Learning objectives
The purpose is to deal in a quantitative way the relevant information for
the financial analyst using advanced computer programming.
1) Knowledge and understanding .The course aims to provide the basic
tools most suitable for the analysis of some fundamental aspects of
monetary and financial market. Particular attention will be paid to time
series of financial issues: exchange rates, interest rates, prices and
equity returns, prices and yields of derivatives. Participation in teaching
activities in conjunction with the exercises, increase the student's ability
to develop, independently, that type of "statistical data" that
characterizes the nature of the degree course in Economics and Finance.
2) Ability to apply knowledge and understanding . At the end of the
course, the student will be able to implement in an autonomous way the
statistical techniques described above. The student will have therefore
developed specific skills, they are associated with critical skills for
diagnostic, which are essential ingredients in building a good statistical
model, with the possible assistance of the appropriate level of computer
tools .
3) Making judgments .At the end of the course, the student will be able to
perform independently all the considerations regarding the problems of
analysis of financial time series. In addition, the student will be able to
correctly interpret the results of such analyzes, even when made by other
users or experts
4) Communication skills . At the end of the course, the student will be
able to use appropriate technical language in communicating with the
operators of financial markets. Also it should be able to summarize the
statistical information of considerable size
5) Learning skills. We want to give the student the opportunity to
assimilate the key results of the statistical theory and probability that
form the basis of building a statistical model. At the end of the course,
the student will have acquired the key concepts to be able to accurately
use quantitative tools, if they become necessary in the solution of
concrete problems of a financial nature.
Prerequisites
Knowledge of basic descriptive and inferential statistics. We recommend th student to attend this course after attending the course of basic statistics.
Course unit content
Elementary theory of stochastic processes for stationary series
1. Recall elements of probability 'for random vectors.
2. Transformation of univariate and multivariate random variables.
3. Gaussian and White Noise processes.
4. Brief introduction to non-stationary processes of type Random Walk
Empirical evidence of the observed time series
Analysis of trend and sesonal component
Empirical characteristics of the time series of financial returns.
Formulas combinations of multi-period returns.
2. The shape of the distribution of returns. Test of symmetry, kurtosis,
and normality .
3. The time dependence (linear and nonlinear) of returns. Autocorrelation
function and tests of significance 'associates.
4. Autoregressive processes for stationary series of returns and
transforms associated with them.
Overview of analysis of the trend of stock market prices and moving averages
Introduction to MATLAB
Full programme
Presentation and classification of the information collected: the matrix of multiple time series
data, relationships between variables, preliminary treatments of data, missing values
and outliers.
Introduction to the use of MATLAB software for statistical analysis and programming.
Exploratory analysis and data visualization: graphical representations of
multiple time series.
Semi-log scale graphs. Subdivision of the graphics window into panels. Importing excel files into a MATLAB table. Data extraction. Comparison between the trend of two historical series. Funnelchart, balloonplots and waterfall charts. Candle charts, price-volume charts, charts Imports of data in timetable format. Timetable management. Creating custom timelines. retime of the time series.
Simple and multiple regression. Analysis of fitted values, residuals and goodness of fit Traditional analysis of time series. Detrendisation and deseasonalization Simple mobile averages, Henderson weighted averages, manual implementation Simple mobile averages, Henderson weighted, automatic implementation Multi-period mobile averages for financial market analysis
Introduction to textual analysis
Bibliography
Riani M., Corbellini A. Laurini F., Morelli G. Perrotta D. Torti F. (2022). Data Science con MATLAB, Giappichelli editore, Torino (second edition)
GOZZI G., Strumenti Statistici per l’Analisi dei Mercati Finanziari, Libreria Medico Scientifica , Parma, Edizione 2019
Material downloadable from web site http://www.riani.it/MQF
Teaching methods
Frontal lessons with PC and practical lesson using Matlab. Additional material can be downloaded from the web site of the course http://www.riani.it/MQF
Assessment methods and criteria
Exam using the computer.
Knowledge and understanding are assessed by methodological questions. The ability of applying knowledge and understanding are assessed by questions on the interpretation of results. Learning skills are assessed by questions on the conclusions to be drawn from an analysis.
Details on examination procedures are provided to the class and made available through http://www.riani.it/MQF before the start of the course.
The result of the test will be published on Elly within 5 days after the exam
Please note that online registration for the exam is mandatory.
Other information
Additional information can be found from the web site http://www.riani.it/MQF together with link to the associated youtube channel.
2030 agenda goals for sustainable development
- - -
