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This work analyses the asymmetric response of conventional and shale oil rig counts to WTI oil price returns. Our analysis shows that the rig count time series exhibited a structural change after the oil glut of 2014. All series are non-stationary in each sub-period but not cointegrated. Therefore, after controlling for possible confounding factors, a vector auto regressive (VAR) model is set up. Our specification accounts for the possible role of oil production and distinguishes between positive and negative oil price changes. It is shown that shale and conventional rig counts reacted differently in each subperiod to signed changes in oil price. Subsequently, by evaluating the response of rig counts to oil price shocks, their intensity and duration over time, we observe that the shale oil rig count reacts more intensively to positive than to negative oil price changes. On the contrary, the conventional rig count exhibits a modest reaction only to positive price changes. Finally, we robustify our findings by focusing on the data of the Permian basin, on the one hand, and the Anadarko, Bakken, Eagle Ford and Niobrara, on the other hand, which are characterized by different patterns in the number of Drilled but not Completed wells. [ABSTRACT FROM AUTHOR]

In the broader landscape of cryptocurrency risk management, this study delves into the nuanced estimation of Value-at-Risk (VaR) for a uniformly weighted portfolio of cryptocurrencies, employing the bivariate Normal Inverse Gaussian distribution renowned for its semi-heavy tails. Utilizing high-frequency data spanning between 1 January 2017 and 25 October 2022, with a primary focus on Bitcoin and Ethereum, our research seeks to accentuate the resilience of VaR methodology as a paramount risk assessment tool. The essence of our investigation lies in advancing the comprehension of VaR accuracy by quantitatively comparing the observed returns of both cryptocurrencies with their corresponding estimated values, with a central theme being the endorsement of the Normal Inverse Gaussian distribution as a potent model for risk measurement, particularly in the domain of high-frequency data. To bolster the statistical reliability of our results, we adopt a forward test methodology, showcasing not only a contribution to the evolution of risk assessment techniques in Finance but also underscoring the practicality of sophisticated distributional models in econometrics. Our findings not only contribute to the refinement of risk assessment methods but also highlight the applicability of such models in precisely modeling and forecasting financial risk within the dynamic realm of cryptocurrencies, epitomized by the case study of Bitcoin and Ethereum. [ABSTRACT FROM AUTHOR]

Although the movement of people from rural to urban areas has caused the increased use of energy, the abundance of water resources can be made into a form of renewable energy known as hydroelectricity. As European countries are ranked as the first users and exporters of hydropower, the production of renewable energy in developed countries such as the Nordic region has caused great impacts on economic growth and human development. The importance of this paper is to investigate the relationship between hydroelectricity and the Human Development Index by depending on some variables such as urbanization, rule of law, corruption, trade openness, and GDP per capita from 2002 to 2021 in Nordic countries. The results were estimated depending on impulse response function after conducting the Vector autoregressive model (VAR) model and Granger causality test. Results showed a negative impact from hydro plants in the short run but a significant positive impact in the long run in Nordic countries. The long-term sustainment of Human Development Index (HDI) is due to policies limiting the immigration of labor as well as protection of energy use. Water batteries are gaining popularity across Europe and their implementation is near mandatory. [ABSTRACT FROM AUTHOR]

Subsurface soil moisture is a primary determinant for root development and nutrient transportation in the soil and affects the tractability of agricultural vehicles. A statistical forecasting model, Vector AutoRegression (VAR), and a Long Short-Term Memory network (LSTM) were developed to forecast the subsurface soil moisture at a 20 cm depth using 9 years of historical weather data and subsurface soil moisture data from Fort Wayne, Indiana, USA. A time series analysis showed that the weather data and soil moisture have a stationary seasonal tendency and demonstrated that soil moisture can be forecasted from weather data. The VAR model estimates volumetric soil moisture of one-day ahead with an R2, MAE (m3m−3), MSE (m6m−6), and RMSE (m3m−3) of 0.698, 0.0561, 0.0046, and 0.0382 for 2021 corn cropping season, whereas the LSTM model using inputs of previous seven days yielded R2, MAE (m3m−3), MSE (m6m−6), and RMSE (m3m−3) of 0.998, 0.00237, 0.00002, and 0.00382, respectively as tested for cropping season of 2020 and 0.973, 0.00368, 0.00003 and 0.00577 as tested for the cropping season of 2021. The LSTM model presents a viable data-driven alternative to traditional statistical models for forecasting subsurface soil moisture. [ABSTRACT FROM AUTHOR]

