Undermind Research Topic: I want to find academic papers on using machine learning and/or AI for financial forecasting in equity markets, including both theoretical insights and practical implementations.

Research topic

I want to find academic papers on using machine learning and/or AI for financial forecasting in equity markets, including both theoretical insights and practical implementations.

Report

Many academic papers focus on using machine learning and AI for financial forecasting in equity markets, with significant contributions addressing both theoretical foundations and practical implementations, with prominent examples including [1, 2, 3, 4, 6, 10, 11].

Details:

Comprehensive Surveys and Reviews: Papers such as [1] provide an extensive survey of machine learning methods for equity market forecasting, discussing algorithms, feature engineering, and the application of statistical learning and deep learning.
Practical Predictive Models: Several studies focus on practical frameworks and predictive models. For instance, [2] and [3] discuss the usage of ML algorithms, including linear regression, decision trees, random forests, RNNs, and LSTMs. They emphasize feature engineering and incorporating external data sources like news sentiment and macroeconomic indicators to enhance predictive accuracy.
Deep Learning and Hybrid Models: Many studies explore the potential of deep learning architectures. For example, [3] and [11] delve into LSTM architectures, investigating their performance in capturing temporal correlations in stock prices compared to traditional ML models like KNN, RF, and SVM. [4] and [10] compare CNN and LSTM models, highlighting CNN’s spatial feature extraction and LSTM’s ability to manage long-term dependencies effectively.
Hybrid Architectures: Research in [17] shows novel integrative approaches using hybrid models like LSTM-CNN and CNN-LSTM, highlighting their strengths in capturing long-term temporal dependencies and localized patterns respectively.
Comparative Analysis: Papers such as [6], [10], and [13] perform comparative analyses of various ML and DL models, proposing hybrid models that outperform traditional ones in terms of accuracy for stock market forecasting.
Feature Engineering and Data Enrichment: Studies [5] and [12] emphasize the role of feature engineering, combining fundamental and technical indicators, and the incorporation of macroeconomic data to improve forecasting accuracy.

Conclusion: The literature comprehensively covers both theoretical and practical aspects of using machine learning and AI for financial forecasting in equity markets. Key insights include the necessity of feature engineering, the efficacy of deep learning models like LSTM and CNN, and the integration of external data to enhance model performance. These findings collectively support the advancement and practical application of ML and AI in developing robust forecasting models for equity markets.

References

Last 5 years

Last 2 years

> 1 citation per year

> 5 citations per year

Topic Match

Cit./Year

Year

Paper

Paper Relevance Summary

100.0%

0.0

2022

[1] Machine Learning Methods for Equity Time Series Forecasting: A Compendium Alberto Matuozzo, ..., and Maria H. Kim Journal Not Provided 2022 - 0 citations - Show abstract - Cite 100.0% topic match

Surveys ML methods for equity market forecasting Examines algorithms, feature engineering, and testing on various markets and time horizons Discusses gaps and future research, focusing on both statistical and deep learning methods

100.0%

0.0

2023

[2] Predictive Analytics for Stock Market Trends using Machine Learning Manasa N, ..., and Lakshmi. S.R 2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM) 2023 - 0 citations - Show abstract - Cite Abstract: Navigating the intricacies of stock market trends demands a novel approach capable of deciphering the web of financial data and market sentiment. This research embarks on a transformative journey into the realm of machine learning, where we harness the power of data to forecast stock market trends with increased precision and accuracy. Commencing with an exploration of stock market dynamics and the inherent limitations of traditional forecasting techniques, this paper takes a bold step into the future by embracing the potential of machine learning. The study begins with an in-depth analysis of data preprocessing, unraveling the complexity of feature selection and engineering, setting the stage for a data-driven odyssey. As our exploration progresses, we dive into the deployment of diverse machine learning algorithms, including linear regression, decision trees, random forests, and the formidable deep learning models such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). These algorithms act as our guiding lights, revealing intricate patterns concealed within historical stock price data. Our journey reaches new heights as we recognize the significance of augmenting predictive models with external data sources. Incorporating elements like news sentiment analysis and macroeconomic indicators enriches our understanding of the market landscape, enhancing the predictive capabilities of our models. We also delve into the crucial aspects of model evaluation, guarding against overfitting, and selecting appropriate performance metrics to ensure robust and reliable predictions. The research reaches its zenith with a meticulous analysis of real-world case studies, providing a comparative perspective between machine learning models and traditional forecasting methods. The results underscore the remarkable potential of machine learning in predicting stock market trends more accurately. 100.0% topic match

Provides an analysis of ML algorithms for stock market trend forecasting. Includes data preprocessing, feature engineering, and uses linear regression, decision trees, random forests, RNNs, and LSTMs. Augments models with external data like news sentiment and macroeconomic indicators; focuses on equity markets.

100.0%

0.0

2024

[3] Deep Insights: Revolutionizing Stock Market Predictions with Machine Learning and Deep Learning Techniques Vajrala Manikanta Reddy, ..., and Kovuru Lourd 2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI) 2024 - 0 citations - Show abstract - Cite 100.0% topic match

Provides insights into using ML and deep learning for stock forecasting. Compares multi-layer LSTM architecture with traditional ML models like KNN, RF, SVM, and DT. Focuses on predicting stock market patterns, offering both theoretical and practical insights.

