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Journal articles on the topic 'Long Short-Term Memory and Financial Stability'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Poernomo, Ayu. "Rupiah Exchange Rate Prediction with Long Short-Term Memory Algorithm." Syntax Literate ; Jurnal Ilmiah Indonesia 10, no. 1 (2025): 122–30. https://doi.org/10.36418/syntax-literate.v10i1.55824.

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The fluctuation of the Rupiah exchange rate against foreign currencies in Asia presents a significant challenge in maintaining Indonesia’s economic stability. This study aims to forecast Rupiah exchange rates using the Long Short-Term Memory (LSTM) algorithm. Weekly exchange rate data from 2020 to 2024 were analyzed using a machine learning approach. The process involved data normalization, model training, and evaluation using Mean Absolute Per- centage Error (MAPE) and R-Squared. The results indicate that the LSTM model effectively captures non-linear patterns in time series data with high ac

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Malaikah, Hunida, and Jawaher Faisal Alabdali. "Analysis of Noise on Ordinary and Fractional-Order Financial Systems." Fractal and Fractional 9, no. 5 (2025): 316. https://doi.org/10.3390/fractalfract9050316.

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This study investigated the influence of stochastic fluctuations on financial system stability by analyzing both ordinary and fractional-order financial models under noise. The ordinary financial system experiences perturbations due to bounded random disturbances, whereas the fractional-order counterpart models memory-dependent behaviors by incorporating fractional Gaussian noise (FGN) characterized by a Hurst parameter that governs long-term correlations. This study used data generated through MATLAB simulations based on standard financial models from the literature. Numerical simulations com

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Kumari, Sweta, Naveen Kumar V., Rakshi Gupta, and Pankhuri Agarwal. "An innovative machine learning algorithm-based approach to financial forecasting for business management." Multidisciplinary Science Journal 6 (July 3, 2024): 2024ss0405. http://dx.doi.org/10.31893/multiscience.2024ss0405.

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A crucial component of company management is financial forecasting, which enables firms to deploy resources wisely and make educated decisions. Traditional forecasting techniques frequently rely on statistical models and historical data, which could not adequately account for the complexity of contemporary financial markets. This study uses cutting-edge machine learning algorithms to provide a novel method for financial forecasting. This study proposes a strategy for improving the precision and stability of financial forecasts by harnessing the power of machine learning and financial data. We

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Wang, Shihui. "A Study of Crude Oil Price Forecasting Based on Long Short-Term Memory Model." Advances in Economics, Management and Political Sciences 99, no. 1 (2024): 93–97. http://dx.doi.org/10.54254/2754-1169/99/2024ox0207.

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In industrial production, crude oil prices play a pivotal role, influencing economic stability due to their propensity to induce fluctuations. Predicting these prices accurately is thus a crucial task in economics. This study addresses this challenge by employing a Long Short-Term Memory (LSTM) model, trained on data spanning January 2005 to January 2019, to forecast crude oil prices. Compared against expectations from economists, financial markets, and policymakers, the LSTM model demonstrates robust fitting and reliable predictive capability across various time frames. Notably, it outperform

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Olaniyan, Julius, Deborah Olaniyan, Ibidun Christiana Obagbuwa, Bukohwo Michael Esiefarienrhe, Ayodele A. Adebiyi, and Olorunfemi Paul Bernard. "Intelligent Financial Forecasting with Granger Causality and Correlation Analysis Using Bayesian Optimization and Long Short-Term Memory." Electronics 13, no. 22 (2024): 4408. http://dx.doi.org/10.3390/electronics13224408.

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Financial forecasting plays a critical role in decision-making across various economic sectors, aiming to predict market dynamics and economic indicators through the analysis of historical data. This study addresses the challenges posed by traditional forecasting methods, which often struggle to capture the complexities of financial data, leading to suboptimal predictions. To overcome these limitations, this research proposes a hybrid forecasting model that integrates Bayesian optimization with Long Short-Term Memory (LSTM) networks. The primary objective is to enhance the accuracy of market t

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Tang, Qi, Ruchen Shi, Tongmei Fan, Yidan Ma, and Jingyan Huang. "Prediction of Financial Time Series Based on LSTM Using Wavelet Transform and Singular Spectrum Analysis." Mathematical Problems in Engineering 2021 (June 8, 2021): 1–13. http://dx.doi.org/10.1155/2021/9942410.

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In order to further overcome the difficulties of the existing models in dealing with the nonstationary and nonlinear characteristics of high-frequency financial time series data, especially their weak generalization ability, this paper proposes an ensemble method based on data denoising methods, including the wavelet transform (WT) and singular spectrum analysis (SSA), and long-term short-term memory neural network (LSTM) to build a data prediction model. The financial time series is decomposed and reconstructed by WT and SSA to denoise. Under the condition of denoising, the smooth sequence wi

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Ez-zaiym, Mustapha, Yassine Senhaji, Meriem Rachid, Karim El Moutaouakil, and Vasile Palade. "Fractional Optimizers for LSTM Networks in Financial Time Series Forecasting." Mathematics 13, no. 13 (2025): 2068. https://doi.org/10.3390/math13132068.

