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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.)

We examine the value of due diligence recommendations on Reddit's Wallstreetbets (WSB) platform. Before the Gamestop (GME) short squeeze, recommendations are significant predictors of returns and cash-flow news. This predictability is eliminated post-GME. Post-GME, the fraction of reports emphasizing price-pressure or attention-grabbing stocks dramatically increases, and the decline in informativeness is concentrated in these reports. Similarly, retail trade informativeness is particularly strong following DD reports in the pre-GME period, but not post-GME. Our findings are consistent with the view that the Gamestop event altered the culture of WSB , leading to a deterioration in investment quality that adversely affected smaller investors. [ABSTRACT FROM AUTHOR]

We document a new empirical phenomenon in which the aggregate positions of money managers, who are sophisticated speculators in the commodity futures market, as disclosed by the Disaggregated Commitments of Traders reports, can predict the cross section of commodity producers' stock returns in the subsequent week. We employ a number of cross-sectional methods, including calendar-time regression analysis, single-sort, double-sort, and Fama–MacBeth regressions, to confirm the predictability results. The results are more pronounced in firms with higher information asymmetry. We thus add more empirical evidence to the literature on costly information processing, which leads to gradual information diffusion across asset markets. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to making stock price movement prediction better serve the trading strategy by embedding the strategy into deep learning. The trading strategy is incorporated into the model's training loss function to obtain a higher return under the given trading strategy. To better embed the strategy into the learning model, we also adopt the data of the same day as a batch instead of fixed-size data as a batch. In other words, each parameter update is based on one day's data. We used the data from the Chinese stock market for an empirical experiment, and according to the characteristics of the Chinese stock market, we made some special treatments. The empirical experiment results show that this method improves the trading strategy's performance compared with the benchmark. [ABSTRACT FROM AUTHOR]

Literature abounds with various statistical and machine learning techniques for stock market forecasting. However, Reinforcement Learning (RL) is conspicuous by its absence in this field and is little explored despite its potential to address the dynamic and uncertain nature of the stock market. In a first-of-its-kind study, this research precisely bridges this gap, by forecasting stock prices using RL, in the static as well as streaming contexts using deep RL techniques. In the static context, we employed three deep RL algorithms for forecasting the stock prices: Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimisation (PPO) and Recurrent Deterministic Policy Gradient (RDPG) and compared their performance with Multi-Layer Perceptron (MLP), Support Vector Regression (SVR) and General Regression Neural Network (GRNN). In addition, we proposed a generic streaming analytics-based forecasting approach leveraging the real-time processing capabilities of Spark streaming for all six methods. This approach employs a sliding window technique for real-time forecasting or nowcasting using the above-mentioned algorithms. We demonstrated the effectiveness of the proposed approach on the daily closing prices of four different financial time series dataset as well as the Mackey–Glass time series, a benchmark chaotic time series dataset. We evaluated the performance of these methods using three metrics: Symmetric Mean Absolute Percentage (SMAPE), Directional Symmetry statistic (DS) and Theil's U Coefficient. The results are promising for DDPG in the static context and GRNN turned out to be the best in streaming context. We performed the Diebold–Mariano (DM) test to assess the statistical significance of the best-performing models. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the rapid development of the China's stock market, but investment risks have also emerged. Stock price is always unstable and non-linear, affected not only by historical transaction data but also by national policies, news, and other data. Stock price and textual data are beginning to be employed in the prediction process. However, the challenge lies in effectively integrating feature information derived from stock price and textual information. To address the problem, in this paper, this paper proposes a Hybrid Data-driven Multi-task Learning(HDML) framework to predict stock price. HDML adopts hybrid data as model input, mining the transaction and capital flow data information in the stock market and considering the impact of investors' emotions on the stock market. In addition, we incorporate multi-task learning, which predicts the closing price range of stock based on structured data and then corrects the prediction results through investors' comment text data. HDML effectively captures the relationship between different modal data through multi-task learning and achieve improvements on both tasks. The experimental results show that compared with previous work, HDML reduces the RMSE of the evaluation set by 12.14% and improves the F1 score by an average of 13.64% at the same time. Moreover, value at risk (VaR), together with the HDML model, can help investors weigh the potential gains against the associated risks. [ABSTRACT FROM AUTHOR]