100.0%

0.0

2023

[4] Decoding the Market Trends: Precision Forecasting of Stock Prices Using CNN Based Approach Pranjali Pandhare, ..., and Nutan V. Bansode 2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA) 2023 - 0 citations - Show abstract - Cite 100.0% topic match

Shows improved financial forecasting with CNN and LSTM. Compares CNN and LSTM models for predicting stock prices. Includes theoretical insights and practical prototype implementation.

100.0%

0.0

2023

[5] Development of a Robust Stock Market Prediction Mechanism based on Enhanced Comprehensive Learning Principles G. Ramkumar, ..., and Pette Amruthavalli 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) 2023 - 0 citations - Show abstract - Cite 100.0% topic match

Introduces innovative hybrid methodologies for stock market forecasting Utilizes Long Short-Term Memory (LSTM) Networks to predict stock market indices and stock prices. Focuses on deep learning, relevant features in equity markets, and practical applications for forecasting.

100.0%

0.6

2023

[6] Survey of Stock Market Price Prediction Trends using Machine Learning Techniques Paul Akash Gunturu, ..., and S. Khapre 2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1) 2023 - 1 citations - Show abstract - Cite 100.0% topic match

Compares ML techniques for predicting stock trends. Evaluates LSTM, Prophet, Random Forest, Auto-ARIMA, k-NN, Linear Regression, and hybrid models on historical stock data. Proposed hybrid model achieves higher accuracy, offering practical insights for investors and future research on stock market prediction.

100.0%

0.5

2020

[7] Deep Learning for Equity Time Series Prediction Miquel Noguer i Alonso, ..., and Aymeric Moulin CompSciRN: Other Machine Learning (Topic) 2020 - 2 citations - Show abstract - Cite 100.0% topic match

Examines Deep Learning methods for equity time series prediction. Focuses on modeling returns, risk, and market impact with non-linear architectures. Addresses practical implementation concerns but notes transparency and theoretical limitations.

100.0%

0.0

2023

[8] Harnessing ML and DL for Stock Market Analysis: A Tableau Forecasting Perspective Abhishek Singh, ..., and B. R. Rohini 2023 4th IEEE Global Conference for Advancement in Technology (GCAT) 2023 - 0 citations - Show abstract - Cite 100.0% topic match

Applies ML and DL to predict stock prices. Uses Linear Regression, Random Forest, XG Boost, and LSTM models for forecasting stock trends. Compares predictions with exponential smoothing in Tableau, addressing both theoretical accuracy and practical implementation.

100.0%

0.0

2023

[9] Stock Market Forecasting Using LSTM Neural Network Aditi Singh and Lavnika Markande International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2023 - 0 citations - Show abstract - Cite - PDF 100.0% topic match

Investigates using RNNs and LSTM models for stock market forecasting. Analyzes empirical results, demonstrating RNNs and LSTM models' efficiency with real-world stock data. Focuses on practical implementation and temporal relationship analysis within equity markets.

100.0%

1.0

2023

[10] Enhancing Stock Market Predictability: A Comparative Analysis of RNN And LSTM Models for Retail Investors Nevendra Kr Upadhyay Journal of Management and Service Science (JMSS) 2023 - 2 citations - Show abstract - Cite - PDF 100.0% topic match

Compares RNN and LSTM models for stock price prediction. Utilizes historical market data to evaluate prediction accuracy for retail investors. Highlights LSTM's superior precision, contributing to financial forecasting in equity markets.

100.0%

0.0

2023

[11] Advanced Deep Learning-Based Predictive Modelling for Analyzing Trends and Performance Metrics in Stock Market Ali Raza, ..., and Asfand Yar Khan Journal of Accounting and Finance in Emerging Economies 2023 - 0 citations - Show abstract - Cite - PDF 100.0% topic match

Investigates deep learning for analyzing stock market performance. Integrates LSTM, CNN, RNN with feature engineering from historical stock prices and macroeconomic data. Demonstrates potential of deep learning for more accurate financial forecasting in equity markets.

100.0%

0.0

2024

[12] Implications of Deep Learning for Stock Market Forecasting Supendi, ..., and Maria Lusiana Yulianti International Journal Software Engineering and Computer Science (IJSECS) 2024 - 0 citations - Show abstract - Cite - PDF 100.0% topic match

Explores effectiveness of deep learning for stock market forecasting. Compares LSTM and CNN-LSTM models with traditional methods, finding deep learning superior. Discusses theoretical insights and practical implications, includes recommendations for future research and model integration.

100.0%

0.0

2024

[13] Prediction of Stock Price by Neural Network Based on CNN, LSTM, ANN Hanqing Wen Advances in Economics, Management and Political Sciences 2024 - 0 citations - Show abstract - Cite - PDF 100.0% topic match

Provides a comparative analysis of CNN, LSTM, and ANN models for stock price forecasting. Highlights model performance, with LSTMs generally achieving higher accuracy due to capturing long-term dependencies. Discusses theoretical insights and practical implications, aligning well with the target of financial forecasting in equity markets.

100.0%

0.5

2022

[14] An Integrated Approach Towards Stock Price Prediction using LSTM Algorithm Kummari Vikas, ..., and S. Salvadi 2022 International Conference on Edge Computing and Applications (ICECAA) 2022 - 1 citations - Show abstract - Cite 100.0% topic match

Provides evaluation of LSTM for stock price prediction. Examines multiple ML algorithms including LSTM on Tehran Stock Exchange sectors. Includes practical testing and comparisons; however, focuses on Tehran Stock Exchange, not explicitly on broader equity markets.

