In post-pandemic China, the expanding weight of services in overall economic activity and the stabilising role of official foreign exchange reserves (FER) elevate the informational value of high-frequency business surveys for interpreting equity market conditions. This study investigates the dynamic relations among China’s stock market index, the Manufacturing and Non-Manufacturing Purchasing Managers’ Index (PMI), and official FER using monthly data from April 2022 to July 2025. All series are log-transformed. Stationarity is assessed via Augmented Dickey-Fuller (ADF) tests to determine that all variables enter the model as first differences. A reduced-form vector autoregression (VAR) with a constant and four lags is estimated, followed by in-system Granger causality tests, orthogonalized impulse-response functions under a Cholesky scheme, and forecast-error variance decomposition (FEVD). Results indicate short-run mean reversion in the stock market index and limited immediate pass-through from manufacturing PMI and FER, while the non-manufacturing PMI exhibits the clearest medium-horizon influence. The FEVD results show that own shocks dominate at very short horizons, with services PMI gaining prominence over 6-12 months and reserves contributing moderately. However, manufacturing PMI accounts for the smallest share of the variance. Granger causality tests suggest that both PMI and FER impact the stock market index, while no causal relationship is found from the stock market index to these variables. These results highlight the role of services-sector signals for monitoring economic trends at medium horizons, with reserves primarily viewed as a stability indicator.

This study presents a currency exchange rate prediction model using a Gated Recurrent Unit (GRU) with historical price data and selected economic factors. Historical data, including Open, High, Low, and Close (OHLC) prices, were obtained from Yahoo Finance. Economic factor data, including Non-Farm Payrolls (NFP), Gross Domestic Product (GDP), Purchasing Managers Index (PMI), Retail Sales, and Durable Goods Orders, were collected from Trading View. Data preprocessing involved chronological sorting, missing value handling, feature scaling, and sequence generation. Multiple experiment cases were evaluated: historical data alone, historical data combined with all economic factors, and historical data combined with each individual factor. The GRU model achieved its best performance when incorporating historical data with Durable Goods Orders, indicating that this economic indicator provides significant predictive value, as reflected by the lowest RMSE (0.0076) and MAPE (0.0054), and the highest R² (0.9764) indicating that this economic factor provides significant predictive value. These findings highlight the importance of integrating selected economic factors into exchange rate prediction models to enhance forecasting accuracy.

Abstract Subject and Purpose of Work This study explores the intricate relationship between key macroeconomic variables and India’s equity market segments, specifically the NIFTY Small-cap, Mid-cap, and Large-cap indices. The primary objective is to evaluate how selected macroeconomic factors influence market dynamics and investor sentiment in the Indian context. Materials and Methods The research analyses monthly data spanning five years, from January 2019 to January 2024. The macroeconomic indicators considered include Foreign Institutional Investment (FII), Domestic Institutional Investment (DII), Consumer Price Index (CPI), Purchasing Managers’ Index (PMI), Treasury Bill Rate, Gold Price, and Reverse Repo Rate. Statistical techniques such as the Unit Root Test, Ordinary Least Squares (OLS), and Granger Causality Test are employed to assess the short-term and long-term impacts of these variables on market indices. Results The findings reveal that GDP, CPI, PMI, and Gold Price exhibit no statistically significant influence on the NIFTY Small-cap, Mid-cap, or Large-cap indices, aligning with certain earlier studies. However, variables like FII, DII, Treasury Bill Rate, and Reverse Repo Rate show varying degrees of influence across the indices, highlighting the complex and segmented nature of the Indian equity market. Conclusion These insights are valuable for investors, policymakers, and financial analysts in refining investment strategies, informing policy frameworks, and enhancing market forecasting models. The study underscores the need for continuous evaluation of macroeconomic influences to better navigate market volatility and investor behaviour.