This paper introduces AlphaVision, an innovative decision support model designed for stock price prediction by seamlessly integrating real-time news updates and Return on Investment (ROI) values, utilizing various machine learning and deep learning approaches. The research investigates the application of these techniques to enhance the effectiveness of stock trading and investment decisions by accurately anticipating stock prices and providing valuable insights to investors and businesses. The study begins by analyzing the complexities and challenges of stock market analysis, considering factors like political, macroeconomic, and legal issues that contribute to market volatility. To address these challenges, we proposed the methodology called AlphaVision, which incorporates various machine learning algorithms, including Decision Trees, Random Forest, Naïve Bayes, Boosting, K-Nearest Neighbors, and Support Vector Machine, alongside deep learning models such as Multi-layer Perceptron (MLP), Artificial Neural Networks, and Recurrent Neural Networks. The effectiveness of each model is evaluated based on their accuracy in predicting stock prices. Experimental results revealed that the MLP model achieved the highest accuracy of approximately 92%, outperforming other deep learning models. The Random Forest algorithm also demonstrated promising results with an accuracy of around 84.6%. These findings indicate the potential of machine learning and deep learning techniques in improving stock market analysis and prediction. The AlphaVision methodology presented in this research empowers investors and businesses with valuable tools to make informed investment decisions and navigate the complexities of the stock market. By accurately forecasting stock prices based on news updates and ROI values, the model contributes to better financial management and business sustainability. The integration of machine learning and deep learning approaches offers a promising solution for enhancing stock market analysis and prediction. Future research will focus on extracting more relevant financial features to further improve the model's accuracy. By advancing decision support models for stock price prediction, researchers and practitioners can foster better investment strategies and foster economic growth. The proposed model holds potential to revolutionize stock trading and investment practices, enabling more informed and profitable decision-making in the financial sector. [ABSTRACT FROM AUTHOR]

Recently, a substantial body of literature in finance has implemented deep learning algorithms as predicting approaches. The principal merit of these methods is the ability to approximate any nonlinear and linear behaviors without understanding the data generation process, making them suitable for predicting stock market movement. This paper explores deep learning approaches to forecast stock price movement in the Hong Kong stock market. The forecasting performance of a temporal convolutional network (TCN) approach and several recurrent neural network (RNN) models is compared. The results show that the TCN can outperform all compared RNN models. Further parameter tuning results also show the superiority of the TCN approach. In addition, we demonstrate that a profitable strategy can be built based on the forecasting results of the proposed model. [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]

Copyright of Journal of Computer Science & Technology (JCS&T) is the property of Journal of Computer Science & Technology 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.)

Copyright of Contaduría y Administración is the property of Facultad de Contaduria y Administracion-Universidad Nacional Autonoma de Mexico 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.)

This paper proposes an approach to improving the accuracy of predicting stock prices. The approach is built on integrating Long-Short-Term Memory (LSTM) networks, a Deep Learning (DL) technique, with Shapely Additive Explanations (SHAP), an Explainable Artificial Intelligence (XAI) technique. This integration is expected to improve predictive accuracy and model explainability. Leveraging the strengths of LSTM in capturing complex sequential patterns in financial time series big data, the model incorporates technical indicators to enhance its performance in forecasting stock movements. Deep learning is known to have a "black box" nature, so incorporating XAI techniques aims to offer detailed insights into how input features contribute to model outputs. This integration of XAI enhances the interpretability of predictions and enables users to understand the underlying rationale of the model, fostering greater trust among investors and financial professionals. The study utilized stock price data from the Yahoo Finance Website. The model processed forecasting of Google stock prices. The practical utility of this approach is demonstrated through the decision-making module, which provides actionable buy, sell, or hold recommendations, showcasing its potential in real-world investment scenarios. Our results indicate a balanced synergy between prediction accuracy and explainability, establishing a transparent and reliable AI-driven financial forecasting framework. [ABSTRACT FROM AUTHOR]

Forecasting stock market prices presents a significant challenge due to the complex and unpredictable nature of the data involved. Accurate predictions can yield substantial financial benefits for traders and investors. The complexity, noise, and non-linearity of stock price data make this task especially difficult. Technological advancements have shifted trading strategies towards automated systems, emphasizing the need to determine optimal transaction points dynamically. A popular method to mitigate losses and increase gains involves using technical analysis to set predetermined thresholds for managing trades. This research focuses on dynamically establishing stop-loss (SL) and take-profit (TP) levels using an in-depth analysis of historical stock data, employing techniques such as standard deviation and Sharpe Ratios, and integrating the Fuzzy Soft Set (FSS) approach. The study categorizes TP/SL levels for strategies suited to either selling (short) or buying (long) positions. It compares the returns from end-of-day Open to Close with those from TP/SL levels to evaluate the effectiveness of these strategies. The primary goal is to refine trading strategies to navigate the volatile stock market, help traders and investors minimize losses and maximize profits, and advance trading practices in this dynamic field. [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]

