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Stock Price Prediction using artificial intelligence.pptx
- 1. Department of ISE AccurateForecasting for Investment Decisions Pavani L (1TJ23IS040) Srushti Shettar (1TJ23IS055) Uday Kiran B R (1TJ23IS058) Guide Name : Dr.DoddeGowda Designation : Associate Professor Mini Project STOCK PRICE PREDICTION
- 2. 01. Introduction 02. LiteratureReview 03. Existing System 04. Problem Statement 05. Proposed System 06. Flow Chart 07. Implementation 08. Results 09. Future Enhancements 10. Conclusion Agenda
- 3. Introduction to StockPrice Prediction • Definition: Stock price prediction refers to forecasting the future value of company stocks traded on financial markets. • Importance: Helps investors, traders, and institutions make informed buy/sell/hold decisions. • Data Sources: Historical stock prices, financial statements, market indices, news articles, and social media sentiment. • Applications: • Algorithmic trading • Portfolio management
- 4. Sl.No Title andAuthors Problem Statement Studies and Contributions Implementation Results Overall Findings Future work 01. Prediction in Stock Price Using Python and Machine Learning Authors: Anchal Vij 1 , Komal Saxena2 Ajay Rana3 The study addresses the challenge of predicting stock market trends and closing prices by applying machine learning algorithms to historical financial data. It seeks to improve prediction reliability and aid investors in making informed trading decisions. The research reviews prior stock prediction techniques and contributes by developing and testing a Python-based machine learning model that enhances accuracy and computational efficiency. It demonstrates the potential of ML in financial forecasting applications. The implementation demonstrated that machine learning algorithms can effectively predict stock prices with a satisfactory level of precision using historical data. Machine learning proves to be a valuable tool in forecasting stock prices, reducing manual prediction errors, and supporting better financial planning. Future research can focus on integrating deep learning models and real-time data for more dynamic and accurate stock market predictions.
- 5. Sl.No Title andAuthors Problem Statement Studies and Contributions Implementation Results Overall Findings Future work 02. Prediction in Stock Price Using Machine Learning Techniques Authors : Sumeet Sarode, Harsha G. Tolani, Prateek Kak, Lifnac The research aims to predict stock closing prices using machine learning techniques implemented in Python to assist investors in making informed financial decisions. This study analyzes previous stock prediction approaches and proposes a Python-based machine learning model that improves accuracy and ease of implementation. The implementation using historical datasets shows that machine learning algorithms such as linear regression and random forest yield promising results in predicting future stock prices. The model produced consistent and realistic predictions compared to traditional methods. The study confirms that machine learning can effectively model complex stock price movements and outperform basic statistical approaches. It also highlights that data preprocessing and algorithm choice significantly influence prediction quality. Future work could integrate deep learning models, real-time data, and hybrid predictive systems for enhanced market forecasting accuracy. Incorporating news sentiment and social media data is also recommended to capture market psychology.
- 6. Sl.No Title andAuthors Problem Statement Studies and Contributions Implementation Results Overall Findings Future work 03. The Directionality Function Defect of Performance Evaluation Method in Regression Neural Network for Stock Price Prediction Authors : Yi Wei Vipin Chaudhary The paper addresses the issue that existing neural network evaluation metrics, such as prediction error, fail to capture the directional movement (rise or fall) of stock prices. It emphasizes that this lack of directionality can mislead financial decisions and result in unreliable performance evaluations. The authors analyze the limitations of conventional performance metrics like MAE, MSE, RMSE, and R² in regression neural networks. They demonstrate that absolute value–based evaluation methods lack financial interpretability, proposing that directionality should be incorporated into model assessment. Experiments were conducted using 20 Chinese and 20 U.S. stocks, comparing six neural network models (MLP, RNN, LSTM, GRU, BiRNN, BiLSTM). Results show that all prediction error values fail to represent stock rise or fall trends, proving that current evaluation metrics lack directional insight. The study concludes that while regression neural networks can predict price closeness effectively, their evaluation methods are fundamentally flawed because they ignore directional attributes. Therefore, relying solely on prediction error values is unreliable for financial forecasting. Future research aims to resolve the defect by integrating fractal trend analysis into prediction error evaluation. The goal is to design an improved performance metric that accounts for both price magnitude and directionality in stock prediction models.
- 7. Existing System 01.Statistical Approaches Statisticalmethods analyze historical stock price data, volume, and related indicators to identify trends and make predictions • Time Series Analysis • Linear Regression • GARCH (Generalized Autoregressive Conditional Heteroskedasticity) • Exponential Smoothing Limitations • Poor Handling of Non-Stationary Data • Short-Term Effectiveness Only • Over fitting Risk • Volatility Clustering Issues
- 8. Existing System 02.Deep Learningin Stock Price Prediction Deep learning uses multi-layered neural networks to automatically extract complex patterns and relationships from large volumes of financial data • Pattern Recognition • Sequential Modeling • Integration of Multiple Data Sources Limitations • Lack of Interpretability • Computation & Training Cost • Regulatory and Ethical Concerns •Lack of Interpretability •Lack of Interpretability •Lack of Interpretability
- 9. Problem Statement • Thestock market is highly dynamic, volatile, and uncertain, with prices influenced by multiple factors such as historical data, trading volumes, economic indicators, global events, and market sentiment. Traditional methods often struggle to capture these complex and non-linear relationships • Robust predictive system powered by machine learning in python which can analyze vast amounts of data, detect hidden patterns, and forecast stock prices more effectively. The ultimate goal of this system is to help investors and financial institutions make informed decisions, reduce risks, and maximize returns.
- 10. Proposed System • DataCollection • Data Preprocessing • Prediction & Output • Website Interface • Machine Learning Models
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- 19. Future Enhancement • CombineARIMA with Deep Learning models (like LSTM or GRU) to capture both linear and non-linear stock trends. • Include real-time stock market data for continuous and dynamic prediction updates. • Add sentiment analysis from financial news and social media to improve forecast accuracy. • Integrate technical and economic indicators (volume, moving averages, GDP, etc.) for better feature representation. • Develop a user-friendly web or mobile interface using Flask or Streamlit for interactive visualization. • Enable automated model retraining to adapt to changing market patterns.
- 20. Conclusion • The Python-basedstock price prediction model using the ARIMA algorithm effectively forecasts future stock prices by analyzing historical data and identifying time-series patterns. ARIMA’s strength lies in its ability to model linear trends and seasonality, making it a reliable statistical approach for short-term predictions. • However, since stock markets are influenced by many non-linear and unpredictable factors, the model’s accuracy may vary with sudden market fluctuations. Overall, the project demonstrates how machine learning and statistical modeling in Python can support investors in making informed financial decisions and serve as a foundation for more advanced hybrid forecasting systems in the future.
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