This study investigates the impacts of behavioral finance on stock market volatility. The primary aims are to explain the reasons behind changes in the S&P 500 price within the context of behavioral finance and to analyze investor behavior in response to these changes. To achieve this, the research employs time-series analysis over a 10-year period, focusing on the S&P 500, real interest rates, consumer confidence, market volatility and credit default swaps while considering the effects of behavioral biases. The findings reveal several significant correlations: rising real interest rates negatively affect stocks due to loss aversion and sentiment. Conversely, higher consumer confidence tends to positively influence the stock market, driven by herding behavior and optimism. Additionally, market volatility shows a negative correlation with the S&P 500, influenced by risk aversion, recency bias and herding behavior. Moreover, an increase in credit default swap rates leads to stock market declines, primarily influenced by risk perception, loss aversion and herding behavior.

Prior research based on Swedish data suggests that collective optimism, as measured by monthly incidence of suicides, correlates inversely with selection in utero against male twins in a population. We test this finding in the US, which reports the highest suicide rate of all high-income countries, and examine whether monthly changes in overall suicides precede changes in the ratio of male twin to male singleton live births. Consistent with prior work, we also examine as a key independent variable, suicides among women aged 15−49 years. We retrieved monthly data on suicides and the ratio of male twin to singleton live births from CDC WONDER, 2003 to 2019, and applied Box-Jenkins iterative time-series routines to detect and remove autocorrelation from both series. Results indicate that a 1% increase in monthly change in overall suicides precedes a 0.005 unit decline in male twin live births ratio 6 months later (coefficient = −.005, p value = .004). Results remain robust to use of suicides among reproductive-aged women as the independent variable (coefficient = −.0012, p value = .014). Our study lends external validity to prior research and supports the notion that a decline in collective optimism corresponds with greater selection in utero.

The aim of the present article is to evaluate the use of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model in predicting spatially and temporally localized political violent events using the Integrated Crisis Early Warning System (ICEWS). The performance of the ARFIMA model is compared to that of a naïve model in reference to two common relevant hypotheses: the ARFIMA model would outperform a naïve model and the rate of outperformance would deteriorate the higher the level of spatial aggregation. This analytical strategy is used to predict political violent events in Afghanistan. The analysis consists of three parts. The first is a replication of Yonamine’s study for the period beginning in April 2010 and ending in March 2012. The second part compares the results to those of Yonamine. The comparison was used to assess the validity of the conclusions drawn in the original study, which was based on the Global Database of Events, Language, and Tone, for the implementation of this approach to ICEWS data. Building on the conclusions of this comparison, the third part uses Yonamine’s approach to predict violent events in Afghanistan over a significantly longer period of time (January 1995–August 2021). The conclusions provide an assessment of the utility of short-term localized forecasting.

A large literature examines how citizens in violent conflicts react to the conflict's events, particularly violent escalations. Nevertheless, the temporal nature of these attitudinal changes remains under-studied. We suggest that popular reactions to greater violence are typically immediate but brief, indicating short-term emotional responses to physical threats. Over the longer term, however, public opinion is more commonly shaped by non-violent events signaling the adversary's perceived intentions, reflecting slower but deeper belief-updating processes. We support this argument using dynamic analyses of comprehensive monthly data from Israel spanning two full decades (2001–20). Rather than violence levels, we find that long-term changes in Jewish attitudes on the Israeli–Palestinian conflict follow non-violent events implying Palestinian preferences, particularly failed negotiations and out-group leadership changes. Our findings underscore the importance of public opinion's temporal dynamics and show that non-violent events, which are often overlooked in the literature, play a prominent role in shaping long-term attitudes in conflictual contexts.