Forecasting stock prices, a task complicated by the inherent volatility of the stock market, poses a significant challenge. The ability to accurately forecast stock prices is crucial, as it provides investors with crucial insights, enabling them to make informed strategic decisions. In this paper, we propose a novel investment strategy that relies on predicting stock prices. Our approach utilizes a hybrid predictive model that combines light gradient-boosting machine (LightGBM) and extreme gradient boosting (XGBoost). This model is designed to generate short to medium-term forecasts for a wide range of stocks. The strategy has shown promising results, surpassing the local market indices used as benchmarks in terms of both risk and return. Our findings demonstrate the strategy's effectiveness in both upward and downward market trends, underscoring its potential as a robust tool for portfolio management in diverse market conditions. [ABSTRACT FROM AUTHOR]

Predicting stock price movements remains a major challenge in time series analysis. Despite extensive research on various machine learning techniques, few models have consistently achieved success in automated stock trading. One of the main challenges in stock price forecasting is that the optimal model changes over time due to market dynamics. This paper aims to predict stock prices using automated reinforcement learning algorithms and to analyse their efficiency compared with conventional methods. We automate DQN models and their variants, known for their adaptability, by continuously retraining them using recent data to capture market dynamics. We demonstrate that our dynamic models improve the accuracy of predicting the directions of various DAX stocks from 50.00% to approximately 60.00%, compared with conventional methods. Additionally, we conclude that dynamic models should be updated in response to shifts rather than at fixed intervals. [ABSTRACT FROM AUTHOR]

Stock market investments have become an essential part of our lives as they offer a means of growing wealth and securing financial stability for individuals and businesses alike. However, predicting and investing in a stock is a complex method and demands significant levels of knowledge, proficiency, and skill. Stock market prediction is the act of analyzing historical information and trends in the market in order to make informed forecasts about the potential future worth of a given stock or instrument that is traded on a financial instrument exchange. When making stock predictions, most stockbrokers use both fundamental and technical analysis and time series analysis. The study focuses on the implementation of Multi-Linear Regression, LSTM, CNN, and LSTM + GRU based Machine learning techniques using technical analysis to predict stock's closing values from the NYSE, and NASDAQ markets for multiple days. The dataset has been taken from Yahoo, of 10-year span. The factors taken into consideration for predicting stock prices are open, close, low, and high. The model's effectiveness is measured using common strategic metrics like RMSE, MSE, MAE, and R2. A lower value for these variables suggests that the models are good at forecasting stock closing prices. After conducting a comprehensive evaluation, we found that LSTM + GRU model performs the best among the tested models for predicting multiple days, followed by CNN and LSTM. The tested models demonstrate a remarkable level of accuracy in predicting stock market prices. This research work provides a valuable contribution to the fields of financial and technical analysis in the stock market research community. [ABSTRACT FROM AUTHOR]

Forecasting stock prices is always considered as complicated process due to the dynamic and noisy characteristics of stock data influenced by external factors. For predicting the stock market, several approaches have been put forward. Many academics have successfully forecasted stock prices using soft computing models. Recently, there has been growing interest in applying deep learning techniques in combination with technical indicators to forecast stock prices, attracting attention from both investors and researchers. This paper focuses on developing a reliable model for anticipating future stock prices in one day advance using Long Short-Term Memory (LSTM). Three steps make up the suggested model. The approach begins with ten technical indicators computed from previous data as feature vectors. The second phase involves data normalization to scale the feature vectors. Finally, in the third phase, the LSTM model analyzes the closing price for the next day using the normalized characteristics as input. Two stock markets, NASDAQ and NYSE are chosen to evaluate the efficacy of the developed model. To demonstrate how effective the new model is in making predictions, its performance is compared to earlier models. Comparing the suggested model to other models, the findings revealed that it had a high level of prediction accuracy. [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]

