Data Analytics in Accounting and Finance

In this module students will develop statistical and quantitative skills and familiarise themselves with their applications in the area of finance and business.

1. Empirical analysis in research
   1. Historical data
   2. Survey data (Questionnaire, Interview)
   3. Case study
2. Introduction to Applied Quantitative techniques
   1. Integrate standard functions, definite integrals
3. Descriptive statistics
   1. Key statistical measures. Tables and graphs for empirical data
4. Probability theory
   1. Univariate and Bivariate random variables. Discrete and continuous random variables. Expected mean,
   2. variance and covariance of random variables. Business Applications.
5. Time series analysis 1
   1. Hypotheses in OLS regression
   2. Multiple OLS Regression in time series and cross-sectional data
6. Time series analysis 2
   1. Dummy variables and interaction terms, non-linear OLS regression.
   2. Failures in OLS assumptions and remediating methods
7. Deep dive analysis in Time Series
   1. Forecasting and regression trend analysis
   2. Univariate time series models: AR, MA, ARMA and ARIMA models
8. Linear Probability Models
   1. Formulate limited dependent variable models, including logit and probit models. Estimate and
   2. interpret logit and probit models