A comprehensive review of machine learning for financial market prediction methods
RM Dhokane, OP Sharma - 2023 International Conference on …, 2023 - ieeexplore.ieee.org
Financial market prediction is an important task for placing an investor's hard-earned money
in the financial market to earn profit. Many parameters affect the financial market's valuation …
in the financial market to earn profit. Many parameters affect the financial market's valuation …
[PDF][PDF] ENHANCING STOCK PRICE PREDICTION: A COMPREHENSIVE ANALYSIS UTILIZING MACHINE LEARNING AND DEEP LEARNING APPROACHES
An important tool for assisting businesses and investors in making wise market decisions is
stock price forecasting. Frequently, forecasting models are needed as they help investors to …
stock price forecasting. Frequently, forecasting models are needed as they help investors to …
[HTML][HTML] Exploring the Gaussian investor sentiment process
SS Coşkun - Borsa Istanbul Review, 2023 - Elsevier
Football (soccer) stocks are substantially subject to investor sentiment stemming from
football fields. Evaluating sentiment functions help us understand how investors interpret …
football fields. Evaluating sentiment functions help us understand how investors interpret …
[PDF][PDF] Stock market prediction using machine learning techniques
S Agrawal, D Thakkar, D Soni, K Bhimani… - International Journal of …, 2019 - academia.edu
ABSTRACT Prediction of Stock Market has been an area of interest for investors as well as
researchers from a long time due to its intrinsic volatility, complex and regularly changing in …
researchers from a long time due to its intrinsic volatility, complex and regularly changing in …
[PDF][PDF] XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting
D Pebrianti, H Kurniawan, L Bayuaji… - Jurnal Ilmiah Teknik …, 2023 - eprints.uad.ac.id
Investment in the capital market has become a lifestyle for millennials in Indonesia as seen
from the increasing number of SID (Single Investor Identification) from 2.4 million in 2019 to …
from the increasing number of SID (Single Investor Identification) from 2.4 million in 2019 to …
Prediction of Telecommunication Company Stock Price using Multiple Linear Regression
This study presents case studies in finance, mainly stock trading. The paperwork from a
portfolio data mining effort and data obtained directly from the Indonesian Stock Exchange …
portfolio data mining effort and data obtained directly from the Indonesian Stock Exchange …
Demand forecasting and route optimization in supply chain industry using data Analytics
BA Mohan, B Harshavardhan, S Karan… - … Asian Conference on …, 2021 - ieeexplore.ieee.org
Logistics industry is one of key pillars of the global economy, which involves interdisciplinary
domains. Manufacturing companies need to devise strategies in order to deliver best-quality …
domains. Manufacturing companies need to devise strategies in order to deliver best-quality …
Enhancement of The Performance of Machine Learning Algorithms to Rival Deep Learning Algorithms in Predicting Stock Prices.
RM Al-Amri, AA Hadi, MS Kadhim… - Babylonian …, 2024 - journals.mesopotamian.press
This paper objectives to improve stock market prediction accuracy by training data on
sentiment analysis of tweets, overcoming volatility and complexity. Utilizing the Use of …
sentiment analysis of tweets, overcoming volatility and complexity. Utilizing the Use of …
Enhancing Intraday Trading through Machine Learning: A NSE Nifty 50 Analysis
A Poptani - 2024 5th International Conference on Innovative …, 2024 - ieeexplore.ieee.org
This paper explores the intersection of machine learning (ML), intraday trading, and the
economic landscape of India, focusing on the National Stock Exchange (NSE) Nifty 50 …
economic landscape of India, focusing on the National Stock Exchange (NSE) Nifty 50 …
Stock Market Forecasting Using LSTM
J Aswini, S Dinesh… - 2024 2nd World …, 2024 - ieeexplore.ieee.org
In this research paper, a novel methodology for forecasting stock market trends is presented:
the utilization of Long Short-Term Memory networks, which are a part of RNN network. This …
the utilization of Long Short-Term Memory networks, which are a part of RNN network. This …