Short-term Correlation Research Articles

A multi-factor integrated model for carbon price forecasting: Market interaction promoting carbon emission reduction

Reasonable carbon price can effectively promote the low-carbon transformation of economy. The future carbon price has an important guiding significance for enterprises and the country. However, the nonlinear and high noise characteristics inherent in carbon price make them challenging to predict accurately. A hybrid decomposition and integration prediction model is proposed using the Hodrick-Prescott filter, an improved grey model and an extreme learning machine to solve this problem. First, a large number of factors that influence carbon price are collected by meta-analysis. The final input is selected through a two-stage feature selection process. Second, the HP filter is used to decompose the input into long-term trends and short-term fluctuations predicted by the improved GM and ELM, respectively. Finally, the two prediction sequences are compared to obtain the final result. European Union Allowances futures price data are applied for empirical analysis. The results show that the prediction performance of this model is better than the other 10 benchmark models, the T-bill, Stoxx50, S&P clean energy index and Brent oil price in the financial and energy markets are helpful in the carbon price's prediction. T-bill affects carbon price frequently, Stoxx50 has a negative correlation with the carbon price in the influence period. Under normal circumstances, the S&P clean energy index is positively correlated with the carbon price. However, when the economic situation is depressed, resulting in a short-term negative correlation between them. In general, carbon market is significantly affected by cross spill over between different markets. The method not only improves the accuracy of carbon price forecast, but also the application of the improved GM explains the reasons for the change of carbon price, which is helpful to promote the realization of carbon neutralization by market-oriented means.

Discourses of Inverted Yield Curve and the Reaction on US Stock Markets

An outlook model to predict future economic growth is crucially important for government, financial institution, corporate, investor, as well as household. An inverted yield curve has been trusted for long time as a prediction for economic recession. But current development showed that there is change in IYC pattern that might be because of the new normal of market condition, anxious investor, or something else, hence it created the discourse among market stakeholder whether it remain a reliable prediction model or not. The research will focus on discourse among important economic stakeholders in US market and how they might impact on stock market rationally or irrationally using discourses network analysis, graph analysis, and t-Test analysis. The result confirmed that there is a different pattern on IYC, also there is a short-term correlation between IYC and stock price movement, confirmed the information theory.

Correlation between intraocular pressure reduction and anterior chamber aqueous flare after micropulse transscleral cyclophotocoagulation

BackgroundMicropulse transscleral cyclophotocoagulation (MP-CPC) is a technique that has been approved in recent years to treat glaucoma. MP-CPC causes anterior chamber inflammation; a relationship with reduced intraocular pressure (IOP) has not been reported. Therefore, we analyzed the correlation between IOP and anterior chamber aqueous flare after MP-CPC.MethodsThis retrospective study included 37 eyes of 37 patients who underwent MP-CPC between November 2018 and October 2020. IOP and flare values were measured at 1, 4, and 12 weeks after MP-CPC. Correlations were assessed between the percentage IOP reduction and flare elevation by calculating Spearman’s rank correlation coefficient.ResultsThe percentage IOP reduction at 1 week after surgery was correlated with the flare elevation at 1 week after surgery (ρ = 0.47, P = 0.006). The percentage IOP reduction at 12 weeks after surgery was correlated with the flare elevation at 4 weeks after surgery (ρ = 0.53, P = 0.006).ConclusionsA short-term correlation was implied between reduced IOP and flare elevation after MP-CPC.

An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

With large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

Repeated training of homing pigeons reveals age-dependent idiosyncrasy and visual landmark use