Oil palm has been identified as one of the key agricultural products that can diversify Nigeria's economy. However, its production and export have been hampered by capital constraints. Consequently, the Agricultural Credit Guarantee Scheme Fund (ACGSF) was introduced to encourage lending to farmers and agro-processors to enhance agricultural productivity. Therefore, this study aims to determine the effectiveness of ACGSF allocations to the oil palm production subsector. Time series data on oil palm production, as well as data on ACGSF allocations to oil palm and cash crop production, prime lending rate and commercial banks' loans to agriculture were obtained from FAOSTAT and the Central Bank of Nigeria. Vector autoregression, Granger causality and impulse response functions were used to estimate the short-run relationship. The results reveal that ACGSF allocations to the oil palm subsector and commercial banks' loans to agriculture have negative and positive effects, respectively, on oil palm production. There was unidirectional causality from ACGSF and commercial banks to agriculture to oil palm production. The findings suggest expansion and close monitoring of the ACGSF scheme, and the intensification and esterification of oil palm production. The study shows that estimating the effect of ACGSF on overall agricultural productivity does not provide deep insight into commodity-specific credit packages. [ABSTRACT FROM AUTHOR]

Prediction of renewable energy consumption structure (RECS) can provide important guidance for energy development planning and energy structure transformation. The RECS refer to the proportion of various renewable energy consumptions and belong to compositional data, which could reflect the structural shapes of a complete system better. The multivariate compositional data's vector autoregressive model (CDVAR) on the basis of the Simplex space and its algebraic system is proposed in this study aiming at the multi-dimensional small sample size. Firstly, the algebraic system of the Simplex space is introduced and the statistics of the compositional data are defined. Secondly, the novel model with the form of the compositional data is obtained and the least square parameter estimation of the model is derived according to Aitchison geometry. Third, the validation of the novel model is verified by the data on RECS in countries (China, USA, and Canada). The validation presents that the proposed model performs better in fitting, prediction, stability, and applicability compared with other five models under transformation. Last, the proposed model is applied to analyze and forecast the RECS of the above countries in 2021–2025 to provide an important basis for the optimization of the RECS. [ABSTRACT FROM AUTHOR]

This study constructed a VAR model with cotton futures and spot price data from April 30, 2009 to November 16, 2022, for empirical analysis utilizing the Granger causality test to analyze the dynamic relationship between cotton futures and spot market prices in China. The impulse response function and variance decomposition analysis showed that the cotton spot prices at flowering have a causal relationship with each other; in terms of mutual influence and impact, futures prices are higher than spot prices. Finally, it proposed countermeasures and suggestions from the perspective of establishing a standardized cotton spot market, improving the laws and regulations of the cotton futures market and trading system, and optimizing the structure of investment subjects. [ABSTRACT FROM AUTHOR]

The SME sector in Indonesia comprises 99.99% of businesses, employing 96.9% of the workforce and contributing 60.5% to GDP and non-oil exports. Despite their importance, SMEs face challenges including limited financial access, product hygiene concerns, and fluctuating demand. Accurate demand prediction is crucial for optimizing production, inventory, and resource allocation. SARIMAX and VAR models are commonly used for demand prediction, with SARIMAX proving more effective, especially when integrating weather data. Due to there are quite few literatures about SARIMAX is used at SMEs, in this study we utilized SARIMAX and VAR models with sales and weather data (average temperature and average humidity) from January to June 2023. SARIMAX with optimum parameters optimum parameters (d=1, D=1, p=2, q=3, P=2, Q=2, s=7) outperformed optimized VAR in predicting demand for food and beverage SMEs. SARIMAX obtained AIC 1070.11, MSE 80.393, MAE 7.513, RMSE 8.966 and reduced MSE by 86.35% compared to VAR. This research highlights the significance of accurate demand prediction for SMEs, emphasizing the importance of considering external factors like weather. Understanding and predicting demand patterns are vital for SMEs to make informed decisions and optimize operations efficiently. [ABSTRACT FROM AUTHOR]