Due to China's thriving economy and culture, the performing arts sector has grown remarkably. To study its development, this study has examined the closing prices of performing arts companies. The GA-BPN model was used to analyze the daily closing prices of Funshine Culture (ticker: 300860) and Sanxiang Impression (ticker: 000863) for the predictions of their future daily closing prices. Next, the study compared the predicted prices with the actual closing prices. By comparing four models, namely GA, 7-4-1, 7-4-4-1, and 7-4-4-4-1, the GA-BPN model has a mean square error (MSE) of 2472.580273 and a root mean square error (RMSE) of 49.72504674, which is the smallest value and the smallest error among the four assessment metrics, it was determined that the GA-BPN model yielded the most accurate prediction results, so it was suitable for forecasting stock closing prices. [ABSTRACT FROM AUTHOR]

Stock price prediction is a significant research domain, intersecting statistics, finance, and economics. Accurately forecasting stock price trends has always been a focal point for many researchers. However, traditional statistical methods for time series prediction still lack accuracy. The existing deep learning-based methods for stock price prediction have significantly enhanced the accuracy of predicting individual stock prices. However, they are not effective in forecasting the probability range of future stock price trends. In this paper, to address these limitations, we propose a novel DeepAR model based on the attention mechanism (DeepARA) for both single-point and probabilistic predictions of stock prices. This enhances the accuracy and flexibility of stock price forecasting. Although the attention mechanism was initially developed for natural language processing, it has now found applications in time series forecasting, including the dynamics of the stock market. Attention allocates different weights to time points of varying importance, thereby enhancing the model's ability to capture fundamental market dynamics. We conducted multiple experiments in the Chinese stock market, involving 30 stocks across the top six sectors. Compared with baseline models, the DeepARA model demonstrates superior predictive capabilities. [ABSTRACT FROM AUTHOR]

Stocks with their inherent complexity and dynamic nature influenced by a multitude of external and internal factors, play a crucial role in investment analysis and trend prediction. As financial instruments representing ownership in a company, stocks not only reflect the company's performance but are also affected by external factors such as economic conditions, political climates, and social changes. In a rapidly changing environment, investors and analysts continuously develop models and algorithms to aid in making timely and effective investment decisions. This study applies a Sequential model to predict stock data using a LSTM neural network. The model consists of a single hidden LSTM layer with 200 units. The LSTM layer, the core element of this model, enables it to capture temporal patterns and long-term relationships within the data. The training and testing data were divided into 80% for training and 20% for testing. The Adam optimizer was chosen to optimize the model's learning process, with a learning rate of 0.001. Dropout techniques were applied to reduce overfitting, with a dropout rate of 0.4, along with batch normalization and ReLU activation functions to enhance model performance. Additionally, callback mechanisms, including ReduceLROnPlateau and EarlyStopping, were used to optimize the training process and prevent overfitting. The model was evaluated using MAE and MSE metrics on training, testing, and future prediction data. The results indicate that the model achieved high accuracy, with an MAE of 0.0142 on the test data. However, future predictions showed higher MAE values, suggesting room for improvement in long-term forecasting. The model's ability to accurately predict future stock closing prices can assist investors in making informed investment decisions. [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]

In the field of stock price forecasting, we are actively seeking to integrate various information to more accurately grasp market dynamics. Although historical stock prices and financial news have been widely used in previous studies, it is relatively rare to find research considering news-based, technical, and chip factors simultaneously and evaluating their combined effect. In this study, we innovatively propose a multi-kernel model that not only combines news-based, technical, and chip factor analysis but also utilizes market data provided by the Taiwan Stock Exchange, including institutional trading situations and stock price technical indicators. The aim is to further enhance the prediction accuracy of stock price dynamics. Based on the frequency of word occurrences, we design a new discriminant index to extract features highly correlated with stock prices from financial news. The empirical results show that our multi-kernel model significantly surpasses the single-kernel model in prediction accuracy. However, we also find that although financial news is somewhat correlated with stock price dynamics, information such as chip factors and stock price technical indicators contribute more significantly in our model. This further validates that our multi-kernel learning algorithm can effectively handle multifaceted data sources and give appropriate weights according to the importance of each data point, thereby enhancing the comprehensiveness of prediction. Through this research, we hope to bring new perspectives and inspirations to the field of stock price forecasting. [ABSTRACT FROM AUTHOR]