In this article, we argue that rising housing prices increase voter approval of incumbent governments because such a rise increases personal wealth, which leads to greater voter satisfaction. This effect is strongest under right-wing governments because those who benefit from rising prices—homeowners—are more likely to be right-leaning. Non-homeowners, who are more likely to vote for left-leaning parties, will view rising housing prices as a disadvantage and therefore feel the government does not serve them well, which will mitigate the advantage to left-wing governments. We find support for our arguments using both macro-level data (housing prices and government approval ratings in 16 industrialized countries between 1960 and 2017) and micro-level data (housing prices and individuals’ vote choices in the United Kingdom using the British Household Panel Survey). The findings imply that housing booms benefit incumbent governments generally and right-wing ones in particular.

The Lung Cam expanded stratigraphic succession in Vietnam is correlated herein to the Meishan D section in China, the GSSP for the Permian–Triassic boundary. The first appearance datum of the conodont Hindeodus parvus at Meishan defines the Permian–Triassic boundary, and using published graphic correlation, the Permian–Triassic boundary level has been projected into the Lung Cam section. Using time-series analysis of magnetic susceptibility (χ) data, it is determined that H. parvus arrived at Lung Cam ∼18 kyr before the Permian–Triassic boundary. Data indicate that the Lung Cam section is expanded by ∼90 % relative to the GSSP section at Meishan. Given the expanded Lung Cam section, it is possible to resolve the timing of significant events during the Permian–Triassic transition with high precision. These events include major stepped extinctions, beginning at ∼135 kyr and ending at ∼110 kyr below the Permian–Triassic boundary, with a duration of ∼25 kyr, followed by deposition of Lung Cam ash Bed + 13, which is equivalent to Siberian Traps volcanism is graphically correlated to a precession Time-series model, placing onset of this major volcanic event at ~242 kyr before the PTB. The Meishan Beds 25 and 26, at ∼100 kyr before the Permian–Triassic boundary. In addition, the elemental geochemical, carbon and oxygen isotope stratigraphy, and magnetostratigraphy susceptibility datasets from Lung Cam allow good correlation to other Permian–Triassic boundary succession. These datasets are helpful when the conodont biostratigraphy is poorly known in sections with problems such as lithofacies variability, or is undefined, owing possibly to lithofacies exclusions, anoxia or for other reasons. The Lung Pu Permian–Triassic boundary section, ∼45 km from Lung Cam, is used to test these problems.

A new data analysis toolkit which is suitable for the analysis of large-scale, long-term datasets and the phenomenon/anomalies they represent is described. The toolkit aims to expose and quantify scientific information in a number of forms contained within a time-series based dataset in a quantitative and rigorous manner, reducing the subjectivity of observations made, thereby supporting the scientific observer. The features contained within the toolkit include the ability to handle non-uniform datasets, time-series component determination, frequency component determination, feature/event detection and characterization/parameterization of local behaviours. An application is presented of a case study dataset arising from the 'Lasgit' experiment.

Dengue fever (DF) and leptospirosis are serious public problems in tropical regions, especially in Manila, the Philippines. In attempting to understand the causes of DF and leptospirosis seasonality, meteorological factors have been suspected, but quantitative correlation between seasonality and meteorological factors has not been fully investigated. In this study, we investigated correlation of temporal patterns of reported numbers of laboratory-confirmed cases of both DF and leptospirosis with meteorological conditions (temperature, relative humidity, rainfall) in Manila. We used time-series analysis combined with spectral analysis and the least squares method. A 1-year cycle explained underlying variations of DF, leptospirosis and meteorological data. There was a peak of the 1-year cycle in temperature during May, followed by maxima in rainfall, relative humidity and number of laboratory-confirmed DF and leptospirosis cases. This result suggests that DF and leptospirosis epidemics are correlated not only with rainfall but also relative humidity and temperature in the Philippines. Quantifying the correlation of DF and leptospirosis infections with meteorological conditions may prove useful in predicting DF and leptospirosis epidemics, and health services should plan accordingly.

