1,720,976 research outputs found
Sul ruolo dei sistemi di supporto alle decisioni nelle attività finanziarie. Un'applicazione per la gestione del portafoglio prestiti
No full textI contributi metodologici che sono stati espressi nello studio dei processi decisionali, nonché gli straordinari risultati conseguiti sui meccanismi di acquisizione ed elaborazione della conoscenza, hanno consentito lo sviluppo di molteplici prodotti software, rivolti ad aiutare e/o sostituire l'uomo nell'attività di individuazione, analisi, valutazione e selezione di alternative nel contesto di processi decisionali complessi e mal strutturati.
Tuttavia, molti di questi prodotti non hanno superato la fase sperimentale e prototipale; ne consegue la necessita' di strumenti computazionali innovativi che sappiano rappresentare in modo più appropriato il processo dinamico di adattamento e di cambiamento
delle politiche decisionali. Nel lavoro, quindi, dopo un breve cenno ai fondamenti, alle proprietà ed alle performance dei suddetti strumenti, si e' voluto sottolineare in particolare l'opportunità di utilizzarli in ambito finanziario, esaminando alcuni esempi di applicazione e proponendo, a conclusione del lavoro, in modo dettagliato ed esaustivo un prototipo di sistema esperto per la valutazione dei fidi e la gestione del portafoglio prestiti
CLUSTERING TOURIST LOCAL SYSTEMS BY VERTEX PARTITIONING AND LOCAL SEARCH
No full textNonostante l’industria del turismo venga riconosciuta come un settore trainante per l’intera economia di un Paese, a tutt’oggi si registra una mancanza di criteri e metodologie ai fini della localizzazione, promozione e gestione di un Sistema Turistico Locale (STL). A tal fine, nel
presente lavoro viene proposto un approccio di tipo quantitativo al problema sia della localizzazione che del dimensionamento ottimale di un STL tenendo conto di criteri e/o vincoli di natura geografica, economica e demografica.
Il problema di ottimizzazione è rappresentato su grafo ed è risolto ricorrendo all’algoritmo di ricerca locale di Threshold Accepting (TA).Despite the importance of tourism as a leading industry in the development of a country’s economy, there is a lack of criteria and methodologies for the detection, promotion and governance of local tourism systems. We propose a quantitative approach for the detection of local tourism systems the size of which is optimal with respect to geographical, economic, and
demographical criteria.
To this end, the above stated optimization problem is solved applying both a vertex partitiong
tecnique and a local search algorithm
Selezione di Portafoglio e Metaeuristiche
No full textIl problema della selezione del portafoglio (PSP) ha come obiettivo quello di determinare, disponendo di un dato insieme di titoli, il portafoglio che minimizza una misura di rischio per un dato livello di rendimento minimo richiesto. Sebbene nella sua formulazione originaria il PSP può essere risolto utilizzando algoritmi di programmazione lineare o quadratica, l'inclusione di
ulteriori vincoli ed obiettivi rende il problema computazionalmente difficile.
Nel presente lavoro viene proposto un approccio metaeuristico al PSP in cui vengono considerate diverse misure di distanza tra i portafogli della frontiera efficiente (Mean-Variance)
ed il portafoglio ottimo che si ottiene in corrispondenza di una strategia di gestione passiva
(Index Tracking) e ciò al fine di fornire all’investitore un ulteriore criterio di selezione tra i portafogli non dominati.The Portfolio selection Problem (PSP) is concerned deciding in what assets to invest and by how much, minimizing a risk measure and imposing a minimum required return. Although the original formulation (by Markowitz) can be solved by Quadratic Programming, including further real world constraints makes the problem computationally difficult.
In this work we propose a hybrid approach in which several distance measures are taken into
account to assess the distance between portfolios on the Markowitz Pareto frontier and the Index tracking optimal portfolio, in order to provide the user with a tool to discriminate
amongst non-dominated portfolios
A proposal for optimal VaR and CVaR parameters estimation
No full textTraditional strategies of managing financial portfolios are based on the assumption of normally distributed losses, i.e., the likelihood of observing extreme adverse events is negligible and close to zero. Indeed, many financial data reject the Gaussian model and exhibits fat-tailed distributions.
In this paper we analyze and compare the relative performances of VaR and CVaR estimators with respect to daily stock market returns of four different emerging markets indexes, i.e., MSCI Emerging Markets, Latin America, Middle East Europe and Africa index and South Africa.
All indices exhibit slightly negative skewed distributions and are characterized by a significant positive excess of kurtosis. This implies heavy tails in the distribution of risky returns, which are thus more likely of being affected by extreme values.
In order to discriminate among time-series datasets we conducted two different level of analyses: 1) identify the timing of regional markets crashes along with the related market crisis periods 2) measure the strength of co-movements among time-series across different timescales.
With reference to the first objective, we followed the methodology applied in [1].
What emerges is that all markets have been affected by the same crisis but their effects, in terms of price decline and duration, appear to be less severe for the MSCI South Africa. Moreover, it would seem that the MXZA index is positively lagged with respect to the other markets. In order to give evidence to the above consideration we have run a Wavelet Multiple (Cross) Correlation analysis [2].
By means of the Extreme Value Theory (EVT), we backtest the risk measures over several classes of density distributions and well-known modeling approaches such as the Exponential Weighted Moving Average (EWMA) and the Historical Simulation Method (HSM).
Results give evidence of how the efficacy of the applied risk measures as well as of the selected model strictly depends on the cumbersome parameters’ settings [3]. In this view, we propose two metaheuristic (C)VaR based algorithms for optimally estimating and fitting within an EVT framework the two-sided tail distribution of returns.
