This document presents a comparative analysis of using machine learning and deep learning algorithms to predict stock market trends via continuous and binary data. It discusses using historical data from four stock market groups over 10 years as inputs to nine machine learning and two deep learning models. The results indicate that recurrent neural networks and long short-term memory networks outperformed other models for both continuous and binary data, with better accuracy when the inputs were converted to binary before being fed to the models.

1. Predicting Stock Market Trends Using Machine Learning and Deep
Learning Algorithms Via Continuous and Binary Data; a
Comparative Analysis
PRESENTED BY:
G.AKHIL REDDY-19D41A1213
K.SAMARASIMHA REDDY-19D41A1218
G.SHYLAJA -19D41A1214
V.SAI TEJA -19D41A1247

2. CONTENTS:
• INTRODUCTION
• ABSTRACT
• EXISTING SYSTEM
• PROPOSED SYSTEM
• ARCHITECTURE
• SYSTEM REQUIREMENTS
• RESEARCH DATA
• CONCLUSION

3. INTRODUCTION
•The task of stock prediction has always been a challenging problem for statistics
experts and finance.
•The main reason behind this prediction is buying stocks that are likely to increase in
price and then selling stocks that are probably to fall.
•Generally, there are two ways for stock market prediction.
1.Fundamental analysis: Fundamental analysis is one of them and relies on a company’s
technique and fundamental information like market position, expenses and annual growth
rates.
2.Technical analysis: The technical analysis method, which concentrates on previous stock
prices and values. This analysis uses historical charts and patterns to predict future prices.

4. ABSTRACT
•The nature of stock market movement has always been ambiguous for investors because of various influential factors.
This study aims to significantly reduce the risk of trend prediction with machine learning and deep learning algorithms.
•Four stock market groups, namely
1.Diversified financials, 2.Petroleum, 3.Non-metallic minerals, 4.Basic metals from Tehran stock exchange.
Are chosen for experimental evaluations.
•This study compares nine machine learning models
(Decision Tree, Random Forest, Adaptive Boosting (Adaboost), eXtreme Gradient Boosting (XGBoost), Support Vector
Classifier (SVC), Naïve Bayes, K-Nearest Neighbors (KNN), Logistic Regression and Artificial Neural Network (ANN))
and two powerful deep learning methods (Recurrent Neural Network (RNN) and Long short-term memory (LSTM).
•Ten technical indicators from ten years of historical data are our input values, and two ways are supposed for employing
them.
•Firstly, calculating the indicators by stock trading values as continuous data.
•Secondly converting indicators to binary data before using. Each prediction model is evaluated by three metrics based on
the input ways.
•The evaluation results indicate that for the continuous data, RNN and LSTM outperform other prediction models with a
considerable difference. Also, results show that in the binary data evaluation, those deep learning methods are the best.

5. EXISTING SYSTEM:
• Existing system is also called as fundamental analysis process where the stock market
analyst make decisions and predictions based upon the information gained from the
sources.
•Fundamental analysis concentrates on data from sources, including financial records,
economic reports, company assets, and market share.
•To conduct fundamental analysis on a public company or sector, investors and analysts
typically analyse the metrics on a company’s financial statements – balance sheet, income
statement, cash flow statement, and footnotes.
•These statements are released to the public in the form of a 10-Q or 10-K report through
the database system, EDGAR, which is administered by the U.S. Securities and Exchange
Commission (SEC).
•Also, the earnings report released by a company during its quarterly earnings press release
is analysed by investors who look to ascertain how much in revenue, expenses, and profits
a company made.

6. Proposed system:
• In this proposed system, The system proposes the prediction of stock market by adapting the
machine learning and deep learning concepts.
•Stock markets were normally predicted by financial experts using fundamental analysis in the
past time like Balance sheets,Income statements,Cash flow,Company reputation.
•In our proposed system the analyst analyse the stock market by technical analysis mainly
focusing on the past and present price action to predict the probability of future price movements.
•Technical analysts analyse the financial market as a whole and are primarily concerned with
price and volume, as well as the demand and supply factors that move the market.
•Charts are a key tool for technical analysts as they show a graphical illustration of a
stock’s trend within a stated time period.
•Data scientists and computer scientists are begun to use the machine and deep learning methods
to improve and enhance the stock market prediction.Deep learning method will enhance more
stock market predictions.

7. Architecture:

8. System requirements:
• Hardware Requirements:
• Intel Core i5 processor or equivalent.
• 4 GB RAM (8 GB preferred).
• 15 GB available hard disk space.
• Internet connection.
• Software Requirements:
• Operating system: windows 7/8/8.1/10.
• Coding language: Python.
• Server : Colab.

9. RESEARCH DATA
•In this study, ten years of historical data of four stock market groups (petroleum, diversified
financials, basic metals and non-metallic minerals) from November 2009 to November 2019
is employed.

10. CONCLUSION
•The purpose of this study was the prediction task of stock market movement by machine learning and deep
learning algorithms.
•We supposed two approaches for input values to models, continuous data and binary data, and we employed
three classification metrics for evaluations. Our experimental works showed that there was a significant
improvement in the performance of models when they use binary data instead of continuous one. Indeed, deep
learning algorithms (RNN and LSTM) were our superior models in both approaches.

11. Thank you