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We provide a systematic empirical assessment of the Minsky hypothesis that business fluctuations stem from irrational swings in expectations. Using predictable firm-level forecast errors, we build an aggregate index of irrational expectations and use it to provide three sets of results. First, we show that our index predicts aggregate credit cycles. Next, we show that these predictable credit cycles drive cycles in firm-level debt issuance and investment and similar cycles between financially constrained and unconstrained firms, as Minsky predicts. Finally, we show more pronounced cycles in firm-level financing and investment for firms with ex ante more optimistic expectations. (JEL G31, G32, G40, E32, E44) [ABSTRACT FROM AUTHOR]

This study examines the importance of governance quality of local government in small businesses' future revenue growth forecasting accuracy. Forecasting errors are due to either overestimation or under-estimation. When local governance quality improves, transaction costs are eliminated, boosting firms' actual performance up to the level forecast, thereby reducing overestimation. Also, when local governance quality improves, institutional trust (trust in government) and generalized trust (trust in strangers) improve, lifting the expectation of future performance to the actual level, thereby reducing under-estimation. We investigate these two mechanisms using a set of more than 250,000 small businesses in Vietnam using an instrumental variable approach to control for potential endogeneity. It is found that governance quality enhances forecasting accuracy by reducing under-estimation forecasting errors but, interestingly, without affecting overestimation errors. This paper is one of the first that links governance quality to business forecasting, indicating that local authorities can help local businesses make efficient resource preparation by improving their governance quality. [ABSTRACT FROM AUTHOR]

Analyst forecasts outperform econometric forecasts in the short run but underperform in the long run. We decompose these differences in forecasting accuracy into analysts' information advantage, forecast bias, and forecast noise. We find that noise and bias strongly increase with forecast horizon, while analysts' information advantage decays rapidly. A noise increase with horizon generates a mechanical reversal in the sign of the error-revision (Coibion-Gorodnichenko) regression coefficient at longer horizons, independently of over-/underreaction. A parsimonious model with bounded rationality and a noisy cognitive default matches the term structures of noise and bias jointly. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Alzheimer's disease is a progressive neurological disorder that is very common among older adults. In truth, taking care of Alzheimer's patients can be a very tedious job that requires a lot of time and effort. On the other hand, smartphone applications (apps) have become an essential part of all of our daily life fields. Almost every human activity can be related to an app from checking the weather forecasts to attending a business meeting online. As a natural result of this, many useful apps were developed to serve and help Alzheimer's patients and families around the world. Sadly, the apps launched in the Arabic area were very poor in their content and limited in their features. In this work, we present Alzheimer's care system (ACS) which is a smartphone Android app that aims to serve Alzheimer's early stages patients, their caregivers, and their doctors. ACS has an Arabic interface along with the English one to serve the patients in the Arabic talking countries who may struggle using similar apps in other languages. The app provides three account types and includes many useful features like contacting the caregivers and doctors through messages, keeping track of daily life tasks, reminding the patient of medication doses and times, and many more. Moreover, ACS has a Chatbot that provides general knowledge regarding the disease. Our app has been practically tested for one month by a group of users who were asked to fill out a 12 questions survey at the end of the test period. The survey results were very positive and encouraging in general. As future work, we plan to add other languages, develop an iOS version, and add new features to the app. [ABSTRACT FROM AUTHOR]

Unlike institutional investors, who have expertise in collecting and analyzing firm information to predict firms' future performance, individual investors have a limited ability to conduct this task. Therefore, individual investors are known to commonly rely on financial analysts' earnings forecasts, which are freely available to them. However, as demonstrated by numerous studies, financial analysts' forecasts are not always accurate and free from bias. Drawing on screening theory and the innate financial and structural differences between highly franchised restaurants and their less franchised peers, the current study showed that analysts provide more accurate and less optimistically biased forecasts for the former than the latter. However, the positive impact of an additional number of analysts on the accuracy of forecasts was weaker for the former than the latter. The current study can help individual investors in the restaurant industry improve their understanding of financial analysts' earnings forecasts and make better-informed investment decisions. [ABSTRACT FROM AUTHOR]

I investigate whether algorithmic trading (AT) affects the provision of management guidance. Existing research finds that AT decreases fundamental information acquisition before earnings announcements and consequently reduces the informativeness of prices. To compensate for reduced information acquisition, I predict and find that managers at firms with more AT activity increase the quantity and quality of guidance issued at earnings announcements. Evidence is consistent with managers responding to reduced information acquisition, as opposed to changes in liquidity, and results suggest guidance in response to AT is effective at reducing information asymmetry. These findings identify a new channel through which AT affects stock price informativeness by documenting a link to managers' disclosure decisions. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G14; G19; G10. [ABSTRACT FROM AUTHOR]

