Search

Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study proposes a hybrid model that predicts future stock volatility values by considering the heteroscedasticity element of the stock price. The proposed model is a combination of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a well-known Recurrent Neural Network (RNN) algorithm Long Short-Term Memory (LSTM). This proposed model is referred to as GARCH-LSTM model. The proposed model is expected to improve prediction accuracy by considering heteroscedasticity elements. First, the GARCH model is employed to estimate the model parameters. After that, the ARCH effect test is used to test the residuals obtained from the model. Any untrained heteroscedasticity element must be found using this step. The hypothesis of the ARCH test yielded a p-value less than 0.05 indicating there is valuable information remaining in the residual, known as heteroscedasticity element. Next, the dataset with heteroscedasticity is then modelled using an LSTM-based RNN algorithm. Experimental results revealed that hybrid GARCH-LSTM had the lowest MAE (7.961), RMSE (10.466), MAPE (0.516) and HMAE (0.005) values compared with a single LSTM. The accuracy of forecasting was also significantly improved by 15% and 13% with hybrid GARCH-LSTM in comparison to single LSTMs. Furthermore, the results reveal that hybrid GARCH-LSTM fully exploits the heteroscedasticity element, which is not captured by the GARCH model estimation, outperforming GARCH models on their own. This finding from this study confirmed that hybrid GARCH-LSTM models are effective forecasting tools for predicting stock price movements. In addition, the proposed model can assist investors in making informed decisions regarding stock prices since it is capable of closely predicting and imitating the observed pattern and trend of KLSE stock prices. [ABSTRACT FROM AUTHOR]

Extreme events such as natural and economic disasters leave lasting impacts on society and motivate the analysis of extremes from data. While classical statistical tools based on Gaussian distributions focus on average behaviour and can lead to persistent biases when estimating extremes, extreme value theory (EVT) provides the mathematical foundations to accurately characterise extremes. This motivates the development of extreme value models for extreme event forecasting. In this paper, a dynamic extreme value model is proposed for forecasting volcanic eruptions. This is inspired by one recently introduced for financial risk forecasting with high-frequency data. Using a case study of the Piton de la Fournaise volcano, it is shown that the modelling framework is widely applicable, flexible and holds strong promise for natural hazard forecasting. The value of using EVT-informed thresholds to identify and model extreme events is shown through forecast performance, and considerations to account for the range of observed events are discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel framework for modelling the spread of financial crises in complex networks, combining financial data, Extreme Value Theory and an epidemiological transmission model. We accommodate two key aspects of contagion modelling: fundamentals-based contagion, where the transmission is due to direct financial linkages, and pure contagion, where a crisis might trigger additional crises due to global effects. We use stock price, geographical location and economic sector data for a set of 398 companies to construct multiplex networks of four layers, on which a susceptible-infected-recovered transmission model is defined, in order to model the spread of financial shocks between companies by accounting for their interconnected nature. By utilizing stock price data for the 2008 and 2020 financial crises, we investigate and assess the effectiveness of our model in forecasting the propagation of financial shocks through the network, where a shock is detected by measuring stock price volatility. The results suggest that the proposed framework is effective in predicting the spread of financial crises. Our findings demonstrate the significance of each layer of the multiplex network structure, which differentiates between various transmission pathways, for predicting the number of affected companies, as well as for company-, sector- or location-specific predictions. [ABSTRACT FROM AUTHOR]

El articulo discute sobre la importancia de la planificación y la estrategia en diversos ámbitos, desde lo personal hasta lo empresarial y lo militar. Se destaca sobre los conceptos básicos de la planificación, incluyendo la toma de decisiones anticipadas, la previsión del futuro y la definición de políticas. Además, presenta principios estratégicos fundamentales y destaca la importancia del seguimiento y control en el proceso de planificación.

