Data Analytics for Finance Using Python

Unlock the power of data analytics in finance with this comprehensive guide. Data Analytics for Finance Using Python is your key to unlocking the secrets of the financial markets. In this book, you’ll discover how to harness the latest data analytics techniques, including machine learning and inferential statistics, to make informed investment decisions and drive business success. With a focus on practical application, this book takes you on a journey from the basics of data preprocessing and visualization to advanced modeling techniques for stock price prediction. Through real-world case studies and examples, you’ll learn how to Uncover hidden patterns and trends in financial data Build predictive models that drive investment decisions Optimize portfolio performance using data-driven insights Stay ahead of the competition with cutting-edge data analytics techniques Whether you’re a finance professional seeking to enhance your data analytics skills or a researcher looking to advance the field of finance through data-driven insights, this book is an essential resource. Dive into the world of data analytics in finance and discover the power to make informed decisions, drive business success, and stay ahead of the curve. This book will be helpful for students, researchers, and users of machine learning and financial tools in the disciplines of commerce, management, and economics. Cover Half Title Series Title Copyright Contents Preface Authors Chapter 1 Stock Investments Portfolio Management by Applying K-Means Clustering 1.1 Introduction 1.1.1 Introduction to Cluster Analysis 1.1.2 Literature Review 1.2 Research Methodology 1.2.1 Data Source 1.2.2 Study Time Frame 1.2.3 Tool for Analysis 1.2.4 Model Applied 1.2.5 Limitations of the Study 1.2.6 Future Scope 1.3 Feature Extraction and Engineering 1.4 Data Extraction 1.5 Standardizing and Scaling 1.6 Identification of Clusters by the Elbow Method 1.7 Cluster Formation 1.8 Results and Analysis 1.8.1 Cluster One 1.8.2 Cluster Two 1.8.3 Clusters Three and Four 1.8.4 Cluster Five 1.8.5 Cluster Six 1.9 Conclusion Chapter 2 Predicting Stock Price Using the ARIMA Model 2.1 Introduction 2.2 ARIMA Model 2.2.1 Literature Review 2.3 Research Methodology 2.3.1 Data Source 2.3.2 Period of Study 2.3.3 Software Used for Data Analysis 2.3.4 Model Applied 2.3.5 Limitations of the Study 2.3.6 Future Scope of the Study 2.3.7 Methodology 2.4 Finding Different Lags Autocorrelation 2.5 Creating the Different ARIMA Models 2.5.1 Comparing the AIC Values of Models 2.6 Selecting the Best Model Using Cross-Validation 2.7 Conclusion Chapter 3 Stock Investment Strategy Using a Logistic Regression Model 3.1 Introduction to the Logistic Regression Model 3.1.1 Introduction to a Multinomial Logistic Regression Model 3.1.2 Literature Review 3.1.3 Applied Research Methodology 3.2 Fetching the Data into a Python Environment and Defining the Dependent and Independent Variables 3.3 Data Description and Creating Trial and Testing Data Sets 3.4 Results Analysis for the Logistic Regression Model 3.4.1 The Stats Models Analysis in Python 3.5 Model Evaluation Using Confusion Matrix and Accuracy Statistics 3.5.1 Calculating False Negative, False Positive, True Negative, and True Positive 3.6 Accuracy Statistics 3.6.1 Recall 3.6.2 Precision 3.7 Conclusion Chapter 4 Predicting Stock Buying and Selling Decisions by Applying the Gaussian Naive Bayes Model Using Python Programming 4.1 Introduction 4.1.1 Literature Review 4.2 Research Methodology 4.2.1 Data Collection 4.2.2 Sample Size 4.2.3 Software Used for Data Analysis 4.2.4 Model Applied 4.2.5 Limitations of the Study 4.2.6 Future Scope of the Study 4.3 Methodology 4.4 Feature Engineering and Data Processing 4.5 Training and Testing 4.6 Predicting Naive Bayes Model with Confusion Matrix 4.6.1 Creating Confusion Matrix 4.6.2 Calculating False Negative, False Positive, True Negative, and True Positive 4.6.3 Result Analysis 4.7 Conclusion Chapter 5 The Random Forest Technique Is a Tool for Stock Trading Decisions 5.1 Introduction 5.2 Random Forest Literature Review 5.3 Research Methodology 5.3.1 Data Source 5.3.2 Period of Study 5.3.3 Sample Size 5.3.4 Software Used for Data Analysis 5.3.5 Model Applied 5.3.6 Limitations of the Study 5.3.7 Future Scope of the Study 5.3.8 Methodology 5.4 Defining the Dependent and Independent Variables for the Random Forest Model 5.5 Training and Testing with Accuracy Statistics 5.6 Buying and Selling Strategy Return 5.7 Conclusion Chapter 6 Applying Decision Tree Classifier for Buying and Selling Strategy with Special Reference to MRF Stock 6.1 Introduction 6.2 Decision Tree 6.3 Research Methodology 6.3.1 Data Source 6.3.2 Period of Study 6.3.3 