100.0%

0.0

2024

[15] Stock Market Prediction Aaron Josey and Amrutha N Indian Journal of Data Mining 2024 - 0 citations - Show abstract - Cite Abstract: The prediction of stock market trends is a challenging yet critical task in the financial sector, given its significant implications for investors, traders, and financial institutions. This research leverages the Long Short-Term Memory (LSTM) algorithm, a type of recurrent neural network (RNN), to develop a robust model for forecasting stock prices. The study utilizes historical stock market data sourced from Yahoo Finance, accessed via the yfinance package in Python. The primary objectives are to preprocess the data, implement the LSTM model, and evaluate its performance against traditional models such as Random Forest and Linear Regression. Data preprocessing involved handling missing values, normalizing the dataset, and transforming it into sequences suitable for LSTM training. The model's architecture includes multiple LSTM layers designed to capture temporal dependencies in the data. The study evaluates the model's performance using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and prediction accuracy. Comparative analysis shows that the LSTM model outperforms both Random Forest and Linear Regression models, with lower MSE and RMSE values and higher accuracy in predicting stock prices. This research discovered that LSTM's ability to retain long-term dependencies makes it particularly effective for stock market prediction, where historical trends and patterns significantly influence future prices. The results indicate that the LSTM model provides more reliable and precise predictions, which can enhance decision-making in trading and investment. This research highlights the potential of advanced neural network architectures in financial forecasting, offering a valuable tool for investors aiming to optimize their strategies and mitigate risks. The significance of this study lies in its practical application in the financial industry, demonstrating that machine learning models, particularly LSTM, can substantially improve the accuracy of stock market predictions. Future research could explore the integration of additional features, such as macroeconomic indicators and sentiment analysis, to further enhance model performance. This study underscores the importance of continuous innovation and the adoption of sophisticated algorithms to navigate the complexities of financial markets. 100.0% topic match

Demonstrates the use of LSTM for stock market prediction. Highlights practical application in financial forecasting, showing improved accuracy with advanced neural networks. Suggests future integration of macroeconomic indicators and sentiment analysis to enhance model performance.

100.0%

0.0

2019

[16] Forecasting stock trends through Machine Learning José Diogo Teixeira de Sousa Seca Journal Not Provided 2019 - 0 citations - Show abstract - Cite Abstract: In managing their portfolio of investments, managers are constantly seeking the instruments (e.g. stocks) that consistently provide the highest returns on a long-term horizon. One possibility to improve the search for overperforming stocks is to use predictive Machine Learning techniques. Previous studies attempted to predict alpha, i.e. higher than average returns, by developing linear models based on capital market anomalies. However, the techniques used in these studies lack the capability to capture complex non-linear dependencies. More recently, evidence suggests that some Machine Learning techniques may be able to capture these non-linearities [1], namely Long Short-Term Memory (LSTM) networks. The LSTM networks belong to the class of Recurrent Neural Networks (RNN) and are used to capture patterns in sequential data. Despite the recent technological advances in Deep Learning in the last years, little research has been done to analyze and identify long-term investment opportunities using predictive Deep Learning techniques. Most available research focuses primarily on short-term predictions and only makes use of market data, disregarding other publicly available information, such as fundamentals. Using the financial statements and market prices of companies listed in the NASDAQ Stock Exchange and the New York Stock Exchange (NYSE) over a span of 30 years, different features (e.g. Book-to-Market ratio, Return On Equity), and different Machine Learning techniques were tested, namely the LSTM neural network. As a baseline, the buy-and-hold investment strategy was used, which comprised of buying a stock at the beginning of the time series, without ever selling. All of the developed models were be tested by simulating trading on an out-of-sample period. The models were then be compared using the Accuracy, Year-over-Year Returns, and Maximum Drawdown for the period tested. The models tested proved to achieve Accuracy measurements higher than the Naive Classifier. Moreover, the results suggest that the LSTM neural network is not the most suitable learning algorithm for this context, achieving worse results than Random Forests and Logistic Regression (with or without the use of Bagging). Although the portfolios built using the Classifier’s predictions were more profitable than the benchmarks, the increased Drawdown indicates a necessity for adequate portfolio risk optimization and risk management. 100.0% topic match

Provides an application of LSTM for stock trend forecasting. Uses financial statements and market prices from NASDAQ and NYSE over 30 years. Compares model performance against a buy-and-hold strategy using accuracy, returns, and drawdown metrics.

100.0%

0.0

2023

[17] Automated Stock Trading System using Technical Analysis and Deep Learning Models Weerapat Buachuen and Pittipol Kantavat Proceedings of the 13th International Conference on Advances in Information Technology 2023 - 0 citations - Show abstract - Cite - PDF 100.0% topic match

Integrates advanced machine learning for stock price prediction. Explores LSTM-CNN and CNN-LSTM with Attention Layer for capturing stock data patterns. Includes theoretical insights and preliminary practical results; focuses on equity markets specifically.

100.0%

None

[18] Stock Price Prediction Using Long Short-term Memory B. V. Vidhya and Ajmeera Kiran Journal Not Provided None - 0 citations - Show abstract - Cite 100.0% topic match

Provides insights into using LSTM for stock price prediction. Focuses on theoretical and practical aspects of applying LSTM to forecast stock prices. Does not specify detailed practical implementations or comparisons with equity market-specific data.