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]

Forecasting the future movements of stock market indexes by utilizing historical transaction data is a prominent concern within the realm of finance. The application of graph convolutional networks to incorporate the interrelationships among various indices' patterns is a highly advanced subject within this field. Addressing the inconsistency between historical and future dynamic graphs in current graph convolution-based index prediction, we propose a method called G-Conv that constructs a graph structure based on constituent stocks of the indices for index trend prediction. This approach extracts traditional quantitative features along with deep features from one-dimensional convolutional networks as characteristics of prediction samples. The method produces index trend predictions by constructing a graph structure using constituent stock data of indices and applying graph convolution to different index sample features. The proposed methodology's efficacy is verified by utilizing 42 widely employed indicators in the A-share market. The experimental findings demonstrate that when utilizing mean absolute error (MAE) and mean squared error (MSE) as the loss functions for model training, G-Conv outperforms classic methods such as GC-CNN and ADGAT. Specifically, G-Conv reduces the average prediction errors by 5.10% and 4.20% respectively, as evaluated by the two error criteria. Additionally, G-Conv exhibits favorable generalization performance. [ABSTRACT FROM AUTHOR]

Establishing a scientifically effective systemic financial risk early warning model is of great significance for prudently mitigating systemic financial risks and enhancing the efficiency of financial supervision. Based on the measurement of systemic financial risk and the network sentiment index of 47 financial institutions, this study adopted the "decomposition–reconstruction–integration" approach, utilizing techniques such as extreme-point symmetric empirical mode decomposition (ESMD), empirical mode decomposition (EMD), variational mode decomposition (VMD), hierarchical clustering, fast independent component analysis (FastICA), attention mechanism, bidirectional long short-term memory neural network (BiLSTM), support vector regression (SVR), and their combination, to construct a systemic financial risk prediction model. The empirical results demonstrate that decomposing and reconstructing relevant indicators before predicting systemic financial risks can enhance prediction accuracy. Among the proposed models, the ESMD-HFastICA-BiLSTM-Attention model exhibits superior performance in systemic financial risk early warning. [ABSTRACT FROM AUTHOR]

Many deep learning works on financial time-series forecasting focus on predicting future prices/returns of individual assets with numerical price-related information for trading, and hence propose models designed for univariate, single-task, and/or unimodal settings. Forecasting for investment and risk management involves multiple tasks in multivariate settings: forecasts of expected returns and risks of assets in portfolios, and correlations between these assets. As different sources/types of time-series influence future returns, risks, and correlations of assets in different ways, it is also important to capture time-series from different modalities. Hence, this article addresses financial time-series forecasting for investment and risk management in a multivariate, multitask, and multimodal setting. Financial time-series forecasting, however, is challenging due to the low signal-to-noise ratios typical in financial time-series, and as intra-series and inter-series relationships of assets evolve across time. To address these challenges, our proposed Temporal Implicit Multimodal Network (TIME) model learns implicit inter-series relationship networks between assets from multimodal financial time-series at multiple time-steps adaptively. TIME then uses dynamic network and temporal encoding modules to jointly capture such evolving relationships, multimodal financial time-series, and temporal representations. Our experiments show that TIME outperforms other state-of-the-art models on multiple forecasting tasks and investment and risk management applications. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index's opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This novel approach leverages the ARIMA model's proficiency in linear trend analysis and the deep learning models' capability in modeling nonlinear dependencies, aiming to provide a comprehensive tool for market prediction. Utilizing a comprehensive dataset covering the period from December 20, 1990, to June 2, 2023, the study develops and assesses the efficacy of ARIMA, LSTM, GRU, ARIMA-LSTM, and ARIMA-GRU models in forecasting the Shanghai Composite Index's opening price spread. The evaluation of these models is based on key statistical metrics, including Mean Squared Error (MSE) and Mean Absolute Error (MAE), to gauge their predictive accuracy. The findings indicate that the hybrid models, ARIMA-LSTM and ARIMA-GRU, perform better in forecasting the opening price spread of the Shanghai Composite Index than their standalone counterparts. This outcome suggests that combining traditional statistical methods with advanced deep learning algorithms can enhance stock market prediction. The research contributes to the field by providing evidence of the potential benefits of integrating different modeling approaches for financial forecasting, offering insights that could inform investment strategies and financial decision-making. [ABSTRACT FROM AUTHOR]

The article focuses on the US economy's resilience amidst recent job growth and inflation challenges. Topics include expected Gross Domestic Product growth driven by consumer spending and job gains, economic forecasts suggesting a soft landing with cautious optimism on business investments, and the Federal Reserve's anticipated rate cuts to stimulate growth in 2025.

