ID:
M312
Tipo Insegnamento:
Opzionale
Durata (ore):
24
CFU:
4
SSD:
ECONOMIA POLITICA
Url:
ECONOMIA E FINANZA/BASE Anno: 1
Anno:
2023
Dati Generali
Periodo di attività
Secondo Semestre (05/02/2024 - 04/05/2024)
Syllabus
Obiettivi Formativi
The “Big Data & Behavioral Finance” Lab should allow students to:
1. identify and detect behavioral bias and heuristics in financial choices and financial markets by analyzing “small” datasets obtained through laboratory experiments;
2. find on populations described by "big" data the same type of behavioral distortions detected in small and controlled samples (laboratory data).
From a methodological point of view, students will learn how to use the STATA software (www.stata.com) in order to:
1. analyze experimental data (“experimetrics”);
2. manage large amounts of data, eventually produced with agent-based simulations;
3. run predictive and causal analysis in a context of big data.
1. identify and detect behavioral bias and heuristics in financial choices and financial markets by analyzing “small” datasets obtained through laboratory experiments;
2. find on populations described by "big" data the same type of behavioral distortions detected in small and controlled samples (laboratory data).
From a methodological point of view, students will learn how to use the STATA software (www.stata.com) in order to:
1. analyze experimental data (“experimetrics”);
2. manage large amounts of data, eventually produced with agent-based simulations;
3. run predictive and causal analysis in a context of big data.
Prerequisiti
Basic knowledge of Statistics (undergraduate level).
Metodi didattici
- Coaching (continuous interaction of the course teacher with the students, in the explanation of the features of the datasets and the commands of the STATA software);
- Team Working;
- Computerized Classroom Experiments.
- Team Working;
- Computerized Classroom Experiments.
Verifica Apprendimento
Teams of 2-3 students will work together in a final assignment that consists in running an experimetric and big data analysis on the datasets studied during the course, by testing behavioral hypotheses linked to relevant financial issues.
The final assignment can be done as:
- an oral presentation to the teacher, who will ask questions and details about the used techniques and interpretation of the results;
or
- a final written report sent to the teacher by email.
The final assignment can be done as:
- an oral presentation to the teacher, who will ask questions and details about the used techniques and interpretation of the results;
or
- a final written report sent to the teacher by email.
Testi
BEHAVIORAL FINANCE:
1. Attanasi, G., Centorrino, S., & Moscati, I. (2016). Over-the-counter markets vs. double auctions: A comparative experimental study. Journal of Behavioral and Experimental Economics, 63, 22-35.
2. Attanasi, G., Centorrino, S., & Manzoni, E. (2021). Zero‐intelligence versus human agents: An experimental analysis of the efficiency of Double Auctions and Over‐the‐Counter markets of varying sizes. Journal of Public Economic Theory, 23(5), 895-932.
3. Attanasi, G., & Manzoni, E. (2022). Experimetrics: Econometrics for Experimental Economics, Peter G. Moffatt. Palgrave Macmillan, London, UK (2015). ISBN: 978-0-230-25023-9.
4. Gode, D. K., & Sunder, S. (1993). Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. Journal of Political Economy, 101, 119-137.
5. Gode, D. K., & Sunder, S. (1997). What makes markets allocationally efficient?. Quarterly Journal of Economics, 112, 603-630.
6. Moffatt, P. G. (2015). Experimetrics: Econometrics for experimental economics. Palgrave Macmillan.
7. Thaler, R. H. (2015). Misbehaving. The making of behavioural economics, New York, W. W. Norton & Company, London.
BIG DATA:
1. Correia, S. (2017). Big data in Stata with the ftools package. In 2017 Stata Conference (No. 6). Stata Users Group.
2. Einav, L., & Levin, J. (2014). The data revolution and economic analysis. Innovation Policy and the Economy, 14, 1-24.
3. Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning, Springer, New York.
4. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R, Springer, New York.
5. Maurer, A. (2015). Big Data in Stata. In United Kingdom Stata Users' Group Meetings 2015 (No. 09). Stata Users Group.
6. Stock, J. H., & Watson, M.W. (2011). Introduction to econometrics, Pearson Education, 3rd Edition.
7. Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28, 3-27.