This study aims to analyse housing prices in Taiwan in the post-epidemic era, identify the crucial factors influencing them, and develop a suitable method for analysing and forecasting them. This study collects relevant data such as Taiwan's housing price index data from 2002 to 2020 to identify the crucial factors affecting Taiwan's housing prices; this is achieved by constructing a regression model, forecasting Taiwan's housing prices through a constructed long short-term memory (LSTM) model that employs big data analytics, and verifying the efficiency of the proposed models through R-square and root mean square error values. The results indicate that the top 10 factors affecting Taiwan's housing prices are mostly related to mortgage interest rates, suggesting that in Taiwan, the effect on housing prices in the post-epidemic era may be non-significant. This study collects data on Taiwan's housing price for the period from the first quarter of 2002 to the fourth quarter of 2020 to construct an LSTM for forecasting Taiwan's housing prices. The results indicate that the proposed LSTM exhibits good fitness, indicating that the model is suitable for analysing and forecasting housing prices. Given that analysing and forecasting quantity is also crucial in housing market analyses and that this study focuses only on predicting housing prices, future research should explore the simultaneous prediction and analysis of both price and quantity. [ABSTRACT FROM AUTHOR]

For several years, a growing interest among numerous researchers and investors in predicting stock price movements has spurred extensive exploration into employing advanced deep learning models. These models aim to develop systems capable of comprehending the stock market's complex nature. Despite the immense challenge posed by the diverse factors influencing stock price forecasting, there remains a notable lack of research focused on identifying the essential feature set for accurate predictions. In this study, we propose a Dynamic Feature Selection System (DFSS) to predict stock prices across the 10 major industries, as classified by the FnGuide Industry Classification Standard (FICS) in South Korea. We apply 16 feature selection algorithms from filter, wrapper, embedded, and ensemble categories. Subsequently, we adjust the settings of industry-specific index data to evaluate the model's performance and robustness over time. Our comprehensive results identify the optimal feature sets that significantly impact stock prices within each sector at specific points in time. By analyzing the inclusion ratios and significance of the optimal feature set by category, we gain insights into the proportion of feature classes and their importance. This analysis ensures the interpretability and reliability of our model. The proposed methodology complements existing methods that do not consider changes in the types of variables significantly affecting stock prices over time by dynamically adjusting the input variables used for learning. The primary goal of this study is to enhance active investment strategies by facilitating the creation of diversified portfolios for individual stocks across various sectors, offering robust models and feature sets that consistently demonstrate high performance across industries over time. [ABSTRACT FROM AUTHOR]

A number of authors have focused on this study to examine how huge data are perceived. A novel big data classification paradigm is introduced by the work's preprocessing, feature extraction and classification techniques. Data normalization is carried out at the preprocessing stage. The MapReduce framework is then utilized to manage the massive data. Statistical features (mean, median, min/max and SD), higher-order statistical features (skewness, kurtosis and enhanced entropy), and correlation-based features are all extracted prior to classification. The Bi-LSTM and deep maxout hybrid classification model classifies the data during the reduction stage. To assure classification accuracy, training will also be deployed by the new Hybrid Butterfly Positioned Coot Optimization (HBPCO) algorithm. The proposed method's accuracy of 97.45% beats the methods of NN (85.13%), CNN (83.78%), RNN (78.37%), Bi-LSTM (82.43%) and SVM (87.83%). [ABSTRACT FROM AUTHOR]

Forecasting stock market prices and trends can be a major challenge for investors and traders because of its volatility and various factors that affects it. Because of that, it is crucial for investors to be able to forecast the stock market alongside its trends as accurately as possible to minimize risk and increase profit, given the complexities and uncertainties inherent in the market. This research aims in digging the potential of LSTM models to forecast the open and close price of the 6 Indonesia bank stock market which consist of 3 national bank (BNI, BRI, and Mandiri) and 3 private bank (CIMB, BCA, and OCBC) in which it also helps in enhancing investment strategies in the banking sector, making it a very helpful tools for investors to gain more profit and minimize loss while also test and evaluate its error rate and accuracy while also evaluate the LSTM performance based on its accuracy and error rates. The historical stock data from April 30, 2019, to April 30, 2024, that's used in this study as the main dataset was acquired from Yahoo Finance. The provided model average accuracy and error rate when forecasting the open and close prices can be seen with an exceptional accuracy and very low error rates which can be proven with the predicted open's MSE: 0.000303, RMSE: 0.018430, MAE: 0.013380, and R²: 0.967834, as well as predicted close's MSE: 0.000511, RMSE: 0.022322, MAE: 0.017260, and R²: 0.942172, making it considered as a robust, reliable, and accurate model for investors and traders to forecast the close and open price of the stock market in the future. The LSTM model performance highlights its capabilities to capture important multivariate patterns of the tested bank's stock market data, which offer more advantages over traditional methods. [ABSTRACT FROM AUTHOR]

