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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 …