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Questions and Answers
What is the primary target variable used for prediction in the stock price dataset?
What is the primary target variable used for prediction in the stock price dataset?
Which method can be used to handle missing data during the preprocessing phase?
Which method can be used to handle missing data during the preprocessing phase?
In terms of forecasting future stock prices, which advantage do LSTM networks have over traditional linear regression models?
In terms of forecasting future stock prices, which advantage do LSTM networks have over traditional linear regression models?
How is the dataset transformed for optimal LSTM training?
How is the dataset transformed for optimal LSTM training?
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What impact does high market volatility have on the performance of LSTM models in stock prediction?
What impact does high market volatility have on the performance of LSTM models in stock prediction?
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What is the purpose of residual analysis in the context of stock prediction models?
What is the purpose of residual analysis in the context of stock prediction models?
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Which feature is least relevant when predicting stock prices using LSTM based on the provided dataset description?
Which feature is least relevant when predicting stock prices using LSTM based on the provided dataset description?
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What procedure is primarily used to ensure that the data is suitable for training the LSTM model?
What procedure is primarily used to ensure that the data is suitable for training the LSTM model?
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What role does feature engineering play in stock price prediction models?
What role does feature engineering play in stock price prediction models?
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Which of the following techniques is NOT mentioned as a regularization technique?
Which of the following techniques is NOT mentioned as a regularization technique?
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How does the Efficient Market Hypothesis (EMH) relate to stock price predictions?
How does the Efficient Market Hypothesis (EMH) relate to stock price predictions?
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Why is reinforcement learning being explored in stock trading?
Why is reinforcement learning being explored in stock trading?
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What challenge is associated with using machine learning for stock price predictions?
What challenge is associated with using machine learning for stock price predictions?
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What is a potential benefit of incorporating alternative data in stock predictions?
What is a potential benefit of incorporating alternative data in stock predictions?
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What comparative advantage do LSTMs have over linear regression in stock price prediction?
What comparative advantage do LSTMs have over linear regression in stock price prediction?
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What is a limitation of models that rely solely on historical stock prices?
What is a limitation of models that rely solely on historical stock prices?
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What is one focus of future research in stock market prediction models?
What is one focus of future research in stock market prediction models?
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Which concept helps models adapt to periods of increased market volatility?
Which concept helps models adapt to periods of increased market volatility?
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What does a random distribution of residuals in a model's analysis indicate?
What does a random distribution of residuals in a model's analysis indicate?
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When analyzing deviations in stock price predictions, what common issue does a model face?
When analyzing deviations in stock price predictions, what common issue does a model face?
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Which of the following is a potential improvement for stock price forecasting models?
Which of the following is a potential improvement for stock price forecasting models?
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What does a small number of large residual errors in a model suggest?
What does a small number of large residual errors in a model suggest?
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Why might a model based purely on historical data struggle during times of uncertainty?
Why might a model based purely on historical data struggle during times of uncertainty?
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What is one of the benefits of performing more feature engineering in stock prediction models?
What is one of the benefits of performing more feature engineering in stock prediction models?
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Study Notes
Dissertation Title
- Stock Market Analysis and Prediction using LSTM
Author
- Aradhya Mahanta Borah
Degree
- Bachelor of Technology in Computer Science and Engineering
Supervisor
- Mr. Ved Prakash Chaubey
Institution
- Lovely Professional University, Phagwara, Punjab (India)
Acknowledgement
- Expresses gratitude to teacher Ved Prakash Chaubey for support, guidance, and insights
- Acknowledges resources like Tiingo for datasets instrumental in research
Supervisor's Certificate
- Confirms the dissertation is original work
- States the work has not been submitted elsewhere
Table of Contents
- Includes sections like Abstract, Problem Statement, Dataset Description, Solution Approach, Required Libraries/Libs Used, Introduction, Literature Review, Methodology, Results, Analysis, Conclusion, References, and GitHub Repository
Abstract
- Explores Long Short-Term Memory (LSTM) networks for stock market prediction
- Aims to predict closing stock prices using historical data and LSTM models
- Captures complex temporal dependencies in stock market
- Evaluates LSTM's effectiveness in forecasting
- Shows potential of deep learning techniques in financial markets
- Employs metrics like MAE, MSE, and RMSE to assess accuracy
Problem Statement
- Stock market is dynamic and complex
- Inherent unpredictability and high volatility in stock prices make accurate forecasting challenging
- Traditional statistical methods often struggle with complex and non-linear relationships
- Aims to predict future stock prices using historical data
Dataset Description
- Dataset comprises historical stock price data for various stocks
- Includes daily records of features like Date, Open, High, Low, Close, Volume, Adj Close, Adj High/Low/Open, Div Cash, and Split Factor
- Data is preprocessed, cleaned, and normalized before training
- Focuses on Close price as target variable
Solution Approach
- Deep Learning is used
- Relies on Long Short-Term Memory (LSTM) model for prediction
- LSTM networks are used for handling sequential data
- Processes include data preprocessing, feature selection, normalization, data splitting, model architecture design, and training
Required Libraries
- Pandas (data manipulation and analysis)
- NumPy (numerical computation)
- Matplotlib (data visualization)
- Seaborn (statistical data visualization)
- Scikit-learn (machine learning utilities and scaling)
- Keras (deep learning framework, specifically LSTM)
- TensorFlow (deep learning framework)
- Joblib (model serialization)
- OS (file and directory management)
- DateTime (date and time manipulation)
- TensorFlow Hub (pre-trained models and components)
Model Training
- Uses backpropagation through time (BPTT)
- Employs Mean Squared Error (MSE) loss function for measuring difference between predicted and actual stock prices
- Uses Adam Optimizer
- Divides training data into epochs and batch sizes
Evaluation and Testing
- Tests on unseen data
- Analyzes residuals to check for patterns
- Visualization (predicted vs actual prices)
Future Price Prediction
- Uses last 100 days of stock prices to predict next day's price
- Iterative forecasting where predicted value used as input for next day prediction
Literature Review
- Discusses ARIMA, GARCH, SVM, Decision Trees, and Random Forests, exploring their applications in stock price prediction
- Highlights limitations of traditional approaches, emphasizing the need for machine learning models
- Examines deep learning models, focusing on LSTM's ability to handle long-term dependencies
Methodology
- Details the steps for data collection, preprocessing, feature selection, model development, and evaluation
- Includes methods for handling missing data, feature scaling, sequence creation, model architecture (LSTM), and model training using Keras and TensorFlow
- Includes details of LSTM model architecture and its layers (input, LSTM, and dense)
Results
- Presents evaluation metrics like RMSE, MAE, and R-squared
- Plots predicted vs actual stock prices
- Presents analysis of residuals
Comparison with Other Models
- Compares LSTM model performance with Linear Regression model
- Employs metrics like RMSE and R-Squared to evaluate models
Analysis
- Assesses model's generalization performance
- Explores potential enhancements to improve stock price prediction accuracy
- Details ways to improve on limitations of the model
Future Work
- Suggests incorporating external data sources like news, sentiment analysis, and macroeconomic factors
- Recommends exploring more complex models (e.g., attention-based networks) or hybrid approaches
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Description
This dissertation explores the use of Long Short-Term Memory (LSTM) networks for predicting stock market closing prices. Through various methodologies and dataset descriptions, the research aims to demonstrate the effectiveness of LSTM in financial forecasting. The work integrates insights, resources, and findings that contribute to the field of stock market analysis.