Based on the daily closing price data of the internet finance index from 2012 to 2021, the two-zone MS-GARCH(1,1) model is firstly used to describe the fluctuation process of the internet finance index, and the optimal model MS(2)-EGARCH(1,1)-st is selected through analysis. The results show that the return rate of the internet finance index has two clearly divided states: the mild fluctuation state is more persistent than the shape fluctuation state, and the shape fluctuation state has asymmetric effects. Secondly, the combined model of MS-EGARCH model and quantile regression (QR) model are used to measure the risk of internet finance return series, and the success rate is calculated by Kupiec backtracking test method. The results show that the success rate of value at risk (VaR) obtained by QR-MS(2)-EGARCH(1,1)-st is higher. [ABSTRACT FROM AUTHOR]

The impact of shadow banking on financial stability remains a controversial issue today due to the size and complexity of these activities and the inadequate regulatory frameworks for systemic risks. In this context, shadow banking has become the focus of financial regulators due to its potential effects on financial stability. Therefore, this study was conducted to understand the impact of shadow banking on financial stability. In this study, the relationship between shadow banking and financial instability is examined using the VAR method for the example of the United States, covering the period 2000-2020. In order to create an accurate model, firstly unit root tests were performed, followed by autocorrelation and heteroscedasticity tests. The findings were found to be significant and Johansen cointegration test was applied. In the cointegration results, it was seen that the series were cointegrated, that is, they moved together in the long run. Finally, a Granger causality test was conducted between shadow banking and financial instability, and according to the empirical findings, it was concluded that there was a causality from shadow banking to financial instability for the period in question. [ABSTRACT FROM AUTHOR]

The objective of this research was to analyze the relationship between economic policy uncertainty and the Brazilian stock market. The research adopted a quantitative, descriptive and econometric approach. The operationalization of the study involved collecting data from the Economic Policy Uncertainty Index and Ibovespa between January 2006 and December 2022. Data analysis was carried out using autoregressive vector models. The results showed a bidirectional relationship between the variables. An increase in uncertainty resulted in a reduction in market performance, while an increase in market performance led to a decrease in uncertainty levels. [ABSTRACT FROM AUTHOR]

Inflation and economic growth are two crucial indicators for any country in the world. In light of the importance of these two economic indicators, the forecast of economic growth and inflation has become a significant topic that national governments have traditionally prioritized. This study aims to apply popular machine learning algorithms such as KNN and MLP to build models for predicting economic growth and inflation. We also provide a comparison of the predictive accuracy between these machine learning algorithms and traditional forecasting models such as VAR and LASSO. Specifically, we employ techniques such as VAR, LASSO, KNN, and multi-layer perceptron (MLP) to construct forecasting models for Vietnam’s economic growth and inflation using data collected from 1996 to 2021. The accuracy of the models is assessed using three indices: RMSE, MAE, and MSE. The empirical results show that according to all three indicators, RMSE, MAE, and MSE, the forecasting models of economic growth and inflation by the MLP model are the most accurate. Based on the results, we have concluded that the MLP model is a valuable tool for future forecasting because it can describe the nonlinear relationships between variables in the model and visually map them. [ABSTRACT FROM AUTHOR]

This article considers a first order continuous time vector autoregression with random coefficients. We discuss some difficulties that arise when the exact discrete analogue is used for estimating the continuous time parameters and provide an estimation method based on an approximate discrete model. Some expressions for the estimator of the drift parameter matrix, for its approximated bias and for the covariance matrix of the parameter estimates are derived. The finite sample performance of the proposed method is studied by a Monte Carlo experiment. We also illustrate the advantages of our model in an application on the expectations theory of the term structure of interest rates. Results show that the performance of the proposed methodology is good, and allowing for time variation on coefficients results in large reductions in the root mean square error of the parameter estimates. [ABSTRACT FROM AUTHOR]

One of the crucial jobs of central banks is to rein in inflation as it creates uncertainty in the economy and in private investment and ultimately negatively impacts the economy. If the source of inflation is positive demand shock, then raising the federal funds rate target is the right way to rein in inflation. If the source of inflation is negative supply shock, then raising the federal funds rate target will make things worse. In this study, the impact of FFR (federal funds rate) on CPI (consumer price index) and producer price index (PPI) is examined. Findings indicate that raising the federal funds rate will have a negative impact on both CPI and PPI with a 2-period lag. The possible explanation of this finding is that raising federal funds rate lowers aggregate demand, lowers the price level and thereby the CPI. And when CPI falls, it lowers perunit profit, prompting producers to cut supply, which in turn lowers the demand for producer goods and services, and thereby lowers PPI. [ABSTRACT FROM AUTHOR]

