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Data Analytics for Finance Using Python.pdf
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Unlock the power ofdata analytics in finance with this comprehensive guide. DataAnalyticsforFinance Using Python is your key to unlocking the secrets ofthe financial markets. In this resource, 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 resource 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 ofthe 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 offinance through data-driven insights, this resource 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 resource will be helpful for students, researchers, and users of machine learning and financial tools in the disciplines of commerce, management, and economics.
1 Stock Investments Portfolio Management
by Applying K-Means Clustering 1
1.1 Introduction 1
1.1.1 Introduction to Cluster Analysis 2
1.1.2 Literature Review 3
1.2 Research Methodology 3
1.2.1 Data Source 3
1.2.2 Study Time Frame 4
1.2.3 Tool for Analysis 4
1.2.4 Model Applied 4
1.2.5 Limitations ofthe Study 4
1.2.6 Future Scope 4
1.3 Feature Extraction and Engineering 4
1.4 Data Extraction 5
1.5 Standardizing and Scaling 6
1.6 Identification ofClusters by the Elbow Method 6
1.7 Cluster Formation 7
1.8 Results and Analysis 8
1.8.1 Cluster One 9
1.8.2 Cluster Two 10
1.8.3 Clusters Three and Four 11
1.8.4 Cluster Five 11
1.8.5 Cluster Six 11
1.9 Conclusion 12
V
VI CONTENTS
2 Predicting Stock Price Using the
3
ARIMA Model 15
2.1 Introduction 15
2.2 ARIMA Model 15
2.2.1 Literature Review 16
2.3 Research Methodology 17
2.3.1 Data Source 17
2.3.2 Period of Study 17
2.3.3 Software Used for Data Analysis 17
2.3.4 Model Applied 17
2.3.5 Limitations ofthe Study 17
2.3.6 Future Scope ofthe Study 17
2.3.7 Methodology 17
2.4 Finding Different Lags Autocorrelation 18
2.5 Creating the Different ARIMA Models 21
2.5.1 Comparing the AIC Values ofModels 23
2.6 Selecting the Best Model UsingCross-Validation 24
2.7 Conclusion 24
Stock Investment Strategy Using A
4
Logistic Regression Model 27
3.1 Introduction to the Logistic Regression Model 27
3.1.1 Introduction to a Multinomial Logistic
Regression Model 27
3.1.2 Literature Review 28
3.1.3 Applied Research Methodology 29
3.2 Fetching the Data into a Python Environment and
Defining the Dependent and Independent Variables 30
3.3 Data Description and Creating Trial and Testing
Data Sets 31
3.4 Results Analysis for the Logistic Regression Model 31
3.4.1 The Stats Models Analysis in Python 32
3.5 Model Evaluation Using Confusion Matrix and
Accuracy Statistics 32
3.5.1 Calculating False Negative, False Positive,
True Negative, and True Positive 32
3.6 Accuracy Statistics 33
3.6.1 Recall 33
3.6.2 Precision 34
3.7 Conclusion 34
Predicting Stock Buying and Selling
Decisions by Applying the Gaussian Naive
Bayes Model Using Python Programming 36
4.1 Introduction 36
4.1.1 Literature Review 37
4.2 Research Methodology 37
4.2.1 Data Collection 37
CONTENTS VII
4.2.2 Sample Size 37
4.2.3 Software Used for Data Analysis 38
4.2.4 Model Applied 38
4.2.5 Limitations ofthe Study 38
4.2.6 Future Scope ofthe Study 38
4.3 Methodology 38
4.4 Feature Engineering and Data Processing 38
4.5 Training and Testing 39
4.6 Predicting Naive Bayes Model with Confusion
Matrix 40
4.6.1 Creating Confusion Matrix 40
4.6.2 Calculating False Negative, False Positive,
True Negative, and True Positive 40
4.6.3 Result Analysis 41
4.7 Conclusion 41
5 The Random Forest Technique IS A
Tool for Stock Trading Decisions 43
5.1 Introduction 43
5.2 Random Forest Literature Review 43
5.3 Research Methodology 44
5.3.1 Data Source 44
5.3.2 Period of Study 44
5.3.3 Sample Size 44
5.3.4 Software Used for Data Analysis 44
5.3.5 Model Applied 44
5.3.6 Limitations ofthe Study 44
5.3.7 Future Scope ofthe Study 44
5.3.8 Methodology 45
5.4 Defining the Dependent and Independent
Variables for the Random Forest Model 45
5.5 Training and Testing with Accuracy Statistics 46
5.6 Buying and Selling Strategy Return 46
5.7 Conclusion 48
6 Applying Decision Tree Classifier
for Buying and Selling Strategy with
Special Reference to MRF Stock 51
6.1 Introduction 51
6.2 Decision Tree 51
6.3 Research Methodology 52
6.3.1 Data Source 52
6.3.2 Period of Study 52
6.3.3 Software Used for Data Analysis 52
6.3.4 Model Applied 53
6.3.5 Limitations ofthe Study 53
6.3.6 Methodology 53
6.4 Creating a Data Frame 53
VIII CONTENTS
6.5 Feature Construction and Defining the Dependent
and Independent Variables 54
6.6 Training and Testing of Data for Accuracy Statistics 55
6.7 Buying and Selling Strategy Return 56
6.8 Decision Tree Analysis 56
6.9 Conclusion 58
7 Descriptive Statistics for Stock
Risk Assessment 61
7.1 Introduction 61