This study investigates how Purchasing Managers' Indices (PMI) from the US and China, along with US stock indices, influence crude oil prices. It specifically examines the moderating effects of US and Chinese exchange rates on these relationships. Using data from January 2016 to December 2023 and regression analysis, the result show that US PMI, Chinese PMI, and US stock indices significantly impact oil prices. Interestingly, the US exchange rate moderates the impact of US PMI more, while the Chinese exchange rate moderates the impact of Chinese PMI and US stock indices on oil prices. This suggests China's growing economic influence, as changes in its currency index have a stronger moderating effect on these relationships.

The manufacturing sector in Indonesia, particularly in the automotive cable and component industry, is facing significant challenges, as reflected in the declining Purchasing Manager’s Index (PMI), indicating a contraction in production activities. To remain competitive, managing production process quality becomes essential, especially in improving supply chain performance. This study examines the application of Six Sigma in the copper busbar production process at PT. X, focusing on reducing supply chain costs through enhanced process quality management and waste reduction. Using the DMAIC approach (Define, Measure, Analyze, Improve, Control), this research analyzes copper busbar production data, including defect rates and associated costs before and after improvements. The results show that Six Sigma implementation successfully reduced defect rates, particularly scratch defects, from 7.32% to 0.01%. Additionally, this improvement contributed to a reduction in supply chain costs, with total savings amounting to IDR 3,421,168,671, covering reductions in manufacturing, administration, warehouse, and capital costs. These findings demonstrate that Six Sigma can significantly enhance efficiency and effectiveness in copper busbar production, while positively impacting overall supply chain costs. The study also recommends further research to more thoroughly explore distribution and installation costs in the supply chain context.

Global crises, such as wars or the COVID-19 pandemic, underscore the need for real-time economic monitoring. Traditional economic indicators often fall short, prompting the exploration of alternative data sources, including online and social media content. This study examines the relationship between media sentiment in press articles and traditional economic indicators: the Purchasing Managers' Index (PMI), Consumer Confidence Index (CCI), and average employment in the enterprise sector. We evaluate four pre-trained natural language processing models for sentiment analysis to assess their applicability. The analysis also explores the impact of time shifts in media reporting on the correlation between sentiment scores and economic indicators. Results reveal that a + 24-day shift in article dates produces the strongest correlation with PMI, suggesting media sentiment can predict changes in PMI with a lead time of about 3.5 weeks. Further analysis shows a positive correlation between sentiment scores and the CCI with a + 6-day shift, indicating media sentiment may signal changes in consumer confidence approximately one week in advance. Additionally, a + 70-day shift reveals that media sentiment can predict changes in average employment in the enterprise sector up to 10 weeks before they are officially recorded. These findings highlight the potential of media sentiment as an early indicator of economic trends, emphasizing the importance of considering time dynamics in such analyses. The study demonstrates that sentiment analysis offers valuable insights into economic trends through media reporting, potentially aiding in more timely economic forecasting and decision-making.

The transformation of business expectations in the contemporary BANI world (brittle, anxious, nonlinear, incomprehensible), which has replaced the VUCA environment, is examined. The significance of business expectations as a key tool in economic analysis is explored, particularly their impact on decision-making, investment activity, employment, and pricing. The focus is on the adaptation of businesses in Ukraine to the conditions of the BANI world and methodological approaches to measuring uncertainty and business sentiment. The article identifies international and national organizations engaged in developing methodologies for collecting data on business expectations. Particular attention is given to the analysis of methodologies such as the PMI (Purchasing Managers’ Index), which is used to determine economic activity, and the harmonized EU BCS (Business and Consumer Surveys) program. The research is based on a review of international and national indicators, including indices of trust, economic sentiments, employment, and uncertainty, which allow for the assessment of cyclical changes in the economy. An example is provided of the business confidence index, which currently reflects a general Ukrainian trend toward a decrease in economic activity in the context of the crisis. The authors have also analyzed the Business barometer, initiated by the Chamber of Commerce and Industry of Ukraine, as an important tool for forecasting market conditions. This index takes into account the situation in key sectors of the economy – manufacturing, construction, trade, and services – and allows for the assessment of the current state and expectations of businesses. The methodology for calculating the Business Barometer is based on PMI, ensuring consistency with international standards. The overall conclusion of the article is that measuring business expectations in a BANI world requires continual refinement of methodologies and approaches. In conditions of high uncertainty, adaptability and the ability to respond quickly to changes in economic conditions acquire special significance. The Ukrainian business sector requires further support and the development of forecasting tools to effectively integrate into global economic processes and overcome the challenges of the modern world.

