This document summarizes a student project on stock market price prediction using machine learning. It includes an introduction discussing the importance of stock price prediction and the potential of machine learning techniques. It then covers system analysis aspects for developing predictive models, including problem definition, data collection/preprocessing, feature engineering, model selection/evaluation, and ensuring model interpretability. The overall aim is to explore applying machine learning algorithms to effectively forecast stock prices.
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This document discusses using machine learning techniques to predict stock market prices. It proposes building a machine learning model that uses historical stock data to predict future stock prices. The model would go through preprocessing, processing, and regression analysis of the dataset to make predictions. Predicting stock market movements accurately is challenging, but this model aims to generate results using machine learning and deep learning algorithms on the dataset to help investors make trading decisions.
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This document discusses a data-driven approach to stock market prediction and sentiment analysis. It proposes combining recurrent neural networks with long short-term memory (RNN-LSTM) to predict stock prices based on historical data, and using support vector machines (SVM) to analyze sentiment from news headlines and predict how it may affect stock trends. The paper reviews several related works applying machine learning techniques like RNN, LSTM, and SVM to stock prediction and sentiment analysis. It aims to improve prediction accuracy by combining both historical data analysis and sentiment analysis of news articles.
STOCK MARKET PREDICTION USING MACHINE LEARNING IN PYTHONIRJET Journal
This document discusses using machine learning techniques to predict stock market prices. Specifically, it evaluates using support vector machines, random forests, and regression models. It finds that support vector regression with an RBF kernel performed best compared to other models at accurately predicting stock prices based on historical data. The paper also reviews several related works applying machine learning methods like neural networks and support vector machines to financial time series data for stock prediction.
STOCK MARKET PREDICTION AND ANALYSIS USING MACHINE LEARNING ALGORITHMSIRJET Journal
This document discusses using machine learning algorithms like LSTM and linear regression to predict stock market prices. It proposes using techniques like stacked LSTM on historical stock data to make predictions. The document outlines collecting data, preprocessing it, training models on training data and evaluating them on test data. It suggests comparing the accuracy of linear regression, stacked LSTM and other models to determine the most accurate for predicting 30 days of future stock prices. The goal is to reduce investment risk through more accurate machine-learned stock predictions.
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This document discusses various machine learning algorithms that have been used for stock market prediction, including CNN, ARIMA, LSTM, random forests, and support vector machines. It provides a literature review of past research applying these algorithms to predict stock prices using historical data. The document concludes that LSTM and ARIMA models generally provide the best predictions based on evaluating various algorithms on large datasets of historical stock market data.
This document discusses using LSTM artificial intelligence to forecast stock prices. It developed a user interface using Streamlit and used steps like importing and cleaning data, splitting it into training and test sets, creating and training a model, making predictions, and evaluating and improving predictions. Future work includes predicting stock prices based on multiple factors and implementing different algorithms because different data requires different techniques.
This document summarizes a student project on stock market price prediction using machine learning. It includes an introduction discussing the importance of stock price prediction and the potential of machine learning techniques. It then covers system analysis aspects for developing predictive models, including problem definition, data collection/preprocessing, feature engineering, model selection/evaluation, and ensuring model interpretability. The overall aim is to explore applying machine learning algorithms to effectively forecast stock prices.
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This document discusses using machine learning techniques to predict stock market prices. It proposes building a machine learning model that uses historical stock data to predict future stock prices. The model would go through preprocessing, processing, and regression analysis of the dataset to make predictions. Predicting stock market movements accurately is challenging, but this model aims to generate results using machine learning and deep learning algorithms on the dataset to help investors make trading decisions.
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This document discusses a data-driven approach to stock market prediction and sentiment analysis. It proposes combining recurrent neural networks with long short-term memory (RNN-LSTM) to predict stock prices based on historical data, and using support vector machines (SVM) to analyze sentiment from news headlines and predict how it may affect stock trends. The paper reviews several related works applying machine learning techniques like RNN, LSTM, and SVM to stock prediction and sentiment analysis. It aims to improve prediction accuracy by combining both historical data analysis and sentiment analysis of news articles.
