Predicting financial risk is an essential task in financial market. Prior research has shown that textual information in a firm's financial statement can be used to predict its stock's risk level. Nowadays, firm CEOs communicate information not only verbally through press releases and financial reports, but also nonverbally through investor meetings and earnings conference calls. There are anecdotal evidences that CEO's vocal features, such as emotions and voice tones, can reveal the firm's performance. However, how vocal features can be used to predict risk levels, and to what extent, is still unknown. To fill the gap, we obtain earnings call audio recordings and textual transcripts for S&P 500 companies in recent years. We propose a multimodal deep regression model (MDRM) that jointly model CEO's verbal (from text) and vocal (from audio) information in a conference call. Empirical results show that our model that jointly considers verbal and vocal features achieves significant and substantial prediction error reduction. We also discuss several interesting findings and the implications to financial markets. The processed earnings conference calls data (text and audio) are released for readers who are interested in reproducing the results or designing trading strategy.

Background Evidence of hand, foot, and mouth disease (HFMD) in neonates is limited. The aim of this study was to evaluate the clinical symptoms, pathogens, possible transmission routes, and prognosis of neonatal HFMD in Shanghai. Methods This was a case-control study based on the HFMD registry surveillance system. All neonates and infected family members were enrolled between 2016 and 2017 in Shanghai. Neonates with HFMD were followed for at least half a year. Detailed questionnaires, medical history, and physical examination were recorded. Routine blood examination, liver and renal function, immunophenotypes of peripheral blood lymphocytes (CD3, CD4, and CD8 T-cells; NK cells), immunoglobulin (Ig) M, IgG, and IgA, and cytokine interleukin (IL-1β, IL-2R, IL-6, IL-8, IL-10, and TNF-α) levels were measured. All rectal swab specimens were collected and genotyped for enterovirus, and phylogenetic analysis based on the VP1 sequences of coxsackievirus A6 (CV-A6) was performed to investigate molecular and evolutionary characteristics. T -test or nonparametric test was used to evaluate the differences. Logistic analysis was applied to calculate the risk of clinical manifestations in the group of HFMD neonates and their paired siblings. Results There were 16 neonates among the 12,608 diagnosed patients with HFMD, accounting for 0.13%. All neonatal infections were transmitted by other members of the family, mainly the elder siblings, and were caused by CV-A6. CV-A6 was the emerging and predominant causative agent of HFMD in Shanghai. None of the neonates with HFMD experienced fever, onychomadesis, or severe complications. However, two elder sibling patients showed lethargy, and one developed hypoperfusion. In the elder siblings with HFMD, the proportion of white blood cells was generally higher than in neonates with HFMD. The immunologic function of the neonates with HFMD was basically normal. The levels of inflammatory markers were higher in both neonates and elder siblings with HFMD compared to age-matched controls. The clinical symptoms receded about 1 week after onset. None of the neonates had sequelae. Conclusions In our study, CV-A6 infection in neonates was benign, but had the character of family clustering. Due to the two-child policy in China, elder siblings may be the main route of HFMD transmission.

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Financial risk is an essential indicator of investment, which can help investors to understand the market and companies better. Among the many influencing factors of financial risk, researchers find the earnings conference call is the most significant one. Predicting financial volatility after the earnings conference call has been critical to beneficiaries, including investors and company managers. However, previous work mainly focuses on the feature extraction from the word-level or document-level.The vital structure of conferences, the alternate dialogue, is ignored. In this paper, we introduced our Multi-Round Q&A Attention Network, which brings into account the dialogue form in the first place. Based on the data of earnings call transcripts, we apply our model to extract features of each round of dialogue through a bidirectional attention mechanism and predict the volatility after the earnings conference call events. The results prove that our model significantly outperforms the previous state-of-the-art methods and other baselines in three different periods.

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