Risk Forecasts Research Articles

The Application of Graph-Structured Cox Model in Financial Risk Early Warning of Companies

An effective financial risk forecast depends on the selection of important indicators from a broad set of financial indicators that are often correlated with one another. In this paper, we address this challenge by proposing a Cox model with a graph structure that allows us to identify and filter out the crucial indicators for financial risk forecasting. The Cox model can be converted to a weighted least squares form for the purpose of solution, where the regularization l0 compresses the signs of the variable coefficients and reduces the error caused by the compression of the coefficients. The graph structure reflects the correlations among different financial indicators and is incorporated into the model by introducing a Laplace penalty term to construct the Graph Regularization–Cox (GR-Cox) model. Monte Carlo simulation results show that the GR-Cox model outperforms the model without a graph structure with respect to the choice of parameters. Here, we apply the GR-Cox model to the forecast of the financial risk of listed companies and find that it shows good classification accuracy in practical applications. The GR-Cox model provides a new approach for improving the accuracy of financial risk early warning.

Forecasting seizure likelihood from cycles of self-reported events and heart rate: a prospective pilot study.

Seizure risk forecasting could reduce injuries and even deaths in people with epilepsy. There is great interest in using non-invasive wearable devices to generate forecasts of seizure risk. Forecasts based on cycles of epileptic activity, seizure times or heart rate have provided promising forecasting results. This study validates a forecasting method using multimodal cycles recorded from wearable devices. Seizure and heart rate cycles were extracted from 13 participants. The mean period of heart rate data from a smartwatch was 562 days, with a mean of 125 self-reported seizures from a smartphone app. The relationship between seizure onset time and phases of seizure and heart rate cycles was investigated. An additive regression model was used to project heart rate cycles. The results of forecasts using seizure cycles, heart rate cycles, and a combination of both were compared. Forecasting performance was evaluated in 6 of 13 participants in a prospective setting, using long-term data collected after algorithms were developed. The results showed that the best forecasts achieved a mean area under the receiver-operating characteristic curve (AUC) of 0.73 for 9/13 participants showing performance above chance during retrospective validation. Subject-specific forecasts evaluated with prospective data showed a mean AUC of 0.77 with 4/6 participants showing performance above chance. The results of this study demonstrate that cycles detected from multimodal data can be combined within a single, scalable seizure risk forecasting algorithm to provide robust performance. The presented forecasting method enabled seizure risk to be estimated for an arbitrary future period and could be generalised across a range of data types. In contrast to earlier work, the current study evaluated forecasts prospectively, in subjects blinded to their seizure risk outputs, representing a critical step towards clinical applications. This study was funded by an Australian Government National Health & Medical Research Council and BioMedTech Horizons grant. The study also received support from the Epilepsy Foundation of America's 'My Seizure Gauge' grant.

Forecasting of financial risks of decline in profit and profitability of companies in the retail industry on the basis of correlation and regression analysis of macroeconomic factors

Aim. Development and realization of the innovative approach to forecasting of financial risks of profit and profitability decrease of retail industry companies on the basis of correlation and regression analysis of macroeconomic factors.Tasks. To identify and substantiate the macroeconomic risk factors of decrease in the retail trade turnover of the Russian companies of the retail industry; to develop a methodology of construction and build regression dependence of the total turnover of the companies of the retail trade industry in the Russian Federation (RF) on the macroeconomic factors; to define parameters of the analytical dependence of the retail trade turnover of the leading Russian retailers on the macroeconomic factors; to reveal and argue the position concerning the restrictions of application of correlation and regression analysis.Methods. The authors applied a systematic approach, providing a comprehensive study of the impact of macroeconomic factors on financial risks of profit and profitability reduction, as well as the correlation and regression analysis, methods of mathematical statistics and econometrics.Results. The methodology of estimation and forecasting of financial risks of profit and profitability decrease of the retail industry companies under the influence of external macroeconomic risk factors based on the use of the regression dependence of the retail industry companies turnover on the selected and grounded macroeconomic factors built in the present article is offered.Conclusions. The methodology of building regression dependences of retail turnover on external macroeconomic factors and the methodology of assessing the impact of these factors on the financial risks of reduced profit and profitability indicators proposed in the study are a methodological basis for forecasting changes in the results and efficiency of economic activities of companies in the retail industry and developing preventive measures aimed at reducing the financial risks of such companies. The quantitative assessments of the impact of macroeconomic factors on the retail turnover of leading Russian retailers obtained in the work allow to identify the most significant external macroeconomic risk factors for each of the companies and give a reasonable forecast of changes in retail turnover, profit and profitability indicators of these companies under the influence of macroeconomic factors.