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This study investigates the theoretical foundations and practical advantages of fractional-order optimization in computational machine learning, with a particular focus on stock price forecasting using long short-term memory (LSTM) networks. We extend several widely used optimization algorithms—including Adam, RMSprop, SGD, Adadelta, FTRL, Adamax, and Adagrad—by incorporating fractional derivatives into their update rules. This novel approach leverages the memory-retentive properties of fractional calculus to improve convergence behavior and model efficiency. Our experimental analysis evaluate

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Hudzaifa, Ashilla Maula, Valerie Vincent Yang, and Defi Yusti Faidah. "THE IMPACT OF THE PRESIDENTIAL ELECTION ON IDX COMPOSITE PREDICTIONS USING LONG SHORT TERM MEMORY." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 4 (2024): 2397–412. http://dx.doi.org/10.30598/barekengvol18iss4pp2397-2412.

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An analysis of the performance of Indonesia's capital market, or Indonesia Stock Exchange (IDX), shows significant growth in recent years, with market capitalization increasing dramatically from IDR 679.95 trillion in 2004 to IDR 11,674.06 trillion by 2023. The IDX plays an important role in the Indonesian economy by facilitating capital formation and providing opportunities for investors to diversify their portfolios. However, the capital market is vulnerable to political events, such as presidential elections, which can affect national stability and economic performance. An analysis of the s

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9

Liu, Yezhen, Xilong Yu, Yanhua Wu, and Shuhong Song. "Forecasting Variation Trends of Stocks via Multiscale Feature Fusion and Long Short-Term Memory Learning." Scientific Programming 2021 (September 21, 2021): 1–9. http://dx.doi.org/10.1155/2021/5113151.

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Forecasting stock price trends accurately appears a huge challenge because the environment of stock markets is extremely stochastic and complicated. This challenge persistently motivates us to seek reliable pathways to guide stock trading. While the Long Short-Term Memory (LSTM) network has the dedicated gate structure quite suitable for the prediction based on contextual features, we propose a novel LSTM-based model. Also, we devise a multiscale convolutional feature fusion mechanism for the model to extensively exploit the contextual relationships hidden in consecutive time steps. The signif

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Bouslimi, Jihen, Sahbi Boubaker, and Kais Tissaoui. "Forecasting of Cryptocurrency Price and Financial Stability: Fresh Insights based on Big Data Analytics and Deep Learning Artificial Intelligence Techniques." Engineering, Technology & Applied Science Research 14, no. 3 (2024): 14162–69. http://dx.doi.org/10.48084/etasr.7096.

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This paper evaluates the performance of the Long Short-Term Memory (LSTM) deep learning algorithm in forecasting Bitcoin and Ethereum prices during the COVID-19 epidemic, using their high-frequency price information, ranging from December 31, 2019, to December 31, 2020. Deep learning (DL) techniques, which can withstand stylized facts, such as non-linearity and long-term memory in high-frequency data, were utilized in this paper. The LSTM algorithm was employed due to its ability to perform well with time series data by reducing fading gradients and reliance over time. The obtained empirical r

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11

Lan, Yi. "A Hybrid CNN-LSTM Model for Stock Price Prediction with Spatial and Temporal Dependencies." Applied and Computational Engineering 155, no. 1 (2025): 236–42. https://doi.org/10.54254/2755-2721/2025.gl23570.

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Stock price prediction is crucial in financial decision-making and investment strategies, significantly influencing investors' profitability and market stability. This paper aims to systematically review and evaluate Machine Learning (ML) and Deep Learning (DL) methodologies, primarily focusing on Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) for stock price forecasting. A hybrid CNN-LSTM model is proposed to enhance predictive accuracy. Specifically, the CNN component initially extracts essential spatial features from historical financial data, while the LSTM

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Ahmad, Zeeshan, Shudi Bao, and Meng Chen. "DeepONet-Inspired Architecture for Efficient Financial Time Series Prediction." Mathematics 12, no. 24 (2024): 3950. https://doi.org/10.3390/math12243950.

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Financial time series prediction is a fundamental problem in investment and risk management. Deep learning models, such as multilayer perceptrons, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM), have been widely used in modeling time series data by incorporating historical information. Among them, LSTM has shown excellent performance in capturing long-term temporal dependencies in time-series data, owing to its enhanced internal memory mechanism. In spite of the success of these models, it is observed that in the presence of sharp changing points, these models fail to

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Korade, Nilesh B., Mahendra B. Salunke, Amol A. Bhosle, et al. "Integrating deep learning and optimization algorithms to forecast real-time stock prices for intraday traders." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2254–63. https://doi.org/10.11591/ijece.v15i2.pp2254-2263.

