endogenous, purely data-driven, nonparametric trend identification method to separate long-term market movements from more short-term ones. Second, we apply SETAR models that allow for asymmetric expansions and contractions around the long-term trend and find systematic stock price cycles. Third, we implement these findings in our indicator and conduct real-time market forecasts, which have so far been neglected in the literature. Applications of our indicator using monthly S&P 500 stock data from 1970 to the end of 2022 show that short-term anomalous market movements can be identified in real time up to one year ahead. We predict all major anomalies, including the 1987 Bubble and the initial phase of the Financial Crisis that began in 2007. In total, our anomaly indicator identifies more than 80% of all – even minor – anomalous episodes. Thus, smoothing market exaggerations through early signaling seems possible. [ABSTRACT FROM AUTHOR]

Predicting the movement of the stock price index has remained a challenging task for financial analysts. They are complex, given how the nation's economic, social, and political conditions affect stock market predictions. The present study is designed to build an efficient model using machine learning and taking monthly data from February 2004 to December 2020. Long- and short-term memory and backpropagation methods of an artificial neural network are utilized to predict the Karachi Stock Exchange (KSE) movement by using twenty-six economic, social, political, and administrative indicators. The model developed in this study to predict the movement of the KSE 100 index gained 99 percent accuracy. The predictions' results showed that the KSE 100 index would remain stagnant at around 40,000 points till September 2023. We compared actual and predicted values from January 2021 to July 2022 to validate our developed model. The results showed that the model developed in this study could be used to forecast stock market trends. The research predicted the KSE 100 index with 99% accuracy using LSTM and backpropagation in a neural network. The KSE 100 should stay around 40,000 points until September 2023. Despite their promise, machine learning forecasts in financial markets require continuing research, market dynamics evaluation, and liability disclosure. A thorough validation procedure and the most recent KSE 100 index prediction using LSTM and backpropagation algorithms demonstrate how machine learning may improve stock market forecasting. The study's accurate projections and consistent data through September 2023 are crucial for investors and financial experts in the Karachi Stock Exchange's changing environment. [ABSTRACT FROM AUTHOR]

This paper analyses the recent evolution of the electricity price of one of the East-European countries' Balancing Markets (BM) - Romania, aiming to understand the prices trend and predict them in the current economic and geopolitical context. This is especially important as the electricity producers have to allocate their output between wholesale electricity market, ancillary services markets and BM targeting to maximize value and achieve a sustainable economic development. Therefore, in this paper, we propose an AI-powered electricity price forecast using several types of standout Machine Learning (ML) algorithms such as classifiers and regressors to predict the electricity price on BM. This approach, consisting of two steps, identifies the imbalance sign and significantly enhances the performance of the price forecast. The proposed method offers valuable insights into the market participants' trading opportunities using two prediction solutions. The first prediction solution consists of averaging the results of five ensemble ML algorithms. The second one consists in weighting the results of the five forecasting ML algorithms using either a linear regression or a decision tree algorithm. Thus, we propose to combine supervised and unsupervised ML algorithms and find the fundamentals for creating optimal bidding strategies for electricity market players. [ABSTRACT FROM AUTHOR]

Stock market forecasting is a classic but challenging problem that has attracted the attention of economists and computer scientists. The activity of trading involves high risks, the investors may lose a part of the totality of the amount they invested. Hence a need for more intelligent techniques to help make investment decisions. The purpose of this study is to provide first an overview of artificial intelligence and machine learning techniques used in recent studies for forecasting the stock market, then to present not only the different data types, commonly used evaluation metrics, and different neural network structures but also to provide a new proposition research method. Our objective is to help researchers stay abreast of the latest advances and help them easily replicate previous studies as a baseline. [ABSTRACT FROM AUTHOR]