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This document discusses using LSTM artificial intelligence to forecast stock prices. It developed a user interface using Streamlit and used steps like importing and cleaning data, splitting it into training and test sets, creating and training a model, making predictions, and evaluating and improving predictions. Future work includes predicting stock prices based on multiple factors and implementing different algorithms because different data requires different techniques.
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This document summarizes research on using deep learning techniques to predict stock market prices. Specifically, it discusses prior research that has used models like LSTM, CNN, random forest and logistic regression with technical indicators as inputs to predict stock prices, trends and trading signals. It also outlines some of the challenges in making accurate stock predictions, such as accessing reliable market data and accounting for the large volume of time series data. The literature review covers several papers that have developed and evaluated deep learning models for stock prediction and generated trading signals.
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This document discusses using a Long Short-Term Memory (LSTM) model to predict stock market prices. It begins by introducing the problem of predicting stock markets and how machine learning techniques like LSTM can help. It then discusses collecting stock price data and designing an LSTM model in Python using Keras and other libraries. The model is trained on historical stock price data to identify patterns and predict future prices. The document suggests LSTM models are well-suited for this due to their ability to use past data in predictions. It evaluates the model's predictions against actual prices to determine accuracy.
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This document discusses using machine learning techniques like LMS and LSTM algorithms to predict stock prices. It summarizes previous research on stock price prediction that used techniques like artificial neural networks, support vector machines, and recurrent neural networks. The document then describes the proposed system for stock price prediction, which involves preprocessing data, splitting it into training and test sets, analyzing the data with LMS and LSTM algorithms, and outputting predictions in graph and report formats. It concludes that combining multiple algorithms into hybrid models can improve prediction accuracy while reducing computational complexity compared to single models.
Predicting Stock Market Prices with Sentiment Analysis and Ensemble Learning ...IRJET Journal
The document describes a study that proposes a hybrid approach for predicting stock market prices using sentiment analysis and ensemble learning techniques. The approach involves collecting stock price and social media data, performing sentiment analysis on the text data, combining the datasets, training various machine learning models, and evaluating the models based on metrics like RMSE and R2 score. The results found that the ensemble XGBoost model outperformed individual models like LSTM and linear regression in predicting stock prices of companies, demonstrating the potential of using sentiment analysis and ensemble learning for stock market prediction.
AlgoB – Cryptocurrency price prediction system using LSTMIRJET Journal
This document describes a cryptocurrency price prediction system called AlgoB that uses an LSTM neural network model. The system was developed by four students to predict cryptocurrency prices with high accuracy. It takes historical price data as input and can predict future prices. The system uses libraries like NumPy, Pandas, TensorFlow and Matplotlib. It achieves 80% prediction accuracy, outperforming regression and tree models. The LSTM model is trained on price data and evaluates predictions against real prices. This helps traders understand market movements and identify good times to buy and sell cryptocurrencies.
This document discusses using machine learning techniques to predict stock market prices. It begins with an introduction to existing stock prediction methods like fundamental and technical analysis. The proposed system would use machine learning models to analyze historical stock price data and sentiment analysis of news articles to predict future stock prices, volatility, and market trends. The methodology section outlines different models, including using only historical prices, classifying sentiment of news, and aspect-based sentiment analysis. Features like stock price volatility, momentum, and index momentum would be used. The conclusion states that accurately predicting the complex stock market requires considering various factors.
This document presents a literature review and proposed framework for stock market prediction. It discusses using long short-term memory (LSTM), support vector regression (SVR), linear regression, and sentiment analysis models individually and in a hybrid ensemble model. The models are trained on historical stock price and sentiment data to predict future stock trends. Results show the hybrid model achieves higher prediction accuracy than individual models. Visualizations of predicted versus actual prices are generated to evaluate model performance. The proposed framework aims to help investors make more informed buy, sell, and hold decisions.