The article focuses on the inadequacy of Sales and Operations Planning (S&OP) in addressing extreme demand-supply uncertainties, particularly highlighted during the COVID-19 pandemic. Topics include the recommendation for 'Quick-Response' planning teams for managing uncertainties, 'Supply-in-Mind' planning teams for optimizing demand under supply constraints, and the need for specialized planning processes to supplement S&OP during volatile conditions.

The article focuses on achieving true collaboration in Sales and Operations Planning (S&OP) by aiming for unity rather than unanimity. Topics include addressing the natural tensions between different functional areas, integrating human judgment with digital processes, and fostering an environment where dissenting opinions are valued to enhance decision-making.

The article focuses on the challenge of accurately forecasting demand in the Fast-Moving Consumer Goods (FMCG) sector. Topics include the 'Animal Farm' segmentation method for categorizing products by demand behavior, the importance of tailored forecasting and management strategies, and the role of Exception-Based Management in optimizing demand planning.

The article focuses on the challenges and strategies in demand forecasting for new product launches. Topics include the complexity of identifying key drivers of demand, the difficulties in estimating the numerical impact of each driver, and the limitations of artificial intelligence in real-world applications for demand forecasting.

The article focuses on the limitations and potential risks of overreliance on artificial intelligence (AI). Topics include AI's tendency to produce mediocrity by relying on existing data, the observed decline in performance of generative AI models over time, and the importance of integrating human creativity and critical thinking with AI tools to avoid complacency and maintain innovation.

Reaching the maximum number of customers at the right time to increase sales and profitability of the business is the primary goal of Electronic Commerce (E-Commerce). However, owing to the low-influenced product, the profitability of e-commerce has been drastically affected in recent years. Therefore, this work proposes an Empirical Probability assigned Satty's method integrated Multicriteria Decision-Making model (EP-Satty-MCDM)-based business decision-making model for improving the sales of low-influenced products by advertising with celebrities. Primarily, the authenticated user securely downloaded the encrypted data using Armstrong number private key generated-Trident Curve-Cryptography (Arm-TCC) in the web application. After that, the data is cleansed and the attributes of review and behavior data are extracted. Then, by utilizing the Interval-valued Atanassov intuitionistic fuzzy-based Mann-Whitney U test (IAF-MWU), the correlation between the review and behavior data is evaluated. The correlated features under each user are mapped under the product, and semantic ontology is constructed, where the data is again mapped with the product's subsections. Afterward, the domains are extracted. Thereafter, to identify whether the product is high-influenced or low-influenced, the obtained ratings from ontology and extracted domains are inputted into the Boosting Regression Tree-Recurrent Neural Network (BRT-RNN). Then, for the decision-making, the positively forecasted celebrities with their garment and low-influenced products are given as the input to EP-Satty-MCDM. The experimental outcomes exhibited that the proposed technique withstands maximum accuracy when contrasted with the existing methodologies. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to verify whether the management of taxable income through aggressive tax practices negatively impacts the accuracy of market analysts' forecasts. Originality/value: The central contribution of this study is to relate tax aggressiveness with more significant errors in analysts' forecasts. Thus, the relevance of this paper is in showing that this practice can lead to a greater perception of companies' risks and more significant informational asymmetry, in addition to influencing stock prices and resulting in lower analyst accuracy. Design/methodology/approach: This research paper was developed from regression models with panel data with random effects, also using estimators based on instrumental variables with the application of Jensen's test to verify the validity of the generalized method of moments (GMM) estimation. The data were treated with the winsorization technique at the 1% level. Findings: A sample of companies listed on the B3 between 2010 and 2017 (with a total of 2,805 valid observations) showed that companies with higher tax aggressiveness are subject to a higher analyst forecast error. Thus, it is inferred that, for more aggressive companies, the degree of analyst predictability decreases. With this drop in the quality of analysts' forecasts, the users of their forecasts and reports are more vulnerable to informational asymmetry. [ABSTRACT FROM AUTHOR]