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The number of stock investors is steadily increasing due to factors such as the availability of high-speed internet, smart trading platforms, lower trading commissions, and the perception that trading is an effective way of earning extra income to enhance financial stability. Accurate forecasting is crucial to earning profits in the stock market, as it allows traders to anticipate price changes and make strategic investments. The traders must skillfully negotiate short-term market changes to maximize gains and minimize losses, as intraday profit mostly depends on the timing of buy and sell dec

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Oyemade, David, and Eseoghene Ben-Iwhiwhu. "An Investigation of Predictability of Traders' Profitability Using Deep Learning." American Journal of Computer Science and Technology 7, no. 2 (2024): 51–61. http://dx.doi.org/10.11648/j.ajcst.20240702.14.

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Trading in the financial market is a daunting task in spite of the attracting increase of the daily turnover of the Forex financial market from 6.5 trillion USD in 2022 to approximately 7.5 trillion USD in 2024. About 80% of retail investors lose money. However, to minimize the risk of losses, investors explore the possibility of profitable trading by resorting to social trading. In social trading of the financial market, the performance statistics and performance charts of traders with diverse trading strategies, methods and characteristics are showcased by the financial market brokers to ena

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15

Zhu, Mingfu, Haoran Qi, and Panke Qin. "IGWO-MALSTM: An Improved Grey Wolf-Optimized Hybrid LSTM with Multi-Head Attention for Financial Time Series Forecasting." Applied Sciences 15, no. 12 (2025): 6619. https://doi.org/10.3390/app15126619.

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In the domain of financial markets, deep learning techniques have emerged as a significant tool for the development of investment strategies. The present study investigates the potential of time series forecasting (TSF) in financial application scenarios, aiming to predict future spreads and inform investment decisions more effectively. However, the inherent nonlinearity and high volatility of financial time series pose significant challenges for accurate forecasting. To address these issues, this paper proposes the IGWO-MALSTM model, a hybrid framework that integrates Improved Grey Wolf Optim

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Akgüller, Ömer, Mehmet Ali Balcı, Larissa Margareta Batrancea, and Lucian Gaban. "Fractional Transfer Entropy Networks: Short- and Long-Memory Perspectives on Global Stock Market Interactions." Fractal and Fractional 9, no. 2 (2025): 69. https://doi.org/10.3390/fractalfract9020069.

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This study addresses the challenge of capturing both short-run volatility and long-run dependencies in global stock markets by introducing fractional transfer entropy (FTE), a new framework that embeds fractional calculus into transfer entropy. FTE allows analysts to tune memory parameters and thus observe how different temporal emphases reshape the network of directional information flows among major financial indices. Empirical evidence reveals that when short-memory effects dominate, markets swiftly incorporate recent news, creating networks that adapt quickly but remain vulnerable to trans

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17

Li, Jin. "Analysis of Evolving Hazard Overflows and Construction of an Alert System in the Chinese Finance Industry Using Statistical Learning Methods." Mathematics 11, no. 15 (2023): 3279. http://dx.doi.org/10.3390/math11153279.

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With the global economic situation still uncertain and various businesses interconnected within the finance system, financial hazards exhibit characteristics such as rapid propagation and wide scope. Therefore, it is of great significance to analyze evolving changes and patterns of hazard overflow in the finance industry and construct a financial hazard alert system. We adopt the time-varying parameter vector auto-regressive model to examine the degree and evolving characteristics of financial hazard alerts from an industry perspective and construct financial hazard measurement indicators. To

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18

Shi, Xiangting, Xiaochen Wang, Yakang Zhang, Xiaoyi Zhang, Manning Yu, and Lihao Zhang. "Innovative novel regularized memory graph attention capsule network for financial fraud detection." PLOS One 20, no. 5 (2025): e0317893. https://doi.org/10.1371/journal.pone.0317893.

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Financial fraud detection (FFD) is crucial for ensuring the safety and efficiency of financial transactions. This article presents the Regularised Memory Graph Attention Capsule Network (RMGACNet), an original architecture aiming at improving fraud detection using Bidirectional Long Short-Term Memory (BiLSTM) networks combined with advanced feature extraction and classification algorithms. The model is tested on two reliable datasets: the European Cardholder (ECH) transactions dataset, which contains 284,807 transactions and 492 fraud instances, and the IEEE-CIS dataset, which has more than 1

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Chaluvadi, Archana, Visrutatma Rao Vallu, Winner Pulakhandam, and R. Lakshmana Kumar. "A Cloud-Based Framework Combining LSTM and Attention Mechanism for Comprehensive Financial Risk Prediction in Banking." International Journal of Advanced Multidisciplinary Research and Studies 4, no. 4 (2024): 1322–28. https://doi.org/10.62225/2583049x.2024.4.4.4417.