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The aim of the study is to present the impact of enterprise markups by sections on changes in the CPI in Poland in the years 2008-2023. The VAR model was developed to reveal the interdependencies between changes in markups in nine major sections of non-financial corporations and changes in the CPI. The results of the model, the impulse response function and the variance decomposition confirmed the differentiated impact of markups on inflation changes. To the greatest extent, changes in the CPI, as much as 30%, were explained by the markups of enterprises in real estate services (L), and mining and quarrying (B) sections. The least pro-inflationary contribution to the CPI explanation was shown by markups from information and communication (J), water supply, sewage and waste management, reclamation (E), trade and repair of motor vehicles (G) and the generation and supply of electricity, gas, steam, and hot water D (less than 1%). The degree of explanation of the CPI by markups projected in the variance decomposition increased in the short term (to 26% in the 1st year), stabilizing at a higher level (35% in the years 3-5) in the medium and long term. [ABSTRACT FROM AUTHOR]

Given the widespread use of technical analysis and the tight relationship between derivatives and the underlying assets, we employ the copula approach to investigate whether the technical indicators based on underlying assets convey extra information about the future movements of implied volatility (IV) indexes. The empirical results, based on long samples of five well‐known IV indexes, suggest that although the technical indicators are not informative for forecasting the future prices of IV indexes, they can provide extra information about the size of forecasting errors of the IV indexes. The findings are also robust to the impact of COVID‐19. The technical indicators are then used to extend Threshold ARCH and Exponencial GARCH models for improving the estimation of Value at Risks (VaRs). The out‐of‐sample forecast results show that the proposed model outperforms the benchmark in estimating the VaRs. These findings have implications for pricing options of IV indexes and managing the risks of IV‐related portfolios. [ABSTRACT FROM AUTHOR]

Most functional magnetic resonance imaging (fMRI) studies in resting state have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant. However, increased interest has recently been in quantifying possible dynamic changes in FC during fMRI experiments. FC study may provide insight into the fundamental workings of brain networks to brain activity. In this work, we focus on the specific problem of estimating the dynamic behavior of pairwise correlations between time courses extracted from two different brain regions. We compare the sliding-window techniques such as moving average (MA) and exponentially weighted moving average (EWMA), dynamic causality with vector autoregressive (VAR) model, dynamic conditional correlation (DCC) based on volatility, and the proposed alternative methods to use differencing and recursive residuals. We investigate the properties of those techniques in a series of simulation studies. We also provide an application with major depressive disorder (MDD) patient fMRI data to demonstrate studying dynamic correlations. [ABSTRACT FROM AUTHOR]

Purpose: Since 2008, bitcoin has continued to attract investors due to its growing capitalization and opportunity for speculation. The purpose of this paper is to analyze the impact of bitcoin (BTC) on gold, the volatility index (VIX) and the dollar index (USDX). Design/methodology/approach: The series used are weekly and cover the period from January 2016 to November 2022. To generate the results, the unrestricted vector autoregression (VAR), structural vector autoregression (SVAR) and wavelet coherence were performed. Findings: The findings are mixed as not all tests show the exact effects of BTC in the three asset classes. However, common to all the tests is the significant influence that BTC maintains on gold and vice versa. The positive shock in BTC significantly increases the gold prices, confirmed in three different tests. The effects on the VIX and USDX are still being determined, where in some tests, it appears to be influential while in others not. Originality/value: BTC's diversification potential with equity stocks and USDX makes it a valuable security for portfolio managers. Furthermore, regulatory authorities should consider that BTC is not an isolated phenomenon and can significantly influence other asset classes such as gold. [ABSTRACT FROM AUTHOR]

This paper examines the causal relationship between human capital and economic factors in BRICS countries using a panel vector autoregressive model and data from 1997 to 2020. The economic factors considered include foreign direct investment (FDI), imports, exports, and gross domestic product (GDP). The study conducts a comparative analysis of Brazil, India, China, Russia, and South Africa by adopting a vector autoregressive (VAR) model. The findings indicate a bidirectional causality between human capital and FDI in China, while a unidirectional causality from FDI to human capital is observed in Brazil. Moreover, a unidirectional causality exists from human capital to GDP in Brazil, Russia, India, and South Africa. Additionally, a unidirectional causality is found from human capital to imports and exports in South Africa. Overall, the results suggest the pivotal role of human capital in achieving sustainable economic development in BRICS countries. Policymakers should ensure sustained investment in human capital, focusing on economic growth, FDI, and international trade. [ABSTRACT FROM AUTHOR]