This study aims to integrate short-term, medium-term, and long-term Composite Leading Indices (CLIs) to establish that interconnected CLIs offer enhanced predictive capabilities compared to individual CLIs. Specifically, it investigates the relationships among CLIs to forecast Indonesia's Manufacturing Cycle (ManC) using Partial Least Squares-Structural Equation Modeling (PLS-SEM).Building on an extensive literature review, the study employs quarterly data spanning from Q1 2010 to Q2 2022, incorporating five constructs representing key economic sectors influencing the manufacturing cycle. The analysis includes two short-term CLIs: the Short Leading Economic Index (SLEI) and the International Trade Channel (ITC). The SLEI is composed of two indicators, the Manufacturing Purchasing Managers’ Index (PMI) and the Composite Stock Price Index from the Indonesia Stock Exchange, while the ITC comprises nine critical export-import CLIs.The Fiscal Cycle (FC) is a potential medium-term CLI, including Gross Domestic Product (GDP) per capita, manufacturing investment, oil prices, and the Consumer Price Index (CPI). Meanwhile, the monetary cycle (MC) comprises the Policy Interest and Real Effective Exchange Rates. This research effectively supports the application of PLS-SEM in forecasting the ManC in Indonesia.

This study investigates the impact of the Purchasing Managers' Index (PMI) on the operational efficiency of 17 commercial banks in Vietnam from 2013 to 2020. Utilizing the Generalized Method of Moments (GMM), the research analyzes how macroeconomic factors such as GDP growth and inflation, alongside internal bank metrics, influence bank performance. The findings reveal an inverse relationship between PMI and bank operational efficiency. Additionally, GDP growth and inflation positively affect ROE, while non-performing loans negatively impact it. These results provide valuable insights for bank managers to enhance bank performance by monitoring the PMI.

This study investigates how Chinese companies are portrayed in American and British economic news tweets, as well as their relationship to Chinese economic fluctuations. The analysis included a corpus of 55,394 tweets (934,155 words) from well-known media outlets between 2007 and 2023. It also incorporated China’s quarterly Gross Domestic Product (GDP) and monthly Purchasing Managers’ Index (PMI) to contextualise the tweets in terms of their actual economic performance. Using van Dijk’s ingroup and outgroup ideologies, this project examined the ideological depiction through sentiments and emotions. RoBERTa-based transformer models were used to analyse sentiments and emotions, whereas Large Language Models (LLMs) were used for evaluative target annotation. Positive and negative sentiments were found to be significantly (p < 0.01) correlated with China’s macroeconomic indices. The representation of Chinese companies trended between ingroup and outgroup portrayals. Positive sentiment diminished as the economy transitioned from expansion to contraction, while negative sentiment increased. American news tweets were most positive during economic balance and most negative during downturns, while British news tweets were most positive during stability or early recovery and most negative when a downturn was predicted. As the economy shifted from growth to decline, positive sentiment emphasised corporate external opportunities alongside corporate strengths, with both evaluative targets reinforcing ingroup representation. Negative sentiment shifted from corporate weaknesses to contextual threats, both strengthening outgroup representation. Moreover, certain emotions had a significant (p < 0.0001) influence on sentiment swings and ideological transitions. These findings emphasise how economic changes and journalistic factors influence the ideological representation of Chinese firms in economic news on social media.