1. Attanasi, G., Centorrino, S., & Moscati, I. (2016). Over-the-counter markets vs. double auctions: A comparative experimental study. Journal of Behavioral and Experimental Economics, 63, 22-35.
2. Attanasi, G., Centorrino, S., & Manzoni, E. (2021). Zero‐intelligence versus human agents: An experimental analysis of the efficiency of Double Auctions and Over‐the‐Counter markets of varying sizes. Journal of Public Economic Theory, 23(5), 895-932.
3. Attanasi, G., & Manzoni, E. (2022). Experimetrics: Econometrics for Experimental Economics, Peter G. Moffatt. Palgrave Macmillan, London, UK (2015). ISBN: 978-0-230-25023-9.
4. Gode, D. K., & Sunder, S. (1993). Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. Journal of Political Economy, 101, 119-137.
5. Gode, D. K., & Sunder, S. (1997). What makes markets allocationally efficient?. Quarterly Journal of Economics, 112, 603-630.
6. Moffatt, P. G. (2015). Experimetrics: Econometrics for experimental economics. Palgrave Macmillan.
7. Thaler, R. H. (2015). Misbehaving. The making of behavioural economics, New York, W. W. Norton & Company, London.
BIG DATA:
1. Correia, S. (2017). Big data in Stata with the ftools package. In 2017 Stata Conference (No. 6). Stata Users Group.
2. Einav, L., & Levin, J. (2014). The data revolution and economic analysis. Innovation Policy and the Economy, 14, 1-24.
3. Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning, Springer, New York.
4. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R, Springer, New York.
5. Maurer, A. (2015). Big Data in Stata. In United Kingdom Stata Users' Group Meetings 2015 (No. 09). Stata Users Group.
6. Stock, J. H., & Watson, M.W. (2011). Introduction to econometrics, Pearson Education, 3rd Edition.
7. Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28, 3-27.
Contenuti
The laboratory focuses on big data analysis applied to behavioral finance.
Behavioral finance deals with the study of several financial decision-making mistakes that we could avoid, if only we were familiar with the distortions that caused them. Every day we take thousands of decisions: Do I have to cross the road right away or wait for the incoming car to pass first? Should I cook pasta or prepare a salad for dinner? How much do I have to tip the cab driver? Usually, we take these decisions without thinking enough, using what psychologists call “heuristics”, i.e., rules of thumb that allow us to “play it by ear”, intuitively and without apparent difficulty, in the complex system where we live nowadays. Without these mental shortcuts, we would be paralyzed by the multitude of daily choices. However, in some circumstances, these shortcuts lead to “predictable” errors, in the sense that we could anticipate them if we knew in advance what to pay attention to. For example, in financial decisions we are naturally inclined to sell financial assets with good performance, while keeping in the portfolio those with poor performance. Or we often get to buy insurance coverage that we do not really need. Richard Thaler, 2017 winner of the Nobel Prize for Economics, has written a long series of articles describing specific market anomalies from a behavioral perspective (https://faculty.chicagobooth.edu/richard-thaler).
Big Data – a collection of heterogeneous, structured or non-structured data, defined in terms of volume, speed, variety, and accuracy – characterize many of the interactions between humans in our modern, increasingly computerized, society. These are satellite, telephone, financial, or social network datasets whose size/volume/complexity is so “big” that it exceeds the ability of relational database systems to capture, store, manage, and analyze them. This data is currently exploited by several economic actors for various reasons: from companies – for management and marketing purposes – to local governments for the development of smart cities; from intelligence agencies for security reasons to families for purchase decisions. Big data is typically the subject of data science analysis, but are becoming more and more relevant to economic research. From a methodological point of view, it allows researchers to overcome the difficulties of working with representative samples, since big data refers to the entire target population.