This study investigates the application of Artificial Intelligence (AI) and Data Science in stock price forecasting, focusing on advanced predictive models such as Long Short-Term Memory (LSTM) networks, Reinforcement Learning (RL), and sentiment analysis. By integrating traditional financial indicators with real-time sentiment data, this research aims to improve the accuracy and adaptability of stock price predictions in dynamic market conditions. Data preprocessing and feature engineering methods were employed to enhance model inputs, while various machine learning and deep learning models were evaluated based on key performance metrics, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results demonstrated that LSTM and RL models outperformed traditional models, particularly in capturing sequential dependencies and adapting to realtime market changes. The inclusion of sentiment scores provided additional predictive power, underscoring the potential of alternative data sources in financial forecasting. While advanced models showed higher accuracy, they also required substantial computational resources and careful tuning to prevent overfitting. These findings suggest that AI-driven predictive models, when properly integrated and rigorously tested, offer significant advantages in stock price forecasting, particularly for institutional and algorithmic trading applications. [ABSTRACT FROM AUTHOR]

This study integrates wavelet transforms with the Adaptive Neuro-Fuzzy Inference System (ANFIS) to forecast the stock prices of Tata Power and Adani Power. Using Daubechies wavelet (Db8) at level 3, the data were decomposed to capture complex patterns. Performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). In the test phase, the ANFIS- Wavelet model improved RMSE by 5.89%, MAE by 8.35%, and MSE by 11.44% for Tata Power, and RMSE by 6.34%, MAE by 0.38%, and MSE by 12.27% for Adani Power compared to the standard ANFIS model. These results highlight the superior accuracy of the hybrid approach for time series forecasting, offering better insights for decision-making in financial analysis. [ABSTRACT FROM AUTHOR]

Accurately predicting stock prices is a complex challenge due to the volatile nature of financial markets. Traditional time series methods like ARIMA are effective in capturing linear trends but often struggle with complex, non-linear relationships. Recent advancements in machine learning, such as Random Forests and Gradient Boosting, offer improved modeling capabilities for intricate data patterns. This paper introduces a hybrid forecasting model that integrates ARIMA with Random Forests and Gradient Boosting to enhance stock price predictions. The approach starts by using ARIMA to model the linear components of stock price data, followed by the application of Random Forests and Gradient Boosting to the residuals to capture non-linear patterns. The performance of the hybrid model is assessed by generating and comparing prediction tables and plots for future stock prices. Results demonstrate that the hybrid model provides more accurate and reliable forecasts compared to the individual ARIMA, Random Forests, and Gradient Boosting models. This approach illustrates the potential of combining traditional time series analysis with advanced machine learning techniques to achieve superior stock price forecasting. [ABSTRACT FROM AUTHOR]

Patents play a vital role in securing technological competitiveness for companies. Acquisition of such means the definitive acquisition of exclusive rights that substantially contribute to the improvement of firm value, so it is appropriate to observe changes in firm value. This study examines the change in firm value according to the announcement of patent acquisition based on data disclosed by companies from February 2009 to January 2017, when the acquisition of patent rights was changed from ad-hoc disclosure to voluntary disclosure. After estimating the abnormal return through the market model of the event study methodology, the factors affecting firm value were analyzed through regression analysis with the cumulative abnormal return as a dependent variable. The main empirical analysis results of the study are as follows. First, in the patent application, the average abnormal return appears positive before the announcement, and then the AAR turns negative on D-0. As for patent acquisition, the AAR appears positive on D-0 and then turns negative after the announcement. Before it is officially announced, information about a patent application is already reflected in stock prices by market forecasts or insider trading, while the information about patent acquisition is reflected in stock prices at the time of public announcement. There is a difference in the reaction time of investors in the stock market depending on patent application and patent acquisition. In particular, although a patent acquisition announcement is self-disclosure, investors still perceive a company's patent acquisition as useful information, which eventually enhances firm value. Second, because of the identifying factors that affect firm value, significant differences were found in firm value depending on the country in which a patent was obtained. As for corporate characteristics, the lower the R&D expense, the leverage ratio, and the operating profitto- sales ratio, the higher the firm value whereas the higher the beta, the higher the firm value. [ABSTRACT FROM AUTHOR]