Loan Fraud Users Detection in Online Lending Leveraging Multiple Data Views

In recent years, online lending platforms have been becoming attractive for micro-financing and popular in financial industries. However, such online lending platforms face a high risk of failure due to the lack of expertise on borrowers' creditworthness. Thus, risk forecasting is important to avoid economic loss. Detecting loan fraud users in advance is at the heart of risk forecasting. The purpose of fraud user (borrower) detection is to predict whether one user will fail to make required payments in the future. Detecting fraud users depend on historical loan records. However, a large proportion of users lack such information, especially for new users. In this paper, we attempt to detect loan fraud users from cross domain heterogeneous data views, including user attributes, installed app lists, app installation behaviors, and app-in logs, which compensate for the lack of historical loan records. However, it is difficult to effectively fuse the multiple heterogeneous data views. Moreover, some samples miss one or even more data views, increasing the difficulty in fusion. To address the challenges, we propose a novel end-to-end deep multiview learning approach, which encodes heterogeneous data views into homogeneous ones, generates the missing views based on the learned relationship among all the views, and then fuses all the views together to a comprehensive view for identifying fraud users. Our model is evaluated on a real-world large-scale dataset consisting of 401,978 loan records of 228,117 users from January 1, 2019, to September 30, 2019, achieving the state-of-the-art performance.

Exploring Hypergraph of Earnings Call for Risk Prediction (Student Abstract)

In financial economics, studies have shown that the textual content in the earnings conference call transcript has predictive power for a firm's future risk. However, the conference call transcript is very long and contains diverse non-relevant content, which poses challenges for the text-based risk forecast. This study investigates the structural dependency within a conference call transcript by explicitly modeling the dialogue between managers and analysts. Specifically, we utilize TextRank to extract information and exploit the semantic correlation within a discussion using hypergraph learning. This novel design can improve the transcript representation performance and reduce the risk of forecast errors. Experimental results on a large-scale dataset show that our approach can significantly improve prediction performance compared to state-of-the-art text-based models.

When can local bird detection radars best complement broad‐scale early‐warning forecasts of risk potential for bird–aircraft strikes as part of an integrated approach to strike mitigation?

Worldwide, wildlife–aircraft strikes cost more than US$1.2 billion in aircraft damage and downtime and jeopardize the safety of aircrews, passengers, and animals. Radar has long been used to monitor flying animal movements and can be a useful tool for strike mitigation. In the USA, the Avian Hazard Advisory System (AHAS) is an early‐warning system that integrates data from next‐generation weather radar (NEXRAD) weather surveillance radars (WSRs) with historic bird occurrence data to quantify avian activity and forecast the relative bird risk within a ~9.3‐km radius of military and civilian airfields. Bird detection radars (BDRs) with both horizontal‐surveillance and vertical‐scanning components are also available for monitoring local avian activity at airports, but we have little information regarding the congruence of broad‐scale warnings and local avian activity where WSRs and BDRs overlap. We quantified trends in biological activity recorded at hourly intervals by a BDR at an airfield in Texas, USA, and in the most frequently assigned AHAS risk forecasts for that site during the same intervals. We then examined the strength of association between these datasets by season and time of day to determine when information from BDRs might best complement forecasts from the broad‐scale AHAS system. We found a strong overall association between the datasets but weak or moderate agreement during daylight periods, when most strikes occur. NEXRAD WSRs see only limited bird activity near the Earth's surface, where the majority of damaging strikes take place and, not surprisingly, AHAS warnings during our study were best predicted by the BDR at higher altitudes. Our results suggest BDRs might best complement early‐warning systems, like AHAS, as part of integrated strike mitigation plans at airfields with large numbers of hazardous birds flying at low altitudes during daylight hours, especially in late afternoon.