100.0%

0.3

2018

[19] Pattern Learning Via Artificial Neural Networks for Financial Market Predictions A. Gabler, ..., and Markus Reitenbach Mutual Funds 2018 - 2 citations - Show abstract - Cite 100.0% topic match

Provides empirical comparison of CNN and LSTM for stock prediction. Uses CNN and LSTM to predict stock movements for European stocks from 1994-2014. Focuses on portfolio performance metrics, demonstrating practical implementations in equity markets.

100.0%

27.8

2022

[20] Stock market index prediction using deep Transformer model Chaojie Wang, ..., and Qiuhui Zhang Expert Syst. Appl. 2022 - 69 citations - Show abstract - Cite 100.0% topic match

100.0%

74.2

2017

[21] Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies Eunsuk Chong, ..., and Frank C. Park Expert Syst. Appl. 2017 - 533 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

18.3

2021

[22] Stock Prediction Based on Optimized LSTM and GRU Models Ya Gao, ..., and Enmin Zhou Sci. Program. 2021 - 59 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

29.3

2019

[23] Predicting the daily return direction of the stock market using hybrid machine learning algorithms Xiao Zhong and D. Enke Financial Innovation 2019 - 162 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

1.0

2022

[24] LSTM-based Stock Prediction Modeling and Analysis Ruobing Zhang Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022) 2022 - 3 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

11.3

2022

[25] Stock Market Analysis and Prediction for Nifty50 using LSTM Deep Learning Approach P. Sisodia, ..., and G. Ameta 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) 2022 - 32 citations - Show abstract - Cite 100.0% topic match

Provides a prediction model for stock prices using LSTM. Uses 10 years of historical data from India's NSE NIFTY 50 index. Achieves an accuracy of 83.88%, focusing on theoretical and practical aspects of equity market forecasting.

100.0%

1.0

2022

[26] Method of Predicting of Trend in the Stock Exchange using ML and DL Algorithms G. D. Arora, ..., and T. V. Kumar 2022 5th International Conference on Contemporary Computing and Informatics (IC3I) 2022 - 2 citations - Show abstract - Cite 100.0% topic match

100.0%

4.6

2020

[27] Machine Learning for Factor Investing Guillaume Coqueret and Tony Guida https://doi.org/10.1201/9781003034858 2020 - 20 citations - Show abstract - Cite 100.0% topic match

100.0%

10.5

2020

[28] Machine Learning for Asset Managers (Chapter 1) Marcos M. López de Prado InfoSciRN: Artificial Intelligence (Topic) 2020 - 50 citations - Show abstract - Cite 100.0% topic match

100.0%

3.5

2019

[29] Artificial Neural Networks: Its Techniques and Applications to Forecasting Anish Gupta, ..., and Nnamdi Ikechi Nwulu 2019 International Conference on Automation, Computational and Technology Management (ICACTM) 2019 - 20 citations - Show abstract - Cite 100.0% topic match

100.0%

42.1

2021

[30] A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction Nan Jing, ..., and Hefei Wang Expert Syst. Appl. 2021 - 157 citations - Show abstract - Cite 100.0% topic match

100.0%

5.6

2022

[31] Clustering-enhanced stock price prediction using deep learning Man Li, ..., and M. Angelova World Wide Web 2022 - 15 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

3.6

2022

[32] Perbandingan Model RNN, Model LSTM, dan Model GRU dalam Memprediksi Harga Saham-Saham LQ45 Andrew Nilsen Jurnal Statistika dan Aplikasinya 2022 - 9 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

27.8

2018

[33] Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets Xingyu Zhou, ..., and Cheng Zhao Mathematical Problems in Engineering 2018 - 186 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

10.3

2021

[34] A Novel Improved Particle Swarm Optimization With Long-Short Term Memory Hybrid Model for Stock Indices Forecast Yi Ji, ..., and Lixia Yang IEEE Access 2021 - 41 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

41.5

2020

[35] Short-term stock market price trend prediction using a comprehensive deep learning system Jingyi Shen and M. O. Shafiq Journal of Big Data 2020 - 179 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

2.1

2020

[36] An Improved Elman Network for Stock Price Prediction Service Bo Liu, ..., and Qian Cao Secur. Commun. Networks 2020 - 9 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

5.2

2023

[37] Prediksi Harga Saham Syariah Menggunakan Algoritma Long Short-Term Memory (LSTM) Gunawan Budiprasetyo, ..., and Darin Zahira Aflah Jurnal Nasional Teknologi dan Sistem Informasi 2023 - 10 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