Changes in economic conditions can occur suddenly with drastic effects. However, economic statistics are published with significant lags, e.g. GDP, and more timely information about the economy is required. Nowcasting methods have become widely popular for providing up-to-date information about the current economic stance. This study adds a novel idea to the previous literature by nowcasting the nowcasting, i.e. the purchasing manager's index (PMI) and the non-manufacturing survey index (NSI) of the ISM Business survey indicators with the weekly Google Trends data. We used two-dimension reduction methods: the principal component analysis (PCA) and partial least squares (PLS) to eliminate 'the curse of dimensionality'. Pseudo-out-of-sample exercises performed with different Google Trends search categories indicated that Google Search data is able to generate useful information to nowcast the nowcasting. In particular, we contribute the existing literature that weekly Google Search data can nowcast the monthly PMI and NSI. [ABSTRACT FROM AUTHOR]

This study examines the impact of employment background on the objectivity of profitability forecasts in the financial sector. We find that investment bank analysts provide relatively biased recommendations and less accurate forecasts than their counterparts at independent research firms (IRFs). The performance discrepancy is greater for bulge bracket banks and for firms involved in syndication with the analysts' employers. A closer look at analysts transitioning between IRFs and investment banks reveals that these biases stem from their affiliation with investment banks rather than personal bias. Our findings emphasize the critical influence of the work environment on analyst behavior. [ABSTRACT FROM AUTHOR]

Forecasting and analyzing corporate financial performance are of significant value to investors, managers, and regulators. In this paper, we constructed the one‐dimensional convolutional neural networks (1D‐CNN) and long short‐term memory (LSTM) deep learning models to investigate the feasibility of forecasting corporate financial performance with deep learning models, using the corporate financial features and environment, social and governance (ESG) rating index of Chinese A‐share listed corporation data from 2015 to 2021. Five evaluation metrics were employed to measure models' forecasting effects, and four competing machine learning models were built to verify the improvement in forecasting accuracy brought by the deep learning models. Furthermore, we also introduced the Accumulated Local Effects method to explore the forecasting processes of the deep learning models. The empirical results show the following: (1) Deep learning models can effectively extract the time‐series information in corporate data, thereby solving the task of predicting corporate financial performance with high accuracy. (2) The introduction of ESG information significantly contributes to the forecasting accuracy of corporate financial performance. For both 1D‐CNN and LSTM models, the ESG rating index can provide additional useful information for forecasting. (3) The interpretable results reveal the preference and emphasis of the two deep learning models for the different features. This further proves the robustness and reliability of deep learning models in forecasting corporate financial performance. [ABSTRACT FROM AUTHOR]

Stock market and cryptocurrency forecasting is very important to investors as they aspire to achieve even the slightest improvement to their buy-or-hold strategies so that they may increase profitability. However, obtaining accurate and reliable predictions is challenging, noting that accuracy does not equate to reliability, especially when financial time-series forecasting is applied owing to its complex and chaotic tendencies. To mitigate this complexity, this study provides a comprehensive method for forecasting financial time series based on tactical input–output feature mapping techniques using machine learning (ML) models. During the prediction process, selecting the relevant indicators is vital to obtaining the desired results. In the financial field, limited attention has been paid to this problem with ML solutions. We investigate the use of feature selection with annealing (FSA) for the first time in this field, and we apply the least absolute shrinkage and selection operator (Lasso) method to select the features from more than 1000 candidates obtained from 26 technical classifiers with different periods and lags. Boruta (BOR) feature selection, a wrapper method, is used as a baseline for comparison. Logistic regression (LR), extreme gradient boosting (XGBoost), and long short-term memory are then applied to the selected features for forecasting purposes using 10 different financial datasets containing cryptocurrencies and stocks. The dependent variables consisted of daily logarithmic returns and trends. The mean-squared error for regression, area under the receiver operating characteristic curve, and classification accuracy were used to evaluate model performance, and the statistical significance of the forecasting results was tested using paired t-tests. Experiments indicate that the FSA algorithm increased the performance of ML models, regardless of problem type. The FSA hybrid models showed better performance and outperformed the other BOR models on seven of the 10 datasets for regression and classification. FSA-based models also outperformed Lasso-based models on six of the 10 datasets for regression and four of the 10 datasets for classification. None of the hybrid BOR models outperformed the hybrid FSA models. Lasso-based models, excluding the LR type, were comparable to the best models for six of the 10 datasets for classification. Detailed experimental analysis indicates that the proposed methodology can forecast returns and their movements efficiently and accurately, providing the field with a useful tool for investors. [ABSTRACT FROM AUTHOR]