7.1.1 Related Work 61
7.2 Research Methodology 61
7.2.1 Data Source 61
7.2.2 Period of Study 62
7.2.3 Software Used for Data Analysis 62
7.2.4 Model Applied 62
7.2.5 Limitations ofthe Study 62
7.2.6 Future Scope ofthe Study 62
7.3 Performing Descriptive Statistics in Python
for Mean 63
7.4 Performing Descriptive Statistics in Python
for Median 63
7.5 Performing Descriptive Statistics in Python
for Mode 64
7.6 Performing Descriptive Statistics in Python
for Range 64
7.7 Performing Descriptive Statistics in Python for
Variance 65
7.8 Performing Descriptive Statistics in Python for
Standard Deviation 65
7.9 Performing Descriptive Statistics in Python for
Quantile 66
7.10 Performing Descriptive Statistics in Python for
Weakness 66
7.11 Performing Descriptive Statistics in Python
for Kurtosis 67
7.12 Conclusion 67
8 Stock Investment Strategy Using A
Regression Model 69
8.1
8.2
Introduction to a Multiple Regression Model 69
Applied Research Methodology 70
8.2.1 Data Source 70
8.2.2 Sample Size 70
8.2.3 Software Used forData Analysis 70
8.2.4 Model Applied 70
8.3 Fetching the Data into a Python Environment and
Defining the Dependent and Independent Variables 71
contents IX
8.4 Correlation Matrix 72
8.5 Result Analysis for the Multiple Regression Model 73
8.5.1 R-Square 73
8.6 Conclusion 74
9 Comparing Stock Risk Using F-Test 76
9.1 Introduction 76
9.1.1 Review ofLiterature 76
9.2 Research Methodology 77
9.2.1 Data Source 77
9.2.2 Period of Study 77
9.2.3 Software Used for Data Analysis 77
9.2.4 Model Applied 77
9.2.5 Limitations ofthe Study 77
9.2.6 Future Scope ofthe Study 77
10 Stock Risk Analysis Using t-Test 80
10.1 Introduction 80
10.2 Research Methodology 80
10.2.1 Data Source 80
10.2.2 Period of Study 80
10.2.3 Software Used for Data Analysis 81
10.2.4 Model Applied 81
10.2.5 Limitations ofthe Study 81
10.2.6 Future Scope ofthe Study 81
10.3 Conclusion 83
11 Stock Investment Strategy Using
A Z-Score 84
11.1 Introduction to Z-Score 84
11.2 Applied Research Methodology 85
11.2.1 Data Source 85
11.2.2 Sample Size 85
11.2.3 Software Used for Data Analysis 85
11.2.4 Model Applied 85
11.3 Fetching the Data into a PythonEnvironment
and Defining the Dependent and Independent
Variables 85
11.4 Calculating the Z-Score for the Stock 86
11.5 Results Z-Score Analysis 88
11.6 Conclusion 88
12 Applying A Support Vector Machine
Model Using Python Programming 90
12.1 Introduction 90
12.1.1 Review ofLiterature 91
12.2 Research Methodology 92
12.2.1 Data Collection 92
12.2.2 Sample Size 92
X CONTENTS
12.2.3 Software Used for Data Analysis 92
12.2.4 Model Applied 92
12.2.5 Limitations ofthe Study 93
12.2.6 Future Scope ofthe Study 93
12.3 Methodology 93
12.4 Feature Engineering and Data Processing 93
12.5 Training and Testing 94
12.6 Predicting a Support Vector Machine Model
with a Confusion Matrix 95
12.6.1 Creating a Confusion Matrix 95
12.7 Calculating False Negative, False Positive,
True Negative, and True Positive 95
12.7.1 Result Analysis 96
12.8 Conclusion 97
13 Data Visualization for Stock Risk
Comparison and Analysis 99
13.1 Introduction to Data Visualization 99
13.1.1 Review ofPast Studies 99
13.1.2 Applied Research Methodology 100
13.2 Fetching the Data into a Python Environment
and Defining the Dependent and Independent
Variables 100
13.2.1 Data Visualization Using Scatter Plot 101
13.3 Data Visualization Using Bar Chat 102
13.4 Data Visualization Using Line Chart 104
13.5 Data Visualization Using Bokeh 104
14 Applying Natural Language Processing
for Stock Investors Sentiment Analysis 107
14.1 Introduction 107
14.2 Research Methodology 108
14.2.1 Data Source 108
14.2.2 Period of Study 108
14.2.3 Software Used forData Analysis 108
14.2.4 Model Applied 108
14.2.5 Limitations oftheStudy 108
14.2.6 Future Scope ofthe Study 108
14.3 Fetching the Data into a Python Environment 108
14.4 Sentiments Count for Understanding Investors’
Perceptions 109
14.5 Performing Data Cleaning in Python 110
14.6 Performing Vectorization in Python 111
14.7 Vector Transformation to Create Trial and
Training Data Sets 111
14.8 Result Analysis Model Testing AUC 113
14.9 Conclusion 113
CONTENTS XI
15 Stock Prediction Applying LSTM 115
15.1 Introduction 115
15.1.1 Review ofLiterature 116
15.2 Research Methodology 117
15.2.1 Data Source 117
15.2.2 Period of Study 117
15.2.3 Software Used for Data Analysis 117
15.2.4 Model Applied 117
15.2.5 Limitations ofthe Study 117
15.2.6 Future Scope ofthe Study 117
15.3 Fetching the Data into a Python Environment 117
15.4 Performing Data Cleaning in Python 118
15.5 Vector Transformation to Create Trial and Training
Data Sets 119
15.6 Result Analysis for the LSTM Model 120
15.7 Conclusion 121
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