The Analysis of Share Market using Random Forest & SVMIRJET Journal
The document discusses using machine learning algorithms like random forest and support vector machines (SVM) to predict stock market movement and values. Specifically, it aims to develop a more accurate technique for forecasting stock behavior by applying these algorithms to preprocessed historical stock data from Yahoo Finance. Random forest and SVM will be used to generate precise predictions. The goal is to build an effective machine learning model that can provide real-world solutions for issues faced by stockholders and market organizations.
ELASTIC PROPERTY EVALUATION OF FIBRE REINFORCED GEOPOLYMER COMPOSITE USING SU...IRJET Journal
This document discusses using machine learning algorithms like random forest and support vector machines to predict stock market prices more accurately. It analyzes using these algorithms on historical stock price data from Yahoo Finance to train models. Specifically, it trains random forest and SVM models on 80% of the data and tests them on the remaining 20% to predict future stock prices. The goal is to develop a more effective technique for stock price forecasting using artificial intelligence methods.
This document provides details about a project aimed at predicting stock market values using Hidden Markov Models. It includes an introduction describing the problem of stock market prediction and the suitability of HMMs for tackling the time-dependent nature of the problem. The document outlines the approach taken, which involves using the daily fractional change in stock value and fractional deviation of intra-day high and low values to train separate HMMs for different stocks. It then discusses testing the models on various stocks and comparing performance to other existing methods. Tables and figures are provided to illustrate the experimental setup, results, and risk analysis.
Stock market analysis and prediction using long short term memory network,predicted the differnt stock market prices using the lstm model which is sequential network in deep learning
Efficient commodity price forecasting using long short-term memory modelIAESIJAI
Predicting commodity prices, particularly food prices, is a significant concern for various stakeholders, especially in regions that are highly sensitive to commodity price volatility. Historically, many machine learning models like autoregressive integrated moving average (ARIMA) and support vector machine (SVM) have been suggested to overcome the forecasting task. These models struggle to capture the multifaceted and dynamic factors influencing these prices. Recently, deep learning approaches have demonstrated considerable promise in handling complex forecasting tasks. This paper presents a novel long short-term memory (LSTM) network-based model for commodity price forecasting. The model uses five essential commodities namely bread, meat, milk, oil, and petrol. The proposed model focuses on advanced feature engineering which involves moving averages, price volatility, and past prices. The results reveal that our model outperforms traditional methods as it achieves 0.14, 3.04%, and 98.2% for root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2 ), respectively. In addition to the simplicity of the model, which consists of an LSTM single-cell architecture that reduced the training time to a few minutes instead of hours. This paper contributes to the economic literature on price prediction using advanced deep learning techniques as well as provides practical implications for managing commodity price instability globally.
The usage of Neural network s has determined a variegated area of packages in the present world. This has caused the
improvement of various fashions for economic markets and funding. This paper represents the idea the way to predict share
market fee the use of artificial Neural community with a given enter parameters of share marketplace. The proportion
marketplace is dynamic in nature approach to expect percentage fee could be very complex method by using trendy prediction
or computation method. Its predominant motive is that there is no linear relationship between market parameters and target last
price. Since there is no linear relationship between input patterns and corresponding output patterns, so use of neural network is
a desire of hobby for share market prediction.
IRJET - Stock Market Analysis and PredictionIRJET Journal
This document discusses using machine learning algorithms to analyze stock market data and predict future stock prices. It proposes collecting historical stock price and Twitter sentiment data and using recurrent neural networks and long short-term memory models to analyze the data and generate predictions and visualizations. The models would allow investors to make informed decisions about buying and selling stocks to potentially achieve returns on their investments.