ЭКОЛОГИЯ: СПЯЩИЕ РИСКИ В ПРЕДПРИНИМАТЕЛЬСКОЙ ДЕЯТЕЛЬНОСТИ

The article considers the entrepreneurial risks associated with environmental safety and company management on the basis of environmental management. Particular attention is paid to the factors in the field of environmental safety and environmental management, which can dramatically affect the financial sustainability, development plans of SME companies in strategic planning and management, as well as investment decisions. The concepts of obvious and non-obvious risks for small and medium-sized businesses in the field of environmental safety, environmental management and sustainable development of the organization are given. Non-obvious risks of subsidiary responsibility for owners of SME companies associated with the lack of knowledge of environmental management were identified. The actions for SME companies focused on preventive forecasting and prevention of environmental risks in entrepreneurial activity were proposed.

Integration of IDPC Clustering Analysis and Interpretable Machine Learning for Survival Risk Prediction of Patients with ESCC.

Precise forecasting of survival risk plays a pivotal role in comprehending and predicting the prognosis of patients afflicted with esophageal squamous cell carcinoma (ESCC). The existing methods have the problems of insufficient fitting ability and poor interpretability. To address this issue, this work proposes a novel interpretable survival risk prediction method for ESCC patients based on extreme gradient boosting improved by whale optimization algorithm (WOA-XGBoost) and shapley additive explanations (SHAP). Given the imbalanced nature of the data set, the adaptive synthetic sampling (ADASYN) is first used to generate the samples with high survival risk. Then, an improved clustering by fast search and find of density peaks (IDPC) algorithm based on cosine distance and K nearest neighbors is used to cluster the patients. Next, the prediction model for each cluster is obtained by WOA-XGBoost and the constructed model is visualized with SHAP to uncover the factors hidden in the structured model and improve the interpretability of the black-box model. Finally, the effectiveness of the proposed scheme is demonstrated by analyzing the data collected from the First Affiliated Hospital of Zhengzhou University. The results of the analysis reveal that the proposed methodology exhibits superior performance, as indicated by the area under the receiver operating characteristic curve (AUROC) of 0.918 and accuracy of 0.881.

Temperature but Not Photoperiod Can Predict Development and Survival of an Invasive Apple Pest.

The apple leaf-curling midge, Dasineura mali Kieffer (Diptera: Cecidomyiidae), is an invasive pest of apple, and can contaminate fresh fruit for export, causing biosecurity problems. To provide crucial information for its pest risk analysis, forecast, and management, we investigated the effects of temperatures (5, 10, 15, 20 and 25 °C) and daylengths (10, 11, 12, 13, 14 and 15 h) on its development and survival. The midge eggs failed to hatch at 5 °C and larvae could not complete development at 10 °C. Pupation and emergence rates were significantly higher at 20 °C than at 15 °C and 25 °C. Daylength had no effect on these parameters. The low temperature threshold and thermal requirement to complete development from eggs to adults were 3.7 °C and 627 degree-days, respectively. The midge had a significantly lower thermal requirement for the completion of its lifecycle at 20 °C (614.5 degree-days) than at 15 °C (650.1 degree-days) and 25 °C (634.8 degree-days). The thermal model developed in this study provided accurate predictions of the number of D. mali generations and adult emergence time in each generation in different regions of New Zealand. We suggest that the model could be used to predict population dynamics of this pest in other parts of the world.

Backtesting Systemic Risk Forecasts Using Multi-Objective Elicitability

Systemic risk measures such as CoVaR, CoES, and MES are widely-used in finance, macroeconomics and by regulatory bodies. Despite their importance, we show that they fail to be elicitable and identifiable. This renders forecast comparison and validation, commonly summarized as “backtesting,” impossible. The novel notion of multi-objective elicitability solves this problem by relying on bivariate scores equipped with the lexicographic order. Based on this concept, we propose Diebold–Mariano type tests with suitable bivariate scores to compare systemic risk forecasts. We illustrate the test decisions by an easy-to-apply traffic-light approach. Finally, we apply our traffic-light approach to DAX 30 and S&P 500 returns, and infer some recommendations for regulators.

