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Finance and Financial Management Commons™
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Articles 1 - 4 of 4
Full-Text Articles in Finance and Financial Management
Effectiveness Of Cnn-Lstm Models Used For Apple Stock Forecasting, Ethan White
Effectiveness Of Cnn-Lstm Models Used For Apple Stock Forecasting, Ethan White
Electronic Theses, Projects, and Dissertations
This culminating experience project investigates the effectiveness of convolutional neural networks mixed with long short-term memory (CNN-LSTM) models, and an ensemble method, extreme gradient boosting (XGBoost), in predicting closing stock prices. This quantitative analysis utilizes recent AAPL stock data from the NASDAQ index. The chosen research questions (RQs) are: RQ1. What are the optimal hyperparameters for CNN-LSTM models in stock price forecasting? RQ2. What is the best architecture for CNN-LSTM models in this context? RQ3. How can ensemble techniques like XGBoost effectively enhance the predictions of CNN-LSTM models for stock price forecasting?
The research questions were answered through a thorough …
An Exploration Of Synergy Evaluation Application Model To Support Implementation On Merger And Acquisition, Jieping Mei
An Exploration Of Synergy Evaluation Application Model To Support Implementation On Merger And Acquisition, Jieping Mei
Electronic Theses, Projects, and Dissertations
ABSTRACT
The project focuses on a comprehensive system’s analysis and design of the front-end of the Synergy Evaluation Application Model (SEAM) system for mergers and acquisitions (M&As). The research questions asked are: Q1. How did the SEAM system incorporate the system requirements and design that incorporated the strategic goals and priorities of both the acquirer and the acquiree? Q2. What data sources will the SEAM system rely on, and how does it overcome data integration, automation, visualization challenges? Q3. How will the model identify build in potential synergies, both quantitative and qualitative? The research questions were analyzed through the SEAM …
Stock Market Forecasting Based On Artificial Intelligence Technology, Yuzhun Liang
Stock Market Forecasting Based On Artificial Intelligence Technology, Yuzhun Liang
Electronic Theses, Projects, and Dissertations
This culminating experience project used artificial intelligence (AI) technology to forecast and analyze the stock market and construct complex nonlinear relationships between the input data and the output data. This project used a radial basis function neural network to forecast and analyze the stock market data. Compared the radial basis function neural network performance with the feed-forward neural network and clearly showed the superiority of the radial basis function neural network over the feed-forward neural network in the data processing. The results showed that AI technology could effectively predict stock market performance. Based on the results, the conclusion is that …
Machine Learning Stock Market Prediction Studies: Review And Research Directions, Troy J. Strader, John J. Rozycki, Thomas H. Root, Yu-Hsiang John Huang
Machine Learning Stock Market Prediction Studies: Review And Research Directions, Troy J. Strader, John J. Rozycki, Thomas H. Root, Yu-Hsiang John Huang
Journal of International Technology and Information Management
Stock market investment strategies are complex and rely on an evaluation of vast amounts of data. In recent years, machine learning techniques have increasingly been examined to assess whether they can improve market forecasting when compared with traditional approaches. The objective for this study is to identify directions for future machine learning stock market prediction research based upon a review of current literature. A systematic literature review methodology is used to identify relevant peer-reviewed journal articles from the past twenty years and categorize studies that have similar methods and contexts. Four categories emerge: artificial neural network studies, support vector machine …