Recent research into the navigational strategies of homing pigeons, Columba livia , in the familiar area has highlighted the phenomenon of route fidelity: birds forming idiosyncratic flight paths to which they are loyal over multiple releases from the same site, and even returning to this path when released from a nearby unfamiliar location. Such results highlight the potential importance of visual landmark cues in the homing process. However, not all birds have been found to produce idiosyncratic routes or show this route-joining behaviour. Here we used birds with and without flight experience to study the formation of idiosyncratic routes when released repeatedly from a single location, followed by two off-route releases with differing topography to see how flight experience and local landmark features can influence navigational strategy in the familiar area. We found that, over the course of 20 sequential releases, birds with greater flight experience tended to form idiosyncratic routes whereas less experienced birds did not show this tendency. When released from nearby sites (from which the birds had not previously been released), a range of navigational strategies were seen, including flying parallel to the learned route (suggestive of a learned compass direction), a direct flight path towards home (again indicative of compass use), rejoining the learned route and following the coastline. These latter strategies are suggestive of landmark usage. Analysis using time lag embedding was also used to assess the off-route releases, and the short-term correlation dimension values produced were also indicative of strategies using one or two factors (landmarks, compass or a combination of these two). Individual birds often showed different strategies at different sites, suggesting that the use of different navigational cues is highly flexible and situationally dependent. • Birds with greater flight experience form idiosyncratic routes more readily. • Multiple homing strategies occur when released close to a familiar site. • Age and site-specific biases occurred in which strategies were used. • Flexibility within individuals was seen in terms of strategy choice and cue use.

MODELING AND DEVELOPMENT INDICATORS OF AGRICULTURAL ENTERPRISES

The article developed modeling and forecasting based on short-term multidimensional forecasting, correlation and regression analysis of gross output of farms specializing in agriculture, using the example of Agro-Boysun farm.

The Influence of Air Pollutants and Meteorological Conditions on the Hospitalization for Respiratory Diseases in Shenzhen City, China.

Air pollutants have significant direct and indirect adverse effects on public health. To explore the relationship between air pollutants and meteorological conditions on the hospitalization for respiratory diseases, we collected a whole year of daily major air pollutants’ concentrations from Shenzhen city in 2013, including Particulate Matter (PM10, PM2.5), Nitrogen dioxide (NO2), Ozone (O3), Sulphur dioxide (SO2), and Carbon monoxide (CO). Meanwhile, we also gained meteorological data. This study collected 109,927 patients cases with diseases of the respiratory system from 98 hospitals. We investigated the influence of meteorological factors on air pollution by Spearman correlation analysis. Then, we tested the short-term correlation between significant air pollutants and respiratory diseases’ hospitalization by Distributed Lag Non-linear Model (DLNM). There was a significant negative correlation between the north wind and NO2 and a significant negative correlation between the south wind and six pollutants. Except for CO, other air pollutants were significantly correlated with the number of hospitalized patients during the lag period. Most of the pollutants reached maximum Relative Risk (RR) with a lag of five days. When the time lag was five days, the annual average of PM10, PM2.5, SO2, NO2, and O3 increased by 10%, and the risk of hospitalization for the respiratory system increased by 0.29%, 0.23%, 0.22%, 0.25%, and 0.22%, respectively. All the pollutants except CO impact the respiratory system’s hospitalization in a short period, and PM10 has the most significant impact. The results are helpful for pollution control from a public health perspective.

Sensor drift fault diagnosis for chiller system using deep recurrent canonical correlation analysis and k-nearest neighbor classifier

Early detection and diagnosis of the chiller sensor drift fault are crucial to maintain normal operation for energy saving. Due to the complex physical structure and operation conditions, sensor drift fault in the chiller system is difficult to discover. To improve the energy efficiency and operation reliability of the chiller system, this paper proposes a novel chiller sensor drift fault diagnosis method using deep recurrent canonical correlation analysis and k-nearest neighbor (KNN) classifier. A deep bidirectional long short-term memory recurrent neural network-based deep recurrent canonical correlation analysis (BLCCA) model is developed, which can automatically extract the nonlinear and temporal features from raw operation data in the chiller system. Based on the proposed BLCCA model, a residual generator is designed to generate the directional residual vector. The cumulative residual vector method is employed to improve the detectability of the sensor drift fault. An efficient KNN-based method is applied to classify the residual vector and judge the faulty sensor. Different distance measures and neighbor numbers are further analyzed to optimize the fault diagnosis performance. The proposed fault detection and diagnosis (FDD) method is validated by using a data set which has been collected from an actual chiller system. Three different state-of-the-art fault diagnosis methods are used for comparison with the proposed method. The comparisons of the experimental results demonstrate that this method achieves significant fault diagnosis performance in terms of diagnosis accuracy, recall, and F measure (F1 score).