Spore dispersal patterns of the ascomycete fungus Ramularia collo-cygni and their influence on disease epidemics

Ramularia leaf spot is a major economic disease of barley caused by the dothidiomycete fungus Ramularia collo-cygni. The fungus has a complex life cycle which includes extensive late season spore release events and a seed-borne phase. Predicting disease epidemics during the growing season remains a difficult challenge. To better understand the interaction between spore movement and disease epidemics, spore samplers were set up in Germany (2 sites over 4 years), Poland (7 sites over 2 years) and the UK (2 sites over 9 years), where the disease has been observed. Spore concentrations were determined using a real-time PCR assay, and meteorological data were obtained from co-located automatic stations. Spore release events were seen to peak in June on mainland Europe and July in the UK. The pattern of spore release was broadly similar across countries with earlier peaks in mainland Europe. A relationship was observed in the UK between July spore levels and disease in following winter barley crops. Rainfall and temperature were proposed as significant drivers of spore release in these months. The major environmental parameter associated with spore release across the two UK sites was crop surface wetness, although some site-specific interactions were noted for rainfall and wind movement. Regression analysis of spore patterns and disease epidemics indicates a relationship between spore levels 75 and 105 days pre-harvest and final disease levels in UK winter barley crops. This relationship was not observed in spring barley. The implications on risk forecasts are discussed.

Regional and global climate risks for reef corals: Incorporating species-specific vulnerability and exposure to climate hazards.

Climate change is driving rapid and widespread erosion of the environmental conditions that formerly supported species persistence. Existing projections of climate change typically focus on forecasts of acute environmental anomalies and global extinction risks. The current projections also frequently consider all species within a broad taxonomic group together without differentiating species-specific patterns. Consequently, we still know little about the explicit dimensions of climate risk (i.e., species-specific vulnerability, exposure and hazard) that are vital for predicting future biodiversity responses (e.g., adaptation, migration) and developing management and conservation strategies. Here, we use reef corals as model organisms (n = 741 species) to project the extent of regional and global climate risks of marine organisms into the future. We characterise species-specific vulnerability based on the global geographic range and historical environmental conditions (1900-1994) of each coral species within their ranges, and quantify the projected exposure to climate hazard beyond the historical conditions as climate risk. We show that many coral species will experience a complete loss of pre-modern climate analogs at the regional scale and across their entire distributional ranges, and such exposure to hazardous conditions are predicted to pose substantial regional and global climate risks to reef corals. Although high-latitude regions may provide climate refugia for some tropical corals until the mid-21st century, they will not become a universal haven for all corals. Notably, high-latitude specialists and species with small geographic ranges remain particularly vulnerable as they tend to possess limited capacities to avoid climate risks (e.g., via adaptive and migratory responses). Predicted climate risks are amplified substantially under the SSP5-8.5 compared with the SSP1-2.6 scenario, highlighting the need for stringent emission controls. Our projections of both regional and global climate risks offer unique opportunities to facilitate climate action at spatial scales relevant to conservation and management.

Part-load operation risk assessment of hydropower units in hydro–wind–solar​ hybrid system considering hydraulic characteristics