Purpose. This study examines the causal relationship between total expenditures and food availability and identifies their shocks in food availability in Saudi Arabia. Methodology / approach. The study uses a multivariate modeling technique of the Vector Autoregression (VAR) and its environment, the Granger Causality Test, Forecast Error Variance Decomposition (FEVD), and Impulse Response Function (IRF) for the observation period of 2000–2020 in Saudi Arabia. Results. The results of the Granger causality show that investment expenditure has a significant impact on food availability in Saudi Arabia. However, consumption and government expenditures do affect food availability in Saudi Arabia, but have an indirect effect. The Impulse Response Functions show that the shocks of the selected variables require a long period to reach the long-run equilibrium level and the greatest response of the food availability variable is explained by its own shock and investment expenditure shocks. Originality / scientific novelty. The novelty of this study is related to the investigation of a new model and focus on a new perspective. While traditional food security research has mostly concentrated on agricultural production, availability, and accessibility of food, as well as nutrition and health outcomes factors, this research conveys a new dimension by highlighting the link between total expenditure and food security. Their contribution expands the scope of food security research and highlights the impact of recognising the role of total expenditure in implementing and supporting food security at the household level. Practical value / implications. It is important to design strategies and develop a budgeting plan to allocate a reasonable portion of total consumption and government expenditures on food items. Adding, regularly reviewing, and adapting the budgeting plan based on new challenges, and evolving priorities are essential to address the dynamic nature of food security.

This paper examines the effect of the riskiness of the top four cryptocurrencies on the riskiness of stock market indexes in Egypt, being recognized as a developing country. The analysis uses daily data on cryptocurrencies and the three stock market indexes covering January 2020 to January 2023. The risk is measured using the holding period Value at Risk (VaR). The GMM results show that (a) cryptocurrency volatility is negatively associated with the volatility of stock market indexes. That is, the higher the investors’ interest in trading cryptocurrencies, the lower the volatility of stock market indexes as investors trade stocks less frequently, (b) cryptocurrencies can provide hedge and diversification benefits, and (c) the relationship between volatilities of cryptocurrencies and stock market indexes varies across indexes, therefore, contingent.

This study examined the connections between Benin's economic expansion, food production, agricultural productivity, and climate change. Using yearly statistics between 1961 and 2021, and R software version 4.2.2, we aim to: (1) Analyze how agricultural added value affects economic expansion; (2) analyze the effects of food production and temperature lagged values on economic growth; (3) investigate the different causality relationships between food production, temperature variation, agricultural added value and economic growth. To achieve these goals, statistical and econometric techniques such as Autoregressive Distributed Lags (ARDL) and the Toda-Yamamoto Granger causality framework were employed. The ARDL model verifies that there is a positive correlation between economic growth and the added value of agriculture based on empirical data. In addition, the Vector Autoregressive (VAR) model highlights the favorable impact of lagged food production values and the adverse effect of temperature fluctuations on economic growth. Granger causality analysis, employing the Toda-Yamamoto approach, unveils unidirectional links between food production and economic growth, as well as between temperature variation and agricultural added value. Interestingly, the study comes to the conclusion that there are no direct causal links between economic expansion and agricultural growth or between economic growth and temperature variance. Notably, bidirectional causality is established between livestock production and both economic growth and agricultural added value. These insights have significant implications for understanding climate change impacts on agriculture and suggest the need for adapted strategies to mitigate climate effects. Future research could focus on evaluating existing policies, exploring social and economic impacts, investigating market dynamics, and utilizing integrated assessment modeling to inform decision-making and foster sustainable economic growth in Benin's agricultural sector.