We investigated the seasonality of age-specific tuberculosis (TB) in Japan. To allow the development of TB control strategies for different age groups we used a time-series analysis, including a spectral analysis and least squares method, to analyse the monthly age-specific numbers of newly registered cases of all forms of active TB in Japan from January 1998 to December 2013. The time-series data are reported in 10-year age groups: 0–9, 10–19, …, 70–79, and ⩾80 years. We defined the contribution ratio of the 1-year cycle, Q1, as the contribution of the amplitude of a 1-year cycle to the whole amplitude of the time-series data. The Q1 values in the age groups corresponding to adolescence and middle life (10–39 years) and old age (⩾70 years) were high. The peaks in the active TB epidemics for the ⩾70 years age group occurred in August and September, 1–2 months behind the peaks for the 10–39 years age group (June and July). An active TB epidemic might be attributable to travel by public transport and irregular employment in the 10–39 years age group and immune system suppression by low winter temperatures in the ⩾70 years age group.

Annual periodicities of reported chickenpox cases have been observed in several countries. Of these, Japan has reported a two-peaked, bimodal annual cycle of reported chickenpox cases. This study investigated the possible underlying association of the bimodal cycle observed in the surveillance data of reported chickenpox cases with the meteorological factors of temperature, relative humidity and rainfall. A time-series analysis consisting of the maximum entropy method spectral analysis and the least squares method was applied to the chickenpox data and meteorological data of 47 prefectures in Japan. In all of the power spectral densities for the 47 prefectures, the spectral lines were observed at the frequency positions corresponding to the 1-year and 6-month cycles. The optimum least squares fitting (LSF) curves calculated with the 1-year and 6-month cycles explained the underlying variation of the chickenpox data. The LSF curves reproduced the bimodal and unimodal cycles that were clearly observed in northern and southern Japan, respectively. The data suggest that the second peaks in the bimodal cycles in the reported chickenpox cases in Japan occurred at a temperature of approximately 8·5 °C.

Campylobacteriosis has been the most common food-associated notifiable infectious disease in Switzerland since 1995. Contact with and ingestion of raw or undercooked broilers are considered the dominant risk factors for infection. In this study, we investigated the temporal relationship between the disease incidence in humans and the prevalence of Campylobacter in broilers in Switzerland from 2008 to 2012. We use a time-series approach to describe the pattern of the disease by incorporating seasonal effects and autocorrelation. The analysis shows that prevalence of Campylobacter in broilers, with a 2-week lag, has a significant impact on disease incidence in humans. Therefore Campylobacter cases in humans can be partly explained by contagion through broiler meat. We also found a strong autoregressive effect in human illness, and a significant increase of illness during Christmas and New Year's holidays. In a final analysis, we corrected for the sampling error of prevalence in broilers and the results gave similar conclusions.

We investigated the seasonality of tuberculosis (TB) in Wuhan, China, to evaluate the increased risk of disease transmission during each season and to develop an effective TB control strategy. We applied spectral analysis to the weekly prevalence data of sputum smear positive (SSP) and sputum smear negative (SSN) pulmonary TB reported from 2006 to 2010. Cases of both SSP and SSN feature 1·0- and 0·5-year periodic modes. The least squares method was used to fit curves to the two periodic modes for SSP and SSN data. The curves demonstrated dominant peaks in spring similar to cases reported previously for other locations. Notably for SSP, dominant peaks were also observed in summer. The spring peaks of SSP and SSN were explained in terms of poorly ventilated and humid rooms and vitamin D deficiency. For the summer peaks of SSP, summer influenza epidemics in Wuhan may contribute to the increase in TB prevalence.

Animal disease surveillance is watching an animal population closely to determine if a specific disease or a group of diseases makes an incursion so that a prior plan of action can be implemented. The purpose of this paper is to review existing tools and techniques for an animal disease-surveillance system that can incorporate the monitoring of climate factors and related data to enhance understanding of disease epidemiology. In recent decades, there has been interest in building information systems by combining various data sources for different purposes. Within the field of animal health, there have only been limited attempts at the integration of surveillance data with relevant climate conditions. Statistical techniques for data integration, however, have been explored and used by several disciplines. Clearly the application of available techniques for linking climate data with surveillance systems should be explored with the aim of facilitating prevention, mitigation, and adaptation responses in the surveillance setting around climate change and animal disease risks. Drawing on this wider body of work, three of the available techniques that can be utilized in the analysis of surveillance data with the available climate data sets are reviewed.