Artificial Intelligence Based Stock Market Prediction Model using Technical I...ijtsrd
The stock market is highly volatile and complex in nature. However, notion of stock price predictability is typical, many researchers suggest that the Buy and Sell prices are predictable and investor can make above average profits using efficient Technical Analysis TA .Most of the earlier prediction models predict individual stocks and the results are mostly influenced by company’s reputation, news, sentiments and other fundamental issues while stock indices are less affected by these issues. In this work, an effort is made to predict the Buy and Sell decisions of stocks, trends of stock by utilizing Stock Technical Indicators STIs As a part of prediction model the Long Short Term Memory LSTM , Support Virtual Machine SVM Artificial intelligence algorithms will be used with Stock Technical Indicators STIs. The project will be carried on National Stock Exchange NSE Stocks of India. Mr. Ketan Ashok Bagade | Yogini Bagade "Artificial Intelligence Based Stock Market Prediction Model using Technical Indicators" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd53854.pdf Paper URL: https://www.ijtsrd.com.com/management/other/53854/artificial-intelligence-based-stock-market-prediction-model-using-technical-indicators/mr-ketan-ashok-bagade
Stock Price Prediction Using Sentiment Analysis and Historic Data of StockIRJET Journal
This document discusses using sentiment analysis and historical stock data to predict stock prices. It proposes analyzing sentiments expressed on Twitter about companies and correlating that with stock price movements. It also discusses using machine learning techniques like naive Bayes classification, time series analysis, and ARIMA models on historical stock data to predict future prices. The proposed system aims to help novice investors make decisions by collectively analyzing news and market sentiments using machine learning algorithms. Accurately predicting stock prices could help investors realize more profits.
The document discusses literature related to stock price prediction using sentiment analysis. It provides an overview of several past studies that have used techniques like naive Bayes classification, support vector machines, and Twitter API to analyze sentiment from social media posts and classify them as positive, negative, or neutral. The studies aimed to predict stock prices, academic success of universities, and movie box office performance based on public sentiment analysis. The document also outlines some of the theoretical concepts involved like time series analysis and the role of sentiment shifts in correlating with stock market trends. It describes the motivation to develop an automated sentiment analysis system to classify reviews without human bias and provide insights for decision making.
Project report on Share Market applicationKRISHNA PANDEY
This is the proposal document for AVS Group of Technology service offering in the website design and development and custom web application development space. The document details our understanding of the brief, the objectives of the services suite, the methodology, and deliverable and commercials.
This document provides an introduction to a seminar on using deep learning and LSTM methods for stock price prediction. It discusses using machine learning algorithms to analyze stock market data and predict future prices. The proposed system would use tools like Pandas, NumPy, and Scikit-learn to get stock data and predict prices using an LSTM model. It presents the modules, architecture, advantages, and requirements of the system. The conclusion states that both machine learning techniques showed improved prediction accuracy compared to traditional methods, with LSTM proving more efficient.
This document provides details of a student project to build a stock market predictor. It includes:
- Names of 4 students working on the project and their guide
- Sections on literature review of papers on stock prediction methods like regression, logistic regression and SVM classification
- A Gantt chart showing the project timeline and tasks from October to April
- Details of the methodology including loading a pre-trained model, making predictions, and displaying results
- Explanations of the modelling process including data collection, preprocessing, training a LSSVR model, and predicting stock values
- A project plan outlining members' roles and responsibilities in planning, data collection, training and testing the model
-
The APCO Geopolitical Radar - Q3 2024 The Global Operating Environment for Bu...APCO
The Radar reflects input from APCO’s teams located around the world. It distils a host of interconnected events and trends into insights to inform operational and strategic decisions. Issues covered in this edition include:
SATTA MATKA SATTA FAST RESULT KALYAN TOP MATKA RESULT KALYAN SATTA MATKA FAST RESULT MILAN RATAN RAJDHANI MAIN BAZAR MATKA FAST TIPS RESULT MATKA CHART JODI CHART PANEL CHART FREE FIX GAME SATTAMATKA ! MATKA MOBI SATTA 143 spboss.in TOP NO1 RESULT FULL RATE MATKA ONLINE GAME PLAY BY APP SPBOSS
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This document presents a literature review and proposed framework for stock market prediction. It discusses using long short-term memory (LSTM), support vector regression (SVR), linear regression, and sentiment analysis models individually and in a hybrid ensemble model. The models are trained on historical stock price and sentiment data to predict future stock trends. Results show the hybrid model achieves higher prediction accuracy than individual models. Visualizations of predicted versus actual prices are generated to evaluate model performance. The proposed framework aims to help investors make more informed buy, sell, and hold decisions.