Systemic lupus Erythematosus and geomagnetic disturbances: a time series analysis

BackgroundTo examine the influence of solar cycle and geomagnetic effects on SLE disease activity.MethodsThe data used for the analysis consisted of 327 observations of 27-day Physician Global Assessment (PGA) averages from January 1996 to February 2020. The considered geomagnetic indices were the AP index (a daily average level for geomagnetic activity), sunspot number index R (measure of the area of solar surface covered by spots), the F10.7 index (measure of the noise level generated by the sun at a wavelength of 10.7 cm at the earth’s orbit), the AU index (upper auroral electrojet index), and high energy (> 60 Mev) proton flux events. Geomagnetic data were obtained from the Goddard Space Flight Center Space Physics Data Facility. A time series decomposition of the PGA averages was performed as the first step. The linear relationships between the PGA and the geomagnetic indices were examined using parametric statistical methods such as Pearson correlation and linear regression, while the nonlinear relationships were examined using nonparametric statistical methods such as Spearman’s rho and Kernel regression.ResultsAfter time series deconstruction of PGA averages, the seasonality explained a significant fraction of the variance of the time series (R2 = 38.7%) with one cycle completed every 16 years. The analysis of the short-term (27-day) relationships indicated that increases in geomagnetic activity Ap index (p < 0.1) and high energy proton fluxes (> 60 Mev) (p < 0.05) were associated with decreases in SLE disease activity, while increases in the sunspot number index R anticipated decreases in the SLE disease activity expressed as PGA (p < 0.05). The short-term correlations became statistically insignificant after adjusting for multiple comparisons using Bonferroni correction. The analysis of the long-term (297 day) relationships indicated stronger negative association between changes in the PGA and changes in the sunspot number index R (p < 0.01), AP index (p < 0.01), and the F10.7 index (p < 0.01). The long-term correlations remained statistically significant after adjusting for multiple comparisons using Bonferroni correction.ConclusionThe seasonality of the PGA averages (one cycle every 16 years) explains a significant fraction of the variance of the time series. Geomagnetic disturbances, including the level of geomagnetic activity, sunspot numbers, and high proton flux events may influence SLE disease activity. Studies of other geographic locales are needed to validate these findings.

PUBLIC DEBT AND ECONOMIC GROWTH IN EURO AREA COUNTRIES. A WAVELET APPROACH

In this paper we propose to analyze the dynamic of the relation between public debt and economic growth rate for Euro area countries by employing a wavelet approach, establishing thus both short-term and long-term correlations between these two variables. In this way we will present time-frequency dependencies between debt and economic growth and differentiate between short term and long-term effects. High levels of public debt have a negative impact on the economic output, because they entail concerns about debt sustainability. Non-linear analysis of the debt-growth nexus shows the existence of thresholds from which rising indebtedness can hamper economic growth. Using wavelet analysis, we demonstrate that there is a strong relation between public debt and economic growth, especially for high frequencies, public debt having a significant impact on economic growth in case of periods situated above 2 years for most Euro Zone member states. High debt levels can cause serious effects on fiscal stability and therefore require fiscal consolidation in order to restore economic growth. Therefore, Euro Zone member states should implement prudent debt policies and establish clear limits for debt increase, in order to comply with fiscal sustainability and ensure conditions for preserving economic growth.

Short-term changes and correlations of plasma spexin, kisspeptin, and galanin levels after laparoscopic sleeve gastrectomy.

To determine the circulating levels of spexin, kisspeptin, galanin, and the correlations between these peptides after laparoscopic sleeve gastrectomy (LSG). The plasma levels of the spexin, kisspeptin, and galanin and metabolic parameters (body mass index, weight loss, % excess weight loss, body fat, fasting glucose, HbA1C, and cholesterol levels) were measured (baseline, 1month, and 3months) and correlated in thirty adult individuals with obesity (22 female and 8 male) after LSG. The body mass index (BMI), body fat, fasting glucose, total and low-density lipoprotein cholesterol decreased, while high-density lipoprotein cholesterol and % EWL (excess weight loss) increased at 3months after surgery. The plasma spexin levels increased at 3months, kisspeptin levels increased at 1month and stabilized afterward, and galanin levels decreased at 3months after LSG. Significant correlations were found between metabolic parameters with spexin, kisspeptin, and galanin. In addition, spexin and kisspeptin were negatively correlated with galanin, while spexin was positively correlated with kisspeptin. The biochemical data reveal evidence that LSG causes an increase in the levels of spexin, and kisspeptin and a decrease in galanin levels. Our findings, therefore, suggest a possible interaction between these novel peptides, which have potential roles in obesity and glucose metabolism.