Establishing hydro–wind–solar hybrid system (HWSHS) is essential to combat the fluctuant and uncertain variable renewable energy (VRE). In HWSHS, the main function of hydropower is gradually changing from electricity generation to equal emphasis on electricity generation and regulation. This means that hydro units are sometimes forced to operate in part-load regions to regulate and avoid power shortage. However, hydropower units operating in the part-load regions exist potential safety risks owing to the hydraulic characteristics (e.g., vibration and pressure fluctuation et al.). Here, an economic operation model of HWSHS considering precise head loss and shared pipeline is proposed. Then, a risk assessment framework of hydro units in HWSHS operating in the part-load regions is presented. Meanwhile, the uncertainty of VRE is also considered. Finally, the model and framework are applied to a case in the Jinsha River basin of China. The results show that (1) the risk framework could be assessed the safety risk of hydropower units for different scenarios during a period and even be applied to future risk forecasts. For example, the risk of hydropower units on May 3, 2019 is lower than the other two days; (2) the risk of hydropower units from May 1, 2019 to May 3, 2019 averagely increases to 3.693 h (duration), 15.389% (probability) and 63.721 MW (mileage) compared with the scenario without VRE; and (3) the main factors of hydropower units being forced to operate in part-load regions are the high proportion and the uncertainty of VRE. Thus, the study supplies support for assessing the risk of hydropower units in HWSHS to regulate VRE.

Risk source identification and diffusion trends of metal(loid)s in stream sediments from an abandoned arsenic-containing mine

Stream sediments from mine area are a converging source of water and soil pollution. The risk and development trends of metal(loid)s pollution in sediments from an abandoned arsenic-containing mine were studied using modelling techniques. The results showed that the combined techniques of geographic information system (GIS), random forest (RF), and numerical simulation (NS) could identify risk sources and diffusion trends of metal(loid)s in mine sediments. The median values of As, Cd, Hg, and Sb in sediments were 5.01, 3.02, 5.67, and 3.20 times of the background values of stream sediments in China, respectively. As (14.09%) and Hg (18.64%) pollution in mine stream sediments were severe while As is the main potential risk source with a strong spatial correlation. High-risk blocks were concentrated in the landfill area, with the surrounding pollution shows a decreasing trend of “step-type” pollution. The risk correlation between Hg and As (55.37%) in the landfill area is high. As a case of arsenic, the diffusion capacity of As within 500m is strong and stabilizes at 1 km when driven by the flows of 0.05, 0.5, and 5 m3/s, respectively. With the worst-case scenario flow (86 m3/s), it would take only 147 days for the waters within 3 km to become highly polluted. The high pollution levels in a stream under forecast of different distance intervals (500, 1500, 2000 m) within 6.5 km is arrived at approximate 344, 357, and 384 days, respectively. The study suggested the combined technique of GIS, RF, and NS can serve the risk source identification of contaminated sites and risk forecast of toxic element diffusion in emergency situations.

Detecting, Monitoring and Foreseeing Wildland Fire Requires Similar Multiscale Viewpoints as Meteorology and Climatology

Achieving sustainable coexistence with wildfires in the Anthropocene requires skilful integrated fire observations, fire behaviour predictions, forecasts of fire risk, and projections of change to fire climates. The diverse and multiscale approaches used by the atmospheric sciences, to understand geographic patterns, temporal trends and likely trajectories of weather and climate, provide a role model for how multiscale assessments of fire danger can be formulated and delivered to fire managers, emergency responders and at-risk communities. Adaptation to escalating risk of fire disasters requires specialised national agencies, like weather services, that provide to provide a diverse range of products to enable detection and near and longer-range prediction of landscape fire activity.

Climate risk and Chinese stock volatility forecasting: Evidence from ESG index

This study employs a generalised autoregressive conditional heteroscedasticity mixed data sampling model (GARCH-MIDAS) to explore the forecasting performance of Chinese climate uncertainty (CU), Chinese climate policy uncertainty (CEU), Chinese economic policy uncertainty (CEPU), and US climate policy uncertainty (UCU) in both CSI 300 ESG and SSEC index volatility forecasting. The empirical results indicate that CU and CEU can significantly drive CSI 300 ESG volatility and outperform CEU and UCU. This may be caused by investors paying more attention to climate risk with the advent of the ESG score.

