Python for Accounting and Finance: An Integrative Approach to Using Python for Research

Acknowledgements
Prologue
Contents
Part I Introduction and Fundamentals
1 Introduction to Python for Accounting and Finance Research
	Benefits of Python in Accounting and Finance Research
	Overview of Python Programming Language
	Installing and Setting up Python Environment
2 Introduction to Python Language
	Data Types, Variables, and Operators
	Control Flow Statements
	Functions and Modules
	Data Structures in Python
	Input and Output
	File Handling in Python
	The os Module
	Object-Oriented Programming in Python
3 Regular Expressions for Python
	re Functions
	Building Blocks of Regex
		Literals
		Metacharacters
		Quantifiers
		Character Classes
		Escape Sequences
		Groups in Regex
		Substitution or Replacement Metacharacters
		Assertions
	Regular Expressions Cheat Sheet
4 Important Python Libraries
	Library
	Data Access Libraries
		BeautifulSoup
		Requests
		Scrapy
	Data Manipulation Libraries
		Pandas
		NumPy
		Dask
	Data Visualization Libraries
		Matplotlib
	Statistical Analysis Libraries
		SciPy
		StatModels
		PyMC3
	Machine Learning Libraries
		Scikit-Learn
		TensorFlow
		PyTorch
		Keras
Part II Data Acquisition and Cleaning
5 Accessing Data from WRDS
6 Accessing Data from SEC EDGAR
	Useful Modifications
		Limiting the Period
		Cleaning the HTML Tags
7 Accessing Data from Other Sources
	Data Contained in a Series of Webpages
	Data on Yahoo Finance
	Data on Cryptocurrency
	National Oceanic and Atmospheric Administration (NOAA) Data
	Twitter Data
	Google Trends Data
8 Text Extraction and Cleaning
	Extracting Useful Parts of Data
	Cleaning HTML Tags
9 Text Normalization
	Removal of Special Characters and Punctuation Marks
	Lowercasing
	Tokenizing
	Stop Word Removal
	Stemming
	Llematization
	Special Considerations in Accounting Data
10 Corpus
	Renaming Files
	Sorting Files
	Creating Corpus
Part III Exploratory Data Analysis and Visualization
11 Data Visualization: Numerical Data
	Matplotlib
		Heatmap
		3D Plot
		Box Plot
	Seaborn
	Plotly
	Bokeh
12 Data Visualization: Text Data
	Wordcloud
	Matplotlib
	Network Map
	Dimensionality Reduction Techniques
13 Descriptive Statistics
	Basic Descriptive Statistics
	Outlier Detection
	Pearson’s Correlation Coefficient
	Time Series Descriptive Statistics
	A Note on Sampling Techniques
Part IV Natural Language Processing and Text Analysis
14 Topic Modeling
	Latent Dirichlet Allocation (LDA)
	Non-negative Matrix Factorization (NMF)
	Probabilistic Latent Semantic Analysis (PLSA)
	Correlated Topic Model (CTM)
	Hierarchical Dirichlet Process (HDP)
15 Word Embeddings
16 Text Classification
	Naive Bayes
	Decision Trees
	Random Forests
	Deep Learning
17 Sentiment Analysis
	Rule-Based Methods
	Lexicon-Based Methods
	Machine Learning Algorithms
Part V Machine Learning and Predictive Analytics
18 Basic Regression
	Linear Regressions
		Simple Linear Regression
		Multiple Regression
	Regression Diagnostics
		Linearity Check
		Multicollinearity
		Heteroscedasticity
		Autocorrelation
		Normality of Residuals
19 Logistic Regression
	Implementing Logistic Regression in Python
		Confusion Matrix
		ROC Curve and AUC
		Precision, Recall, and F1-Score
20 Probit and Logit Regression
	Probit Regression
		Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)
		Model Diagnostics
	Logit Model
21 Polynomial Regression
22 Quantile Regression
23 Advanced Regressions
	Tobit Regression
	Poisson Regression
	Negative Binomial Regression
	Instrumental Variables (IV) Regression
	Two-Stage Least Squares (2SLS) Regression
24 Time Series Analysis
	Autoregressive (AR) Model
	Moving Average (MA) Model
	ARMA, ARIMA, SARIMA, and SARIMAX
	Vector Autoregression (VAR) Model
	Vector Error Correction Model (VECM)
	Advantages of VECM
	GARCH Model
25 Panel Data
	Types of Panel Data Models
	Pooled OLS Models
	Fixed Effect Models
	Random Effect Models
	Dynamic Panel Data Models
	Panel Data Model Diagnostics
26 Special Techniques in Multivariate Analysis
	Principal Component Analysis
	Factor Analysis
	Cluster Analysis
	Canonical Correlation Analysis
		Discriminant Analysis
Part VI Advanced Topics
27 Deep Learning
	Neuron
	Deep Learning Techniques and Architectures
	Implementation of Deep Learning in Accounting and Finance
		Transformer Models
		FNN Models
		LSTM Models
		GRU Model
		CNN Model
		Autoencoder Model
Most Common Errors and Solutions
Index