The design fluency test: a\xa0reliable and valid instrument for the assessment of game intelligence?

The design fluency test (DFT) has been reported to predict successful sports performance of soccer players and has therefore been in the spotlight of sport psychology research. There is, however, a lack of research regarding the psychometric properties of the DFT in elite sports. Thus, the aim of this research was to provide findings of test–retest reliability, practice effects and the diagnostic power of the DFT. Multiple studies of youth and adult elite athletes, as well as nonathlete students, were conducted in applied settings. Test–retest relationship demonstrated poor to acceptable short-term and long-term correlations. Furthermore, significant changes between test and retest were obtained in some variables that differed among samples. The differential value of the DFT was corroborated by significant differences between adolescent students and adolescent elite soccer players. Regarding the prospective value, significant partial correlation coefficients were found between DFT scores and volleyball performance in adult elite players. Although our research partially confirmed previous findings on the differential and prospective power of the DFT, the findings on test–retest reliability indicate that the DFT cannot be recommended for application in sports. The psychometric properties—in particular the findings on test–retest reliability—of the DFT have to be improved before research can be carried out on the application for the selection of team sport athletes and for the prediction of future success in team sports. Further research is needed to develop a scientific instrument for the assessment of game intelligence.

Intelligibility Enhancement Via Normal-to-Lombard Speech Conversion With Long Short-Term Memory Network and Bayesian Gaussian Mixture Model

Speech communications and interactions frequently occur in a variety of environments. Noise in the environment significantly degrades speech intelligibility when speaking and listening. Especially in the listening stage, even if the multimedia terminal outputs clean speech, it is still difficult for listeners to obtain information. Intelligibility enhancement (IENH) of speech is a technique for overcoming the environmental noise in the listening stage. It implements a perceptual enhancement of non-noisy speech. This study focuses on IENH via normal-to-Lombard speech conversion, inspired by a well known acoustic mechanism named the Lombard effect. Our method combines the long short-term memory (LSTM) network and Bayesian Gaussian mixture model (BGMM) to build a conversion architecture. Compared with baselines, it has three main advantages: 1) an LSTM network is used for spectral tilt mapping with fully considering short-term correlations and high-dimensional expression abilities; 2) the aperiodicity (AP) is mapped together with the fundamental frequency ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F_0$</tex-math></inline-formula> ) by a BGMM, which considers their relevance constraints and the importance of APs; 3) the gender-dependent mapping is used for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F_0$</tex-math></inline-formula> and APs to consider distribution differences between genders. Experiments indicate that our method gets better performance in both objective and subjective tests.

Revisiting the effects of relevant factors on Pakistan's agricultural products export

Pakistan is an agriculture-based country, so the agricultural sector is known as the backbone of the national economy. Considering the national economy and the agricultural industry, it is necessary to focus on earnings through agricultural products export to improve the livelihood of local farmers. Therefore, the current study aimed to analyse the short-term and long-term factors affecting agricultural products export. The annual time series of 1976–2016 were collected from World Bank indicators, the Food and Agriculture Organization, and the Statistical Bureau of Pakistan. An autoregressive distributed lag, along with a vector error correction model, was employed. A cointegration test showed long-term associations between the selected variables. While the autoregressive distributed lag model confirmed the short-term correlation between area sown and crop production towards agricultural products export, there is no long-term relationship between the selected variables. In addition, the bidirectional correlation between employment in agriculture and agricultural products export was confirmed by the vector error correction model. Therefore, it is essential to increase agricultural production with the available natural resources to increase foreign earnings.

Did the introduction and increased prescribing of antidepressants lead to changes in long-term trends of suicide rates?