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With the real-time high-speed financial transactions and digitalization of the modern fast-paced finance age, there is an urgent need for intelligent systems that can forecast financial risks at high speed and precision. This paper introduces a cloud-based system that is based on the integration of Long Short-Term Memory (LSTM) networks and Attention Mechanisms to provide robust financial risk predictions for the banking industry. With increasing complexity and magnitude of financial transactions, sophisticated predictive models that are capable of processing in real-time are needed. The conve

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20

Huang, Yijing, and Vinay Vakharia. "Deep Learning-Based Stock Market Prediction and Investment Model for Financial Management." Journal of Organizational and End User Computing 36, no. 1 (2024): 1–22. http://dx.doi.org/10.4018/joeuc.340383.

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This study explores the potential application of deep learning techniques in stock market prediction and investment decision-making. The authors used multi-temporary stock data (MTS) for effective multi-scale feature extraction in reverse cross attention (RCA), combined with improved whale optimization algorithm (IWOA) to select the optimal parameters for the bidirectional long short-term memory network (BiLSTM) and constructed an innovative RCA-BiLSTM stock intelligent trend prediction model. At the same time, a complete RCA-BiLSTM-DQN stock intelligent prediction and investment model was est

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Wan, Fei, and Ping Li. "A Novel Money Laundering Prediction Model Based on a Dynamic Graph Convolutional Neural Network and Long Short-Term Memory." Symmetry 16, no. 3 (2024): 378. http://dx.doi.org/10.3390/sym16030378.

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Money laundering is an illicit activity that seeks to conceal the nature and origins of criminal proceeds, posing a substantial threat to the national economy, the political order, and social stability. To scientifically and reasonably predict money laundering risks, this paper focuses on the “layering” stage of the money laundering process in the field of supervised learning for money laundering fraud prediction. A money laundering and fraud prediction model based on deep learning, referred to as MDGC-LSTM, is proposed. The model combines the use of a dynamic graph convolutional network (MDGC

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Agarwal, Aman, and Yamini Agarwal. "AI-Driven Economic and Financial Forecasting: House Prices, Unemployment, Cryptocurrency, and Business Stability in the USA." International Journal of Science and Social Science Research 2, no. 4 (2025): 228–37. https://doi.org/10.5281/zenodo.15029917.

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Artificial Intelligence (AI) and Machine Learning (ML) have transformed economic and financial forecasting, offering valuable insights into key market indicators such as housing prices, unemployment rates, cryptocurrency trends, and business stability in the United States. This study examines AI-driven approaches for forecasting economic trends, identifying patterns, and mitigating financial risks. Various ML models, including Random Forest, Gradient Boosting, Long Short-Term Memory (LSTM) networks, and Support Vector Machines (SVM), are applied across economic domains. For instance, house pri

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Zhang, Hui. "A Deep Learning Model for ERP Enterprise Financial Management System." Advances in Multimedia 2022 (July 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/5783139.

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With the advent of the information age, the need for information technology construction is beginning to be realized when working in corporate financial management. The application of ERP systems to financial management has become a major trend in the development of modern society. This can help companies collect financial information in real time and analyze and process the obtained information. This paper first gives the significance and models of the ERP financial management system. Then, a financial risk prediction model based on a deep learning model is designed. The method proposes an im

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Gao, Xia, Xiaoqian Yang, and Yuchen Zhao. "Rural micro-credit model design and credit risk assessment via improved LSTM algorithm." PeerJ Computer Science 9 (September 26, 2023): e1588. http://dx.doi.org/10.7717/peerj-cs.1588.

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Rural microcredit plays an important role in promoting rural economic development and increasing farmers’ income. However, traditional credit risk assessment models may have insufficient adaptability in rural areas. This study is based on the improved Long Short Term Memory (LSTM) algorithm using self organizing method, aiming to design an optimized evaluation model for rural microcredit risk. The improved LSTM algorithm can better capture the long-term dependence between the borrower’s historical behavior and risk factors with its advantages in sequential data modeling. The experimental resul

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Rai, Kovat, and Amit Vijayan. "Performance Comparison of Long Short-Term Memory and Convolutional Neural Network for Prediction of Exchange Rate of Indian Rupee against US Dollar." International Journal Artificial Intelligent and Informatics 3, no. 1 (2025): 9–15. https://doi.org/10.33292/ijarlit.v3i1.41.