2.0

2021

[38] IMPLEMENTASI ALGORITMA SUPPORT VECTOR MACHINE (SVM) DALAM MEMPREDIKSI HARGA SAHAM PT. GARUDA INDONESIA TBK R. Wulandari and Dian Anubhakti IDEALIS : InDonEsiA journaL Information System 2021 - 7 citations - Show abstract - Cite - PDF Abstract: Abstrak- Saham adalah salah satu bentuk investasi yang dapat memindah tangankan kepemilikan perusahaan. Saham diperjual-belikan di lantai bursa, salah satunya yang berada di Indonesia adalah IDX (Indonesia Stcok Exchange). IDX adalah pihak penyelenggara dan penyedia sistem dan sarana yang mempertemukan penawaran jual beli Efek pihak-pihak lain dengan tujuan memperdagangkan Efek diantara mereka. Harga saham yang sangat fluktuatif sangat beresiko dapat menimbulkan kerugian kepada para trader ataupun investor, tetapi harga saham masih dapat diprediksi. Model prediksi pergerakan harga saham yang akurat dapat membantu para investor dalam pertimbangan pengambilan keputusan. Ada beberapa variabel yang mempengaruhi nilai return saham, yang sifatnya linier dan non-linier terhadap return harga saham. Prediksi harga saham ini akan sangat berguna bagi investor untuk mengetahui bagaimana alur investasi bekerja dan tentunya akan sangat membantu untuk mengurangi tingkat kerugian. Penelitian ini bertujuan untuk menerapkan metode Support Vector Machine (SVM) dalam melakukan prediksi harga saham harian PT. Garuda Indonesia, Tbk yang mana hasil akhirnya berupa nilai akurasi prediksi harga saham. Data yang digunakan dalam penelitian ini adalah data sekunder berupa data historis harga saham harian sejak tanggal 18 Maret 2019 sampai dengan tanggal 23 April 2021. Penelitian ini menunjukkan bahwa menggunakan metode SVM mampu memprediksi harga saham dengan nilai akurasi prediksi sebesar 0.545. Abstract- Stock is one of the investment firms that can transfer ownership of a company. Shares are traded on the stock exchange floor, one of which is in Indonesia is IDX (Indonesia Stock Exchange). IDX is the provider of systems and facilities that bring together other parties' buying and selling offers for the purpose of trading Securities between them. Very volatile stock prices are very risky, which can cause losses to traders or investors, but stock prices are still predictable. An accurate prediction model for stock price movements can help investors in considering decision-making. Several variables affect the value of stock returns, which are linear and non-linear on stock price returns. This stock price prediction will be very useful for investors to find out how the investment flow works and of course will be very helpful to reduce the level of losses. This study aims to apply the Support Vector Machine (SVM) method in predicting the daily stock price of PT. Garuda Indonesia, Tbk, where the result is the accuracy of the stock price prediction. The data used in this study is secondary data in the form of historical data on daily stock prices from March 18, 2019, to April 23, 2021. This study shows that using the SVM method can predict stock prices with a predictive accuracy value of 0.545. 100.0% topic match

100.0%

0.5

2022

[39] Analysis of Stock Market Value Prediction using Simple Novel Long Short Term Memory Algorithm in Comparison with Back Propagation Algorithm for Increased Accuracy Rate N. J. Reddy and K. Jaisharma 2022 5th International Conference on Contemporary Computing and Informatics (IC3I) 2022 - 1 citations - Show abstract - Cite 100.0% topic match

100.0%

1.0

2022

[40] Prediksi Saham PT. Aneka Tambang Tbk. dengan K-Nearest Neighbors Lathifah Alfat, ..., and Refi Tandjilal JSAI (Journal Scientific and Applied Informatics) 2022 - 2 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

1.4

2017

[41] Prediksi Indeks Harga Saham dengan Metode Gabungan Support Vector Regression dan Jaringan Syaraf Tiruan Lisbeth Evalina Siahaan https://doi.org/10.21108/INDOJC.2017.2.1.45 2017 - 10 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

1.3

2022

[42] Optimasi Parameter Support Vector Machine Menggunakan Algoritma Genetika untuk Meningkatkan Prediksi Pergerakan Harga Saham Sudriyanto Sudriyanto, ..., and Setyo Agung Edho Wicaksono COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi 2022 - 3 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

21.5

2019

[43] Deep learning for decision making and the optimization of socially responsible investments and portfolio Nhi N. Y. Vo, ..., and Guandong Xu Decis. Support Syst. 2019 - 114 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

0.0

2024

[44] Forecasting Stock Price: A Deep Learning Approach with LSTM And Hyperparameter Optimization Yifan You Highlights in Science, Engineering and Technology 2024 - 0 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

0.0

2020

[45] A Deep Learning-based Approach for Stock Price Prediction Preeti Chaudhary Turkish Journal of Computer and Mathematics Education (TURCOMAT) 2020 - 0 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

0.0

2024

[46] A Study on Stock Forecasting Using Deep Learning and Statistical Models Himanshu Gupta and Aditya Jaiswal ArXiv 2024 - 0 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

0.0

2023

[47] A study of stock price prediction models based on time series analysis: LSTM and CNN Luoyuan Zhang Applied and Computational Engineering 2023 - 0 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

39.8

2020

[48] Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis Mojtaba Nabipour, ..., and A. Mosavi IEEE Access 2020 - 198 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

0.0

2024

[49] HybStock: A Hybrid Deep Learning-Based Model to Predict Stock Market on Dhaka Stock Exchange Md. Sakif, ..., and Muhammad Ibrahim Khan 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT) 2024 - 0 citations - Show abstract - Cite 100.0% topic match

100.0%

0.0

2023

[50] Proposing a Novel Framework for Prediction of Stock using Machine Learning A. Bist, ..., and Ahmad Bayu Yadila 2023 11th International Conference on Cyber and IT Service Management (CITSM) 2023 - 0 citations - Show abstract - Cite 100.0% topic match

100.0%

0.0

2023

[51] Stock Market Prediction using different Machine Learning Algorithms Vidushi Tiwari, ..., and Bhavdeep Dhariwal 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) 2023 - 0 citations - Show abstract - Cite 100.0% topic match

100.0%

0.0

2023

[52] Stock Price Prediction using Various LSTMs S. Narayana, ..., and Teki Veera Venkata Satya Prakash 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) 2023 - 0 citations - Show abstract - Cite 100.0% topic match

100.0%

0.0

2023

[53] Stock Price Forecasting Using Machine Learning Techniques: A Systematic Review D. Patnaik, ..., and Srikanta Patnaik 2023 International Conference on Data Science & Informatics (ICDSI) 2023 - 0 citations - Show abstract - Cite 100.0% topic match

