1,721,074 research outputs found
Riflessioni comparate sui principi teorici della finanza islamica e della moderna finanza occidentale
No full textDalla finanza classica a quella comportamentale
No full textThe aim of this book is to draw a conjunction line between traditional corporate finance and behavioral one, trying to merge models in order to better represent markets’ dynamics.
Many traditional models appear to work only under rigid and static assumptions in a one-period frame and even those models considering different periods time series can’t capture random market’s behavior, which remain a key issue in financial markets’ theory.
Research questions to which the book try to answer are:
Do economic behaviors tend to be optimal?
Is it possible to think a theory likely to explain markets’ trends better than EMH?
Is it possible to come up with a reliable measure of information efficiency as posed by Fama?
How can contradictions between information efficiency and Tobin’s fundamental efficiency be solved?
How can experimental psychology contribute to financial markets’ theory?
Is it possible to think to traders’ behavior in terms of cognitive dissonances?
How can we derive a trading model from a mental model?
All these problems are developed within the framework of the debate about utility functions, including the one developed by Kahneman and Tversky in their seminal work about Prospect theor
A critical analysis of the impact measurement in impact finance
Full text linkAcademic literature on impact finance has not yet covered all aspects of the topic, nor has significantly contributed, so far, to solve several relevant problems arising from the field. Defining the metrics and measurement models suitable to assess impact is probably, among them, the most important one. Practitioners seem willing to exploit the potential value and, although useful heuristics and practical solutions have been found, no satisfactory and widely accepted valuation model is available. The present paper tries to summarize the state of the art, through the analysis of the available literature and tries to address some possible development in future research. The underlying idea is that the field is still very new, on one side, and extremely diverse in its manifestation, therefore no traditional theory fully applies to it. At the same time, the research on the topic still relays on practitioners‟ effort, rather than on academia, a gap that ought to be filled. The paper concludes that Impact Finance and Investing are perhaps too narrow labels that limit the possibility to fully grasp the core of it and propose to widen up it by using “Positive Finance” as a more comprehensive one. Indeed, it has been found that academic empirical studies are so far very few and statistical findings far from being robust. The absence of accepted market models, prevent researchers from delivering a theoretical effective interpretation of the growing market
HOW PSYCHOLOGY AFFECTS DECISIONS IN CORPORATE FINANCE: TRADITIONAL VS. BEHAVIOURAL APPROACH
No full textThe aim of this research is to draw a theoretical line to connect on a common conceptual base, behavioural fi-nance with what is internationally known as Modern Fi-nance. The debate often involves discussions about the prevalence of rationality over irrationality. This paper will address mainly two questions: as an economist, should I propend for traditional or for behavioural finance? And, perhaps more important, are they in opposition to each other? Linking the principles upon which the traditional theory of finance is based to behavioural finance appear also to be useful to better understand recent global turmoil in the world financial system. In finding such links, behavioural finance studies will help on driving research to define market models much closer to reality than they are today. Thus a literature recognition will be carried out, starting from the most important contribution to fundamental analysis, value theory, going through modern portfolio theory and efficient market hypothesis to seminal contributions on behavioural finance, reaching recent findings of Neuronomics, in order to establish some common theoretical base in corporate finance studie
Synthetic pattern generation for imbalanced learning in image retrieval
No full textNowadays very large archives of digital images are easily produced thanks to the wide availability of digital cameras, that are often embedded into a number of portable devices. One of the ways of exploring an image archive is to search for similar images. Relevance feedback mechanisms can be employed to refine the search, as the most similar images according to a set of visual features may not contain the same semantic concepts according to the users’ needs. Relevance feedback allows users to label the images returned by the system as being relevant or not. Then, this labelled set is used to learn the characteristics of relevant images. As the number of images provided to users to receive feedback is usually quite small, and relevant images typically represent a tiny fraction, it turns out that the learning problem is heavily imbalanced. In order to reduce this imbalance, this paper proposes the use of techniques aimed at artificially increasing the number of examples of the relevant class. The new examples are generated as new points in the feature space so that they are in agreement with the local distribution of the available relevant examples. The locality of the proposed approach makes it quite suited to relevance feedback techniques based on the Nearest-Neighbor (NN) paradigm. The effectiveness of the proposed approach is assessed on two image datasets and comparisons with editing techniques that eliminate redundancies in non-relevant examples are also reported
Information fusion in content based image retrieval: A comprehensive overview
Full text linkAn ever increasing part of communication between persons involve the use of pictures, due to the cheap availability of powerful cameras on smartphones, and the cheap availability of storage space. The rising popularity of social networking applications such as Facebook, Twitter, Instagram, and of instant messaging applications, such as WhatsApp, WeChat, is the clear evidence of this phenomenon, due to the opportunity of sharing in real-time a pictorial representation of the context each individual is living in. The media rapidly exploited this phenomenon, using the same channel, either to publish their reports, or to gather additional information on an event through the community of users. While the real-time use of images is managed through metadata associated with the image (i.e., the timestamp, the geolocation, tags, etc.), their retrieval from an archive might be far from trivial, as an image bears a rich semantic content that goes beyond the description provided by its metadata. It turns out that after more than 20 years of research on Content-Based Image Retrieval (CBIR), the giant increase in the number and variety of images available in digital format is challenging the research community. It is quite easy to see that any approach aiming at facing such challenges must rely on different image representations that need to be conveniently fused in order to adapt to the subjectivity of image semantics. This paper offers a journey through the main information fusion ingredients that a recipe for the design of a CBIR system should include to meet the demanding needs of users
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