1,720,959 research outputs found
Fewer is better: the cases of portfolio selection and of operational risk management
Full text linkThis thesis aims to show that in some applications the appropriate selection of a small number of available items can be beneficial with respect to the use of all available items. In particular, we focus on portfolio selection and on operational risk management and we use operations research techniques to identify the few important elements that are needed in both cases. In the first part of this work - based on an article published in Economics Bulletin [Cesarone et al (2016)], we show that, for several portfolio selection models, the best portfolio which uses only a limited number of assets has in-sample performance very close
to that of an optimized portfolio which could include all assets, but generally obtains better out-of-sample performance. This is true for various performance measures, and it is often possible to identify a "golden range" of sizes where the best performances are obtained. These general empirical findings are consistent with theoretical results obtained by Kondor and Nagy (2007) under very restrictive assumptions. We also note that small portfolios are preferable for several practical reasons including monitoring, availability for small investors, and transaction costs. In the second part of the thesis, we develop an operational risk
management framework for the assessment of the exposure of a company (with particular reference to a financial institution) to potential risk events arising from the launch of a new product. This framework is based on the Analytic Hierarchy Process and on the 80/20 rule which allows one to rank and to identify the
most relevant risk events, respectively. By means of appropriate integer programming models we then address the problem of identifying the mitigation actions that secure the internal processes of a company with minimum cost. This corresponds to the primary goal of an operational risk manager: reducing the exposure to potential risk events. An alternative approach, when the budget is fixed, consists in selecting the subset of mitigation actions that provide the greatest reduction in operational risk exposure for that budget. A parametric analysis with respect to the budget level provides additional information for the management to take decisions about possible budget adjustments
Optimally chosen small portfolios are better than large ones
No full textOne of the fundamental principles in portfolio selection models is minimization of risk through diversification of the
investment. However, this principle does not necessarily translate into a request for investing in all the assets of the
investment universe. Indeed, following a line of research started by Evans and Archer almost fifty years ago, we
provide here further evidence that small portfolios are sufficient to achieve almost optimal in-sample risk reduction
with respect to variance and to some other popular risk measures, and very good out-of-sample performances. While
leading to similar results, our approach is significantly different from the classical one pioneered by Evans and Archer.
Indeed, we describe models for choosing the portfolio of a prescribed size with the smallest possible risk, as opposed
to the random portfolio choice investigated in most of the previous works. We find that the smallest risk portfolios
generally require no more than 15 assets. Furthermore, it is almost always possible to find portfolios that are just 1%
more risky than the smallest risk portfolios and contain no more than 10 assets. Furthermore, the optimal small
portfolios generally show a better performance than the optimal large ones. Our empirical analysis is based on some
new and on some publicly available benchmark data sets often used in the literature
Optimally chosen small portfolios are better than large ones
Full text linkOne of the fundamental principles in portfolio selection models is minimization of risk through diversification of the investment. However, this principle does not necessarily translate into a request for investing in all the assets of the investment universe. Indeed, following a line of research started by Evans and Archer almost fifty years ago, we provide here further evidence that small portfolios are sufficient to achieve almost optimal in-sample risk reduction with respect to variance and to some other popular risk measures, and very good out-of-sample performances. While leading to similar results, our approach is significantly different from the classical one pioneered by Evans and Archer. Indeed, we describe models for choosing the portfolio of a prescribed size with the smallest possible risk, as opposed to the random portfolio choice investigated in most of the previous works. We find that the smallest risk portfolios generally require no more than 15 assets. Furthermore, it is almost always possible to find portfolios that are just 1% more risky than the smallest risk portfolios and contain no more than 10 assets. Furthermore, the optimal small portfolios generally show a better performance than the optimal large ones. Our empirical analysis is based on some new and on some publicly available benchmark data sets often used in the literature
Does Greater Diversification Really Improve Performance in Portfolio Selection?
No full textOne of the fundamental principles in portfolio selection models is minimization of risk through diversification of the investment. This seems to require that in a given working universe, or market, the investment should be spread among all (or almost all) the available assets. Indeed, this is what some classical investment strategies, like Equally-Weighted portfolios, or more recent and refined ones, like Risk Parity, actually recommend.
The purpose of this work consists in giving some empirical evidence of the fact that diversifying through the use of larger portfolios is not the best way to achieve an improvement in out-of-sample performance. More precisely, we investigate the role of the restriction on the number of assets in a portfolio (a cardinality constraint) on the in-sample and out-of-sample outcomes of the Equally-Weighted approach and of some well-known portfolio selection models that minimize risk through the use of Variance, Semi-Mean Absolute Deviation, and Conditional Value-at-Risk.
Our empirical analysis is based on some new and on some publicly available benchmark data sets often used in the literature
Operational risk assessment of a new product using AHP
No full textThe risk assessment of a new product is one of the most critical activities performed
by the Operational Risk Management (ORM) of a company operating in
the financial sector. For a new product there are few reference points to assess its
riskiness for ORM, due both to the lack of operational loss data and to the inexperience
of the process owners in handling the new operation. To overcome these two
limitations, we propose an operational risk methodological framework to identify
and prioritize the most dangerous operational risk events with respect to the introduction
of a new product in a financial institution. The methodology provides the
use of a checklist based on risk factors (causes) to assess the operational riskiness of
a new product before its launch. Then, after the launch and with particular reference
to the management of a new product, we use the Analytic Hierarchy Process
(AHP) approach to prioritize operational risk events, and the 80/20 rule to allocate
them in appropriate risk rating classes. As a further element with respect to the
aforementioned framework, we then develop an optimization model to minimize the
total cost of investments required to cover all the important risks. Furthermore, we
to study the relationship between the total cost of investments and the exposure
coverage by means of another optimization model
An alternative approach for the operational risk assessment of a new product
No full textThe risk assessment of a new product is one of the most critical activities performed
by the operational risk management of a company operating in the financial sector.
There are few reference points for the operational risk management to assess the
riskiness of a new product, due both to the lack of operational loss data and to the
inexperience of the process owners in handling the new operation. To overcome these
two limitations, we propose a methodological operational risk framework to identify
and prioritize the most dangerous operational risk events with respect to the introduction
of a new product in a financial institution. The methodology starts with the use
of a checklist based on risk factors (causes) to assess the operational riskiness of a
new product before its launch. Then, after the launch and with particular reference to
the management of the new product, we use the analytic hierarchy process approach
to prioritize operational risk events, and the “80/20 rule” to allocate them to appropriate
risk rating classes. Finally, we develop two optimization models to minimize the total cost of investments required to cover all the important risks, and to study the relationship between the total cost of investments and the exposure coverage
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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