Problem definition: Commodity prices have exhibited significant volatility in recent times, which poses an exogenous risk factor for commodity-processing and commodity-trading firms. Accurate commodity price forecasts can help firms leverage data-driven procurement policies that incorporate the underlying price volatility for financial and operational hedging decisions. However, historical prices alone are insufficient to obtain reasonable forecasts because of the extreme volatility. Methodology/results: Building on the hypothesis that commodity prices are driven by real-world events, we propose a method that automatically extracts events from news articles and combines them with price data using a neural network-based predictive model to forecast prices. In addition to achieving a high prediction accuracy that outperforms several benchmarks (by up to 13%), our proposed model is also interpretable, which allows us to identify meaningful events driving the price fluctuations. We found that the events frequently associated with major fluctuations in the price include "natural," "hike," "policy," and "elections," all of which are known drivers of price change. We used a corpus containing about 1.6 million news articles of a major Indian newspaper spanning 15 years and daily prices of four crops (onion, potato, rice, and wheat) in India to perform this study. Our proposed approach is flexible and can be used to predict other time series data, such as disease incidence levels or macroeconomic indicators, that are also influenced by real-world events. Managerial implications: Firms can leverage price forecasts from our system to design inventory and procurement policies in the face of uncertain commodity prices. Commodity merchants can also use the forecasts to design optimal storage policies for physical trading of commodities when prices are volatile. Our findings can also significantly impact policymakers, who can leverage the information of impending price changes and associated events to mitigate the negative effects of price shocks. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0641. [ABSTRACT FROM AUTHOR]

Cryptocurrencies are digital assets that have attracted a lot of investment and attention. It is challenging and essential for investors and traders to predict their stock price movements. Making accurate predictions about cryptocurrency prices is crucial for avoiding losses and gaining profits. Our research proposes a novel method for predicting the stock closed prices of three popular cryptocurrencies: Bitcoin, Ethereum and Polkadot. The SVR (Support vector regression) machine learning method can provide robust and accurate predictions for nonlinear and nonstationary data. This paper compares SVR radial basis functions (RBFs) and hybrid kernels based on cryptocurrency data characteristics. SVR parameters such as regularization, gamma, and epsilon can also be tuned using grid search. Our approach is tested on real-world cryptocurrency stock prices collected from Yahoo Finance. Prediction performance is measured using regression metrics like MAPE (Mean absolute percentage error) and R² score. In our work, a MAPE value of 0.07772 and an R² score of 0.9999 have been obtained. The results of our experiments indicate that our approach is significantly more accurate and reliable than existing methods. [ABSTRACT FROM AUTHOR]

We apply a machine-learning algorithm, calibrated using general human vision, to predict the visual salience of prices of stock price charts. We hypothesize that the visual salience of adjacent prices increases the decision weights on returns computed from those prices. We analyze the inferred impact of these weights in two experimental studies that use either historical price charts or simpler artificial sequences. We find that decision weights derived from visual salience are associated with experimental investments. The predictability is not subsumed by statistical features and goes beyond established models. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

These days, there is a lot of demand for cryptocurrencies, and investors are essentially investing in them. The fact that there are already over 6,000 cryptocurrencies in use worldwide because of this, investors with regular incomes put money into promising cryptocurrencies that have low market values. Accurate pricing forecasting is necessary to build profitable trading strategies because of the unique characteristics and volatility of cryptocurrencies. For consistent forecasting accuracy in an unknown price range, a variation point detection technique is employed. Due to its bidirectional nature, a Bi-LSTM appropriate for recording long-term dependencies in data that is sequential. Accurate forecasting in the cryptocurrency space depends on identifying these connections, since values are subject to change over time due to a variety of causes. In this work, we employ four deep learning-based models that are LSTM, FBProphet, LSTM-GRU and Bidirectional-LSTM(Bi-LSTM) and these four models are compared with Silverkite. Silverkite is the main algorithm of the Python library Graykite by LinkedIn. Using historical bitcoin data from 2012 to 2021, we utilized to analyse the models' mean absolute error (MAE) and root mean square error (RMSE). The Bi-LSTM model performs better than others, with a mean absolute error (MAE) of 0.633 and a root mean square error (RMSE) of 0.815. The conclusion has significant ramifications for bitcoin investors and industry experts. [ABSTRACT FROM AUTHOR]