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This study compares the effectiveness of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models in predicting the exchange rate of the Indian Rupee (INR) against the United States Dollar (USD). Using historical data from 2017 to 2023 obtained from Yahoo Finance, both models were trained and evaluated based on several performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R²), and accuracy. The results showed that the hybrid LSTM model consistently outperformed the CNN mode

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Raissa, Zabrina. "A COMPARATIVE ANALYSIS OF FINANCIAL PERFORMANCE FORECASTING MODELS: ARIMA, ARIMA-GARCH & LSTM IN INDONESIAN BANKING STOCKS." JMBI UNSRAT (Jurnal Ilmiah Manajemen Bisnis dan Inovasi Universitas Sam Ratulangi). 12, no. 1 (2025): 328–40. https://doi.org/10.35794/jmbi.v12i1.61515.

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The banking sector is a crucial generator of economic activity and financial stability in Indonesia's stock market, so precise forecasting of bank stock prices is critical for making informed investment decisions. This study evaluates the forecasting performance of ARIMA, ARIMA-GARCH, and Long Short-Term Memory (LSTM) models for predicting daily closing prices of five key Indonesian banking stocks: BBCA.JK, BBNI.JK, BBRI.JK, BMRI.JK, and BNGA.JK. Using historical data from January 2022 to December 2024, the models are evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), a

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Xu, Jialing, Jingxing He, Jinqiang Gu, et al. "Financial Time Series Prediction Based on XGBoost and Generative Adversarial Networks." International Journal of Circuits, Systems and Signal Processing 16 (January 15, 2022): 637–45. http://dx.doi.org/10.46300/9106.2022.16.79.

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Considering the problems of the model collapse and the low forecast precision in predicting the financial time series of the generative adversarial networks (GAN), we apply the WGAN-GP model to solve the gradient collapse. Extreme gradient boosting (XGBoost) is used for feature extraction to improve prediction accuracy. Alibaba stock is taken as the research object, using XGBoost to optimize its characteristic factors, and training the optimized characteristic variables with WGAN-GP. We compare the prediction results of WGAN-GP model and classical time series prediction models, long short term

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Abir, Shake Ibna, Mohammad Hasan Sarwer, Mahmud Hasan, et al. "Deep Learning for Financial Markets: A Case-Based Analysis of BRICS Nations in the Era of Intelligent Forecasting." Journal of Economics, Finance and Accounting Studies 7, no. 1 (2025): 01–15. https://doi.org/10.32996/jefas.2025.7.1.1.

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In this paper, we develop a method based on a deep learning method in financial market prediction, which includes BRICS economies as the test cases. Financial markets are rife with volatility that is affected by a "bed of complexity," coddled by local and distal factors. To leverage these vast datasets both deep learning models such as Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks as well as hybrid architectures are used in this study. The paper evaluates the predictive accuracy of the models, and by so doing, identifies their strengths in predicting temporal dep

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Wang, Jingyang, Tianhu Zhang, Tong Lu, and Zhihong Xue. "A Hybrid Forecast Model of EEMD-CNN-ILSTM for Crude Oil Futures Price." Electronics 12, no. 11 (2023): 2521. http://dx.doi.org/10.3390/electronics12112521.

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Crude oil has dual attributes of finance and energy. Its price fluctuation significantly impacts global economic development and financial market stability. Therefore, it is necessary to predict crude oil futures prices. In this paper, a hybrid forecast model of EEMD-CNN-ILSTM for crude oil futures price is proposed, which is based on Ensemble Empirical Mode Decomposition (EEMD), Convolutional Neural Network (CNN), and Improved Long Short-Term Memory (ILSTM). ILSTM improves the output gate of Long Short-Term Memory (LSTM) and adds important hidden state information based on the original output

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Tan, Jiaqi. "NVIDIA Stock Price Prediction by Machine Learning." Highlights in Business, Economics and Management 24 (January 22, 2024): 1072–76. http://dx.doi.org/10.54097/dsz8ns50.

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This study explores how to forecast NVIDIA stock values using machine learning models. The chosen prediction models are Long Short-Term Memory (LSTM), Random Forest, and Linear Regression. Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R² score are only a few of the metrics used to assess the effectiveness of a model. The empirical findings showcase the remarkable prowess of the LSTM model in prognosticating NVIDIA stock prices, exhibiting a heightened level of accuracy and predictive acumen in contrast to Random Forest and Linear Regression counterpar

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Shi, Xiangting, Yakang Zhang, Manning Yu, and Lihao Zhang. "Deep learning for enhanced risk management: a novel approach to analyzing financial reports." PeerJ Computer Science 11 (January 27, 2025): e2661. https://doi.org/10.7717/peerj-cs.2661.