This study explores the trust dynamics in financial forecasting by comparing how individuals perceive the credibility of AI and human experts during significant structural market changes. We specifically examine the impact of two types of structural breaks on trust: Additive Outliers, which represent a single yet significant anomaly, and Level Shifts, which indicate a sustained change in data patterns. Grounded in theoretical frameworks such as attribution theory, algorithm aversion, and the Technology Acceptance Model (TAM), this research investigates psychological responses to AI and human advice under uncertainty. This experiment involved 157 participants, recruited via Amazon Mechanical Turk (MTurk), who were asked to forecast stock prices under different structural break scenarios. Participants were randomly assigned to either the AI or human expert treatment group, and the experiment was conducted online. Through this controlled experiment, we find that, while initial trust levels in AI and human experts are comparable, the credibility of advice is more severely compromised following a structural break in the Level Shift condition, compared to the Additive Outlier condition. Moreover, the decline in trust is more pronounced for human experts than for AI. These findings highlight the psychological factors influencing decision making under uncertainty and offer insights into the behavioral responses to AI and human expert systems during structural market changes. [ABSTRACT FROM AUTHOR]

The global financial landscape has witnessed a significant shift towards Exchange-Traded Funds (ETFs), with their market capitalization surpassing USD 10 trillion in 2023, due to advantages such as low management fees, high liquidity, and broad market exposure. As ETFs become increasingly central to investment strategies, accurately forecasting their performance has become crucial. This study addresses this need by comparing the efficacy of deep learning models against the traditional Fama-French three-factor model in predicting daily ETF returns. The methodology employs eight artificial neural network architectures, including ANN, LSTM, GRU, CNN, and their variants, implemented in Python and applied to data ranging from 2010 to 2020, while also exploring the impact of additional factors on forecast accuracy. Empirical results reveal that LSTM and the Fama-French three-factor model exhibit a superior performance in ETF return prediction. This study contributes to the literature on financial forecasting and offers practical insights into investment decision making. By leveraging advanced artificial intelligence techniques, this study aims to enhance the toolkit available for ETF performance analysis, potentially improving investment strategies in this dynamic market segment. [ABSTRACT FROM AUTHOR]

This study explores market efficiency and behavior by integrating key theories such as the Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH), Informational Efficiency and Random Walk theory. Using LSTM enhanced by optimizers like Stochastic Gradient Descent (SGD), Adam, AdaGrad, and RMSprop, we analyze market inefficiencies in the Standard and Poor's (SPX) index over a 22-year period. Our results reveal "pockets in time" that challenge EMH predictions, particularly with the AdaGrad optimizer at a size of the hidden layer (HS) of 64. Beyond forecasting, we apply the Dominguez–Lobato (DL) and General Spectral (GS) tests as part of the Martingale Difference Hypothesis to assess statistical inefficiencies and deviations from the Random Walk model. By emphasizing "informational efficiency", we examine how quickly new information is reflected in stock prices. We argue that market inefficiencies are transient phenomena influenced by structural shifts and information flow, challenging the notion that forecasting alone can refute EMH. Additionally, we compare LSTM with ARIMA with Exponential Smoothing, and LightGBM to highlight the strengths and limitations of these models in financial forecasting. The LSTM model excels at capturing temporal dependencies, while LightGBM demonstrates its effectiveness in detecting non-linear relationships. Our comprehensive approach offers a nuanced understanding of market dynamics and inefficiencies. [ABSTRACT FROM AUTHOR]

This paper systematically reviews the behavior of numerical indicators in predicting future bankruptcy of companies through statistical analysis models. Following the PRISMA standard, ten primary studies were included in the review. The obtained results underline (1) the ability of numerical indicators, through simple statistical analysis models, to forecast the bankruptcy of businesses and companies and (2) the reliability of cash flows in predicting financial distress through statistical analysis, and (3) models are built with indicators from a specific economy; it is impossible to consider them stable and unchanging, as changes in a country's economic conditions can potentially impact their predictive accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes an explicable early warning machine learning model for predicting financial distress, which generalizes across listed Nordic corporations. We develop a novel dataset, covering the period from Q1 2001 to Q2 2022, in which we combine idiosyncratic quarterly financial statement data, information from financial markets, and indicators of macroeconomic trends. The preferred LightGBM model, whose features are selected by applying explainable artificial intelligence, outperforms the benchmark models by a notable margin across evaluation metrics. We find that features related to liquidity, solvency, and size are highly important indicators of financial health and thus crucial variables for forecasting financial distress. Furthermore, we show that explicitly accounting for seasonality, in combination with entity, market, and macro information, improves model performance. [ABSTRACT FROM AUTHOR]

