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The fundamental idea developed throughout this work is the introduction of new
metrics in Social Sciences (Economics, Finance, opinion dynamics, etc). The concept
of metric, that is the concept of measure, is usually neglected by mainstream
theories of Economics and Finance. Financial Markets are the natural starting
point of such an approach to Social Sciences because a systematic approach can
be undertaken and the methods of Physics has shown to be very effective. In fact
since a decade there exists a very huge amount of high frequency data from stock
exchanges which permit to perform experimental procedures as in Natural Sciences.
Financial markets appear as a perfect playground where models can be tested and
where repeatability of empirical evidences are well-established features differently
from, for instance, Macro-Economy and Micro-Economy. Thus Finance has been
the first point of contact for the interdisciplinary application of methods and tools
deriving from Physics and it has been also the starting point of this work.
We investigated the origin of the so-called Stylized Facts of financial markets (i.e.
the statistical properties of financial time series) in the framework of agent-based
models. We found that Stylized Facts can be interpreted as a finite size effect in
terms of the number of effectively independent agents (i.e. strategy) which results
to be a key variable to understand the self-organization of financial markets.
As a second issue we focused our attention on the order book dynamics both
from a theoretical and a data oriented point of view. We developed a zero intelligence
model in order to investigate the role of vanishing liquidity in the price
response to incoming orders. Within the framework of this model we have analyzed
the effect of the introduction of strategies pointing out that simple strategic
behaviors can explain bursts of intermittency and long memory effects. On the
other hand we quantitatively showed that there exists a feedback effect in markets
called self-fulfilling prophecy which is the mechanism through which technical trading
can exist and work. This feature is a very interesting quantitative evidence
of a self-reinforcement of agents’ belief. Last but not least nowadays we live in
a computerized and networked society where many of our actions leave a digital
trace and affect other people’s actions. This has lead to the emergence of a new
data-driven research field. In this work we highlighted how non financial data can
be used to track financial activity, in detail we investigate query log volumes, i.e.
the volumes of searches for a specific query done by users in a search engine, as a
proxy for trading volumes and we find that users’ activity on Yahoo! search engine
anticipates trading volume by one-two days.
Differently from Finance, Economics is far from being an ideal candidate to
export the methodology of Natural Sciences because of the lack of empirical data
since controlled (and repeatable) experiments are totally artificial while real experiments
are almost incontrollable and non repeatable due to a high degree of non
stationarity of economical systems. However, the application of method deriving
from complexity to the Economics of Growth is one of the more important achievement
of the work here developed. The basic idea is to study the network defined
by international trade flows and introduce a (non-monetary) metric to measure the
complexity and the competitiveness of countries’ productive system. In addition
we are able to define a metric for products’ quality which overcomes traditional
economic measure for the quality of products given in terms of hours of qualified
labour needed to produce a good. The method developed provides some impressive
results in predicting economical growth of countries and offers many opportunities
of improvements and generalizations
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