This article aims to examine the potential relationship between Brent crude oil futures prices and the index of the European renewable energy companies. After the overview of the European legislation and the most recent literature review on the topic, the article deploys a method of the Vector Autoregressive Model (VAR). The analysis includes weekly data over eight years (2015-2022). Our results indicate a positive correlation between Brent crude oil futures prices and the value of the European Renewable Energy Total Return (ERIX) index. The estimated bivariate VAR model indicates a statistically significant relationship, meaning that past values of the ERIX Index may be used to predict future Brent crude oil prices in the long run. Considering the most recent systemic disturbance in the world’s commodity market, future research should consider longer time series and possible relationships of other macroeconomic factors.

Since the advent of floating exchange rates in Nigeria, exchange rate has been highly volatile. Excessive volatility has severe implications for international trade. This study revisits the long- standing debates on the link between exchange rate volatility and trade flow for the Nigerian economy, 1980-2020. We employ the Granger causality tests based on VECM\VAR model. The results show evidence for a bi-directional causality from trade to exchange rate volatility and vice versa. Since the Nigeria seeks export promotion, there is need to undertake measures that will check excessive fluctuations beyond fundamentals needed for the economy. Hence, we suggest that monetary authority should continue its periodic exchange rate intervention to curtail excessive swings. This should be carefully done to maintain policy rate that will not be counter-productive.

There is no doubt that women play a vital role in all aspects of economic activities around the globe. However, despite the great efforts that governments have made over the past three decades to increase women’s integration into the labor market, their participation is still relatively low compared to men. On the other hand, economic literature argues that the government can use fiscal policy tools such as tax revenue and spending to decrease gender inequality in the labor market. The aim of this paper is to investigate the impact of government spending and tax revenue shocks on the female labor force participation rate (the share of women in the total labor force) in Egypt. Annual time-series data were collected from the Central Bank of Egypt and the World Bank from 1990 to 2021, where the vector autoregressive (VAR) model and impulse response functions have been used. The results suggest that government spending and tax revenue shocks increase gross domestic product (GDP) growth rate, female labor force participation, and inflation. Results validated the research hypotheses and showed that a one standard deviation shock to either government spending or tax revenue has a positive impact on female labor force participation. Therefore, the study recommends that using an expansionary fiscal policy may increase the accessibility of Egyptian women to the labor market.

Following the introduction of EUR/USD futures and USD/JPY futures on 31 October 2022, Thailand Futures Exchange first entered the top 11 list of derivatives exchanges based on foreign exchange derivative volumes in 2022. This paper investigates the dynamics of foreign exchange futures trading volumes in Thailand through the VAR(2) model. Trading volumes of EUR/USD futures, USD/JPY futures, and USD/THB futures are considered over the sample period from 31 October 2022 to 12 January 2024. The empirical results provide no evidence that the trading volume of EUR/USD futures is dependent on the past trading volumes of USD/JPY futures and USD/THB futures. The Granger causality test results show the existence of bidirectional causality between the trading volumes of USD/JPY futures and USD/THB futures. The results of the impulse response function are consistent with the sign results of the VAR(2) model, showing that the USD/JPY futures trading volume has a negative impact on the USD/THB futures trading volume, and vice versa. The analysis of variance decomposition shows that the variability of the USD/JPY futures trading volume and USD/THB futures trading volume, apart from its own shock, is explained by other FX futures trading volume shocks. Therefore, traders should pay more attention to new FX futures trading activity due to its negative impact on the USD/THB futures trading volume and its contribution to the variance in the USD/THB futures trading volume. Understanding the futures trading volume relationship also helps Thailand Futures Exchange develop new products and services that can foster market liquidity and stability.

Nowadays, online public opinions (OPOs) significantly impact corporate brand value (CBV). To prevent corporate brand crises caused mainly by OPOs, it is essential to detect anomalies in OPOs related to corporate reputation in a timely manner. This study explores how dramatic changes in OPOs affect market capital value (MCV), the primary indicator of CBV, and aims to construct a CBV early warning evaluation model. First, a set of OPO indicators dedicated to CBV are selected based on correlation analysis between various popular OPO and CBV indicators collected through a literature review. The method of Criteria Importance Through Intercriteria Correlation (CRITIC) is then employed to determine the indicator weights using data collected from popular social media platforms. Finally, the vector auto-regression (VAR) model is applied to validate the effectiveness of the proposed evaluation model. A case study involving several Chinese enterprises shows that abnormal changes in their MCVs consistently follow abnormal fluctuations observed in their OPOs, with a significant delay. This finding enables managers to promptly detect potential crises from the internet and take actions to avoid unexpected shocks.