Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 6 of 6 for “"Stock price prediction"”.
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Applying fuzzy logic to stock price prediction
… is to develop a system that can predict future prices in the stock markets by taking samples of past prices. Stock markets are complex. Their dramatic movements, and unexpected booms and crashes, dull all traditional tools. This study attempts to resolve such complexity using the subtractive …
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A comparative evaluation of machine learning models for stock price prediction and uncertainity estimation
This study compares machine learning models for stock price prediction and uncertainty estimation using high-frequency one-minute stock data. The research looks at how different models perform across developed and emerging markets, which helps with model selection for practical financial …
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Application of machine learning : automated trading informed by event driven data
Models of stock price prediction have traditionally used technical indicators alone to generate trading signals. In this paper, we build trading strategies by applying machine-learning techniques to both technical analysis indicators and market sentiment data. The resulting prediction models can be …
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Forecasting the Next Winning Stock: A Comparative Analysis of Machine Learning Models
Stock price prediction is a common and complex problem due to the high volatility of financial markets. This master’s thesis presents a new approach to stock price forecasting by reformulating the problem as a multiclass classification task. The main objective is to predict which stock will yield …
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Portfolio Optimization Using a Hybrid Machine Learning Stock Selection Model
… in machine learning, there are significant prediction tools available that can be applied to portfolio selection. Financial markets are known to be dynamic and complex, but algorithms are designed to capture patterns in the data. In this paper, seven machine learning techniques are used for …