Many infectious diseases exhibit repetitive or regular behaviour over time. Time-domain approaches, such as the seasonal autoregressive integrated moving average model, are often utilized to examine the cyclical behaviour of such diseases. The limitations for time-domain approaches include over-differencing and over-fitting; furthermore, the use of these approaches is inappropriate when the assumption of linearity may not hold. In this study, we implemented a simple and efficient procedure based on the fast Fourier transformation (FFT) approach to evaluate the epidemic dynamic of scarlet fever incidence (2004–2010) in China. This method demonstrated good internal and external validities and overcame some shortcomings of time-domain approaches. The procedure also elucidated the cycling behaviour in terms of environmental factors. We concluded that, under appropriate circumstances of data structure, spectral analysis based on the FFT approach may be applicable for the study of oscillating diseases.

The objective of this study was to evaluate the effect of age-adjusted comorbidity and alcohol-based hand rub on monthly hospital antibiotic usage, retrospectively. A multivariate autoregressive integrated moving average (ARIMA) model was built to relate the monthly use of all antibiotics grouped together with age-adjusted comorbidity and alcohol-based hand rub over a 5-year period (April 2005–March 2010). The results showed that monthly antibiotic use was positively related to the age-adjusted comorbidity index (concomitant effect, coefficient 1·103, P = 0·0002), and negatively related to the use of alcohol-based hand rub (2-month delay, coefficient −0·069, P = 0·0533). Alcohol-based hand rub is considered a modifiable factor and as such can be identified as a target for quality improvement programmes. Time-series analysis may provide a suitable methodology for identifying possible predictive variables that explain antibiotic use in healthcare settings. Future research should examine the relationship between infection control practices and antibiotic use, identify other infection control predictive factors for hospital antibiotic use, and evaluate the impact of enhancing different infection control practices on antibiotic use in a healthcare setting.

Hong Kong has a subtropical climate and an influenza seasonality lying approximately mid-way (March–June) between those of the Northern (November–March) and Southern (June–September) hemispheres. Respiratory syncytial virus (RSV) shares a similar seasonality to that of influenza in Hong Kong and is another important respiratory infection of childhood. Daily virus incidence data from public hospitals in Hong Kong's New Territory East Cluster, together with Hong Kong climate data were obtained for 2000–2007. Statistical time-series analysis using monthly time windows showed that influenza A and RSV incidence increased with higher environmental relative humidity, whereas influenza B incidence decreased with higher environmental temperatures. The other climate variables (including vapour pressure as a measure of absolute humidity) were not significantly related to the incidence of these respiratory viruses. Data from this study further reinforces the concept that the relationship between climate factors and respiratory virus incidence differ between subtropical/tropical and temperate countries.

Using time-series analysis, we investigated the monthly cholera incidence in Dhaka, Bangladesh during an 18-year period for its relationship to the sea surface temperature (SST) linked to El Niño, and to the sunspot number. Dominant periodic modes identified for cholera incidence were 11·0, 4·8, 3·5, 2·9, 1·6, 1·0 and 0·5 years. The majority of these modes, e.g. the 11·0-, 4·8-, 3·5-, 1·6- and 1·0-year modes, were essentially consistent with those obtained for the SST data (dominant modes: 5·1, 3·7, 2·5, 2·1, 1·5, 1·0 years) and the sunspot number data (dominant modes: 22·1, 11·1, 7·3, 4·8, 3·1 years). We confirmed that the variations of cholera incidence were synchronous with SSTs, and were inversely correlated to the sunspot numbers. These results suggest that the cholera incidence in Bangladesh may have been influenced by the occurrence of El Niño and also by the periodic change of solar activity.