The potential value of zakat in Indonesia in 2021 was estimated at RP 13.529 trillion; however, the actual amount of zakat collected was only RP 571 billion. Thus, this study aims to develop a precise financial soundness forecasting model for the National Board of Zakat (BAZNAS) in Indonesia using multiple linear regression (MLR) and artificial neural networks (ANN) with three algorithms to investigate, compare, and interpret the obtained results for better forecasting. The information was extracted from the financial statements of six BAZNAS for the period spanning year 2017 to 2021. The study determined unique and best models for each current ratio, cash ratio, and quick ratio. The results highlighted two models deemed best for forecasting Indonesia's BAZNAS financial soundness: MLR and ANN with Scaled Conjugate Gradient (SCG) training algorithm and MLR and ANN with Bayesian Regularization (BR). The research implications could help decision-makers strategise for the financial health of other Zakat organisations accordingly. [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]

Economic forecasting is crucial since it benefits many different parties, such as governments, businesses, investors, and the general public. This paper presents a novel methodology for forecasting business cycles using an autoregressive integrated moving average (ARIMA), a popular linear model in time series forecasting, and a neural network with weighted fuzzy membership functions (NEWFM) as a forecasting model generator. The study used a dataset that included seven components of the leading composite index, which is used to predict positive or negative trends in several economic sectors before the GDP is compiled. The preprocessed time series data comprising the leading composite index using ARIMA were used as input vectors for the NEWFM to predict comprehensive business fluctuations. The prediction capability significantly improved through the duplicated refining process of the dataset using ARIMA and NEWFM. The combined ARIMA and NEWFM techniques exceeded ARIMA in both classification and prediction, yielding an accuracy of 91.61%. [ABSTRACT FROM AUTHOR]

Accurately predicting the prices of financial time series is essential and challenging for the financial sector. Owing to recent advancements in deep learning techniques, deep learning models are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. This shift in model selection has led to a notable rise in research related to applying deep learning models to price forecasting, resulting in a rapid accumulation of new knowledge. Therefore, we conducted a literature review of relevant studies over the past 3 years with a view to aiding researchers and practitioners in the field. This review delves deeply into deep learning‐based forecasting models, presenting information on model architectures, practical applications, and their respective advantages and disadvantages. In particular, detailed information is provided on advanced models for price forecasting, such as Transformers, generative adversarial networks (GANs), graph neural networks (GNNs), and deep quantum neural networks (DQNNs). The present contribution also includes potential directions for future research, such as examining the effectiveness of deep learning models with complex structures for price forecasting, extending from point prediction to interval prediction using deep learning models, scrutinizing the reliability and validity of decomposition ensembles, and exploring the influence of data volume on model performance. This article is categorized under:Technologies > PredictionTechnologies > Artificial Intelligence [ABSTRACT FROM AUTHOR]

Financial market prediction is generally regarded as one of the most challenging tasks in data mining. Recent deep learning models have achieved success in improving the accuracy of financial time-series forecasting (TSF), but as implicit complex information, and there have few available labeled data, the generalization capability of current benchmarks is poor in this field. To alleviate the restriction of overfitting caused by insufficient clean data, a novel deep transfer learning framework incorporating adversarial domain adaptation is proposed for financial TSF task, dubbed as ADA-FTSF for short, in improving reliable, accurate and competitive deep forecasting models. Concretely, we implement a typical adversarial domain adaptation architecture to transfer feature knowledge and reduce the distribution discrepancy between financial datasets. To reduce the shape difference during the pre-train process, a smoothed formulation of dynamic time warping (DTW) is also introduced tactfully in adversarial training phase to measure the shape loss. Notably, the confident selection of source domain from potential source datasets will make significant impact on forecasting performance. In our study, appropriate source dataset is selected using temporal causal discovery method via transfer entropy derived from copula entropy. The feasibility and effectiveness of the proposed framework are validated by the empirical experiments, ablation study, Diebold–Mariano test and parameter sensitivity analysis conducting on different financial datasets of three domains (financial indexes, energy futures and agricultural futures). [ABSTRACT FROM AUTHOR]