100.0%

2.6

2022

[54] Stock Price Prediction using Recurrent Neural Network and LSTM Pasala Sandhya, ..., and D. D. Himabindu 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) 2022 - 7 citations - Show abstract - Cite 100.0% topic match

100.0%

0.0

2023

[55] A Systematic Literature Review: Forecasting Stock Price Using Machine Learning Approach Niclauss Lumoring, ..., and A. A. S. Gunawan 2023 International Conference on Data Science and Its Applications (ICoDSA) 2023 - 0 citations - Show abstract - Cite 100.0% topic match

100.0%

0.0

2023

[56] Implementation of Long Short-Term Memory (LSTM) Networks for Stock Price Prediction Vivek Deshpande Research Journal of Computer Systems and Engineering 2023 - 0 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

5.0

2019

[57] Multi-factor Based Stock Price Prediction Using Hybrid Neural Networks with Attention Mechanism Chen Li, ..., and Y. Morimoto 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) 2019 - 27 citations - Show abstract - Cite 100.0% topic match

100.0%

9.3

2020

[58] Forecasting Stock Prices Using a Hybrid Deep Learning Model Integrating Attention Mechanism, Multi-Layer Perceptron, and Bidirectional Long-Short Term Memory Neural Network Qian Chen, ..., and Yu Lou IEEE Access 2020 - 42 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

1.7

2023

[59] Prediction of Stock Market Using LSTM-RNN Model Nimesh Raj 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) 2023 - 2 citations - Show abstract - Cite 100.0% topic match

100.0%

0.6

2021

[60] Stock Trend Prediction by Fusing Prices and Indices with LSTM Neural Networks Vasilis Karlis, ..., and G. Mentzas 2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) 2021 - 2 citations - Show abstract - Cite 100.0% topic match

100.0%

1.6

2021

[61] Fuzzy transfer learning in time series forecasting for stock market prices Shanoli Samui Pal and S. Kar Soft Computing 2021 - 5 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

0.3

2022

[62] Research on stock prediction algorithm based on CNN and LSTM Keren He and Qian Jiang Academic Journal of Computing & Information Science 2022 - 1 citations - Show abstract - Cite - PDF 100.0% topic match

100.0%

10.5

2020

[63] Machine Learning in Finance: From Theory to Practice Guillaume Coqueret Quantitative Finance 2020 - 44 citations - Show abstract - Cite 100.0% topic match

100.0%

0.0

2023

[64] Unveling the Precision of Deep Learning Models for Stock Price Prediction: A Comparative Analysis of Bi-LSTM, LSTM, and GRU Thirza Baihaqi, ..., and Hasitha Erandi 2023 International Conference on Converging Technology in Electrical and Information Engineering (ICCTEIE) 2023 - 0 citations - Show abstract - Cite 100.0% topic match

100.0%

5.8

2022

[65] Volatility Forecasting for Stock Market incorporating Macroeconomic Variables based on GARCH‐MIDAS and Deep Learning Models Yuping Song, ..., and Zhiren Ma Journal of Forecasting 2022 - 14 citations - Show abstract - Cite 100.0% topic match

100.0%

59.1

2020

[66] A CNN-LSTM-Based Model to Forecast Stock Prices Wenjie Lu, ..., and Jingyang Wang Complex. 2020 - 241 citations - Show abstract - Cite - PDF 100.0% topic match

29.8%

8.9

2021

[67] Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic Rohitash Chandra and Yixuan He PLoS ONE 2021 - 31 citations - Show abstract - Cite - PDF 29.8% topic match

10.9%

0.0

2024

[68] PREDICTING STOCK MARKET TRENDS WITH PYTHON Mariia Kapinus, ..., and Valerii Danylov Grail of Science 2024 - 0 citations - Show abstract - Cite Abstract: Artificial Intelligence (AI) and machine learning (ML) have revolutionized the realm of stock market prediction, offering sophisticated tools to analyze vast volumes of data and anticipate market trends. This article provides a comprehensive overview of AI techniques, focusing on Python as the preferred platform for implementation. Beginning with an exploration of AI fundamentals, including machine learning and deep learning, it delves into various techniques employed for stock market prediction. Traditional statistical models such as linear regression and ARIMA are under scientific discussion alongside advanced ML algorithms like random forests and support vector machines. Moreover, the article highlights the efficacy of deep learning methodologies, particularly recurrent neural networks (RNNs) and long & short-term memory (LSTM) networks, in capturing temporal dependencies within stock market data. We also explored innovative developments such as Generative Adversarial Networks (GANs) for their potential in revealing hidden patterns influencing price movements. Throughout the discussion, we concluded that Python emerges as the preferred programming language due to its simplicity, extensive libraries, and versatility. Key libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow play a pivotal role in data manipulation, preprocessing, and model development. The article outlines a structured approach to building predictive models, encompassing data collection, preprocessing, feature engineering, model selection, training, evaluation, and prediction. Despite the advancements in AI, challenges persist in stock market prediction, including market volatility, data quality issues, complexity of influencing factors, and risks of overfitting. Ultimately, we may witness AI and Python synergy, which empowers analysts and investors with deeper insights, enabling informed decision-making amidst the complexities of financial markets. 10.9% topic match

Provides an overview of AI techniques for stock market prediction. Explores AI fundamentals, traditional statistical models, and advanced ML algorithms using Python. Focuses on theoretical insights but does not detail practical implementations in equity markets.