Predicting the stock market is a crucial but difficult task due to the complexity and volatility of financial markets. In recent years, multimodal techniques utilizing the fusion of diverse data sources have demonstrated great promise for enhancing the accuracy of stock market prediction models. From an empirical standpoint, this paper provides a comprehensive review of machine learning techniques for multimodal stock market prediction. This review's primary objective is to compare and assess the functionality, precision, accuracy, recall, delay, and scalability of various multimodal techniques. By analyzing a large number of empirical studies, researchers shed light on the strengths and weaknesses of various multimodal approaches used for stock market forecasting. In addition, researchers develop an effective Stock Market Predictive Rank (SMPR) to address the need for a comprehensive evaluation metric that combines multiple performance measures. SMPR evaluates the capability of a model to handle multimodal data, in addition to its precision, accuracy, recall, delay, and scalability. By assigning a rank based on these metrics, researchers hope to identify the best models for predicting stock prices in real-time situations. This review is necessitated by the growing interest in multimodal techniques for stock market forecasting and the absence of a comprehensive analysis of various performance metrics. By synthesizing the existing literature, researchers provide a holistic perspective on the advantages and disadvantages of various multimodal approaches, thereby facilitating future research and practical application. researchers aim to provide researchers, practitioners, and decision-makers with valuable insights into the current state of multimodal stock market prediction techniques through this review. The findings presented in this paper can aid in the selection and development of effective models for real-time stock market forecasting, thereby enhancing investment decision-making and financial risk management process. [ABSTRACT FROM AUTHOR]

In this work, we develop a methodology to detect structural breaks in multivariate time series data using the t-distributed stochastic neighbour embedding (t-SNE) technique and non-parametric spectral density estimates. By applying the proposed algorithm to the exchange rates of Indian rupee against four primary currencies, we establish that the coronavirus pandemic (COVID-19) has indeed caused a structural break in the volatility dynamics. Next, to study the effect of the pandemic on the Indian currency market, we provide a compact and efficient way of combining three models, each with a specific objective, to explain and forecast the exchange rate volatility. We find that a forward-looking regime change makes a drop in persistence, while an exogenous shock like COVID-19 makes the market highly persistent. Our analysis shows that although all exchange rates are found to be exposed to common structural breaks, the degrees of impact vary across the four series. Finally, we develop an ensemble approach to combine predictions from multiple models in the context of volatility forecasting. Using model confidence set procedure, we show that the proposed approach improves the accuracy from benchmark models. Relevant economic explanations to our findings are provided as well. [ABSTRACT FROM AUTHOR]

Stock prediction is a challenging task due to multiple influencing factors and complex market dependencies. Traditional solutions are based on a single type of information. With the success of multi-source information in different fields, the combination of different types of information such as numerical and textual information has become a promising option. Although multi-source information provides rich multi-view information, how to mine and construct structured relationships from them is a difficult problem. Specifically, most existing methods usually extract features from commonly used multi-source information as predictive information sources, without further pre-constructing stock relationship graphs with dependencies using broader information. More importantly, they typically treat each stock as an isolated forecasting, or employ stock market correlations based on a fixed predefined graph structure, but current methods are not sensitive enough to aggregate the attribute features extracted from multi-source information and stock relationship graph, to obtain the dynamic update of market relations and relationship strength. The stock market is highly temporally, and the attributes of nodes are affected by the time perception of other attributes, which is not fully considered. To address these problems, we propose a novel dynamic attributes-driven graph attention networks incorporating sentiment (DGATS) information, transaction data, and text data. Inspired by behavioral finance, we separately extract sentiment information as a factor of technical indicators, and further realize the early fusion of technical indicators and textual data through Kronecker product-based tensor fusion. In particular, by LSTM and temporal attention network, the short-term and long-term transition features are gradually grasped from the local composition of the fused stock trading sequence. Furthermore, real-time intra-market dependencies and key attributes information are captured with graph networks, enabling dynamic updates of relationships and relationship strengths in predefined graphs. Experiments on the real datasets show that the architecture can outperform the previous methods in prediction performance. [ABSTRACT FROM AUTHOR]