BackgroundEcological studies have explored associations between suicide rates and antidepressant prescriptions in the population, but most of them are limited as they analyzed short-term correlations that may be spurious. The aim of this long-term study was to examine whether trends in suicide rates changed in three European countries when the first antidepressants were introduced in 1960 and when prescription rates increased steeply after 1990 with the introduction of the serotonin reuptake inhibitors (SSRIs).MethodsData were extracted from the WHO Mortality Database. Suicide rates were calculated for people aged 10–89 years from 1951–2015 for Italy, 1955–2016 for Austria and 1951–2013 for Switzerland. Trends in suicide rates stratified by gender were analyzed using joinpoint regression models.ResultsThere was a general pattern of long-term trends that was broadly consistent across all three countries. Suicide rates were stable or decreasing during the 1950s and 1960s, they rose during the 1970s, peaked in the early 1980s and thereafter they declined. There were a few notable exceptions to these general trends. In Italian men, suicide rates increased until 1997, then fell sharply until 2006 and increased again from 2006 to 2015. In women from all three countries, there was an extended period during the 2000s when suicide rates were stable. No trend changes occurred around 1960 or 1990.ConclusionsThe introduction of antidepressants around 1960 and the sharp increase in prescriptions after 1990 with the introduction of the SSRIs did not coincide with trend changes in suicide rates in Italy, Austria or Switzerland.

Fundus image-based cataract classification using a hybrid convolutional and recurrent neural network

Cataract is the most prevailing reason for blindness across the globe, which occupies about 4.2% population of the world. Even with the developments in visual sciences, fundus image-based diagnosis is deemed as a gold standard for cataract detection and grading. Though the increase in the workload of ophthalmologists and complexity of fundus images, the results may be subject to intelligence. Therefore, the development of an automatic method for cataract detection is necessary to prevent visual impairment and save medical resources. This paper aims to provide a novel hybrid convolutional and recurrent neural network (CRNN) for fundus image-based cataract classification. The proposed CRNN fuses the advantages of convolution neural network and recurrent neural network to preserve long- and short-term spatial correlation between the patches. Coupled with transfer learning, we adopt AlexNet, GoogLeNet, ResNet and VGGNet to extract multilevel feature representation and to analyse how well these models perform cataract classification. The results demonstrate that the proposed method outperforms state-of-the-art methods with an average accuracy of 0.9739 for four-class cataract classification and provides a compelling reason to be applied for other retinal diseases.

A non-invasive method to assess the reproductive status of the European badger (Meles meles) from urinary sex-steroid metabolites

Due to their unique reproductive physiology and behaviour, European badgers (Meles meles) are often used as a model to study mammalian reproduction. For reproductive endocrinology, circulating hormone levels are conventionally measured directly from blood samples. However, routine blood sampling is often not practical for wild animals and may induce stress affecting measurement accuracy. Non-invasive alternatives are thus of interest. Circulating hormones are metabolized through different routes, either by the kidneys, to be excreted through urine, or by the liver, to be excreted through faeces. These metabolites can thus be used as a proxy of hormone measurements, provided the species-specific metabolic characteristics are known. Here we tested the suitability of measuring urinary metabolites of circulating plasma sex-steroid hormones (testosterone in males and oestrogen in females) with enzyme immunoassays to assess the reproductive status of the European badger (Meles meles). Biological validation evidenced that urinary testosterone metabolite (UTM) and urinary total oestrogen metabolite (UEM) excretion patterns both corresponded with seasonal badger reproductive patterns on a population level, signaling correlation over a broad time frame. On an individual level, concurrent sampling of urine and plasma showed that male plasma testosterone and UTM levels correlated significantly across seasons, but no short term correlation was evident for total oestrogen and UEM in females. Thus, in badgers, urinary sex-steroid metabolites can be used reliably in the short term to assess male reproductive status at the individual level, but only at the broader population level for females.

Assessing correlations between short-term exposure to atmospheric pollutants and COVID-19 spread in all Italian territorial areas.

