Style, Fees and Performance of Italian Equity Funds

Using a clustering procedure,we classify Italian funds ex-post on the basis of the composition of their portfolios and find that the optimal number of clusters is equal to 4. The four groups which result from the statistical classification closely match the 4-level aggregation of the 20 ex-ante categories used by the Italian mutual funds association. We then estimate the risk-adjusted performance of Italian equity funds, using both net and gross returns and employing both one-factor CAPM benchmarks and multi-factor benchmarks. In addition to the standard Jensen's a, we measure risk-adjusted performance using the Positive Period Weighting measure (PPW), which is not influenced by managers' market-timing strategy.Using net returns (calculated after management fees and taxes but before load fees) the Italian equity funds' performance is not significantly different from zero. However, when the funds'performance is evaluated on the basis of gross returns (i.e.returns computed adding back management fees paid each year by the funds), the performance of the Italian equity funds is always positive. In particular, when both a 2-index benchmark that takes account of the funds' investments in government bonds and a 5-factor APT benchmark are considered, performance is positive and significant using both Jensen's a and the PPW. This result supports Grossman and Stiglitz's (1980) view of market efficiency, suggesting that informed investors (investment funds) are compensated for their information gathering.mutual funds; performance measures; investment style; management fees; market timing

Supplementary pension schemes in Italy: features, development and opportunities for workers

Participation in supplementary pension funds allows workers to exploit tax benefits and payroll employees to take advantage of employer contributions. The simulations reported in the paper show that these two components can considerably increase workers' retirement wealth. Data show that returns on supplementary pension funds may be greater than the revaluation rate of the so-called Trattamento di fine rapporto (Tfr, severance pay entitlements that also serve as provision for old age and are funded by workers' contributions). As for the liquidity of accrued positions, recent changes in the law give retirement wealth held in pension funds a degree of flexibility comparable to that of the Tfr. The paper shows that scale economies may be substantial. Cost moderation also requires transparency and comparability of charges and fees: they are also essential in stimulating competition and allowing workers to move freely from expensive retirement schemes to schemes charging lower fees. In this respect the limits on the portability of employer contributions discourage worker mobility across different pension schemes. Italian workers seem to overestimate the level of the future public pension. This result suggests the importance of strengthening public efforts aimed at providing workers with appropriate information, to make them aware of their retirement position.pension funds, retirement, financial education, employer contributions, management fees, TFR

Insurance Fraud Detection: A Statistically-Validated Network Approach

Fraud is a social phenomenon, and fraudsters oftencollaborate with other fraudsters, taking on differentroles. The challenge for insurance companies is toimplement claim assessment and improve frauddetection accuracy. We developed an investigativesystem based on bipartite networks, highlighting therelationships between subjects and accidents or vehi-cles and accidents. We formalize filtering rules throughprobability models and test specific methods to assessthe existence of communities in extensive networksand propose new alert metrics for suspicious struc-tures. We apply the methodology to a real database—the Italian Antifraud Integrated Archive—and compare the results to out‐of‐sample fraud scams underinvestigation by the judicial authoritie

Dress Code: High-Resolution Multi-Category Virtual Try-On

Image-based virtual try-on strives to transfer the appearance of a clothing item onto the image of a target person. Existing literature focuses mainly on upper-body clothes (e.g. t-shirts, shirts, and tops) and neglects full-body or lower-body items. This shortcoming arises from a main factor: current publicly available datasets for image-based virtual try-on do not account for this variety, thus limiting progress in the field. In this research activity, we introduce Dress Code, a novel dataset which contains images of multi-category clothes. Dress Code is more than 3x larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images (1024 x 768) with front-view, full-body reference models. To generate HD try-on images with high visual quality and rich in details, we propose to learn fine-grained discriminating features. Specifically, we leverage a semantic-aware discriminator that makes predictions at pixel-level instead of image- or patch-level. The Dress Code dataset is publicly available at https://github.com/aimagelab/dress-code

Dress Code: High-Resolution Multi-Category Virtual Try-On

Image-based virtual try-on strives to transfer the appearance of a clothing item onto the image of a target person. Prior work focuses mainly on upper-body clothes (e.g. t-shirts, shirts, and tops) and neglects full-body or lower-body items. This shortcoming arises from a main factor: current publicly available datasets for image-based virtual try-on do not account for this variety, thus limiting progress in the field. To address this deficiency, we introduce Dress Code, which contains images of multi-category clothes. Dress Code is more than 3x larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images (1024 x 768) with front-view, full-body reference models. To generate HD try-on images with high visual quality and rich in details, we propose to learn fine-grained discriminating features. Specifically, we leverage a semantic-aware discriminator that makes predictions at pixel-level instead of image- or patch-level. Extensive experimental evaluation demonstrates that the proposed approach surpasses the baselines and state-of-the-art competitors in terms of visual quality and quantitative results. The Dress Code dataset is publicly available at https://github.com/aimagelab/dress-code.Comment: Dress Code - Video Demo: https://www.youtube.com/watch?v=qr6TW3uTHG

Dual-Branch Collaborative Transformer for Virtual Try-On

Image-based virtual try-on has recently gained a lot of attention in both the scientific and fashion industry communities due to its challenging setting and practical real-world applications. While pure convolutional approaches have been explored to solve the task, Transformer-based architectures have not received significant attention yet. Following the intuition that self- and cross-attention operators can deal with long-range dependencies and hence improve the generation, in this paper we extend a Transformer-based virtual try-on model by adding a dual-branch collaborative module that can exploit cross-modal information at generation time. We perform experiments on the VITON dataset, which is the standard benchmark for the task, and on a recently collected virtual try-on dataset with multi-category clothing, Dress Code. Experimental results demonstrate the effectiveness of our solution over previous methods and show that Transformer-based architectures can be a viable alternative for virtual try-on