1,720,969 research outputs found
Tensor decomposition in mortality: identifying subgroups, modeling, forecasting and exploring causes of death
Full text linkWith the increasing availability of temporal data, a researcher often analyzes information stored in matrices, in which entries are replicated on different occasions. For
example, in the context of underwriting, pricing, or forecast, an actuary manages a greater amount of information and could have to deal with the death rates (or
with log-death rates) by age and year (or different countries). The occasions can be time-varying or refer to different conditions, and in these situations, data can be
stored in a 3-way array or tensor. Also, we can consider an additional dimension (the second occasions) and the data are stored in a 4-way array or (4-way) tensor.
More in general, data can be stored in a N-way array or (N-way) tensor. These data are called multi-way data and they are analysed and handled by multi-way models.
The aim of this work is to illustrate the different uses of DEDICOM, Tucker and CANDECOMP/PARAFAC models in the context of mortality, such as: identifying subgroups, modeling, forecasting and exploring causes of death. To achieve this aim, we gradually approach the problem, considering respectively three and four dimensions in different order and in various applications. In particular we focus on the Tucker method to modeling and exploring data, on Canonical Polyadic Decomposition (CANDECOMP) or Parallel Factors (PARAFAC) (CANDECOMP/PARAFAC) to forecasting data and on the nonnegative 3-way DEcomposition into DIrectional COMponents (DEDICOM) method, that is a special case of Tucker decomposition, to identify subgroups in the data. Aiming at identifying subgroups, we show how the DEDICOM is able to extract meaningful relational patterns from multi population log centered death rate mortality data. Our work, by specifically describing the mesoscale interactions between countries, could help to design appropriate actions against longevity risk that may impact on the stability conditions of life assurance and pensions. Concerning the mortality modeling, firstly we refer to the three-way Lee Carter model [91], that is based on Tucker 3 decomposition, and that can be considered an extension of the classic Lee carter model [61]. The proposed approach allows us to simplify the data structure and to obtain a rank reduced representation. Then, following this line of research and focusing on the forecasting, we propose a coherent mortality forecasting using a four-way CANDECOMP/PARAFAC decomposition, hence considering another dimension. Our proposal based on the four-way
structure allows managing mortality data aggregated in multi-dimensional settings, according to common demographic features: age class, time, country, and gender.
We deal with four-dimensional mortality data using two main approaches proposed in the literature, the first one which works on centered mortality rates as in [28],
and the second one working on compositional data as in [13]. Here, we provide two steps further on methodological developments in the field of mortality analysis and
forecasting in a high-dimensional space. Firstly, compared to the current literature, we use an additional dimension, implementing a 4-way tensor decomposition. Thus,
we further extend this framework including the CoDa analysis in the spirit of [14]. In the last part, we apply the Tucker 4 method to the mortality by cause of death, hence considering again four dimensions and referring to death rates. This four-way component analysis is useful for the exploratory analysis of four-way data and in this
context it reveals some peculiar aspects of the mortality phenomenon. In particular, this analysis lets us understand how the longevity improvements, witnessed in many
high-income countries during the twentieth century, were determined especially by the reduction in a few specific major causes of death groups
Robo-advisors: A systematic literature review
No full textUsing a systematic literature review, our study analyzes articles on robo-advisors between 2017 and 2022. Our review identifies four relevant research streams: early classification of robo advisors, behavioral topics, performance, and algorithm modelization. Finally, we propose relevant research questions for each stream, providing scholars with new research angles. Our considerations are also valuable for asset managers, banks, and other financial companies since adopting robo-advisors affects their business models through clients' preferences and cost structure
The Credit Risk of Sustainable Firms during the Pandemic
Full text linkThis study investigates how the credit risk of more sustainability-oriented firms changes when national governments intervene in their economies to counterbalance the COVID-19 pandemic. For this reason,
we examine how the credit default swap spread changes on a database of all listed firms—for which a
credit default swaps (CDS) contract is available—in Europe and the United Kingdom during the whole
year of 2020. We find that when national governments intervene in the local economies, the CDS
spreads for these firms decrease more than for other firms. Furthermore, the CDS spread changes
are more sensitive to those policies aimed at supporting household and business income during the
pandemic rather than those policies related to stay-at-home measures and investments in healthcare.
Our results corroborate previous theories linking firm sustainability, equity, and credit risk
Investor behavior around targeted liquidity announcements
Full text linkWe exploit announcements related to targeted longer-term financing operations (TLTROs) as exogenous shocks in investor perceptions to test recent theories on bank funding liquidity (Ahnert et al., 2019; Liu, 2015). We find that banks with high derivative holdings and more exposed to sovereign credit risk respond better to the announcements, consistent with the view that lower funding costs benefit banks with higher asset encumbrance and located in more vulnerable Eurozone countries. The TLTRO announcements also elicit reductions in short positions on bank stocks relative to stocks of non-financial corporations without impairing their market liquidity. Robustness tests rule out that our results are driven by confounding events and anticipation effects. Placebo tests confirm that the TLTRO announcements are driving the estimated price reactions and changes in short positions
Is it all about noise? Investor sentiment and risk nexus: evidence from China
Full text linkWe investigate how online investor sentiment impacts stock risk, measured as Value-at-Risk (VaR). We extrapolate online investor sentiment from information on the stock forum on the 100 constituent stocks of the Shenzhen index using a self-written code to collect daily online postings from 2016 to 2022. Then, we rely on algorithms to classify them. Using quantile regressions and controlling for firm-specific factors and COVID-19, we document that stronger sentiment increases VaR while decreasing VaR on a lagged 7-day horizon. As we move to a longer horizon (20 days), the effect vanishes as more information becomes incorporated into the stock prices
COVID‐19, ESG investing, and the resilience of more sustainable stocks: Evidence from European firms
Full text linkFollowing the COVID-19 outbreak, orientation toward sustainability is a critical factor in ensuring firm survival and growth. Using a large sample of 1,204 firms in Europe during the year 2020, this study investigates how more sustainable firms fare during the pandemic compared with other firms in terms of risk–return trade-off and stock market liquidity. We also highlight the drivers of the resilience of more sustainable firms to the pandemic. Particularly, we document that higher levels of cash holdings and liquid assets in the pre-COVID period help these firms to perform and absorb the COVID-19 externalities better than other firms. Our results are robust to a host of econometric models, including GMM estimations and several measures of stock market performance. These findings contribute to the theoretical and empirical debate on the role of the sustainability as a source of corporate resilience to unexpected shocks
Market Reaction to the Expected Loss Model in Banks
Full text linkThis is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordWe investigate how investors perceive the adoption of the expected-loss model (ELM) for impairment incorporated in IFRS 9. Using a sample of European listed banks covering the period of the standard-setting process of IFRS 9, we examine whether the market perceives the new regulation to increase shareholder wealth. First, we document a positive market reaction to the ELM adoption events. Second, we find that investors perceive that the potential benefits of ELM are more pronounced for larger banks, banks with lower profitability and higher systemic risk, and for those that received a public bailout and with more positively skewed returns. Overall, these results support a “monitoring” channel suggesting that ELM may lead to greater bank transparency and more effective market discipline, fundamental for improving financial stability
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
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