Reliable forecasts are key to decisions in areas ranging from supply chain management to capacity planning in service industries. It is encouraging then that recent decades have seen dramatic advances in forecasting methods which have the potential to significantly increase forecast accuracy and improve operational and financial performance. However, despite their benefits, we have evidence that many organizations have failed to take up systematic forecasting methods. In this paper, we provide an overview of recent advances in forecasting and then use a combination of survey data and in-depth semi-structured interviews with forecasters to investigate reasons for the low rate of adoption. Finally, we identify pathways that could lead to the greater and more widespread use of systematic forecasting methods. [ABSTRACT FROM AUTHOR]

When two people coincidentally have something in common (such as a name or birthday), they tend to like each other more and are thus more likely to offer help and comply with requests. This dynamic can have important legal and ethical consequences whenever these incidental similarities give rise to unfair favoritism. Using a large-scale, longitudinal natural experiment, covering nearly 200,000 annual earnings forecasts over more than 25 y, we show that when a CEO and a securities analyst share a first name, the analyst's financial forecast is more accurate. We offer evidence that name matching improves forecast accuracy due to CEOs privately sharing pertinent information with name-matched analysts. Additionally, we show that this effect is especially pronounced among CEO-analyst pairs who share an uncommon first name. Our research thus demonstrates how incidental similarities can give way to special treatment. Whereas most investigations of the effects of similarity consider only one-shot interactions, we use a longitudinal dataset to show that the effect of name matching diminishes over time with more interactions between CEOs and analysts. We also point to the findings of an experiment suggesting that favoritism born of sharing a name may evade straightforward regulation in part due to people's perception that name similarity would exert little influence on them. Taken together, our work offers insight into when private disclosures are likely to be made. Our results suggest that the effectiveness of regulatory policies can be significantly impacted by psychological factors shaping the context in which they are implemented. [ABSTRACT FROM AUTHOR]

The article focuses on the prediction of rate cuts in 2024 by Fed Chairman Powell, indicating a potential slowdown in the US economy. Topics discussed include projections of GDP growth, unemployment rates, and interest rate decreases, suggesting cautious consumer spending and potential risks in the commercial real estate market.

The use of deep learning in conjunction with models that extract emotion-related information from texts to predict financial time series is based on the assumption that what is said about a stock is correlated with the way that stock fluctuates. Given the above, in this work, a multivariate forecasting methodology incorporating temporal convolutional networks in combination with a BERT-based multi-label emotion classification procedure and correlation feature selection is proposed. The results from an extensive set of experiments, which included predictions of three different time frames and various multivariate ensemble schemes that capture 28 different types of emotion-relative information, are presented. It is shown that the proposed methodology exhibits universal predominance regarding aggregate performance over six different metrics, outperforming all the compared schemes, including a multitude of individual and ensemble methods, both in terms of aggregate average scores and Friedman rankings. Moreover, the results strongly indicate that the use of emotion-related features has beneficial effects on the derived forecasts. [ABSTRACT FROM AUTHOR]

Nicola Gennaioli and Andrei Shleifer's I A Crisis of Beliefs i is a rich, elaborate and ambitious book. Gennaioli and Shleifer first conveniently dismiss the two common explanatory theories of the 2008 financial crisis - the moral hazard theory (banks knew they were too big to fail and were wilfully engaging in reckless and fraudulent behaviour);[2] and the theory that the liquidity crisis was due to a bank run (a system that is fundamentally healthy runs into a liquidity problem, which causes a domino effect). 14 In 2011, the U.S. Financial Crisis Inquiry Commission concluded that "many of these [financial] institutions acted recklessly" and that the "financial crisis was avoidable" if only financial institutions would not have engaged in "predatory lending practices, dramatic increases in household mortgage debt, exponential growth in financial firms' trading activities." Given the book's multifaced nature, it would be fair to assess its explanatory power against its own stated goals: (1) to show that survey expectations data are a valid and useful source of information; (2) to provide an empirically motivated and psychologically grounded formal model of expectation formation that can be used across various domains; and (3) to use the model of expectation formation to account for the main features of the 2008 financial crisis. [Extracted from the article]

