1,720,969 research outputs found
Scoring models for roboadvisory platforms: A network approach
No full textAutomated digital consultancy platforms ("robot advisors") reduce costs and improve the perceived service quality, speeding it up and making user involvement more transparent. These improvements are often offset by risk classification models that are simpler than those employed in traditional consultancy. We show how to exploit the available data to build portfolios that better fit the risk profiles of investors. This is made possible, on the one hand, by constructing groups of homogeneous risk profiles based on user responses to the markets in financial instruments directive (MIFID) questionnaire, and, on the other hand, by constructing homogeneous clusters of financial assets based on their risk and return performance. We also show that machine learning methods and, specifically, neural network models can be used to "automatize" the previous classifications and, eventually, to assess whether an investor's portfolio matches their risk profile
Network models to improve robot advisory portfolios
Full text linkRobot advisory services are rapidly expanding, responding to a growing interest people have in directly managing their savings. Robot-advisors may reduce costs and improve the quality of asset allocation services, making user’s involvement more transparent. Against this background, there exists the possibility that robot advisors underestimate market risks, especially during crisis times, when high order interconnections arise. This may lead to a mismatch between investors’ expected and actual risk. The aim of this paper is to overcome this issue, taking into account not only investors’ risk preference but also their attitude towards interconnectdness. To achieve this aim, we combine random matrix theory with correlation networks and extend the Markowitz’ optimisation problem to a third dimension. To demonstrate the practical advantage of our proposed approach we employ daily returns of a large set of Exchange Traded Funds, which are representative of the financial products employed by robot-advisors
A tail-revisited Markowitz mean-variance approach and a portfolio network centrality
Full text linkA measure for portfolio risk management is proposed by extending the Markowitz mean-variance approach to include the left-hand tail effects of asset returns. Two risk dimensions are captured: asset covariance risk along risk in left-hand tail similarity and volatility. The key ingredient is an informative set on the left-hand tail distributions of asset returns obtained by an adaptive clustering procedure. This set allows a left tail similarity and left tail volatility to be defined, thereby providing a definition for the left-tail-covariance-like matrix. The convex combination of the two covariance matrices generates a “two-dimensional” risk that, when applied to portfolio selection, provides a measure of its systemic vulnerability due to the asset centrality. This is done by simply associating a suitable node-weighted network with the portfolio. Higher values of this risk indicate an asset allocation suffering from too much exposure to volatile assets whose return dynamics behave too similarly in left-hand tail distributions and/or co-movements, as well as being too connected to each other. Minimizing these combined risks reduces losses and increases profits, with a low variability in the profit and loss distribution. The portfolio selection compares favorably with some competing approaches. An empirical analysis is made using exchange traded fund prices over the period January 2006–February 2018
Network Models to Enhance Automated Cryptocurrency Portfolio Management
No full textThe usage of cryptocurrencies, together with that of financial automated consultancy, is widely spreading in the last few years. However, automated consultancy services are not yet exploiting the potentiality of this nascent market, which represents a class of innovative financial products that can be proposed by robo-advisors. For this reason, we propose a novel approach to build efficient portfolio allocation strategies involving volatile financial instruments, such as cryptocurrencies. In other words, we develop an extension of the traditional Markowitz model which combines Random Matrix Theory and network measures, in order to achieve portfolio weights enhancing portfolios' risk-return profiles. The results show that overall our model overperforms several competing alternatives, maintaining a relatively low level of risk
Agglomeration vs amenities? Unraveling the latent engine of growth in metropolitan Greece
No full textEconomic downturns, social change, and migrations shape population expansion and shrinkage, making city life cycles particularly complex over time and intrinsically diversified over space. Identifying local drivers of population change plays a major role when addressing metropolitan cycles of growth and decline and provides insights to any policy and planning strategy aimed at promoting together local development, economic competitiveness, and socio-environmental sustainability at large. Timing of metropolitan cycles is, however, heterogeneous and reflects the individual development path of any city. Assuming economic downturns and the associated social processes at the base of spatially heterogeneous patterns of population growth and decline in Mediterranean Europe, we adopted a spatial econometric approach investigating short-term and long-term demographic dynamics (1960–2010) in metropolitan Athens (Greece), with the aim at identifying contextual drivers of population change. Spatial regressions evaluated the role of economic and non-economic dimensions of metropolitan growth, quantifying the impact of agglomeration, scale, accessibility, and amenities at different phases of the city life cycle. Settlement models grounded on scale and agglomeration processes—with growing population in high- and medium-density municipalities—were observed under economic expansion. Recession consolidated a settlement model with population growth in socially dynamic and accessible (low density) districts with natural/cultural amenities, reflecting the inherent decline of agglomeration economies. Based on such dynamics, the polarized hierarchy of central and peripheral locations resulting from radio-centric population expansion was replaced with a settlement model grounded on population increase in “intermediate-density,” attractive locations