The article focuses on enhancing stock market price prediction through artificial neural networks and machine learning. It underscores the significance of improving forecast accuracy by incorporating historical stock prices, macroeconomic indicators, news events, and technical indicators. Exploring deep learning principles, it delves into convolutional neural networks (CNN), recurrent neural networks (RNN), including long shortterm memory (LSTM), and gated recurrent unit (GRU) modifications. This financial time series processing study covers data preprocessing, creating training/test sets, and selecting evaluation metrics. Results suggest promising applications for the developed forecasting models in stock markets, stressing the importance of considering various factors for precise forecasts in dynamic financial environments. Historical reserve data serves as the model foundation. Integration of macroeconomic, news, and technical indicators offers a holistic approach, aiding trend and anomaly identification for enhanced forecasts. The article recommends suitable deep learning architectures, highlighting LSTM and GRU's effectiveness in adapting to intricate data dependencies. Experimental outcomes showcase these architectures' benefits in predicting stock market prices, offering valuable insights for finance and asset management professionals in financial analysis and machine learning realms. [ABSTRACT FROM AUTHOR]

This article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each method, identifying their impact on the accuracy and reliability of borrower creditworthiness assessments. Current trends in machine learning and credit scoring are also covered, warning of challenges and discussing prospects. The analysis highlights the significant contributions of methods such as LGBM classifier, LR, LDA, DT classifier, gradient boosting classifier and XGB classifier to the development of modern credit scoring practices, highlighting their potential for improving the accuracy and reliability of borrower creditworthiness forecasts in the financial services industry. Additionally, the article discusses the importance of careful selection of machine learning models and the need to continually update methodology in light of the rapidly changing nature of the financial market. [ABSTRACT FROM AUTHOR]

The motivation for this research was the desire to disclose the social nature of the financial risks of global companies: the authors attempted a scientific explanation of the influence of corporate social responsibility, which is manifested through the preservation and creation of additional jobs, on the financial risks of global companies. The research aims to establish the interdependence between financial risks and sustainable employment in global companies. This goal is achieved using the SEM (structural equation modeling) method based on corporate statistics from the Fortune "Global 500" rankings for 2021–2023. As a result, the consequences of global companies' CSR (corporate social responsibility) practices in personnel management and financial risk management are modeled and described through quantitative and qualitative patterns. The established regularities proved that for developed and developing countries, the larger the number of employees, the lower the financial risks of global companies—the risk of a decrease in profitability, the risk of loss of profit, and the risk of depreciation of assets. The main conclusion is that there is a close systemic relationship between the financial risks of global companies and their workforce size, suggesting that CSR is key to highly effective financial risk management. A clear distinction between the practices of financial risk management through CSR in developed and developing countries forms the basis of the theoretical significance of the research results. The authors provide recommendations to improve the current practice of financial risk management in global companies by integrating it more closely with personnel management practices, highlighting their managerial relevance. It is proposed that corporate strategies for global companies in developed countries should focus on reducing the risk of declining profitability, as CSR has the most pronounced and consistent impact on this particular financial risk. In developing countries, corporate strategies are recommended to be structured by diversifying the areas of CSR application, with the most promising in financial risk management being the reduction in asset depreciation risk and the reduction in profitability risk. The findings of this research have practical significance because they enhance the predictability of CSR activities of global companies and open up opportunities for highly accurate forecasting of the financial risk implications of ensuring sustainable employment by global companies, considering the specificities of developed and developing countries. [ABSTRACT FROM AUTHOR]

We examine the effect of a common auditor within a supply chain, where the auditor serves both the supplier and its customer(s). This dual role allows auditors to leverage and disseminate crucial chain-specific knowledge. Considering that supplier firms are relatively smaller and at a disadvantage compared with their customers, such supply-chain knowledge is valuable for suppliers to make better demand forecasts and business plans. Consistent with this argument, we find that a supplier sharing a common auditor with its customer(s) has a higher ROA, a higher profit margin, a shorter receivable conversion period, and a smaller demand distortion from the bullwhip effect. Performance enhancement is more pronounced when the common auditor has more opportunities to collect and transfer information and when such information transfer is more valuable to the supplier. Our results are robust to alternative measures of common-auditor presence, alternative explanations, and potential endogeneity concerns. Data Availability: The data that support the findings of this study are openly available. JEL Classifications: D82; D83; L25; M42. [ABSTRACT FROM AUTHOR]