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Predicting commodity prices, particularly food prices, is a significant concern for various stakeholders, especially in regions that are highly sensitive to commodity price volatility. Historically, many machine learning models like autoregressive integrated moving average (ARIMA) and support vector machine (SVM) have been suggested to overcome the forecasting task. These models struggle to capture the multifaceted and dynamic factors influencing these prices. Recently, deep learning approaches have demonstrated considerable promise in handling complex forecasting tasks. This paper presents a novel long short-term memory (LSTM) network-based model for commodity price forecasting. The model uses five essential commodities namely bread, meat, milk, oil, and petrol. The proposed model focuses on advanced feature engineering which involves moving averages, price volatility, and past prices. The results reveal that our model outperforms traditional methods as it achieves 0.14, 3.04%, and 98.2% for root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2 ), respectively. In addition to the simplicity of the model, which consists of an LSTM single-cell architecture that reduced the training time to a few minutes instead of hours. This paper contributes to the economic literature on price prediction using advanced deep learning techniques as well as provides practical implications for managing commodity price instability globally.
The usage of Neural network s has determined a variegated area of packages in the present world. This has caused the
improvement of various fashions for economic markets and funding. This paper represents the idea the way to predict share
market fee the use of artificial Neural community with a given enter parameters of share marketplace. The proportion
marketplace is dynamic in nature approach to expect percentage fee could be very complex method by using trendy prediction
or computation method. Its predominant motive is that there is no linear relationship between market parameters and target last
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The document discusses literature related to stock price prediction using sentiment analysis. It provides an overview of several past studies that have used techniques like naive Bayes classification, support vector machines, and Twitter API to analyze sentiment from social media posts and classify them as positive, negative, or neutral. The studies aimed to predict stock prices, academic success of universities, and movie box office performance based on public sentiment analysis. The document also outlines some of the theoretical concepts involved like time series analysis and the role of sentiment shifts in correlating with stock market trends. It describes the motivation to develop an automated sentiment analysis system to classify reviews without human bias and provide insights for decision making.
Project report on Share Market applicationKRISHNA PANDEY
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This document provides details of a student project to build a stock market predictor. It includes:
- Names of 4 students working on the project and their guide
- Sections on literature review of papers on stock prediction methods like regression, logistic regression and SVM classification
- A Gantt chart showing the project timeline and tasks from October to April
- Details of the methodology including loading a pre-trained model, making predictions, and displaying results
- Explanations of the modelling process including data collection, preprocessing, training a LSSVR model, and predicting stock values
- A project plan outlining members' roles and responsibilities in planning, data collection, training and testing the model
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This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
Key highlights include Microsoft's Digital Transformation Framework, which focuses on driving innovation and efficiency, and McKinsey's Ten Guiding Principles, which provide strategic insights for successful digital transformation. Additionally, Forrester's framework emphasizes enhancing customer experiences and modernizing IT infrastructure, while IDC's MaturityScape helps assess and develop organizational digital maturity. MIT's framework explores cutting-edge strategies for achieving digital success.