The spread of SARS-CoV-2, the beta coronavirus responsible for the current pneumonia pandemic outbreak, has been speculated to be linked to short-term and long-term atmospheric pollutants exposure. The present work has been aimed at analyzing the atmospheric pollutants concentrations (PM10, PM2.5, NO2) and spatio-temporal distribution of cases and deaths (specifically incidence, mortality and lethality rates) across the whole Italian national territory, down to the level of each individual territorial area, with the goal of checking any potential short-term correlation between these two phenomena. The data analysis has been limited to the first quarter of 2020 to reduce the lockdown-dependent biased effects on the atmospheric pollutant levels as much as possible. The analysis looked at non-linear, monotonic correlations using the Spearman non-parametric correlation index. The statistical significance of the Spearman correlations has also been evaluated. The results of the statistical analysis suggest the hypothesis of a moderate-to-strong correlation between the number of days exceeding the annual regulatory limits of PM10, PM2.5 and NO2 atmospheric pollutants and COVID-19 incidence, mortality and lethality rates for all the 107 territorial areas in Italy. A weak-to-moderate correlation seems to exist when considering the 36 territorial areas in four of the most affected regions (Lombardy, Piedmont, Emilia-Romagna and Veneto). Overall, PM10 and PM2.5 showed a higher non-linear correlation than NO2 with incidence, mortality and lethality rates. As to particulate matters, PM10 profile has been compared with the incidence rate variation that occurred in three of the most affected territorial areas in Northern Italy (i.e., Milan, Brescia, and Bergamo). All areas showed a similar PM10 time trend but a different incidence rate variation, that was less severe in Milan compared with Brescia and Bergamo.

Individual difference in serial dependence results from opposite influences of perceptual choices and motor responses.

Natural image statistics exhibit temporal regularities of slow changes and short-term correlations and visual perception, too, is biased toward recently seen stimuli, i.e., a positive serial dependence. Some studies report strong individual differences in serial dependence in perceptual decision-making: some observers show positive serial effects, others repulsive effects, and some show no bias. To understand these contrasting results, this study separates the influences of physical stimuli per se, perceptual choices, and motor responses on serial dependence in perceptual decision making. In two experiments, human observers reported which orientation (45° or −45°, at threshold contrast) they perceived. Experiment 1, used a consistent mapping between stimulus and response buttons whereas in Experiment 2, observers did two tasks: one with a consistent stimulus-response mapping, the other with a random stimulus-response mapping (perceptual choice and motor response unrelated). Results show that the stimulus percept (not the physical stimulus per se) affected subsequent perceptual choices in an attractive way and that motor responses produced a repulsive serial effect. When the choice-response mapping was consistent (inseparable choice and response, typical of most experiments), individual differences in the overall serial effect was observed: some were positive, some repulsive, and some were bias-free. The multiple regression analysis revealed that observers’ overall serial effects in the consistent choice-response mapping task could be predicted by their serial effects for choices and motor responses in the random mapping task. These individual differences likely reflect relative weightings of a positive choice bias and a repulsive motor bias.

Analysis of the Linkage between South African’s Exchange Rate and Stock Price Index

In this study, we used a wavelet analysis method to analyze how stock price index is correlated with exchange rate in South African stock market andinterest rate was choosen as the control variable. As indicated by the empirical results, first, stock index is significantly correlated with exchange rate in the stock market of South Africa, no matter in the short term (1-4 years) or the long term (4-8 years). There is a significant correlation period, with correlation coefficient greater than 0.8. Second, for the short-term (1-4 years) relationship, after adding control variables, South Africa’s short-term negative correlation will be led by the stock exchange rate. That is, in condition of the negativity of both stock market and exchange rate, the stock market leads. And interest rate greatly affects the short-term (1-4 years) exchange rate and the stock price index in South Africa. And in the short term, the linkage between those two variables is not subject to the influence of 2008 financial crisis. Third, for the long-term (4-8 years) relationship, when we add control variables, regardless of its term, there is a negative correlation between stock market index and exchange rate in South African stock market, and stock market affects exchange rate. The long-term (4-8 years) correlation of stock price index and exchange rate in South African stock market was affected by the 2008 financial crisis. South African market is indeed the goal for investors to participating enthusiastically now. The above conclusions can serve as a lesson for hedging in corporate exchange rate, stock market, and also reference for global investors and all sorts of investor asset allocation.