The German economy continues to stagnate. After it appeared to finally be growing at the start of 2024, it experienced a slight setback in the second quarter. Although incomes are rising and inflation is now near the two-percent target, people in Germany are saving their money. As investments and exports faltered due to the sluggish industrial sector, the upturn has been delayed for the time being. Overall economic output is likely to stagnate in 2024, but it will likely pick up at the end of the year; growth of 0.9 and 1.4 percent is expected in 2025 and 2026, respectively. Private consumption, which will increase more slowly than expected but still provide a stimulating effect, will primarily support this growth. As interest rates continue to fall worldwide, foreign trade and investments are also supporting growth. The global economy has returned to its recovery path, but expanded more weakly in the second quarter compared to the first. Growth in China in particular has stalled somewhat, and there are also signs of an upcoming economic slowdown in the United States. Following growth of 3.8 percent in 2024, the global economy is expected to expand by only 3.5 percent in 2025, but develop more dynamically again in 2026. [ABSTRACT FROM AUTHOR]

Copyright of Deutsches Institut für Wirtschaftsforschung: DIW-Wochenbericht is the property of DIW Berlin and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article discusses the current state of the U.S. economy, focusing on modest Gross Domestic Product (GDP) growth and inflation trends. Topics discussed include the Federal Reserve's (Fed) anticipated rate cuts, the slowdown in job openings and the labor market, and the expected growth in M2 money supply, a measure of the money supply that includes cash, checking deposits, and easily convertible near money to support economic stability.

The article examines strategic demand-shaping techniques as part of the Integrated Business Planning (IBP) process, focusing on aligning demand and supply with business objectives. Topics include using pricing, promotions, and product substitution to manage inventory levels, measuring demand-shaping efficacy with Forecast Value Add (FVA) Key Performance Indicators (KPIs), and understanding the role of Artificial Intelligence (AI) and human oversight in refining demand forecasts.

Financial forecasting involves predicting the future financial states and performance of companies and investors. Recent technological advancements have demonstrated that machine learning-based models can outperform traditional financial forecasting techniques. In particular, hybrid approaches that integrate diverse methods to leverage their strengths have yielded superior results in financial prediction. This study introduces a novel hybrid model, entitled EMD-TI-LSTM, consisting of empirical mode decomposition (EMD), technical indicators (TI), and long short-term memory (LSTM). The proposed model delivered more accurate predictions than those generated by the conventional LSTM approach on the same well-known financial datasets, achieving average enhancements of 39.56%, 36.86%, and 39.90% based on the MAPE, RMSE, and MAE metrics, respectively. Furthermore, the results show that the proposed model has a lower average MAPE rate of 42.91% compared to its state-of-the-art counterparts. These findings highlight the potential of hybrid models and mathematical innovations to advance the field of financial forecasting. [ABSTRACT FROM AUTHOR]

This study addresses the pressing need for improved stock price prediction models in the financial markets, focusing on the Indonesian stock market. It introduces an innovative approach that utilizes the custom activation function RunReLU within a concave long short-term memory (LSTM) framework. The primary objective is to enhance prediction accuracy, ultimately assisting investors and market participants in making more informed decisions. The research methodology used historical stock price data from ten prominent companies listed on the Indonesia Stock Exchange, covering the period from July 6, 2015, to October 14, 2021. Evaluation metrics such as RMSE, MAE, MAPE, and R2 were employed to assess model performance. The results consistently favored the RunReLUbased model over the ReLU-based model, showcasing lower RMSE and MAE values, higher R2 values, and notably reduced MAPE values. These findings underscore the practical applicability of custom activation functions for financial time series data, providing valuable tools for enhancing prediction precision in the dynamic landscape of the Indonesian stock market. [ABSTRACT FROM AUTHOR]

In an increasingly complex and volatile environment, government officials, researchers, and investors alike would like to possess models that accurately forecast markets in order to make appropriate decisions. This research investigates the efficacy of Transformer-based deep neural networks in predicting financial market returns compared to traditional models, focusing on ten different market indexes. The study employs a comprehensive methodology that involves iterative dropout tests and batch size optimization to enhance model performance. By leveraging the power of deep learning, the research aims to improve prediction accuracy and capture complex patterns in stock market data. Twelve neural network architectures are compared across ten indexes to measure their performance, finding that the proposed Transformer variants produce significantly better results compared to benchmark models in all cases. The results of ablative experiments reveal the superiority of Transformer models in capturing long-term dependencies and extracting meaningful features from time series data. The findings suggest that Transformer neural networks outperform LSTM networks and other traditional models in forecasting financial market trends. This research contributes to the growing body of literature on deep learning applications in finance and provides valuable insights for government officials, researchers, and investors seeking to make informed decisions in the stock market. The implications of this study extend beyond academia, offering practical implications for enhancing prediction accuracy and optimizing investment strategies in the dynamic and volatile financial market landscape. [ABSTRACT FROM AUTHOR]