Agglomeration economies and the spatial configuration of local labour systems in Italy
No full textChanges in the Italian metropolitan hierarchy are explored by investigating the spatial distribution of per-land value added, assumed as a proxy of urban concentration and economic agglomeration. The analysis is carried out at the level of 686 local labour systems over the period 1996-2005. A spatial autocorrelation analysis of per-land value added standardized by population density identified the spatial structure of the main urban agglomerates and geographical gradients in Italy. An index of local competitiveness based on the elasticity of per-land value added to population density evaluated direction and intensity of change in the Italian urban hierarchy. A Principal Component Analysis (PCA) and non-parametric correlation analysis were run to disentangle the complexity of local development processes beyond the observed spatial and functional configuration of local districts, considering a large set of socioeconomic and territorial variables. Different levels of per-land value added discriminated local labour systems along the urban gradient, evidencing a clear North-South divide in Italy. Results definitely outline per-land value added as a proxy of the (evolving) metropolitan hierarchy in Italy
Longevity-risk-Adjusted Global Age Indicators in Russia and Italy
No full textThe goal of this study is to make a comparison between chronological and biological ages across the Italian and the Russian population. To serve this purpose we employ a measure of age recently introduced in the literature by Milevski (so-called Longevity-risk-Adjusted global age, hereafter L-RaG). Data for the Italian and the Russian population, split by regions, sex, and age groups have been collected for the years 2019 and 2020. Results show that there are significant differences between the chronological and the perceived ages for females and males across Italian regions. The difference exacerbates if we make a comparison between the North and the South regions. Looking at the Russian population, the gap appears extremely high for the North Caucasian area. Finally, we compare the median values of the gaps between the years 2019 and 2020. We found that in the latter year, the median values of the gap has been decreased. This could be attributed to the consequences of the Covid-19 pandemic
Toward a ‘reverse density dividend’? Population growth and socioeconomic evolution of Greek districts before and after crisis
No full textSocial dynamics and economic cycles have driven population growth in Europe toward
heterogeneous and hardly predictable spatial patterns. To assess the role of economic
expansion and recession, our study identifies contextual factors of population growth
and decline at the prefectural scale in Greece, a peripheral economy in Europe, estimating the differential impact of economic scale, agglomeration, accessibility, and amenities since the early 2000s. With economic expansion (2002–2009), population growth was largely dependent on agglomeration forces in both high and medium-density prefectures.
The spatial model observed during recession (2010–2017) has instead reflected the inherent decline of agglomeration economies—with population increasing in accessible, rural districts with (natural and cultural) amenities. In more recent years, population growth in low-density coastal areas definitely suggests how demographic trends have been decoupled from the geography of income and wealth, reducing the divide in central and peripheral locations. The dominance of Athens and Thessaloniki in the Greek urban hierarchy progressively lowered, leading to a settlement model based on population growth in ‘intermediate towns’ and attractive/accessible rural locations. Such dynamics delineate a development path grounded on the spatial distribution of amenities, suggesting the existence of a ‘reverse density dividend’ that requires a specific investigation in advanced economies
Crypto price discovery through correlation networks
Full text linkWe aim to understand the dynamics of crypto asset prices and, specifically, how price information is transmitted among different bitcoin market exchanges, and between bitcoin markets and traditional ones. To this aim, we hierarchically cluster bitcoin prices from different exchanges, as well as classic assets, by enriching the correlation based minimum spanning tree method with a preliminary filtering method based on the random matrix approach. Our main empirical findings are that: (i) bitcoin exchange prices are positively related with each other and, among them, the largest exchanges, such as Bitstamp, drive the prices; (ii) bitcoin exchange prices are not affected by classic asset prices, but their volatilities are, with a negative and lagged effect
- …
