Stock market fluctuation and stroke incidence: A time series study in Eastern China

Li, Fudong; Zhong, Jieming; He, Fan; Wang, Hao; Lin, Junfen; Yu, Min

doi:10.1016/j.socscimed.2022.114757

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…The case-period was defined as the time (i.e., the lag hours mentioned thereafter) before the symptom onset, and the control-periods (3 or 4 per case) were identified using the time-stratified approach by matching the same hours of the same day of the week within the same calendar month and year. 20,21 For example, if the first ACS symptom occurred at 10 AM on Tuesday, May 18, 2021, we would define 10 AM on Tuesday, May 18, 2021 as the case index hour and 10 AM on all other Tuesdays in May 2021 (May 4, 11, and 25) as the control index hours.…”

Section: Methodsmentioning

confidence: 99%

Stock volatility may trigger the onset of acute coronary syndrome: A nationwide case-crossover analysis

Zhu,

Chen,

Liu

et al. 2023

TIME

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<p>Stock volatility, an adverse psychological stressor, has been linked to cardiovascular mortality or hospitalization. However, no studies had quantified the risk of acute coronary syndrome (ACS) onset from stock market fluctuation. Using the time-stratified case-crossover design, which could control for the potential confounding from time-invariant, we conducted the analysis based on a nationwide registry comprising of 2,113,728 ACS patients in China from January, 2015 to December, 2021. We obtained daily returns (%) of three main stock indexes in Chinese Mainland, and matched the case and control days of ACS onset on hourly level. The data were analyzed using conditional logistic regression models. The associations between daily stock returns and ACS onset were U-shaped with stock rise and fall both increasing the risk of ACS onset. The excess risks associated with each 1%-decrease of daily stock returns for Shanghai, Shenzhen and ChiNext index were 0.96% (95%CI: 0.40,1.51), 0.65% (95%CI: 0.27,1.03) and 0.87% (95%CI: 0.52,1.21) at the concurrent day, respectively; and a 1%-increase of daily stock returns was associated with 1.06% (95%CI: 0.61,1.52), 0.45% (95%CI: 0.11,0.80) and 0.17% (95%CI: -0.08,0.41) increases of ACS onset, respectively. Thereafter, most of the associations gradually attenuated, disappeared after 3 days. The associations were significant for unstable angina and ST-segment-elevation myocardial infarction, but not for non-ST-segment-elevation myocardial infarction. This nationwide study provides novel and robust evidence that both stock rise and fall could increase the risk of ACS onset, highlighting the importance of heart care and psychological support during fluctuations of stock market.</p>

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Section: Methodsmentioning

confidence: 99%

Stock volatility may trigger the onset of acute coronary syndrome: A nationwide case-crossover analysis

Zhu,

Chen,

Liu

et al. 2023

TIME

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“…Stroke and spinal cord injury are the two leading causes of DCNS, often leading to hemiplegia or paraplegia. Stroke is a clinical manifestation of acute cerebrovascular disease characterized by high morbidity and disability 2 . Hemi-plegia can significantly reduce an individual's mobility, usually resulting in altered metabolic function and body composition 345 .…”

Section: Introductionmentioning

confidence: 99%

Modeling and Control of Functional Electrical Stimulation Cycling Training System

Sun,

Cheng,

Wang

et al. 2024

Preprint

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As a rehabilitation technique, functional electrical stimulation cycling training system can improve patients' cardiopulmonary function, enhance muscle strength, and restore some motor function. However, for functional electrical stimulators, the pattern of electrical stimulation in cycling training is usually obtained by physiotherapist based on trial and error or clinical experience, Inability to maintain a training speed for an extended period of time. Therefore, this study proposed a kind of pedal-Hill modeling to establish an optimal stimulus mode with the maximum torque efficiency optimization objective. A particle swarm optimization (PSO) algorithm, back propagation (BP) neural network algorithm and a composite algorithm of proportional integral differentiation (PID) control were used to construct an adaptive controller. We conducted experiments on six subjects to validate the system. First, the six subjects' own stimulation patterns were found separately according to the proposed modeling approach, and then each subject performed three sets of experiments for cycling without electrical stimulation, cycling with fixed pulse width, and cycling with adaptive adjustment of pulse width by the fabricated controller. In the traditional training mode, the cycling speed of the six subjects fluctuated greatly, in the FES-engaged mode, six subjects experienced longer sustained riding and more stable cycling training speeds. RMSE: fixed pulse width < adaptive adjustment of pulse width, AVE: traditional training mode > fixed pulse width >28 adaptive adjustment of pulse width, training time: traditional training mode < fixed pulse width < adaptive adjustment29 of pulse width, number of stops: traditional training mode > fixed pulse width > adaptive adjustment of pulse width. The results of the study showed that the adaptive pulse width control system of the fabricated controller allows subjects to train at a more stable speed compared to the no control and fixed control conditions.

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“…Many financial companies have made great efforts to develop information technology, hoping that the analysis and mining of historical data can obtain hidden patterns to assist market forecasts and financial investment portfolio. At the same time, the prediction of stock market trend is also considered as a challenging direction in the study of financial time series [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Research on trend prediction of component stock in fuzzy time series based on deep forest

Yin

et al. 2022

CAAI Trans on Intel Tech

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With the continuous development of machine learning and the increasing complexity of financial data analysis, it is more popular to use models in the field of machine learning to solve the hot and difficult problems in the financial industry. To improve the effectiveness of stock trend prediction and solve the problems in time series data processing, this paper combines the fuzzy affiliation function with stock‐related technical indicators to obtain nominal data that can widely reflect the constituent stocks in the case of time series changes by analysing the S&P 500 index. Meanwhile, in order to optimise the current machine learning algorithm in which the setting and adjustment of hyperparameters rely too much on empirical knowledge, this paper combines the deep forest model to train the stock data separately. The experimental results show that (1) the accuracy of the extreme random forest and the accuracy of the multi‐grain cascade forest are both higher than that of the gated recurrent unit (GRU) model when the un‐fuzzy index‐adjusted dataset is used as features for input, (2) the accuracy of the extreme random forest and the accuracy of the multigranular cascade forest are improved by using the fuzzy index‐adjusted dataset as features for input, (3) the accuracy of the fuzzy index‐adjusted dataset as features for inputting the extreme random forest is improved by 18.89% compared to that of the un‐fuzzy index‐adjusted dataset as features for inputting the extreme random forest and (4) the average accuracy of the fuzzy index‐adjusted dataset as features for inputting multi‐grain cascade forest increased by 5.67%.

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Stock market fluctuation and stroke incidence: A time series study in Eastern China

Cited by 4 publications

References 28 publications

Stock volatility may trigger the onset of acute coronary syndrome: A nationwide case-crossover analysis

Stock volatility may trigger the onset of acute coronary syndrome: A nationwide case-crossover analysis

Modeling and Control of Functional Electrical Stimulation Cycling Training System

Research on trend prediction of component stock in fuzzy time series based on deep forest

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