The stock market is an important segment of the economy that circulates a large volume of assets. Several factors may affect the stock market transactions, leading to fluctuations in the stock values that may pose a problem for those who seek to forecast future stock values and maximize their profits. This issue is more serious when the stock values present the concept drift phenomenon, which means that the stock value's patterns change over time. In this work, we aimed to evaluate whether machine learning-based predictors that incorporate mechanisms to deal with concept drift are suitable for stock market forecasting. To do so, a historic database of stock prices of 10 companies, negotiated in the Brazilian stock exchange and collected over 20 years, was used. We compared the performance of predictors based on different paradigms, with and without mechanisms to deal with concept drift, and the results showed that, although the strategies that handle concept drift demand longer computational times, they also tend to present smaller prediction errors. The highlight was the EOS-D approach, which had the best performance in 6 of the 10 stocks analyzed considering one-to-one comparisons. [ABSTRACT FROM AUTHOR]

The COVID-19 lockdowns have transitioned to a new normal and triggered commodity supply disruption and trade uncertainty, yet little is known about the seafood trade resilience of developing and developed countries amid pandemic-related shocks. Here, employing a newly developed geographical transition-net model, we simulate a set of idealized lockdown scenarios in a real-world seafood network. The results show that (1) even if restrictions from regions with high strictness policies were eventually lifted globally at the end of 2022, the pandemic-induced disruption will continue to affect global seafood trade until 2030, and the annual growth rate of the global seafood market would be around 1% lower than that during 2006–2019; (2) Due to the continued high level of stringency in China in 2022 and the soaring demand of seafood in the developed countries in the post-COVID-19 era, developed countries are increasingly reliant on their intra-regional trade until 2030; (3) The global seafood supply chains will magnify export losses beyond the direct effects of COVID-19, and there would be 17 to 57 million people in the developing countries in 2030 facing seafood supply shortage. The new long-term challenge is to call for the multilateral cooperation of major exporters for global seafood trade recovery. Our study provides a new perspective to evaluate the economic impact of COVID-19 as well as the cascading effect caused by the supply-chain linkages in the global seafood system. [ABSTRACT FROM AUTHOR]

The paper focuses on the use of Artificial Neural Networks (ANNs) for forecasting time series data of the stock market since ANNs are dynamic and are more capable of handling complex data sets in comparison to conventional forecasting techniques such as regression, Logistic regression, and have massive potential for the prediction of stock market prices. Artificial neural networks are an effective method for forecasting time series. Therefore, this study aims to forecast the closing price of the BSE Sensex using artificial neural networks (ANNs). The study uses nine input variables, including macroeconomic and global stock market factors, to estimate the BSE Sensex using scaled conjugate gradient algorithm artificial neural networks (SCGANNs) and Bayesian regularized artificial neural networks (BRANN). As per the empirical results of the study, the ANN model can forecast the closing values of the BSE Sensex with a Bayesian Regularization (BR) method with an accuracy of over 99 percent, thus leading to significant implications for domestic institutional investors (DIIs), foreign institutional investors (FIIs), investment houses, and so on. This study adds more value to the existing literature by proving that the BRANN models outperform SCGANN in stock market forecasting. This is the first study to employ macroeconomic variables as input variables for predicting the Indian stock market using ANN. The study highlights the ANN model's forecasting potential, giving investors robust and accurate stock value prediction capabilities. [ABSTRACT FROM AUTHOR]

This paper aims to study the forecasting capabilities of several models under the Markov regime‐switching (MRS) and the extreme value theory (EVT) frameworks applied to daily electricity prices in the New Zealand electricity market. The MRS models in this study include up to five regimes, with time‐varying transition probabilities and incorporation of external market variables. We apply Hamilton's filter with maximum likelihood estimation for parameter estimation. The EVT peaks‐over‐threshold (EVT‐PoT) framework is also considered, and its relationship to the MRS class of models is discussed. We generate out‐of‐sample forecasts under various market scenarios. The MRS models are able to replicate real price densities under stable market conditions. The EVT‐PoT model performs well despite its lack of complexity compared to the MRS framework. We attribute this to the usage of the generalized Pareto distribution to model price extremities. [ABSTRACT FROM AUTHOR]