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Risk management is a critical component of today’s financial environment because of the enormity and complexity of data contained in financial statements. Business situations, plans, and schedule risk assessment with the help of conventional ways which involve analytical, technical, and heuristic models are inadequate to address the complex structures of the latest data. This research brings out the Hybrid Financial Risk Predictor (HFRP) model, using the convolutional neural networks (CNN) and long-short term memory (LSTM) networks to improve financial risk prediction. A combination of quantit

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Vásquez-Serpa, Luis-Javier, Ciro Rodríguez, Jhelly-Reynaluz Pérez-Núñez, and Carlos Navarro. "Challenges of Artificial Intelligence for the Prevention and Identification of Bankruptcy Risk in Financial Institutions: A Systematic Review." Journal of Risk and Financial Management 18, no. 1 (2025): 26. https://doi.org/10.3390/jrfm18010026.

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The identification and prediction of financial bankruptcy has gained relevance due to its impact on economic and financial stability. This study performs a systematic review of artificial intelligence (AI) models used in bankruptcy prediction, evaluating their performance and relevance using the PRISMA and PICOC frameworks. Traditional models such as random forest, logistic regression, KNN, and neural networks are analyzed, along with advanced techniques such as Extreme Gradient Boosting (XGBoost), convolutional neural networks (CNN), long short-term memory (LSTM), hybrid models, and ensemble

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Liu, Juan, Wei Huang, and Pingping Kong. "Deep Learning and Variational Modal Decomposition in Stock Price Prediction." Scientific Journal of Economics and Management Research 6, no. 12 (2024): 211–20. https://doi.org/10.54691/wf3sbh45.

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This study explores stock price forecasting, a critical topic for economic stability and investor decision-making. Traditional models like ARIMA struggle with stock market complexity due to their linear assumptions. To address this, the study examines advanced methods, focusing on deep learning techniques such as CNNs and LSTMs for their predictive strengths. It proposes a hybrid model combining Variational Mode Decomposition (VMD) and Bi-directional Long Short-Term Memory Networks (BiLSTM). VMD reduces time series non-stationarity, while BiLSTM captures sequence features via bi-directional pr

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Matviychuk, Andriy, Oleksandr Novoseletskyy, Serhii Vashchaiev, Halyna Velykoivanenko, and Igor Zubenko. "Fractal analysis of the economic sustainability of enterprise." SHS Web of Conferences 65 (2019): 06005. http://dx.doi.org/10.1051/shsconf/20196506005.

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The article deals with the method of calculating the fractal analysis, the time series of economic sustainability of the industrial enterprise on the trend-resistant sustainability were investigated by estimating the depth of the long-term memory of the time series and constructing a phase portrait. According to the approach used, the “depth of the long memory” is estimated in terms of fuzzy sets. The approach to the estimation of the index of economic stability is developed, based on the methods of forming an integrated indicator consisting of an assessment of such subsystems as the industria

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Tran, Dat, and Allan W. Tham. "Accuracy Comparison Between Feedforward Neural Network, Support Vector Machine and Boosting Ensembles for Financial Risk Evaluation." Journal of Risk and Financial Management 18, no. 4 (2025): 215. https://doi.org/10.3390/jrfm18040215.

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Loan defaults have become an increasing concern for lending institutions, presenting significant challenges to profitability and operational stability. However, with the advent of advanced data processing capabilities, greater data availability, and the development of sophisticated machine learning techniques—particularly neural networks—new opportunities have emerged for classifying and predicting loan defaults beyond traditional manual methods. This, in turn, can reduce risk and enhance overall financial performance. In recent years, institutions have increasingly employed these advanced tec

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Li, Jingyao. "Comparison of Different Machine Learning Approaches for Forecasting Stock Prices." Highlights in Science, Engineering and Technology 94 (April 26, 2024): 17–23. http://dx.doi.org/10.54097/2re5n809.

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Predicting stock prices is a crucial task that has significant implications for investment decisions, business strategies, and financial market stability. Accurate predictions can help investors make informed decisions, capture opportunities, and minimize risks. Understanding financial markets, economic data, company-specific elements, and a variety of statistical and analytical approaches are all necessary for making accurate stock price predictions. This paper employs three machine learning methods to forecast stock price data from a three-year time series dataset. Stock prediction plays a f

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Luo, Yiyang. "Application of deep learning algorithms in predicting the exchange rate of Chinese yuan against the US dollar." Applied and Computational Engineering 52, no. 1 (2024): 170–76. http://dx.doi.org/10.54254/2755-2721/52/20241539.