The complexity in stock index futures markets, influenced by the intricate interplay of human behavior, is characterized as nonlinearity and dynamism, contributing to significant uncertainty in long-term price forecasting. While machine learning models have demonstrated their efficacy in stock price forecasting, they rely solely on historical price data, which, given the inherent volatility and dynamic nature of financial markets, are insufficient to address the complexity and uncertainty in long-term forecasting due to the limited connection between historical and forecasting prices. This paper introduces a pioneering approach that integrates financial theory with advanced deep learning methods to enhance predictive accuracy and risk management in China's stock index futures market. The SF-Transformer model, combining spot-forward parity and the Transformer model, is proposed to improve forecasting accuracy across short and long-term horizons. Formulated upon the arbitrage-free futures pricing model, the spot-forward parity model offers variables such as stock index price, risk-free rate, and stock index dividend yield for forecasting. Our insight is that the mutual information generated by these variables has the potential to significantly reduce uncertainty in long-term forecasting. A case study on predicting major stock index futures prices in China demonstrates the superiority of the SF-Transformer model over models based on LSTM, MLP, and the stock index futures arbitrage-free pricing model, covering both short and long-term forecasting up to 28 days. Unlike existing machine learning models, the Transformer processes entire time series concurrently, leveraging its attention mechanism to discern intricate dependencies and capture long-range relationships, thereby offering a holistic understanding of time series data. An enhancement of mutual information is observed after introducing spot-forward parity in the forecasting. The variation of mutual information and ablation study results highlights the significant contributions of spot-forward parity, particularly to the long-term forecasting. Overall, these findings highlight the SF-Transformer model's efficacy in leveraging spot-forward parity for reducing uncertainty and advancing robust and comprehensive approaches in long-term stock index futures price forecasting. [ABSTRACT FROM AUTHOR]

Understanding the volatile nature of palm oil prices is essential due to its profound economic and market implications. The complexity of forecasting palm oil prices stems from the interplay of various demand and supply forces, making it challenging for scholars to pinpoint the key determinants. This study addresses the intricate challenges in the palm oil industry, including price volatility, shifting consumer preferences, and environmental sustainability, by analysing the factors influencing Malaysian Crude Palm Oil (CPO) pricing dynamics. Utilising data from the Malaysian Palm Oil Board, covering the period from January 2004 to December 2021, we examined the impact of these variables on CPO prices. Methodologically, we employed Autoregressive Distributed Lag (ARDL) and Long Short-Term Memory (LSTM) models to evaluate and forecast CPO prices. Our findings indicate that the LSTM method outperformed the ARDL method in terms of forecasting accuracy. Specifically, the LSTM model showed superior performance when using a selection of ten independent variables identified through LASSO and SHAP estimation, compared to using eleven or four variables based on ARDL regression results. The analysis underscores the significant influence of weather conditions and macroeconomic factors, particularly tax rates, on CPO prices. These findings contribute to a deeper understanding of market dynamics and enhance the accuracy of CPO price forecasting. [ABSTRACT FROM AUTHOR]

The proposed model is a Deep Learning (DL) based method employing Long Short-Term Memory (LSTM) networks for forecasting stocks. The aim of this approach is forecasting stock prices of Apple Inc. using statistics on previous stock prices obtained from Tiingo. The proposed model consists of several stages of processing and modelling, including data cleaning, feature selection, feature scaling, model building, model evaluation, model improvement, and prediction. Cleaning, organising, and transforming raw data into a format appropriate for analysis are all parts of data pre-processing. Feature engineering involves the data extraction and selection of relevant features for accuracy improvement of the model. The scaling of features involves normalising the data to prevent bias in the model. The LSTM models are built and evaluated using multiple metrics such as Mean Squared Error (MAE) and Root Mean Squared Error (RMSE). The model is iteratively improved using a combination of hyperparameter tuning and feature engineering. Finally, the model is then used to forecast stock prices for the following 30 days, and the accuracy of the forecasts is determined. The proposed methodology is designed to outperform traditional LSTM models for predicting the future price of stock by incorporating novel techniques, for feature engineering and model refinement. The suggested design is a comprehensive approach for forecasting future stock prices using DL based techniques. The model is designed to be flexible and adaptable, allowing for customization for different datasets and prediction horizons. It represents a significant improvement over existing LSTM models for stock price prediction to be valuable in a variety of financial industry applications. This paper collects data from Tiingo API and uses stacked LSTM to train the model. The experimental results give only 0.0813 RMSE, which proves that the model is more accurate and precise. [ABSTRACT FROM AUTHOR]