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Wydział Fizyki UW > Badania > Seminaria i konwersatoria > Multimedialne seminarium z ekono- i socjofizyki

Multimedialne seminarium z ekono- i socjofizyki

2009/2010 | 2010/2011 | 2011/2012 | 2012/2013 | 2013/2014 | 2014/2015 | 2015/2016 | 2016/2017 | 2017/2018 | 2018/2019 | 2019/2020 | 2021/2022 | 2022/2023 | 2023/2024

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2019-06-04 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:15  Calendar icon
Ewa Thomson (Wydział Fizyki UW)

Ilościowe oszacowania parametrow rynku finansowego za pomocą wybranych metod fizyki stosowanych w ekonomii

Quantitative predictions of financial market properties by applying physics methods to problems in economics

There has been a lot of effort to apply physics methods to problems in economics. Those were particularly successful at option pricing theory, however not only. During my talk, I will describe a model introduced by a group of scientists from the Santa Fe Institute and King's College London. They used a range of methods: dimensional analysis, simulation, and mean field theory to derive quantitive parameters of the financial market. By using this model they managed to make predictions of price diffusion rates and the spread and price impact functions.
2019-05-28 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:15  Calendar icon
Ewa Thomson (Wydział Fizyki UW)

Ilościowe oszacowania parametrów rynku finansowego za pomocą metod zaczerpniętych z fizyki

Quantitive predictions of financial market properties by applying physics methods

There has been a lot of effort to apply physics methods to problems in economics. Those were particularly successful at option pricing theory, however not only. During my talk, I will describe a model introduced by a group of scientists from the Santa Fe Institute and King's College London. They used a range of methods: dimensional analysis, simulation, and mean field theory to derive quantitive parameters of the financial market. By using their model they managed to make predictions of price diffusion rates and the spread and price impact functions.
2019-05-21 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:30  Calendar icon
Michał Chorowski (Wydział Fizyki UW)

Model Sznajdów i jego zastosowania

Sznajd Model and its applications

The Sznajd Model (SM) is a sociophysics model of opinion formation. It applies the idea of social validation, which means that people are doing what they observe other people doing. The hope is to create a model of local psychological interactions that will result in global sociological phenomena. In my talk, I will explain how this psychological idea has been applied in the SM using only simple rules for spin interactions. I will present some of the results of the model and discuss its applications.
2019-05-14 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:15  Calendar icon
Jarosław Klamut (Wydział Fizyki UW)

Klastrowanie aktywności w formalizmie błądzenia losowego w czasie ciągłym

Activity clustering in continuous-time random walk formalism

Over 50 years ago, two physicists Montroll and Weiss in the physical context of dispersive transport and diffusion introduced the stochastic process, named Continuous-Time Random Walk (CTRW). The trajectory of such a process is created by elementary events ‘spatial’ jumps preceded by waiting time. Since introduction, CTRW found innumerable application in different fields [1] including high-frequency finance [2], where jumps are considered as price increments and waiting times represent inter-trade times. Our latest results [3] suggest that dependencies between inter-trade times are the key element to explain activity clustering in financial time-series. We introduce the new CTRW model with long-term memory in waiting times, able to successfully describe power-law decaying time autocorrelation of the absolute values of price changes. We test our model on the empirical data from the Polish stock market. Bibliografia: [1] Kutner, R., Masoliver, J. (2017), The continuous time random walk, still trendy: fifty-year history, state of art and outlook, Eur. Phys. J. B, 90(3), 50; [2] Scalas, E. (2006), Five years of continuous-time random walks in econophysics: The complex networks of economic interactions (pp. 3-16), Springer, Berlin, Heidelberg; [3] Klamut, J. & Gubiec, T. (2019), Directed continuous-time random walk with memory, Eur. Phys. J. B 92:69. 

2019-04-30 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:15  Calendar icon
Grzegorz Link (Wydział Fizyki UW)

W jaki sposób znana strategia inwestycja może wciąż działać i pozostawać zyskowna?

How can a widely known investment strategy still work and remain profitable?

A common misconception about investing strategies is that they need to be kept in secret in order to work. The idea is that once a profitable strategy — some phenomenon or precise algorithm of investing behavior — becomes known, investors flock to it and it becomes depleted, the returns of the strategy drop to zero. This is most often not the case. While it is true there are some highly secretive firms in the investment space, an informational advantage is just one of several types of inefficiencies exploited by profitable investment approaches. In this talk, I will present some examples of known, yet profitable strategies, examine their returns and discuss how they stay profitable. I will start with a portfolio toy model and switch to empirical examples of functioning strategies. I will supplement the talk with empirical data from the quantitative research community and quantitative investment space.
2019-04-16 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:15  Calendar icon
Mateusz Wiliński (Scuola Normale Superiore di Pisa oraz Wydział Fizyki UW)

Wykrywanie Makroskopowych Struktur w Sieciach Finansowych z użyciem Modeli Blokowych

Disentangling Macroscopic Structures in Financial Networks: A Stochastic Block Model Approach