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This paper mainly studies the feasibility of using deep learning algorithms to predict the exchange rate between the Chinese yuan and the US dollar. Firstly, this study chooses the Long Short Term Memory Network (LSTM) as the main algorithm, which is a deep learning model suitable for processing time series data. Then, the study collects a large amount of data, including historical exchange rate data, macroeconomic indicators, and political events that may affect exchange rates. These data provide sufficient information for the model to learn and predict exchange rate fluctuations. The study f

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Liu, Yuxin, Yuhan Zhang, Minxuan Hu, Yuming Tu, and Xinqi Dong. "User Behavior Analysis and Prediction Based on Differential Evolution Algorithm Optimized Transformer Combined with Bidirectional Long Short-Term Memory Neural Network." Applied and Computational Engineering 116, no. 1 (2024): 93–101. https://doi.org/10.54254/2755-2721/2025.20430.

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This paper discusses a method combining differential evolution algorithm and bidirectional long short-term memory neural network (BiLSTM) to optimize Transformer model, aiming to improve the accuracy of bank customer credit analysis and prediction. By optimizing the parameters of Transformer model through differential evolution algorithm and combining with the powerful time series analysis capability of BiLSTM, an efficient credit prediction model is constructed. In the process of model training, with the increase of the number of iterations, the correct rate of the model steadily improves and

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Pasupuleti, Murali Krishna. "Deep Learning for Fraud Detection in Real-Time Transaction Networks." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 05 (2025): 641–51. https://doi.org/10.62311/nesx/rphcr24.

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Abstract: The rapid expansion of digital transactions has intensified the need for real-time fraud detection systems capable of identifying anomalous patterns with high accuracy. Traditional rule-based and classical machine learning approaches often fall short in handling the complex, high-dimensional, and sequential nature of transaction data. This study investigates the application of deep learning models—specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs)—to detect fraudulent behavior in real-time transaction networks. Using the

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Huynh, Tran Trong, and Bui Thanh Khoa. "Financial Bubble Detection Using GSADF and LSTM-RNN Model: Evidence from Emerging Markets." International Journal of Analysis and Applications 23 (June 26, 2025): 150. https://doi.org/10.28924/2291-8639-23-2025-150.

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Forecasting financial bubbles is a crucial task in financial economics due to the disruptive impact of asset price collapses on markets and economic stability. This study proposes a novel approach to bubble prediction by integrating the PSY (Phillips, Shi, and Yu) procedure for bubble detection with Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), a machine learning technique well-suited for modeling nonlinear time-series patterns. Using weekly data from the Vietnamese stock market covering the period from 2015 to 2025, we construct a binary dependent variable indicating the presen

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Korade, Nilesh B., Mahendra B. Salunke, Amol Bhosle, et al. "Integrating deep learning and optimization algorithms to forecast real-time stock prices for intraday traders." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2254. https://doi.org/10.11591/ijece.v15i2.pp2254-2263.

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The number of stock investors is steadily increasing due to factors such as the availability of high-speed internet, smart trading platforms, lower trading commissions, and the perception that trading is an effective way of earning extra income to enhance financial stability. Accurate forecasting is crucial to earning profits in the stock market, as it allows traders to anticipate price changes and make strategic investments. The traders must skillfully negotiate short-term market changes to maximize gains and minimize losses, as intraday profit mostly depends on the timing of buy and sell dec

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Upadhyay, Nevendra Kr. "Enhancing Stock Market Predictability: A Comparative Analysis of RNN And LSTM Models for Retail Investors." Journal of Management and Service Science (JMSS) 3, no. 1 (2023): 1–9. http://dx.doi.org/10.54060/jmss.v3i1.42.

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The stock markets are important components of the global financial system and have a considerable impact on an economy's growth and stability. This research article uses algorithms, notably deep learning, to increase the prediction of stock values. The efficacy and precision of long short-term memory (LSTM) and recurrent neural networks (RNN) algorithms to estimate stock prices are compared in this study. The paper investigates the potential of deep learning algorithms in creating a more predictable and trustworthy environment for the stock market. The study utilizes historical market data obt

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Ab.Khalil, Mohd Ridzuan, and Azuraliza Abu Bakar. "A Comparative Study of Deep Learning Algorithms in Univariate and Multivariate Forecasting of the Malaysian Stock Market." Sains Malaysiana 52, no. 3 (2023): 993–1009. http://dx.doi.org/10.17576/jsm-2023-5203-22.

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As part of a financial institution, the stock market has been an essential factor in the growth and stability of the national economy. Investment in the stock market is risky because of its price complexity and unpredictable nature. Deep learning is an emerging approach in stock market prediction modeling that can learn the non-linearity and complexity of stock market data. To date, not much study on stock market prediction in Malaysia employs the deep learning prediction model, especially in handling univariate and multivariate data. This study aims to develop a univariate and multivariate st

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Tian, Ruimin. "Google Stocks Prediction by Machine Learning of RNN and LSTM Techniques." Advances in Economics, Management and Political Sciences 57, no. 1 (2024): 285–93. http://dx.doi.org/10.54254/2754-1169/57/20230771.