References
[1]. Sandeep A. Patel and Asani Sarkar. Crises in developed and emerging stock markets. Financial Analysts Journal, 54(6):50–61, 1998.
[2]. J. Fern ́andez-Macho. Wavelet multiple correlation and cross-correlation: A multiscale analysis of eurozone stock markets. Physica A: Statistical Mechanics and its Applications, 391(4):1097–1104, 2012.
[3]. J. Andria. A computational proposal for a robust estimation of the pareto tail index: An application to emerging markets. Applied Soft Computing, 114:108048, 2022
Integrating the gender dimension to disclose the degree of businesses’ articulation of innovation
Full text linkIn this contribution, we examine the relationship between the presence of women in companies’ Boards and innovation communication claims: we propose a framework to quantitatively assess the presence of women and the online articulation of innovation, in order to understand whether some correlations hold between these two variables. We also introduce a neural network approach to predict the innovation metric that uses, amongst the predictors, the gender component, and we compare it with a linear regression analysis. Results indicate that neural networks may be used to predict the articulation of innovation by using a predictor set that includes the gender component of the Board of Directors, and also that the use of the gender metric improves previous predictions about the articulation of innovation model’s output
Biclustering sustainable local tourism systems by the Tabu search optimization algorithm
Full text linkTourism is nowadays fully acknowledged as a leading industry contributing to boost the economic development of a country. This growing recognition has led researchers and policy makers to increasingly focus their attention on all those concerns related to optimally detecting, promoting and supporting territorial areas with a high tourist vocation, i.e., Local Tourism Systems. In this work, we propose to apply the biclustering data mining technique to detect Local Tourism Systems. By means of a two-dimensional clustering approach, we pursue the objective of obtaining more in-depth and granular information than conventional clustering algorithms. To this end, we formulate the objective as an optimization problem, and we solve it by means of Tabu-search. The obtained results are very promising and outperform those provided by classic clustering approaches
Fuzzy Multi-Criteria Decision Making: An entropy-based approach to assess tourism sustainability
No full textIn this paper we propose a method for ranking tourist destinations and evaluating their performances under a sustainability perspective: a Fuzzy Multiple Criteria Decision-Making method is applied for determining sustainability performance values and ranking destinations accordingly. We select a set of sustainability evaluation criteria and we use a Fuzzy Analytic Hierarchy Process to weight the selected criteria. We also optimize each evaluator's membership function support by means of a fuzzy entropy maximization criteria.
A case study is illustrated and results are compared with two DEA-based models. The simplicity of the proposed approach along with the easy readability of the results allow its direct applicability for all involved stakeholders
The Predictive Power of Social Media Sentiment: Evidence from Cryptocurrencies and Stock Markets Using NLP and Stochastic ANNs
No full textCryptocurrencies are nowadays seen as an investment opportunity, since they show some peculiar features, such as high volatility and diversification properties, that are triggering research interest into investigating their differences with traditional assets. In our paper, we address the problem of predictability of cryptocurrency and stock trends by using data from social online communities and platforms to assess their contribution in terms of predictive power. We extend recent developments in the field by exploiting a combination of stochastic neural networks (NNs), an extension of standard NNs, natural language processing (NLP) to extract sentiment from Twitter, and an external evolutionary algorithm for optimal parameter setting to predict the short-term trend direction. Our results point to good and robust accuracy over time and across different market regimes. Furthermore, we propose to exploit recent advances in sentiment analysis to reassess its role in financial forecasting; in this way, we contribute to the empirical literature by showing that predictions based on sentiment analysis are not found to be significantly different from predictions based on historical data. Nonetheless, compared to stock markets, we find that the accuracy of trend predictions with sentiment analysis is on average much higher for cryptocurrencies
Propagation of Bankruptcy Risk over Scale-Free Economic Networks
Full text linkThe propagation of bankruptcy-induced shocks across domestic and global economies is sometimes very dramatic; this phenomenon can be modelled as a dynamical process in economic networks. Economic networks are usually scale-free, and scale-free networks are known to be vulnerable with respect to targeted attacks, i.e., attacks directed towards the biggest nodes of the network. Here we address the following question: to what extent does the scale-free nature of economic networks and the vulnerability of the biggest nodes affect the propagation of economic shocks? We model the dynamics of bankruptcies as the propagation of financial contagion across the banking sector over a scale-free network of banks, and perform Monte-Carlo simulations based on synthetic networks. In addition, we analyze the public data regarding the bankruptcy of US banks from the Federal Deposit Insurance Corporation. The dynamics of the shock propagation is characterized in terms of the Bank Failures Diffusion Index, i.e., the average number of new bankruptcies triggered by the bankruptcy of a single bank, and in terms of the Shannon entropy of the whole network. The simulation results are in-line with the empirical findings, and indicate the important role of the biggest banks in the dynamics of economic shocks
The predictive power of power-laws: An empirical time-arrow based investigation
No full textThe efficient market hypothesis forbids any predictability towards future, but there is no such restriction in the case of reversed-looking approaches. We analyze if this asymmetry in non-predictability is reflected in the statistical features of financial time series. Our study is based on the analysis of the length-distribution of periods with high variability, and introduces time-asymmetric modifications of the method which are capable of revealing differences of the time series in forward and reversed time. We show that the future and reversed-looking time-series possess very similar properties, with some features being distinguishable with our method. Our findings give also evidence of the differences in the dynamics of markets before and after crisis, this implying the possibility to predict a forthcoming crisis
- …