These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
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Microsoft’s Digital Transformation Framework
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Forrester’s Digital Transformation Framework
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Cisco’s Digital Transformation Framework
Cognizant’s Digital Transformation Framework
DXC Technology’s Digital Transformation Framework
The BCG Strategy Palette
McKinsey’s Digital Transformation Framework
Digital Transformation Compass
Four Levels of Digital Maturity
Design Thinking Framework
Business Model Canvas
Customer Journey Map
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2. RAJIV GANDHI COLLEGE OF ENGINEERING ,RESEARCH & TECHNOLOGY,CHANDRAPUR
(DR.BABASAHEB AMBEDKAR TECHNOLOGICAL UNIVERSITY,LONERE)
(2023-24)
DEPARTMENTOFCOMPUTER SCIENCEAND ENGINEERING
Semester VI Mini project– II
Submitted By
1.Mohit Titarmare
2.Gaurav Wankar
3.Gaurav Thapliyal
4.Yash Raut
Guided By:
Dr. Bireshwar Ganguly
Mini project Incharge :
Prof. Madhavi Sadu
Dr. Nitin Janwe
HOD , CSE
3. INTRODUCTION
A market where shares are publicly issued and traded is known as a share market.
Implementing the concept of algorithmic trading, which uses automated, pre-
programmed trading strategies to predict stock prices.
Time series forecasting (predicting future values based on historical values) applies
well to stock forecasting.
Predicting stock prices is a challenging task that blends finance, mathematics, and
computer science. It involves using historical data, market trends, and various
analytical techniques to forecast the future movements of stock prices.
4. Need of Project
The stock market is known for being volatile, dynamic,
& nonlinear
Accurate stock price prediction is extremely challenging
because of multiple factors.
But, all of this also means that there’s a lot of data to
find patterns in.
So, we keep exploring analytics techniques to detect
stock market trends.
So, they can be analyzed as a sequence of discrete-time
data
Despite the volatility, stock prices aren’t just randomly
generated numbers.
5. STEPS PERFORMED
1. Importing data
2. Split the Data into training / test sets
3. Creating and Training the Model
4. Making Predictions
5. Evaluating and Improving Predictions
6. METHODOLOGY
• Data Preprocessing: Normalizing or standardizing stock price data so that the LSTM network
can train more effectively.
• Model Training: Feeding historical stock data into the LSTM, which learns from sequences of
past stock prices, volumes, etc.
• Prediction: Using the trained LSTM to predict future stock prices; these predictions can
be visualized or used to trigger trading actions in the app.
• Backtesting: Using historical data to validate the model’s predictions, which is crucial
for understanding the effectiveness of the LSTM model before it’s used in live trading.
• Deployment: Integrating the LSTM model into a stock trading application where it provides
regular predictions, updates based on new data, and potentially adapts to changing market
conditions.
10. Algorithm Used
• Long Short-Term Memory (LSTM) networks, a type of recurrent neural network
(RNN), play a crucial role in applications involving sequence prediction
• Handling Time-Series Data: Stock market data is inherently sequential and
time-dependent. LSTM networks are designed to recognize patterns in
sequences of data, making them particularly suitable for modeling stock price
movements
• Avoiding Vanishing Gradient Problem: LSTMs solve the vanishing gradient
problem commonly encountered with standard RNNs through their unique
structure of gates, including input gates, output gates, and forget gates.
• Predictive Performance: In stock prediction apps, LSTM networks can be
trained to predict not just one-step ahead (next day’s price) but also multiple
steps ahead, providing forecasts over a horizon that can be tuned according to
user needs or specific application requirements.
13. FUTURE WORK
Machine learning and Data science is a game changer in this domain so
there is a lot of data to find patterns in for predicting with high degree
of accuracy.
In future we’ll try to predict the values based on multiple factors such
as politics, global economic conditions, unexpected events like covid,
companies financial performance, and so on.
Decided to implement a simple User Interface to operate this whole
process for users so to make people engage in Stock market.
14. CONCLUSION
Stock price prediction is a challenging task influenced by numerous variables and
uncertainties. While various methodologies, including statistical models and
machine learning algorithms, aim to forecast future prices, no approach can
consistently predict market movements with absolute accuracy.
The dynamic nature of financial markets, coupled with the complexity of factors
influencing stock prices, makes it difficult to develop models that reliably capture all
relevant information. Additionally, the efficient market hypothesis suggests that
stock prices reflect all available information, further complicating prediction efforts.