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The objective of this study is to utilize a combined model of two algorithms, namely Long Short-Term Memory network and Recurrent Neural Network, to forecast the stock price of Google. Using Google stock price data from 2010 to 2022 as the training set and performed data preprocessing and feature engineering. This then build a deep neural network model consisting of multiple LSTM and RNN layers and train it by the backpropagation algorithm. During training, this paper employs an appropriate loss function and optimizer to minimize the prediction error. In conclusion, the performance of the mode

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Liu, Jiazhen. "Stock Market Prediction Model Based on Deep Learning and Enhancement of Interpretabilit." Academic Journal of Science and Technology 13, no. 1 (2024): 147–50. http://dx.doi.org/10.54097/6r8hhv32.

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This paper's objective is to delve into the utilization of deep learning technology within the realm of stock market forecasting, specifically emphasizing the enhancement of model interpretability. To accomplish this, we employ a deep learning model rooted in the long-term and short-term memory network (LSTM). We proceed to construct three distinct models: the foundational LSTM model, an LSTM model augmented with an attention mechanism, and an LSTM model incorporating an integrated learning strategy. By conducting comparative experiments, we assess the effectiveness of these models in both reg

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Kirubadevi M, Mrs. "Recurrent Neural Network Based Financial Data Analysis and Forecasting." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46140.

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Abstract -- The stock market is inherently volatile and influenced by a wide array of factors including economic indicators, political events, market sentiment, and company-specific news. Accurately predicting stock prices has long been a challenge for investors, traders, and researchers alike. This project, titled "Stock Market Prediction", aims to leverage advanced machine learning techniques to forecast future stock prices based on historical data and key market indicators. The study employs a combination of supervised learning algorithms such as Linear Regression, Support Vector Machines (

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Enajero, Jude. "The Impact of AI-Driven Predictive Models on Traditional Financial Market Volatility: A Comparative Study with Crypto Markets." International Journal of Advances in Engineering and Management 7, no. 1 (2025): 416–27. https://doi.org/10.35629/5252-0701416427.

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This study investigates the effectiveness of AIdriven predictive models in managing volatility in traditional financial markets compared to cryptocurrency markets. By examining the impact of these models on market stability, the research aims to identify best practices and areas for improvement in the deployment of AI technologies across different asset classes. We employed a Maximum Likelihood (ML) ARCH model to analyze market volatility, effectively capturing the time-varying volatility characteristics inherent in financial data. Our empirical framework integrates AI-driven metrics, such as

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Sakshi, Vora, Shaikh Rayees, Bhanushali Kartik, and Pradnya Patil Prof. "Stock Price Prediction using LSTM." Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) 2, no. 4 (2022): 1–5. https://doi.org/10.54105/ijainn.D1052.062422.

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<strong>Abstract:</strong> The movement of stock prices is non-linear and complicated. In this study, we compared and analyzed various neural network forecasting methods based on real problems related to stock price demand forecasting. We ultimately selected the LSTM (Long Short-Term Memory) [1] neural network as traditional RNN's long-term reliance is improved by LSTM, which substantially enhances prediction accuracy and stability. The practicality of this method and the pertinence of the model are then inspected, and final conclusions are drawn through a detailed examination of stock pri

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Kamenshchikov, Sergey A. "Transport Catastrophe Analysis as an Alternative to a Monofractal Description: Theory and Application to Financial Crisis Time Series." Journal of Chaos 2014 (September 14, 2014): 1–8. http://dx.doi.org/10.1155/2014/346743.

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The goal of this investigation was to overcome limitations of a persistency analysis, introduced by Benoit Mandelbrot for monofractal Brownian processes: nondifferentiability, Brownian nature of process, and a linear memory measure. We have extended a sense of a Hurst factor by consideration of a phase diffusion power law. It was shown that precatastrophic stabilization as an indicator of bifurcation leads to a new minimum of momentary phase diffusion, while bifurcation causes an increase of the momentary transport. An efficiency of a diffusive analysis has been experimentally compared to the

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Kofidis, Kinstantinos, and Cătălina Lucia Cocianu. "COMPARATIVE ANALYSIS OF RF, SVR WITH GAUSSIAN KERNEL AND LSTM FOR PREDICTING LOAN DEFAULTS." Journal of Financial Studies 9, no. 17 (2024): 91–106. http://dx.doi.org/10.55654/jfs.2024.9.17.06.

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This investigation elucidates the paramount endeavour of predicting loan defaults, which is imperative for the efficacious management of financial risk and the overall stability of financial institutions. Conventional statistical methodologies frequently encounter challenges in effectively capturing the nonlinear and sequential dynamics inherent in financial data, thereby necessitating the examination of more sophisticated machine learning methodologies. This research reports an experimental-based comparative evaluation of three ML and DL models—Long Short-Term Memory (LSTM) networks, Random F

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