Modeling and Forecasting of the financial market have been an attractive topic to scholars and researchers from various academic fields. The financial market is an abstract concept where financial commodities such as stocks, bonds, and precious metals transactions happen between buyers and sellers. In the present scenario of the financial market world, especially in the stock market, forecasting the trend or the price of stocks using machine learning techniques and artificial neural networks are the most attractive issue to be investigated. As Giles explained, financial forecasting is an instance of signal processing problem which is difficult because of high noise, small sample size, non-stationary, and non-linearity. The noisy characteristics mean the incomplete information gap between past stock trading price and volume with a future price. The stock market is sensitive with the political and macroeconomic environment. However, these two kinds of information are too complex and unstable to gather. The above information that cannot be included in features are considered as noise. The sample size of financial data is determined by real-world transaction records. On one hand, a larger sample size refers a longer period of transaction records; on the other hand, large sample size increases the uncertainty of financial environment during the 2 sample period. In this project, we use stock data instead of daily data in order to reduce the probability of uncertain noise, and relatively increase the sample size within a certain period of time. By non-stationarity, one means that the distribution of stock data is various during time changing. Non-linearity implies that feature correlation of different individual stocks is various. Efficient Market Hypothesis was developed by Burton G. Malkiel in 1991.
Table of Contents
1. INTRODUCTION
1.1 General Introduction
1.2 Problem Statement
1.3 Technologies
1.3.1 Python
1.3.2 Numpy
1.3.3 Scikit Learn
1.3.4 TensorFlow
1.3.5 Keras
1.3.6 Compiler Option
2. LITERATURE SURVEY
3. DATA & TOOLS
3.1 Data
3.1.1 Choosing the data-set
3.1.2 Gathering the data-set
4. PREVIOUS ANALYSIS
4.1 Technical Analysis Methods
4.2 Fundamental Analysis Techniques
4.3 Traditional Time Series Prediction
4.4 Machine Learning Methods
4.5 Deep Learning
4.5.1 Artificial Neural Network
4.5.1.1 Artificial Neural Network in Stock Market Prediction
4.5.2 Convolution Neural Network(CNN)
4.5.2.1 Convolution Neural Network in Stock Market Prediction
5. THE PROPOSED MODEL
5.1 Recurrent Neural Networks
5.2 Long Short Term Memory(LSTM)
5.3 Advantages of LSTM
6. SYSTEM DESIGN
6.1 System Architecture
6.1.1 Collect data set
6.1.2 Import Training data
6.1.3 Applying Scaling Features
6.1.4 Creating a neural network
6.1.5 Train the Model
6.1.6 Import test data
6.1.7 Visualize Result
6.1.8 Calculate Efficiency
6.2 LSTM Architectural Diagram
7. SYSTEM REQUIREMENTS
8. IMPLEMENTATION
8.1 Data Preprocessing
8.1.1 Libraries Import
8.1.2 Importing the training set
8.1.3 Feature Scaling
8.1.4 Inputs and Outputs
8.1.5 Reshaping
8.2 Building the Recurrent Neural Network (LSTM)
8.2.1 Libraries Import
8.2.2 LSTM Construction
8.2.3 Model Fitting
8.3 Prediction
8.3.1 Importing the Test Data
8.3.2 Scaling and Reshaping Test Data
8.3.3 Predicting Test Data
8.4 Visualization and Results
8.4.1 Visualization
8.4.2 Results
Objectives & Core Themes
This project aims to improve the accuracy of stock market price prediction by leveraging Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models. By addressing the limitations of traditional fundamental and technical analysis, the study explores the feasibility of using deep learning techniques to identify hidden patterns in historical stock data, utilizing financial datasets to build a more reliable predictive model.
- Feasibility and performance evaluation of LSTM in stock market forecasting.
- Implementation of data mining techniques to optimize neural network configurations.
- Comparison of model performance using Root Mean Square Error (RMSE) metrics.
- Architecture design of Recurrent Neural Networks for sequential data processing.
- Application of deep learning to reduce prediction error margins compared to existing methods.