The existing body of research on dynamic customer segmentation has primarily focused on segment-level customer purchasing behavior (CPB) analysis to tailor marketing strategies for distinct customer groups. However, these approaches often lack the granularity required for personalized marketing at the individual level. Moreover, the analysis of customer transitions between different groups has largely been overlooked. This study addresses these gaps by developing an efficient framework that enables businesses to forecast customer behavior, assess the impact of various strategies on each customer separately, and analyze customer transition between segments. This can facilitate providing personalized marketing strategies, fostering a gradual transition toward a desired customer status, and enhancing the overall marketing precision. In this study, we employ time series feature vectors encompassing recency, frequency, monetary value, and lifespan, applying the K-means algorithm with a range of distance metrics for customer segmentation along with classification algorithms to predict customer behavior. Leveraging counterfactual analysis, we establish a solution for analyzing customer transitions between groups and evaluating personalized marketing strategies. Our findings underscore the superior performance of the Euclidean distance metric, closely followed by the Manhattan distance, in distinguishing the patterns in time series customer behavior, with logistic regression excelling in predicting customer status. This study enables decision-makers to forecast the impact of diverse marketing strategies on customer behavior which facilitates customer retention and engagement through well-informed decisions. [ABSTRACT FROM AUTHOR]

In the realm of financial decision-making, it is crucial to consider multiple factors, among which lies the pivotal concern of a firm's potential insolvency. Numerous insolvency prediction models utilize machine learning techniques try to solve this critical aspect. This paper aims to assess the financial performance of financial investment firms listed on the Iraq Stock Exchange (ISX) from 2012 to 2022. A Multi-Layer Perceptron predicting model with a parameter optimizer is proposed integrating an additional feature selection process. For this latter process, three methods are proposed and compared: Principal Component Analysis, correlation coefficient, and Particle Swarm Optimization. Through the fusion of financial ratios with machine learning, our model exhibits improved forecast accuracy and timeliness in predicting firms' insolvency. The highest accuracy model is the integrated MLP + PCA model, at 98.7%. The other models, MLP + PSO and MLP + CC, also exhibit strong performance, with 0.3% and 1.1% less accuracy, respectively, compared to the first model, indicating that the first model serves as a powerful predictive approach. [ABSTRACT FROM AUTHOR]

The volatile and non-linear nature of stock market data, particularly in the post-pandemic era, poses significant challenges for accurate financial forecasting. To address these challenges, this research develops advanced deep learning and machine learning algorithms to predict financial trends, quantify risks, and forecast stock prices, focusing on the technology sector. Our study seeks to answer the following question: "Which deep learning and supervised machine learning algorithms are the most accurate and efficient in predicting economic trends and stock market prices, and under what conditions do they perform best?" We focus on two advanced recurrent neural network (RNN) models, long short-term memory (LSTM) and Gated Recurrent Unit (GRU), to evaluate their efficiency in predicting technology industry stock prices. Additionally, we integrate statistical methods such as autoregressive integrated moving average (ARIMA) and Facebook Prophet and machine learning algorithms like Extreme Gradient Boosting (XGBoost) to enhance the robustness of our predictions. Unlike classical statistical algorithms, LSTM and GRU models can identify and retain important data sequences, enabling more accurate predictions. Our experimental results show that the GRU model outperforms the LSTM model in terms of prediction accuracy and training time across multiple metrics such as RMSE and MAE. This study offers crucial insights into the predictive capabilities of deep learning models and advanced machine learning techniques for financial forecasting, highlighting the potential of GRU and XGBoost for more accurate and efficient stock price prediction in the technology sector. [ABSTRACT FROM AUTHOR]

