Acomparative study of Neural Network Architectures
Acomparative study of Neural Network Architectures for stock market prediction in the era of accessible machine learning
Designed the project using Tensorflow & Keras in Jupyter Notebooks. ● Developed and tested LSTM neural networks to predict stock prices using time series analysis, comparing performance against major index funds like Fidelity 500 (FXAIX) and Vanguard 500 (VFIAX). ● Applied K-Means clustering to optimize a diversified investment portfolio, leveraging financial indicators such as RSI and ATR to maximize returns and minimize risk. ● https://github.com/gino23odar/ThesisMLmodels
Designed the project using Tensorflow & Keras in Jupyter Notebooks
Developed and tested LSTM neural networks to predict stock prices using time series analysis, comparing performance against major index funds like Fidelity 500 (FXAIX) and Vanguard 500 (VFIAX)
Applied K-Means clustering to optimize a diversified investment portfolio, leveraging financial indicators such as RSI and ATR to maximize returns and minimize risk
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Posted Nov 29, 2024
A study on stock prediction using LSTM, TensorFlow, and Keras, optimizing portfolios with K-Means and financial indicators for better returns and lower risk
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