Excerpt from the Book
1.1 GENERAL INTRODUCTION
Modeling and Forecasting of the financial market have been an attractive topic to scholars and researchers from various academic fields. The financial market is an abstract concept where financial commodities such as stocks, bonds, and precious metals transactions happen between buyers and sellers. In the present scenario of the financial market world, especially in the stock market, forecasting the trend or the price of stocks using machine learning techniques and artificial neural networks are the most attractive issue to be investigated. As Giles explained, financial forecasting is an instance of signal processing problem which is difficult because of high noise, small sample size, non-stationary, and non-linearity. The noisy characteristics mean the incomplete information gap between past stock trading price and volume with a future price. The stock market is sensitive with the political and macroeconomic environment. However, these two kinds of information are too complex and unstable to gather. The above information that cannot be included in features are considered as noise. The sample size of financial data is determined by real-world transaction records. On one hand, a larger sample size refers a longer period of transaction records; on the other hand, large sample size increases the uncertainty of financial environment during the 2 sample period. In this project, we use stock data instead of daily data in order to reduce the probability of uncertain noise, and relatively increase the sample size within a certain period of time. By non-stationarity, one means that the distribution of stock data is various during time changing. Non-linearity implies that feature correlation of different individual stocks is various. Efficient Market Hypothesis was developed by Burton G. Malkiel in 1991. In Burton’s hypothesis, he indicates that predicting or forecasting the financial market is unrealistic, because price changes in the real world are unpredictable. All the changes in prices of the financial market are based on immediate economic events or news.
Summary of Chapters
1. INTRODUCTION: Provides an overview of financial market modeling challenges and outlines the project's objective to use neural networks for better stock price prediction.
2. LITERATURE SURVEY: Reviews previous research papers concerning LSTM architectures, neural networks, and their applications in time series and financial forecasting.
3. DATA & TOOLS: Details the dataset selection (Google stocks) and the gathering process using Quandl, alongside the programming environment.
4. PREVIOUS ANALYSIS: Explores existing methodologies like technical analysis, fundamental analysis, and standard machine learning approaches used in the field.
5. THE PROPOSED MODEL: Explains the theoretical framework of RNNs and the specific advantages of the LSTM architecture in managing long-term dependencies.
6. SYSTEM DESIGN: Outlines the architectural flow from data collection and feature scaling to neural network creation, training, and result visualization.
7. SYSTEM REQUIREMENTS: Lists the hardware and software specifications necessary for implementing the proposed project.
8. IMPLEMENTATION: Describes the technical execution, including data preprocessing, building the Keras-based LSTM model, and the final prediction and evaluation process.
Key Terms
Stock Market Prediction, Recurrent Neural Network, RNN, Long Short Term Memory, LSTM, Artificial Neural Network, ANN, Data Mining, Time Series Analysis, Financial Forecasting, Machine Learning, Root Mean Square Error, RMSE, Python, Keras, TensorFlow
Frequently Asked Questions
What is the core focus of this project?
The project focuses on developing a stock market prediction model using Recurrent Neural Networks and Long Short-Term Memory units to achieve higher prediction accuracy compared to traditional methods.
What are the central research themes?
The research themes include time series forecasting, neural network architectural design, feature scaling for financial datasets, and the evaluation of model reliability using Root Mean Square Error.
What is the primary goal of the research?
The primary goal is to examine the feasibility of using LSTM networks to identify hidden, non-linear patterns in stock market data and to provide a more stable forecasting tool for investors.
Which scientific methods are employed?
The study employs data mining, machine learning techniques, and specifically deep learning architectures, utilizing the Backpropagation algorithm to train the neural networks.
What is covered in the implementation section?
This section covers the practical aspects of the project, including library imports (Pandas, Numpy, Keras), data preprocessing steps like feature scaling and array reshaping, and the actual building and fitting of the LSTM model.
Which keywords define this work?
Key terms include Stock Market Prediction, RNN, LSTM, ANN, Data Mining, Time Series Analysis, and Deep Learning.
How does the project handle the "vanishing gradient" problem?
The project uses LSTM (Long Short-Term Memory) units, which incorporate memory blocks and specific gate architectures to preserve error signals over longer sequences, effectively mitigating the vanishing gradient challenge.
Why was Python chosen as the development language?
Python was selected for its robust community support, extensive scientific computing libraries like Numpy and Keras, and its ability to simplify the implementation of complex mathematical algorithms.
How is the accuracy of the model measured?
The performance of the model is measured using the Root Mean Square Error (RMSE), which helps in quantifying the difference between the predicted and actual stock prices.
What role do the 'gates' play in the LSTM architecture?
The gates (input, output, and forget gates) in the LSTM cell regulate the flow of information, allowing the model to decide which data is important to store or discard during the learning process.
- Arbeit zitieren
- Joish Bosco (Autor:in), Fateh Khan (Autor:in), 2018, Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network, München, GRIN Verlag, https://www.grin.com/document/419380