Software Used for Data Analysis 6.3.4 Model Applied 6.3.5 Limitations of the Study 6.3.6 Methodology 6.4 Creating a Data Frame 6.5 Feature Construction and Defining the Dependent and Independent Variables 6.6 Training and Testing of Data for Accuracy Statistics 6.7 Buying and Selling Strategy Return 6.8 Decision Tree Analysis 6.9 Conclusion Chapter 7 Descriptive Statistics for Stock Risk Assessment 7.1 Introduction 7.1.1 Related Work 7.2 Research Methodology 7.2.1 Data Source 7.2.2 Period of Study 7.2.3 Software Used for Data Analysis 7.2.4 Model Applied 7.2.5 Limitations of the Study 7.2.6 Future Scope of the Study 7.3 Performing Descriptive Statistics in Python for Mean 7.4 Performing Descriptive Statistics in Python for Median 7.5 Performing Descriptive Statistics in Python for Mode 7.6 Performing Descriptive Statistics in Python for Range 7.7 Performing Descriptive Statistics in Python for Variance 7.8 Performing Descriptive Statistics in Python for Standard Deviation 7.9 Performing Descriptive Statistics in Python for Quantile 7.10 Performing Descriptive Statistics in Python for Weakness 7.11 Performing Descriptive Statistics in Python for Kurtosis 7.12 Conclusion Chapter 8 Stock Investment Strategy Using a Regression Model 8.1 Introduction to a Multiple Regression Model 8.2 Applied Research Methodology 8.2.1 Data Source 8.2.2 Sample Size 8.2.3 Software Used for Data Analysis 8.2.4 Model Applied 8.3 Fetching the Data into a Python Environment and Defining the Dependent and Independent Variables 8.4 Correlation Matrix 8.5 Result Analysis for the Multiple Regression Model 8.5.1 R-Square 8.6 Conclusion Chapter 9 Comparing Stock Risk Using F-Test 9.1 Introduction 9.1.1 Review of Literature 9.2 Research Methodology 9.2.1 Data Source 9.2.2 Period of Study 9.2.3 Software Used for Data Analysis 9.2.4 Model Applied 9.2.5 Limitations of the Study 9.2.6 Future Scope of the Study Chapter 10 Stock Risk Analysis Using t-Test 10.1 Introduction 10.2 Research Methodology 10.2.1 Data Source 10.2.2 Period of Study 10.2.3 Software Used for Data Analysis 10.2.4 Model Applied 10.2.5 Limitations of the Study 10.2.6 Future Scope of the Study 10.3 Conclusion Chapter 11 Stock Investment Strategy Using a Z-Score 11.1 Introduction to Z-Score 11.2 Applied Research Methodology 11.2.1 Data Source 11.2.2 Sample Size 11.2.3 Software Used for Data Analysis 11.2.4 Model Applied 11.3 Fetching the Data into a Python Environment and Defining the Dependent and Independent Variables 11.4 Calculating the Z-Score for the Stock 11.5 Results Z-Score Analysis 11.6 Conclusion Chapter 12 Applying a Support Vector Machine Model Using Python Programming 12.1 Introduction 12.1.1 Review of Literature 12.2 Research Methodology 12.2.1 Data Collection 12.2.2 Sample Size 12.2.3 Software Used for Data Analysis 12.2.4 Model Applied 12.2.5 Limitations of the Study 12.2.6 Future Scope of the Study 12.3 Methodology 12.4 Feature Engineering and Data Processing 12.5 Training and Testing 12.6 Predicting a Support Vector Machine Model with a Confusion Matrix 12.6.1 Creating a Confusion Matrix 12.7 Calculating False Negative, False Positive, True Negative, and True Positive 12.7.1 Result Analysis 12.8 Conclusion Chapter 13 Data Visualization for Stock Risk Comparison and Analysis 13.1 Introduction to Data Visualization 13.1.1 Review of Past Studies 13.1.2 Applied Research Methodology 13.2 Fetching the Data into a Python Environment and Defining the Dependent and Independent Variables 13.2.1 Data Visualization Using Scatter Plot 13.3 Data Visualization Using Bar Chat 13.4 Data Visualization Using Line Chart 13.5 Data Visualization Using Bokeh Chapter 14 Applying Natural Language Processing for Stock Investors Sentiment Analysis 14.1 Introduction 14.2 Research Methodology 14.2.1 Data Source 14.2.2 Period of Study 14.2.3 Software Used for Data Analysis 14.2.4 Model Applied 14.2.5 Limitations of the Study 14.2.6 Future Scope of the Study 14.3 Fetching the Data into a Python Environment 14.4 Sentiments Count for Understanding Investors’ Perceptions 14.5 Performing Data Cleaning in Python 14.6 Performing Vectorization in Python 14.7 Vector Transformation to Create Trial and Training Data Sets 14.8 Result Analysis Model Testing AUC 14.9 Conclusion Chapter 15 Stock Prediction Applying LSTM 15.1 Introduction 15.1.1 Review of Literature 15.2 Research Methodology 15.2.1 Data Source 15.2.2 Period of Study 15.2.3 Software Used for Data Analysis 15.2.4 Model Applied 15.2.5 Limitations of the Study 15.2.6 Future Scope of the Study 15.3 Fetching the Data into a Python Environment 15.4 Performing Data Cleaning in Python 15.5 Vector Transformation to Create Trial and Training Data Sets 15.6 Result Analysis for the LSTM Model 15.7 Conclusion