Neglected tropical diseases risk correlates with poverty and early ecosystem destruction

BackgroundNeglected tropical diseases affect the most vulnerable populations and cause chronic and debilitating disorders. Socioeconomic vulnerability is a well-known and important determinant of neglected tropical diseases. For example, poverty and sanitation could influence parasite transmission. Nevertheless, the quantitative impact of socioeconomic conditions on disease transmission risk remains poorly explored.MethodsThis study investigated the role of socioeconomic variables in the predictive capacity of risk models of neglected tropical zoonoses using a decade of epidemiological data (2007–2018) from Brazil. Vector-borne diseases investigated in this study included dengue, malaria, Chagas disease, leishmaniasis, and Brazilian spotted fever, while directly-transmitted zoonotic diseases included schistosomiasis, leptospirosis, and hantaviruses. Environmental and socioeconomic predictors were combined with infectious disease data to build environmental and socioenvironmental sets of ecological niche models and their performances were compared.ResultsSocioeconomic variables were found to be as important as environmental variables in influencing the estimated likelihood of disease transmission across large spatial scales. The combination of socioeconomic and environmental variables improved overall model accuracy (or predictive power) by 10% on average (P < 0.01), reaching a maximum of 18% in the case of dengue fever. Gross domestic product was the most important socioeconomic variable (37% relative variable importance, all individual models exhibited P < 0.00), showing a decreasing relationship with disease indicating poverty as a major factor for disease transmission. Loss of natural vegetation cover between 2008 and 2018 was the most important environmental variable (42% relative variable importance, P < 0.05) among environmental models, exhibiting a decreasing relationship with disease probability, showing that these diseases are especially prevalent in areas where natural ecosystem destruction is on its initial stages and lower when ecosystem destruction is on more advanced stages.ConclusionsDestruction of natural ecosystems coupled with low income explain macro-scale neglected tropical and zoonotic disease probability in Brazil. Addition of socioeconomic variables improves transmission risk forecasts on tandem with environmental variables. Our results highlight that to efficiently address neglected tropical diseases, public health strategies must target both reduction of poverty and cessation of destruction of natural forests and savannas.

Less disagreement, better forecasts: Adjusted risk measures in the energy futures market

AbstractThis paper develops a generic adjustment framework to improve in the market risk forecasts of diverse risk forecasting models, which indicates the degree to which risk is under‐ and overestimated. In the context of the energy commodity market, a market in which tail risk management is of crucial importance, the empirical analysis shows that after this adjustment framework is applied, the forecasting performance of various risk models generally improves, as verified by a battery of backtesting methods. Additionally, our method also lessens the risk model disagreement among post‐adjusted risk forecasts.

A Novel DBN-EFA-CFA-Based Dimensional Reduation for Credit Risk Measurement

Affected by the Federal Reserve's interest rate hike and the downward pressure on the domestic economy, the phenomenon of default is still prominent. The credit risk of the listed companies has become a growing concern of the community. In this paper we present a novel credit risk measurement method based on a dimensional reduation technique. The method first extracts the risk measure indexes from the basal financial data via dimensional reduation by using deep belief network (DBN), exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) in turn. And then the credit risk is measured by a systemic structural equation model (SEM) and logistic distribution. To validate the proposed method, we employ the financial data of the listed companies from Q1 2019 to Q2 2022. The empirical results show its effectiveness on statistical evaluation, assessment on testing samples and credit risk forecasting.

Deep learning based depression detection from social media text

Depression perception be a complex task on social media Complex properties associated with mental illness. There was a recent development in this area of research; social media platforms have established themselves as growing popularity. A basic part of people`s daily life. Social media platforms and their users share goals Relationships where the user's personal life is reflected on these platforms at several levels. Aside from the complexity associated with detecting mental illness through social media platforms, it is inherently difficult to obtain an enough annotated training data, so a supervised deep learning approach such as deep neural networks the implementation has not yet been widely implemented. We tried to find them for these reasons. The most effective deep learning model of the architectures selected in the previous architecture Achievements of supervised learning methods. The selected model will be used for recognition online users showing depression. Due to the limited amount of unstructured text data Extracted from social media text. Recently, Deep learning has been effectively used to a variety of application challenges, including stock market forecasting, traffic flow and accident risk forecasting, and mental disease diagnosis. Furthermore, deep learning has been used to predict sadness on social media and has outperformed classical machine learning method.