10.4%

3.3

2020

[69] IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG) Eka Patriya Jurnal Ilmiah Teknologi dan Rekayasa 2020 - 15 citations - Show abstract - Cite - PDF 10.4% topic match

10.4%

3.1

2024

[70] A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach Pham Hoang Vuong, ..., and T. Trinh Science Progress 2024 - 3 citations - Show abstract - Cite - PDF 10.4% topic match

10.3%

4.2

2021

[71] Importance of Machine Learning in Making Investment Decision in Stock Market Akhilesh Prasad and A. Seetharaman Vikalpa: The Journal for Decision Makers 2021 - 13 citations - Show abstract - Cite 10.3% topic match

10.2%

0.0

2024

[72] Stock Market Analysis Using Machine Learning D. N INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 2024 - 0 citations - Show abstract - Cite - PDF 10.2% topic match

10.2%

0.0

2024

[73] Security Risk Analysis and Price Predictions with Machine and Deep Learning Models (LSTM) Muhammad Mehdi Muhammad Mehdi, ..., and Atif Ikram Atif Ikram Journal of Innovative Computing and Emerging Technologies 2024 - 0 citations - Show abstract - Cite - PDF 10.2% topic match

10.1%

0.0

2024

[74] Forecasting Stock Prices with Long Short-Term Memory Networks K. Singh, ..., and Neeraj Varshney 2024 International Conference on Automation and Computation (AUTOCOM) 2024 - 0 citations - Show abstract - Cite 10.1% topic match

9.4%

0.0

2024

[75] Research on the application of deep learning techniques in stock market prediction and investment decision-making in financial management Rui Zhao, ..., and Ziyu Zhao Frontiers in Energy Research 2024 - 0 citations - Show abstract - Cite - PDF Abstract: Introduction: This paper introduces a deep learning approach based on Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and attention mechanism for stock market prediction and investment decision making in financial management. These methods leverage the advantages of deep learning to capture complex patterns and dependencies in financial time series data. Stock market prediction and investment decision-making have always been important issues in financial management.Methods: Traditional statistical models often struggle to handle nonlinear relationships and complex temporal dependencies, thus necessitating the use of deep learning methods to improve prediction accuracy and decision effectiveness. This paper adopts a hybrid deep learning model incorporating CNN, BiLSTM, and attention mechanism. CNN can extract meaningful features from historical price or trading volume data, while BiLSTM can capture dependencies between past and future sequences. The attention mechanism allows the model to focus on the most relevant parts of the data. These methods are integrated to create a comprehensive stock market prediction model. We validate the effectiveness of the proposed methods through experiments on real stock market data. Compared to traditional models, the deep learning model utilizing CNN, BiLSTM, and attention mechanism demonstrates superior performance in stock market prediction and investment decision-making.Results and Discussion: Through ablation experiments on the dataset, our deep learning model achieves the best performance across all metrics. For example, the Mean Absolute Error (MAE) is 15.20, the Mean Absolute Percentage Error (MAPE) is 4.12%, the Root Mean Square Error (RMSE) is 2.13, and the Mean Squared Error (MSE) is 4.56. This indicates that these methods can predict stock market trends and price fluctuations more accurately, providing financial managers with more reliable decision guidance. This research holds significant implications for the field of financial management. It offers investors and financial institutions an innovative approach to better understand and predict stock market behavior, enabling them to make wiser investment decisions. 9.4% topic match

9.3%

None

[76] Stock Market Prediction Using Machine Learning Akash Maurya, ..., and Sujal Kumar Gupta https://doi.org/10.21090/ijaerd.rtde14 None - 0 citations - Show abstract - Cite - PDF 9.3% topic match

9.2%

2.3

2022

[77] Deep Reinforcement Learning for Stock Prediction Junhao Zhang and Yifei Lei Scientific Programming 2022 - 6 citations - Show abstract - Cite - PDF 9.2% topic match

9.1%

142.7

2012

[78] LSTM Neural Networks for Language Modeling M. Sundermeyer, ..., and H. Ney Interspeech 2012 - 1852 citations - Show abstract - Cite - PDF 9.1% topic match

9.1%

0.5

2020

[79] Analysing Stock Market Trend Prediction using Machine & Deep Learning Models: A Comprehensive Review Doan Yen Nhi Le, ..., and Suntharalingam Senthilananthan 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) 2020 - 2 citations - Show abstract - Cite 9.1% topic match

9.0%

1.0

2022

[80] ARIMA vs LSTM Algorithm – A Comparative Study Based on Stock Market Prediction Jaikishan Bagul, ..., and Nikhita Mangaonkar 2022 5th International Conference on Advances in Science and Technology (ICAST) 2022 - 2 citations - Show abstract - Cite 9.0% topic match

8.1%

None

[81] ENHANCING STOCK MARKET PREDICTION USING MACHINE LEARNING Akhilesh Bhagat, ..., and Student Journal Not Provided None - 0 citations - Show abstract - Cite 8.1% topic match

7.9%

None

[82] Advancements In Machine Learning For Stock Market Trend Analysis: A Comprehensive Review Akash Chourasia Journal Not Provided None - 0 citations - Show abstract - Cite 7.9% topic match

7.8%

0.0

2024

[83] A Comparative study of the Envisaged and Definite Stock Prices of BSE SMEs Using RNN during the COVID-19 Pandemic S. Kaur, ..., and A. K. Goyal Finance: Theory and Practice 2024 - 0 citations - Show abstract - Cite - PDF 7.8% topic match