While studies on corporate philanthropy have blossomed in recent times, little is known empirically about its practice formalized through foundations. Drawing on semi-structured interviews with seven corporate foundations, this paper explored the giving strategies, opportunities, and challenges of corporate foundations in Ghana. Our findings based on thematic analysis revealed that corporate foundations rely on a combination of strategies (request, media-lead, adoption, and contest) to identify potential beneficiaries and implement their giving programs. Further evidence indicates that giving of corporate foundations presents opportunities to the foundations themselves (serve society, get partnership offers from other companies, and obtain goodwill from the public) and their parent companies (indirect business and advertising opportunities). Nonetheless, corporate foundation giving is constrained by insufficient funding, lack of support from stakeholders, managing expectations of individuals, poor maintenance culture, and cultural rites. The findings have implications for practitioners as it present insights that could serve as a model to guide new entrants into the corporate foundation landscape of developing economies. In addition, the findings could assist the development of government interventions necessary to foster more formalized corporate giving. [ABSTRACT FROM AUTHOR]

The article focuses on key success factors for effective Sales and Operations Planning (S&OP), including visible executive leadership, alignment in objectives, collaborations, and disciplined implementation. Topics include the challenges faced during S&OP implementation at a European office supplies retailer, the importance of clear organizational structures and roles, and the significant benefits achieved through successful S&OP, such as cost reductions and improved service levels.

The article focuses on the significance of forecasting accuracy in business decision-making and its actual economic impact. Topics include the potential overemphasis on forecast accuracy, the relationship between forecast accuracy and economic performance, and the need for a more value-centric approach to forecasting.

The article highlights the U.S. unemployment rate's slight increase to 3.80 present in August, despite efforts by the Fed to address inflation, with experts like doctor Ray Perryman and Wells Fargo noting the economy's resilience. It mentions Rajeev Dhawan expresses concern about a 'white-collar recession' due to job compressions in various sectors, affecting upward mobility and leading to reluctance in taking on higher-rate mortgages in certain segments of the workforce.

Cryptocurrencies are nowadays seen as an investment opportunity, since they show some peculiar features, such as high volatility and diversification properties, that are triggering research interest into investigating their differences with traditional assets. In our paper, we address the problem of predictability of cryptocurrency and stock trends by using data from social online communities and platforms to assess their contribution in terms of predictive power. We extend recent developments in the field by exploiting a combination of stochastic neural networks (NNs), an extension of standard NNs, natural language processing (NLP) to extract sentiment from Twitter, and an external evolutionary algorithm for optimal parameter setting to predict the short-term trend direction. Our results point to good and robust accuracy over time and across different market regimes. Furthermore, we propose to exploit recent advances in sentiment analysis to reassess its role in financial forecasting; in this way, we contribute to the empirical literature by showing that predictions based on sentiment analysis are not found to be significantly different from predictions based on historical data. Nonetheless, compared to stock markets, we find that the accuracy of trend predictions with sentiment analysis is on average much higher for cryptocurrencies. [ABSTRACT FROM AUTHOR]

In recent years, federated learning has been widely used in various fields, such as smart healthcare and financial forecast, due to its ability to protect the privacy of user secret data. Although federated learning has the capability of protecting users' data privacy, recent research results demonstrated that federated learning still suffers from many privacy attacks. Among them, membership inference attacks are the most common privacy attacks in which attackers infer whether the record belongs to a member message or not. However, the current studies are unable to provide further depth to infer membership information, meaning that existing attack methods have difficulty deducing specifically which user the record belongs to. Moreover, there is a lack of training data in the training process which seriously impacts the effectiveness of membership inference attacks. In this paper, from the perspective of inferring both model-level and user-level membership information, we not only infer whether a record belongs to members but furthermore identify which member the record belongs to. In addition, we augment the training dataset by leveraging the generative adversarial networks (GANs) approach and address the lack of labeling of the newly generated data with the aid of the active learning approach. To demonstrate the effectiveness of our method, we implement our proposed methods on the five benchmark datasets. Extensive experimental results demonstrate that both model-level and user-level membership inference attacks can be achieved with good effectiveness. [ABSTRACT FROM AUTHOR]