In the last decade, financial networks became an extremely important subject, both for researchers and regulators. European Central Bank, Bank of England and other central banks across the world invested significant resources into understanding the relation between financial networks properties and the systemic risk associated with them. One of the most crucial elements of such research is being able to identify macroscopic structures, such as core-periphery, which is considered to be very common in the financial world. In my talk, I will show how Stochastic Block Models can be used in order to easily distinguish between different structures. Moreover, I will present results obtained for real data from the Italian interbank market, which suggest that including or neglecting part of the information about the network can lead to surprisingly different results. Finally, I will show the dynamics of interbank market structures in the years 2010-2014, and how they depend on the used timescale.
2019-03-26 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:15  Calendar icon
Krzysztof Piwoński (Wydział Fizyki UW)

Modelowanie agentowe ruchu lotniczego

Agent-based modeling of the air traffic

Sieć transportu lotniczego stanowi złożony układ, zarządzany przez wiele rozproszonych centrów decyzyjnych o różnorodnych celach i ograniczeniach oraz podlegający chaotycznym wymuszeniom, przez co bezustannie balansujący opłacalność i zdolność do absorpcji różnorodnych zaburzeń. Przedmiotem omawianego projektu jest zbadanie wpływu konkretnego typu zaburzenia, czasowego wyłączenia lotniska, na stabilność i efektywność sieci -- zjawisko takie może być całkowicie nieistotne, albo spowodować konieczność kaskadowego przekierowania dużej liczby lotów, co generuje ogromne koszty finansowe i trudności organizacyjne. Ponieważ wierne odwzorowanie wszystkich czynników sterujących ruchem lotniczym jest niemożliwe, zaproponowany został uproszczony, stochastyczny model agentowy, pozwalający analizować różne scenariusze wyłączeń i strategie przekierowań ruchu przez statystyczną analizę wiązek symulacji. W trakcie seminarium zostanie przedstawiony wspomniany model, jak również wstępne wyniki uzyskane dla scenariusza opartego o faktyczne wydarzenia.
2019-03-19 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:15  Calendar icon
Tomasz Raducha (Wydział Fizyki UW)

Wprowadzenie do modelu wyborcy

Introduction to voter model

The behavior of ants, decision making of stock market players, voting process and opinion dynamics - do they have something in common? On a basic level, all of these phenomena can be captured by the so-called voter model. Although it is usually associated with opinion dynamics, it has also other explanations. Simple definition with just two states of the nodes leads to multiple possible interpretations. It can be also seen as a non-equilibrium spin model. During the talk, I will cover origins of the voter model, its development and some recent results.
2018-11-06 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:15  Calendar icon
Mateusz Wiliński (Scuola Normale Superiore di Pisa)

Detekcja struktur makroskopowych w sieciach skierowanych: podejście od strony modelu stochastycznych bloków

Detectability of Macroscopic Structures in Directed Networks: a Stochastic Block Model Approach

Disentangling network macroscopic structures is one of the funding problems in complexity science. One of the most basic models of communities in networks is the stochastic block model. It was recently shown that in this case the detectability of real communities only from the network topology is limited. Even though the results were shown only for planted partition, where there are only two parameters, the conclusions are universal. We examined a more general case of directed stochastic block model. More interestingly, we have shown that by introducing an asymmetry of direction, we are able to increase the range of the detectable phase. Importantly, this qualitative change holds for an entire class of hardly detectable models, where both the average in- and out-degree are the same across all groups.
2018-10-23 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:15  Calendar icon
Tomasz Raducha (Wydział Fizyki UW)

Model interakcji społecznych: pomiędzy fizyką a socjologią

Model of social interactions: between physics and sociology

In the 90s Robert Axelrod has proposed a canonical model of social interactions explaining one of the possible and important mechanisms of the dissemination of culture. His idea started with a question: if peopletend to become more similar during an interaction, why don't all the differences eventually disappear? The model was very fruitful and managed to explain the issue. He has found that depending on initial conditions the system can end up in one of two states: ordered with global culture or disordered with many small subcultures. Others have studied the model deeper on complex networks and discovered that the structure has a crucial influence on the system. Despite this development, there was still a great unsolved issue - the model wascontradictory to empirical data considering language diversity. In mytalk, I will cover the most important publications on the model of socialinteractions and describe modifications necessary in order to make itconsistent with the empirical data.
2018-10-09 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:15  Calendar icon
Jarosław Duda (Uniwersytet Jagielloński)

Estymowanie wspólnego rozkładu wielomianami na przykład dla przewidywania szeregów czasowych

Estimating joint distribution with polynomial for example for time series prediction

We can inexpensively model probability distribution as a polynomial: using orthonormal basis, the estimated coefficient of a function is just average of this function over the sample. Each such coefficient has a concrete cumulant-like interpretation, for joint distribution they describe statistical dependencies, we have some control of their accuracy. We can also model their time evolution and evolution of the density. Among others, I will talk about their application for time series analysis - for example modeling joint distribution of a few neighboring values to predict probability distribution of the next one based on a few previous values on the example of DJIA and yield curve parameters.
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