000			04380nam a22002297a 4500
005			20250102170437.0
008			250102b |||||||| |||| 00| 0 eng d
020			_a9789354644177
082			_a332.0028 _bMOH
100			_aMohanty, Pitabas
245			_aFinancial analytics
260			_bWiley India Pvt. Ltd. _aNew Delhi _c2023
300			_axx, 664 p.
365			_aINR _b1019.00
490			_aWiley Analytics Series for Management
500			_aTable of content: 1. Introduction to Finance Analytics 1.1 Analytics in Finance 1.2 Data-Driven Finance 1.3 Organization of the Book 1.4 Use of R and Python 1.5 What this Book is Not About 1.6 Data and Codes Used in this Book 1.7 Skills and Resources Needed to Excel in Finance Analytics 2. Data in Finance 2.1 Introduction 2.2 Fundamental Data 2.3 Obtaining Fundamental Data 2.4 Marker Data 2.5 Analysts’ Data 2.6 Alternate Data 2.7 Downloading Data Using an API 3. Wrangling Financial Data 3.1 Reading Financial Data 3.2 Check Data Types (Variable Types) 3.3 Clean Variable Names 3.4 Managing Missing Data 3.5 Managing Invalid Data 3.6 Managing Outliers 3.7 Long and Wide Form Data 4. Exploratory Analysis of Financial Data 4.1 Introduction 4.2 Univariate Analysis of Fundamental Data 4.3 Bi-variate and Multi-variate Analysis of Fundamental Data 4.4 Analysis of Time Series Data 5. Understanding Basic Finance using R and Python 5.1 Introduction 5.2 Time Value of Money 5.3 Risk and Return 5.4 Asset Valuation 6. Accounting Data Analytics 6.1 Introduction 6.2 Case 1: Detecting Patterns in Financial Statements 6.3 Case 2: Predicting Corporate Bankruptcy 7. Applications of Natural Language Processing in Finance 7.1 Sourcing Text Data 7.2 Text Preprocessing 7.3 Case 1: Summarizing a Document 7.4 Case 2: Sentiment Analysis 7.5 Case 3: Sentiment Analysis Using Machine Learning 8. Financial Fraud Analytics 8.1 Benford’s Law 8.2 Predicting Credit Card Frauds 9. Valuation Analytics 9.1 Introduction 9.2 Theory of Valuation 9.3 Building a Valuation Model 9.4 Creating a Valuation Function 9.5 Estimating Implied Returns 9.6 Extension of the Model 9.7 Building the Valuation Function in Python 9.8 Valuation of Walmart Inc. 10. Portfolio Analytics 10.1 Introduction 10.2 Return and Risk of a Portfolio 10.3 Markowitz Optimization Process 10.4 Portfolio Optimization using Python 10.5 Portfolio Performance Evaluation 10.6 Portfolio Insurance 11. Developing and Backtesting Technical Trading Rules 11.1 Trend Indicators 11.2 Momentum Indicators 11.3 Volatility Indicators 11.4 Volume Indicators 11.5 Backtesting 11.6 Technical Analysis in Python 11.7 Comparing 5-day EMA with 21-day EMA in Python 11.8 Quantstrat to Automate Backtesting 12. Predicting Stock Prices/Returns 12.1 Predicting Stock Returns Based on Accounting Ratios 12.2 Predicting Stock Returns (Prices) using Past Stock Returns (Prices) Data 12.3 Predicting Stock Prices using Technical Indicators 12.4 Predicting Stock Returns using Valuation Multipliers and Value Drivers 12.5 Predicting Returns Based on Factor Exposures/Stock Characteristics Appendix 1: Installing R and Python Appendix 2: Introduction to R Appendix 3: Introduction to Python Appendix 4: A Concise Introduction to Machine Learning Index [https://www.wileyindia.com/financial-analytics.html]
520			_aFinancial Analytics applies modern data science tools to explore and understand interesting financial data patterns. Though the use of quantitative tools is nothing new in finance, modern financial analytics is different due to three recent trends: i) improved computing power with the availability of GPUs and TPUs, ii) advanced ML and AI algorithms, and iii) access to large volumes of practically all types of data. The new-age Financial Analytics takes a data-driven approach to study finance. Instead of making assumptions about the distribution of data or the relationship between variables, it simply lets the data speak for itself. (https://www.wileyindia.com/financial-analytics.html)
650			_aFinance-Databases
650			_aFinance-Mathematical models
650			_aData processing _920049
942			_cBK _2ddc
999			_c8086 _d8086