7.4%

173.2

2015

[84] An Empirical Exploration of Recurrent Network Architectures R. Józefowicz, ..., and I. Sutskever International Conference on Machine Learning 2015 - 1639 citations - Show abstract - Cite 7.4% topic match

7.4%

43.8

2023

[85] Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation Natalia Díaz Rodríguez, ..., and Francisco Herrera Inf. Fusion 2023 - 72 citations - Show abstract - Cite - PDF Abstract: Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system's entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors that are part of the system's life cycle, and considers previous aspects from different lenses. A more holistic vision contemplates four essential axes: the global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the mentioned pillars and requirements. The seven requirements (human agency and oversight; robustness and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; societal and environmental wellbeing; and accountability) are analyzed from a triple perspective: What each requirement for trustworthy AI is, Why it is needed, and How each requirement can be implemented in practice. On the other hand, a practical approach to implement trustworthy AI systems allows defining the concept of responsibility of AI-based systems facing the law, through a given auditing process. Therefore, a responsible AI system is the resulting notion we introduce in this work, and a concept of utmost necessity that can be realized through auditing processes, subject to the challenges posed by the use of regulatory sandboxes. Our multidisciplinary vision of trustworthy AI culminates in a debate on the diverging views published lately about the future of AI. Our reflections in this matter conclude that regulation is a key for reaching a consensus among these views, and that trustworthy and responsible AI systems will be crucial for the present and future of our society. 7.4% topic match

7.2%

4.5

2021

[86] Machine Learning Algorithm for Cryptocurrencies Price Prediction J. B. Awotunde, ..., and T. O. Aro https://doi.org/10.1007/978-3-030-72236-4_17 2021 - 18 citations - Show abstract - Cite 7.2% topic match

6.7%

1.6

2022

[87] Interpreting Attention of Stock Price Prediction K. Yoshida 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) 2022 - 4 citations - Show abstract - Cite 6.7% topic match

6.5%

0.0

2022

[88] Stock Price Prediction Based on Spatio-Temporal Coupling with Deep Learning Heming Lai, ..., and Qinxin Wang BCP Business & Management 2022 - 0 citations - Show abstract - Cite - PDF 6.5% topic match

6.4%

0.0

2023

[89] Analysis of Machine Learning Models for Stock Market Prediction Laksh Jethani, ..., and Tanuja Sarode 2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS) 2023 - 0 citations - Show abstract - Cite 6.4% topic match

5.2%

0.0

2024

[90] Stock Market Prediction and Analysis using Supervised Learning Ashutosh Talekar, ..., and Reshma Kohad International Journal For Multidisciplinary Research 2024 - 0 citations - Show abstract - Cite - PDF 5.2% topic match

5.0%

82.4

2018

[91] Neural Networks and Deep Learning C. Aggarwal Cambridge International Law Journal 2018 - 521 citations - Show abstract - Cite 5.0% topic match

4.8%

0.0

2023

[92] Predicting Stock Prices using Machine Learning Techniques: An Analysis of Historical Market Data Pratik Vispute, ..., and N. A. Natraj 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) 2023 - 0 citations - Show abstract - Cite 4.8% topic match

4.7%

27.4

2022

[93] An Intelligent LoRa based Women Protection and Safety Enhancement using Internet of Things A. G, ..., and R. Prabu 2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) 2022 - 58 citations - Show abstract - Cite 4.7% topic match

3.9%

0.0

2023

[94] Forecasting Stock Markets Trends using Machine Learning Algorithms Lahari Kotapati, ..., and M. Enduri 2023 IEEE 15th International Conference on Computational Intelligence and Communication Networks (CICN) 2023 - 0 citations - Show abstract - Cite 3.9% topic match

3.3%

0.0

2024

[95] A Comparative Analysis of Deep Learning Models for Short- Term Stock Price Prediction Dr. Elma Sibonghanoy Groenewald, ..., and Mehta Journal of Informatics Education and Research 2024 - 0 citations - Show abstract - Cite - PDF 3.3% topic match

3.1%

0.6

2023

[96] PENGARUH FASILITAS KEPABEANAN TERHADAP NILAI EKSPOR DAN KINERJA KEUANGAN PERUSAHAAN No author found Indonesian Treasury Review Jurnal Perbendaharaan Keuangan Negara dan Kebijakan Publik 2023 - 1 citations - Show abstract - Cite 3.1% topic match

2.7%

0.0

2023

[97] Application of Machine Learning and Deep Learning Algorithms to the Prediction of Stock Market Trends Shravan Khunti, ..., and Mabhu Subhani 2023 Global Conference on Information Technologies and Communications (GCITC) 2023 - 0 citations - Show abstract - Cite 2.7% topic match

1.9%

19.4

2021

[98] Bridging observations, theory and numerical simulation of the ocean using machine learning M. Sonnewald, ..., and V. Balaji Environmental Research Letters 2021 - 71 citations - Show abstract - Cite - PDF 1.9% topic match

1.6%

10.7

2001

[99] Long short-term memory in recurrent neural networks Felix Alexander Gers https://doi.org/10.5075/EPFL-THESIS-2366 2001 - 257 citations - Show abstract - Cite 1.6% topic match

1.1%

0.0

2024

[100] Machine Learning Strategies for Understanding Stock Market Behaviour Vivek Patil INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 2024 - 0 citations - Show abstract - Cite - PDF 1.1% topic match

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