Research background: Generative artificial intelligence (AI) and machine learning algorithms support industrial Internet of Things (IoT)-based big data and enterprise asset management in multiphysics simulation environments by industrial big data processing, modeling, and monitoring, enabling business organizational and managerial practices. Machine learning-based decision support and edge generative AI sensing systems can reduce persistent labor shortages and job vacancies and power productivity growth and labor market dynamics, shaping career pathways and facilitating occupational transitions by skill gap identification and laborintensive manufacturing job automation by path planning and spatial cognition algorithms, furthering theoretical implications for management sciences. Generative AI fintech, machine learning algorithms, and behavioral analytics can assist multi-layered payment and transaction processing screening with regard to authorized push payment, account takeover, and synthetic identity frauds, flagging suspicious activities and combating economic crimes by rigorous verification processes. Purpose of the article: We show that edge device management functionalities of cloud industrial IoT and virtual robotic simulation technologies configure plant production and route planning processes across cyber-physical production and industrial automation systems in multi-cloud immersive 3D environments, leading to tangible business outcomes by reinforcement learning and convolutional neural networks. Labor-augmenting automation and generative AI technologies can impact employment participation, increase wage and wealth inequality, and lead to potential job displacement and massive labor market disruptions. The deep learning capabilities of generative AI fintech in terms of adaptive behavioral analytics and credit scoring mechanisms can enhance financial transaction behaviors and algorithmic trading returns, identify fraudulent payment transactions swiftly, and improve financial forecasts, leading to customized investment recommendations and well-informed financial decisions. Methods: Machine learning-based study selection process and text mining systematic review management software and tools leveraged include Abstrackr, CADIMA, Colandr, DistillerSR, EPPI-Reviewer, JBI SUMARI, METAGEAR package for R, SluRp, and SWIFT-Active Screener. Such reference management systems are harnessed for methodologically rigorous evidence synthesis, study selection and characteristic extraction, predictive document classification, machine learning-based citation and record screening, bias assessment, article retrieval automation, and document classification and prioritization. Findings & value added: Industrial IoT and 3D augmented reality technologies can create business value by streamlining virtual product and remote asset management across extended reality-based navigation and robotic autonomous systems in smart factory environments by generative AI and machine learning algorithms, articulating business organizational level and theory of management implications. 3D simulation and operational modeling tools can execute and complete complex cognitive task-oriented and knowledge economy jobs, producing first-rate quality outputs swiftly while leading to unemployment spells, labor market disruptions, job displacement losses, and reduced earnings by machine learning clustering and spatial cognition algorithms. Generative AI decentralized finance, interoperable blockchain networks, cash flow management tools, and asset tokenization can mitigate fraud risks, enable digital fund and crypto investing servicing, and automate treasury operations by integrating real-time payment capabilities, routing and configurable workflows, and lending and payment technologies. [ABSTRACT FROM AUTHOR]

IT is recognized as the engine of the digital world. The fact that this technology has multiple sub-sectors makes it the driving force of the economy. With these characteristics, the sector is becoming the center of attention of investors. Considering that investors prioritize profitability, it becomes a top priority for managers to make accurate and reliable profitability forecasts. The aim of this study is to estimate the profitability of IT sector firms traded in Borsa Istanbul using machine learning methods. In this study, the financial data of 13 technology firms listed in the Borsa Istanbul Technology index and operating between March 2000 and December 2023 were used. Return on assets (ROA) and return on equity (ROE) were estimated using machine learning methods such as neural networks, multiple linear regression and decision tree regression. The results obtained reveal that the performance of artificial neural networks (ANN) and multiple linear regression (MLR) are particularly effective. [ABSTRACT FROM AUTHOR]

The aim of this study is to enhance the understanding of volatility dynamics in commodity returns, such as gold and cocoa, as well as the financial market index S&P500. It provides a comprehensive overview of each model's efficacy in capturing volatility clustering, asymmetry, and long-term memory effects in asset returns. By employing models like sGARCH, eGARCH, gjrGARCH, and FIGARCH, the research offers a nuanced understanding of volatility evolution and its impact on asset returns. Using the Skewed Generalized Error Distribution (SGED) in model optimization shows how important it is to understand asymmetry and fat-tailedness in return distributions, which are common in financial data. Key findings include the sGARCH model being the preferred choice for Gold Futures due to its lower AIC value and favorable parameter estimates, indicating significant volatility clustering and a slight positive skewness in return distribution. For Cocoa Futures, the FIGARCH model demonstrates superior performance in capturing long memory effects, as evidenced by its higher log-likelihood value and lower AIC value. For the S&P500 Index, the eGARCH model stands out for its ability to capture asymmetry in volatility responses, showing superior performance in both log-likelihood and AIC values. Overall, identifying superior modeling approaches like the FIGARCH model for long memory effects can enhance risk management strategies by providing more accurate estimates of Value-at-Risk (VaR) and Expected Shortfall (ES). Additionally, the out-of-sample evaluation reveals that Support Vector Regression (SVR) outperforms traditional GARCH models for short-term forecasting horizons, indicating its potential as an alternative forecasting tool in financial markets. These findings underscore the importance of selecting appropriate modeling techniques tailored to specific asset classes and forecasting horizons. Furthermore, the study highlights the potential of advanced techniques like SVR in enhancing forecasting accuracy, thus offering valuable implications for portfolio management and risk assessment in financial markets. [ABSTRACT FROM AUTHOR]

