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The applications of artificial neural networks, support vector machines, and long-short term memory for stock market prediction

Chhajer, Parshv; Shah, Manan; Kshirsagar, Ameya - In: Decision analytics journal 2 (2022)

opportunity is the application of machine learning and artificial intelligence for stock market prediction. The stock market is …. We discuss the strengths and weaknesses of machine learning for stock market prediction and provide some insight into the … opportunities and threats in applying advanced technologies for stock market prediction. We further study the applications of three …

Persistent link: https://www.econbiz.de/10013163098

Stock Market Index Prediction Using Artificial Neural Network

Hedayati, Amin - 2017

In this study the ability of artificial neural network (ANN) in forecasting the daily NASDAQ stock exchange rate was …. Networks for NASDAQ index prediction for two type of input dataset (four prior days and nine prior days) were developed and … exchange rates of NASDAQ from January 28, 2015 to 18 June, 2015 are used to develop a robust model. First 70 days (January 28 …

Persistent link: https://www.econbiz.de/10012951809

Markov Switching Artificial Neural Networks and Volatility Modeling with an Application to a Turkish Stock Index

Bildirici, Melike - 2012

The study analyzes the family of regime switching GARCH neural network models, which allow the generalization of MS type RS-GARCH models to MS-GARCH-NN models by incorparating with neural network architectures with different dynamics and forecasting capabilities both in addition to the family of...

Persistent link: https://www.econbiz.de/10013103071

Predict stock prices using supervised learning algorithms and particle swarm optimization algorithm

Bazrkar, Mohammad Javad; Hosseini, Soodeh - In: Computational economics 62 (2023) 1, pp. 165-186

Persistent link: https://www.econbiz.de/10014327292

A new neural network approach for predicting the volatility of stock market

Koo, Eunho; Kim, Geonwoo - In: Computational economics 61 (2023) 4, pp. 1665-1679

Persistent link: https://www.econbiz.de/10014327101

Identification of nonlinear determinants of stock indices derived by Random Forest algorithm

Tratkowski, Grzegorz - In: International journal of management and economics 56 (2020) 3, pp. 209-217

In this paper, the use of the machine learning algorithm is examined in derivation of the determinants of price movements of stock indices. The Random Forest algorithm was selected as an ideal representative of the nonlinear algorithms based on decision trees. Various brokering and investment...

Persistent link: https://www.econbiz.de/10012303034

Forecasting global stock market volatility : the impact of volatility spillover index in spatial-temporal graph-based model

Son, Bumho; Lee, Yunyoung; Park, Seongwan; Lee, Jaewook - In: Journal of forecasting 42 (2023) 7, pp. 1539-1559

Persistent link: https://www.econbiz.de/10014432719

SENSEX price fluctuation forecasting comparison between global indices and companies making it

Sharma, Aviral; Bhatnagar, Vishal; Bansal, Abhay - In: Journal of global information management : an official … 26 (2018) 3, pp. 90-104

Persistent link: https://www.econbiz.de/10011926114

Application of machine learning in algorithmic investment strategies on global stock markets

Grudniewicz, Jan; Ślepaczuk, Robert - In: Research in international business and finance 66 (2023), pp. 1-24

Persistent link: https://www.econbiz.de/10014463386

Financial Machine Learning

Kelly, Bryan T.; Xiu, Dacheng - 2023

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

Persistent link: https://www.econbiz.de/10014349681