The Delphi method has been used as a way to reach consensus among experts established in the 1950s. The method was originally conceived to be used as a forecasting instrument for business in a mixed-method study. The Delphi method is usually conducted with elements of anonymity, iteration, controlled feedback and statistical group response. Delphi method had traditionally used expert opinions through methods such as interviews and brainstorming for the initial point of studies conducted. However, with the expanding volumes of research papers and articles in this age, can techniques such as Traditional Literature Review (TLR) and Systematic Literature Review (SLR) also be viable technique to be an initial point of studies for a Delphi study. As such this study aims 1) to examine and discover whether TLR and SLR are appropriate techniques to be used in a Delphi study and 2) construct a Delphi study process overview. This study adopts a methodology in which seven articles that have used TLR and SLR in their Delphi study will be analyzed. The results shows that TLR and SLR are appropriate techniques to be used in a Delphi study and that there are two types of processes to incorporate them into the study. We have also constructed a Delphi process overview through our analysis. We hope that the results of this study will be able help researchers who are interested in doing a Delphi study to know on what are the benefits of TLR and SLR in these studies along with how to incorporate them. [ABSTRACT FROM AUTHOR]

This article evaluates the forecast performance of model averaging forecasts in a nonstationary vector autoregression with mixed roots in the vicinity of unity. The deviation from unit root allows for local to unity, moderate deviation from unity and strong unit root, and the direction of such deviation could be from either the stationary or the explosive side. We provide a theoretical foundation for comparison among various forecasts, including the least squares estimator, the constrained estimator imposing the unit root constraint, and the selection or average over these two basic estimators. Furthermore, three new types of estimators are constructed, i.e., the bagging versions of the pretest estimator, the Mallows-pretest estimator that marries the Mallows averaging criterion and the Wald test, and the Mallows-bagging estimator that combines the Mallows averaging criterion and bagging technique. The asymptotic risks are shown to depend on the local parameters, which are not consistently estimable. Via Monte Carlo simulations, graphic comparisons indicate that the Mallows averaging estimator has both robust and outstanding forecasting performance. Model averaging over the vector autoregressive lag order is further considered to address the issue of model uncertainty in the lag specification. Finite sample simulations show that the Mallows averaging estimator performs superior to other frequently used selection and averaging methods. The application to forecasting the financial indices popularly used in the predictive regression further illustrates the practical merit of the proposed estimator. [ABSTRACT FROM AUTHOR]

Data plane development kit (DPDK) provides an effective solution for the rapid processing of general multi-core processor platforms. Research based on DPDK on a general-purpose multi-core processor platform can use CPU cache to improve packet forwarding and processor core performance, as well as network function virtualization performance. In the parallel development of finance and network, people find that complex financial problems can be well-solved through big data network. The rapidly changing financial market is affected by economic and political factors, and the generation of financial data is also highly uncertain. However, financial forecast analysis and forecast modeling are of great significance. As a whole, this trend could help inform policy; from a meso-level, it can be insightful. The changes in financial data are conducive to the planning of business strategies for enterprises and gaining market competitive advantages; from a micro-level, individual investors will pay attention to historical data changes and the development of industries or companies before making investment decisions in order to obtain benefits. Internet technology can strengthen the accuracy of financial forecasting, so as to effectively avoid risks, but also can promote the development of digital economy, reduce labor costs, improve the breadth and depth of financial services in digital economy. [ABSTRACT FROM AUTHOR]

The aim of this study is to create the best forecasting model by comparing the forecasting power of Discriminant Analysis and Artificial Neural Networks models, which are widely used in the literature, among various forecasting models developed to date, in order to predict the financial failures of companies. In the study, the probability of financial failure of the companies was tried to be estimated 1, 2 and 3 years ago by using the financial ratios of 355 companies traded in BIST. For this purpose, separate forecasting models were created for each year before the financial failure, according to discriminant analysis and artificial neural networks. As a result of the study, the artificial neural network model, which was created with the discriminant analysis rates of the models that best predicted the probability of financial failure of the companies, compared to one and three years ago; Two years ago, it was determined that there was an artificial neural network model created with all financial ratios. [ABSTRACT FROM AUTHOR]