Purpose. Development of a dynamic mathematical model of an innovative company operating in accordance with Ukrainian legislation, allowing for the analysis and adjustment of its financial activities. Methodology. When developing a model and description of financial flows, a factor analysis of the necessary indicators of the financial condition of the enterprise in question was carried out, which were used in the calculations of the presented mathematical modeling. A mathematical apparatus has been developed for calculating financial indicators depending on time. Using inductive, deductive and logical methods, an analysis of the financial situation for the year was carried out. Findings. The main methods and tools for describing financial flows have been analyzed. Mathematical modeling of the innovative company is carried out, which allows analyzing the costs of the enterprise and determining the size of its free funds. The calculation of financial indicators of the enterprise, which are demonstrated in the form of tables, is performed and the dynamics is shown in charts and graphs. All calculations were made on the basis of existing Ukrainian legislation using modern information technologies. Originality. A mathematical dynamic model of changes in financial flows at the enterprise has been developed and implemented. The proposed model takes into account various items of expenses and profits in accordance with the state legislation. Calculations allow for dynamic analysis and determination of the impact of various indicators on the financial condition of the enterprise, which allows for faster adoption and implementation of decisions in managing financial flows. This dynamic model is a convenient tool for any enterprise in Ukraine. Practical value. The results that were obtained during the study can become the basis for creating the necessary digital tools for researching, analyzing and adjusting the financial flows of a particular enterprise. Now methods are available that allow analyzing and describing financial flows more accurately and forecasting their changes in the future. The methodology presented allows the creation of dynamic models that can account for complex dependencies between different indicators and forecast the behavior of financial flows in real time. The financial model allows the simulation of cash flows of planned activities and the evaluation of financial planning in advance, taking into account the conducted research. It is straightforward to use and allows the analysis of different scenarios of business development, significantly reducing the time required. [ABSTRACT FROM AUTHOR]

In our paper, we examine the simultaneous impact of the agglomeration economy (AE) and the innovation ecosystem (IE) on the competency development of SMEs in the Piemonte region in three high-tech sectors: aerospace, mechatronics, and automotive. This study focuses on the critical challenge for SMEs: survival and market position expansion amidst rapidly changing external environments. We used the capability approach, which includes identifying and assessing a company's competencies through the capability matrix, as a tool to analyse a company's competitiveness in the industry based on structural changes; we determined the key insights from managerial practices of SMEs that help to comprehend the behaviour of high-performing, sustainable-performing, and low-performing companies in the development of competencies. The key finding is that SMEs' sustainability in the context of structural changes in the external environment is primarily influenced by factors such as investment in the development of company competencies, industrial cooperation, strategic planning, cross-fertilisation, and the impact of company age and size on the availability of resources, along with the level of internationalisation. This study also identifies the main challenges these SMEs face, particularly the lack of highly skilled human resources to meet the sector's specific needs, as well as the difficulties companies face in adapting to changes in consumer behaviour and forecasting future business strategies. In addition, this study introduces the AE and IE variables as strategic tools to enable company representatives to improve their development strategies. [ABSTRACT FROM AUTHOR]

In the present scenario, retail sales forecasting has a great significance in E-commerce companies. The precise retail sales forecasting enhances the business decision making, storage management, and product sales. Inaccurate retail sales forecasting can decrease customer satisfaction, inventory shortages, product backlog, and unsatisfied customer demands. In order to obtain a better retail sales forecasting, deep learning models are preferred. In this manuscript, an effective Bi-GRU is proposed for accurate sales forecasting related to E-commerce companies. Initially, retail sales data are acquired from two benchmark online datasets: Rossmann dataset and Walmart dataset. From the acquired datasets, the unreliable samples are eliminated by interpolating missing data, outlier's removal, normalization, and de-normalization. Then, feature engineering is carried out by implementing the Adaptive Particle Swarm Optimization (APSO) algorithm, Recursive Feature Elimination (RFE) technique, and Minimum Redundancy Maximum Relevance (MRMR) technique. Followed by that, the optimized active features from feature engineering are given to the Bi-Directional Gated Recurrent Unit (Bi-GRU) model for precise retail sales forecasting. From the result analysis, it is seen that the proposed Bi-GRU model achieves higher results in terms of an R2 value of 0.98 and 0.99, a Mean Absolute Error (MAE) of 0.05 and 0.07, and a Mean Square Error (MSE) of 0.04 and 0.03 on the Rossmann and Walmart datasets. The proposed method supports the retail sales forecasting by achieving superior results over the conventional models. [ABSTRACT FROM AUTHOR]