One widely known risk measure is Tail Value-at-Risk (TVaR), which is the average of the values of random risk that exceed the Value-at-Risk (VaR). This classic risk measure of TVaR does not take into account the excess of another random risk (associated risk) that may have an effect on target risk. Copula function expresses a methodology that represents the dependence structure of random variables and has been used to create a risk measure of Dependent Tail Value-at-Risk (DTVaR). Incorporating copula into the forecast function of the ARMA-GJR-GARCH model, this article argues a novel approach, called ARMA-GJR-GARCH-copula with Monte Carlo method, to calculate the DTVaR of dependent energy risks. This work shows an implementation of the ARMA-GJR-GARCH-copula model in forecasting the DTVaR of energy risks of NYH Gasoline and Heating oil associated with energy risk of WTI Crude oil. The empirical results demonstrate that, the simpler GARCH-Clayton copula is better in forecasting DTVaR of Gasoline energy risk than the MA-GJR-GARCH-Clayton copula. On the other hand, the more complicated MA-GJR-GARCH-Frank copula is better in forecasting DTVaR of Heating oil energy risk than the GARCH-Frank copula. In this context, energy sector market players should invest in Heating oil because the DTVaR forecast of Heating oil is more accurate than that of Gasoline. [ABSTRACT FROM AUTHOR]

The prediction of the stock market offers information for the business to increase the profit in the share market. The prediction of a stock's price is a challenging task because of the non-linearity, instability of time series information, and significant noise. In this research, an effective stock market prediction (SMP) is done by proposing the combination of long-short term memory (LSTM) and neural prophet (NP) namely the LSTMNP model. The interpretable features of NP are integrated with the LSTM to improve the prediction performances. On the other hand, the safety net incorporated in the LSTMNP is used to confirm the better prediction even if one of the models provides a lower performance. The main objective of this LSTMNP-SMP method is to improve the prediction with less errors. The LSTMNP-SMP method is analyzed using the parameters of mean absolute error (MAE), root-mean-square error (RMSE) and mean squared error (MSE). The existing approaches such as accelerated gradient-LSTM (AG-LSTM), support vector machine (SVM)-particle swarm optimization (PSO) and LSTM with artificial rabbits optimization (ARO) are used to compare the LSTMNP-SMP. The MSE of IBM for LSTMNP-SMP is 0.1110 which is less when compared to the AG-LSTM, SVM-PSO and LSTM-ARO. [ABSTRACT FROM AUTHOR]

This study aims to shed light on the prospects of the sharing economy by taking Airbnb as an example. We firstly collected listing data of Airbnb to analyze the growth patterns using the diffusion model for Taiwan and major cities in the United States. Afterward, we applied the Lotka–Volterra (LV) competition model to study the relationship between the Airbnb and local hotel industry in Taiwan with data from the Tourism Bureau of Taiwan. The study found that growth rates have been reaching their limits in both sets of markets. Moreover, the competition between Airbnb and the local hotel industry in Metro Taipei reflects a predator-and-prey relationship. [ABSTRACT FROM AUTHOR]

Stock market forecasting is one of the most challenging problems in today's financial markets. According to the efficient market hypothesis, it is almost impossible to predict the stock market with 100% accuracy. However, Machine Learning (ML) methods can improve stock market predictions to some extent. In this paper, a novel strategy is proposed to improve the prediction efficiency of ML models for financial markets. Nine ML models are used to predict the direction of the stock market. First, these models are trained and validated using the traditional methodology on a historic data captured over a 1-day time frame. Then, the models are trained using the proposed methodology. Following the traditional methodology, Logistic Regression achieved the highest accuracy of 85.51% followed by XG Boost and Random Forest. With the proposed strategy, the Random Forest model achieved the highest accuracy of 91.27% followed by XG Boost, ADA Boost and ANN. In the later part of the paper, it is shown that only classification report is not sufficient to validate the performance of ML model for stock market prediction. A simulation model of the financial market is used in order to evaluate the risk, maximum draw down and returns associate with each ML model. The overall results demonstrated that the proposed strategy not only improves the stock market returns but also reduces the risks associated with each ML model. [ABSTRACT FROM AUTHOR]