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Computational intelligence in finance has been a very popular topic for both academia and financial industry in the last few decades. Numerous studies have been published resulting in various models. Meanwhile, within the Machine Learning (ML) field, Deep Learning (DL) started getting a lot of attention recently, mostly due to its outperformance over the classical models. Lots of different implementations of DL exist today, and the broad interest is continuing. Finance is one particular area where DL models started getting traction, however, the playfield is wide open, a lot of research opportunities still exist. In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications. We not only categorized the works according to their intended subfield in finance but also analyzed them based on their DL models. In addition, we also aimed at identifying possible future implementations and highlighted the pathway for the ongoing research within the field. © 2020 Elsevier B.V.

Computational intelligence in finance has been a very popular topic for both academia and financial industry in the last few decades. Numerous studies have been published resulting in various models. Meanwhile, within the Machine Learning (ML) field, Deep Learning (DL) started getting a lot of attention recently, mostly due to its outperformance over the classical models. Lots of different implementations of DL exist today, and the broad interest is continuing. Finance is one particular area where DL models started getting traction, however, the playfield is wide open, a lot of research opportunities still exist. In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications. We not only categorized the works according to their intended subfield in finance but also analyzed them based on their DL models. In addition, we also aimed at identifying possible future implementations and highlighted the pathway for the ongoing research within the field. © 2020 Elsevier B.V.

People routinely manage the impressions they make on others, attempting to project a favorable self-image. The bulk of the literature has portrayed people as savvy self-presenters who typically succeed at conveying a desired impression. When people fail at making a favorable impression, such as when they come across as braggers, regulatory resource depletion is to blame. Recent research, however, has identified antecedents and strategies that foster systematic impression management failures (independently of regulatory resource depletion), suggesting that self-presenters are far from savvy. In fact, they commonly mismanage their impressions without recognizing it. We review failed perspective taking and narcissism as two prominent antecedents of impression mismanagement. Further, we argue that failed perspective taking, exacerbated by narcissism, contributes to suboptimal impression management strategies, such as hubris, humblebragging, hypocrisy, and backhanded compliments. We conclude by discussing how self-presenters might overcome some of the common traps of impression mismanagement.

In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN) model is trained in the learning stage on the daily stock prices between 1997 and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used in the training stage. The trained model is then tested with data from 2007 to 2017. The results indicate that by choosing the most appropriate technical indicators, the neural network model can achieve comparable results against the Buy and Hold strategy in most of the cases. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance., Comment: ACM Southeast Conference, ACMSE 2017, Kennesaw State University, GA, U.S.A., 13-15 April, 2017

The European Myeloma Network provides recommendations for the management of the most common complications of multiple myeloma. Whole body low-dose computed tomography is more sensitive than conventional radiography in depicting osteolytic disease and thus we recommend it as the novel standard for the detection of lytic lesions in myeloma (grade 1A). Myeloma patients with adequate renal function and bone disease at diagnosis should be treated with zoledronic acid or pamidronate (grade 1A). Symptomatic patients without lytic lesions on conventional radiography can be treated with zoledronic acid (grade 1B), but its advantage is not clear for patients with no bone involvement on computed tomography or magnetic resonance imaging. In asymptomatic myeloma, bisphosphonates are not recommended (grade 1A). Zoledronic acid should be given continuously, but it is not clear if patients who achieve at least a very good partial response benefit from its continuous use (grade 1B). Treatment with erythropoietic-stimulating agents may be initiated in patients with persistent symptomatic anemia (hemoglobin <10g/dL) in whom other causes of anemia have been e

The role of high-dose therapy followed by autologous stem cell transplantation (ASCT) in the treatment of multiple myeloma (MM) continues to evolve in the novel agent era. The choice of induction therapy has moved from conventional chemotherapy to newer regimens incorporating the immunomodulatory derivatives thalidomide or lenalidomide and the proteasome inhibitor bortezomib. These drugs combine well with traditional therapies and with one another to form various doublet, triplet, and quadruplet regimens. Up-front use of these induction treatments, in particular 3-drug combinations, has affected unprecedented rates of complete response that rival those previously seen with conventional chemotherapy and subsequent ASCT. Autotransplantation applied after novel-agent-based induction regimens provides further improvement in the depth of response, a gain that translates into extended progression-free survival and, potentially, overall survival. High activity shown by immunomodulatory derivatives and bortezomib before ASCT has recently led to their use as consolidation and maintenance therapies after autotransplantation. Novel agents and ASCT are complementary treatment strategies for MM. This article reviews the current literature and provides important perspectives and guidance on the major issues surrounding the optimal current management of younger, transplantation-eligible MM patients.

The aim of this study was to identify factors associated significantly with hospitalised cancer patients' satisfaction with care. Patients were recruited from four geographical/cultural groups, including five European countries and Taiwan. They rated their level of satisfaction by completing the EORTC IN-PATSAT32 questionnaire at home. Additionally, data were collected on the sociodemographic and clinical characteristics and the quality of life of the patients, as well as on institutional characteristics. Of 762 patients recruited, 647 (85%) returned a completed questionnaire. The number of nurses and doctors per bed, institution size, geo-cultural origin, ward setting, teaching/non-teaching setting, treatment toxicity, global health status, participation in clinical trials and education level were all associated significantly at the multivariate level with satisfaction with doctor and nurse interpersonal skills, information provision, availability, and/or overall satisfaction. A number of patient-, institutional- and culture-related factors are associated with the perceived quality of cancer care. Future studies, with appropriate sampling frames and stratification procedures, are needed to better understand cross-national and cross-cultural differences in cancer patient satisfaction., Journal Article, Multicenter Study, Research Support, N.I.H. Extramural, Research Support, Non-U.S. Gov't, SCOPUS: ar.j, info:eu-repo/semantics/published

The aim of this study was to identify factors associated significantly with hospitalised cancer patients' satisfaction with care. Patients were recruited from four geographical/cultural groups, including five European countries and Taiwan. They rated their level of satisfaction by completing the EORTC IN-PATSAT32 questionnaire at home. Additionally, data were collected on the sociodemographic and clinical characteristics and the quality of life of the patients, as well as on institutional characteristics. Of 762 patients recruited, 647 (85%) returned a completed questionnaire. The number of nurses and doctors per bed, institution size, geo-cultural origin, ward setting, teaching/non-teaching setting, treatment toxicity, global health status, participation in clinical trials and education level were all associated significantly at the multivariate level with satisfaction with doctor and nurse interpersonal skills, information provision, availability, and/or overall satisfaction. A number of patient-, institutional- and culture-related factors are associated with the perceived quality of cancer care. Future studies, with appropriate sampling frames and stratification procedures, are needed to better understand cross-national and cross-cultural differences in cancer patient satisfaction., Journal Article, Multicenter Study, Research Support, N.I.H. Extramural, Research Support, Non-U.S. Gov't, SCOPUS: ar.j, info:eu-repo/semantics/published

info:eu-repo/semantics/published

info:eu-repo/semantics/published