Big data will become more and more essential in the financial world. Markets, intermediaries and financial instruments are subject to epochal changes: a recent research by Hays (a company specialized in the field of personnel recruitment) has shown that 54% of the sample of interviewed Italian professionals in Finance believe that in 2030 banking branches will disappear from the streets of our cities, while 71% among them believe that artificial intelligence and big data will be the basis of all financial advices and forecasts, thereby crucially affecting the work of financial operators. Also for this reason, the skill to evaluate the actual ability of behavioral finance to provide insights that will guide a decision maker into a financial world that will produce more and more “big data” in the future becomes fundamental.
Behavioral finance deals with the study of several financial decision-making mistakes that we could avoid, if only we were familiar with the distortions that caused them. Every day we take thousands of decisions: Do I have to cross the road right away or wait for the incoming car to pass first? Should I cook pasta or prepare a salad for dinner? How much do I have to tip the cab driver? Usually, we take these decisions without thinking enough, using what psychologists call “heuristics”, i.e., rules of thumb that allow us to “play it by ear”, intuitively and without apparent difficulty, in the complex system where we live nowadays. Without these mental shortcuts, we would be paralyzed by the multitude of daily choices. However, in some circumstances, these shortcuts lead to “predictable” errors, in the sense that we could anticipate them if we knew in advance what to pay attention to. For example, in financial decisions we are naturally inclined to sell financial assets with good performance, while keeping in the portfolio those with poor performance. Or we often get to buy insurance coverage that we do not really need. Richard Thaler, 2017 winner of the Nobel Prize for Economics, has written a long series of articles describing specific market anomalies from a behavioral perspective (https://faculty.chicagobooth.edu/richard-thaler).
Big Data – a collection of heterogeneous, structured or non-structured data, defined in terms of volume, speed, variety, and accuracy – characterize many of the interactions between humans in our modern, increasingly computerized, society. These are satellite, telephone, financial, or social network datasets whose size/volume/complexity is so “big” that it exceeds the ability of relational database systems to capture, store, manage, and analyze them. This data is currently exploited by several economic actors for various reasons: from companies – for management and marketing purposes – to local governments for the development of smart cities; from intelligence agencies for security reasons to families for purchase decisions. Big data is typically the subject of data science analysis, but are becoming more and more relevant to economic research. From a methodological point of view, it allows researchers to overcome the difficulties of working with representative samples, since big data refers to the entire target population.
Big data will become more and more essential in the financial world. Markets, intermediaries and financial instruments are subject to epochal changes: a recent research by Hays (a company specialized in the field of personnel recruitment) has shown that 54% of the sample of interviewed Italian professionals in Finance believe that in 2030 banking branches will disappear from the streets of our cities, while 71% among them believe that artificial intelligence and big data will be the basis of all financial advices and forecasts, thereby crucially affecting the work of financial operators. Also for this reason, the skill to evaluate the actual ability of behavioral finance to provide insights that will guide a decision maker into a financial world that will produce more and more “big data” in the future becomes fundamental.
Risultati di Apprendimento Attesi
Students will learn how behavioral finance provides explanations for market outcomes that are not in line with rational explanations and market efficiency (e.g., incorrect price evaluation, irrational decision making, and anomalies on financial yields). Furthermore, they will learn how the results of behavioral finance studies, usually based on very small samples and on (controlled) laboratory data, can be extended to current financial markets, whose description is increasingly characterized by big data. Finally, they will learn how to use the STATA software (www.stata.com) for experimental and big data analysis.
Criteri Necessari per l'Assegnazione del Lavoro Finale
Teams of 2-3 students will work together in a final assignment that consists in running an experimetric and big data analysis on the datasets studied during the course, by testing behavioral hypotheses linked to relevant financial issues.
The final assignment can be done as:
- an oral presentation to the teacher, who will ask questions and details about the used techniques and interpretation of the results;
or
- a final written report sent to the teacher by email.
The final assignment can be done as:
- an oral presentation to the teacher, who will ask questions and details about the used techniques and interpretation of the results;
or
- a final written report sent to the teacher by email.
Corsi
Corsi
ECONOMIA E FINANZA
Laurea Magistrale
2 anni
No Results Found
Persone
Persone (3)
Altro personale docente
Professore Emerito
Altro personale docente
No Results Found