Subject. The article discusses precious metal prices, namely, prices for gold, silver, platinum, and palladium. Objectives. The purpose is to assess factors influencing precious metal prices in the world market, focusing on mutual influence of prices. Methods. The study employs a seasonal autoregressive integrated moving average with exogenous regressors (SARIMAX) econometric model. Results. The obtained econometric estimates for the four models (four precious metals) based on the statistics for 2014–2023 enabled to highlight one factor that is significant for the said precious metals pricing, S&amp;P500 index. Moreover, we found that some of the precious metals are influenced by the USA dollar exchange rate, Purchasing Managers Index (PMI), and inflation. The gold price is a factor for pricing of all the precious metals, and they influence the price of gold at the same time. The mutual influence of prices of other precious metals is observed in all cases, though with different degree. Conclusions. Precious metal prices are influenced by different macroeconomics and financial market indices. Mutual influence of precious metal prices on world financial markets can be explained by investors’ attitude to them as a whole group of financial instruments. We suppose that this fact may strengthen the role of psychological aspects of investing in this part of the financial market.

The purpose of this study is to examine the impact of a variety of macroeconomic indicators on the investment decisions of manufacturing firms. Key macroeconomic indicators such as GDP growth rate, interest rate, consumer price index (CPI), purchasing managers' index (PMI), employment rate, industrial production index (PPI), money supply (M2), and imports are analyzed to assess their impacts on manufacturing firms' capital expenditures. A multiple linear regression model is used to empirically analyze the data from Chinese manufacturing firms. The results of the study show that the Consumer Price Index (CPI) and the Industrial Production Index (PPI) have a significant negative effect on the capital expenditures of manufacturing firms, while the increase in the money supply (M2) has a positive effect on capital expenditures. Changes in the GDP growth rate and the Purchasing Managers' Index (PMI) also affect the investment decisions of manufacturing firms. In particular, an increase in foreign trade imports promotes capital expenditures by export-oriented manufacturing firms. This paper analyzes in-depth the impact mechanism of macroeconomic indicators on the investment of manufacturing companies to provide a reference for the investment decisions of enterprises in different economic environments. At the same time, this study also provides an important basis for policy makers to help formulate and adjust economic policies to support the development of the manufacturing industry and stable economic growth. The results of the study have important theoretical and practical significance for understanding the impact of macroeconomic environment on the investment behavior of enterprises.

Modeling and forecasting of coal price based on influencing factors and time series

ABSTRACT This article decodes the pass-through behaviour from input prices to output prices, which is a key component in assessing second-order effects of cost-push pressures. Using panel GMM model, this paper has tried to empirically test the degree of pass-through of Purchasing Managers Index (PMI) input prices to output prices in a cross-country framework for 28 countries for the period 2011–2023. Empirical results support modest pass through with the sensitivity of output prices to input prices being lower for advanced nations and inflation targeting countries and higher for manufacturing sector with signs of non-linearity.

Constructing the Agriculture Purchasing Managers’ Index in Taiwan Rice Industry

In this study, the relationship between Turkey’s Purchasing Managers' Index (PMI) and various economic and financial indicators was investigated. The data for the period 2018.4 - 2021.2 were used in the analysis, and the Vector Autoregression (VAR) method was preferred. According to the results, although there is a significant relationship between various indicators, it has been determined that the most effective index on PMI is the Export Climate Index. A positive relationship was found between PMI and the Export Climate Index in the short run. There is also a unidirectional causality relationship from the Export Climate Index to PMI. This situation has revealed that exports are influential on the investment decisions of purchasing managers, and therefore market-oriented strategies have an important place in business policies. It is thought that this study will make important contributions to policymakers, researchers and literature.

At the end of 2019, the world was startled by the emergence of a new disease caused by the Sars CoV-2 virus, now known as Coronavirus Disease-19 (Covid-19). The manufacturing sector is one of the industries affected by the onset of Covid-19 in Indonesia. Covid-19 forced manufacturing companies to lay off employees and caused a decline in the Manufacturing Purchasing Managers Index in 2020. This research aims to analyze the financial performance of manufacturing companies in Indonesia, specifically focusing on profitability ratios before and during the Covid-19 pandemic in 2020. This study is a quantitative research type using descriptive analysis. The hypothesis testing in this research utilizes non-parametric statistical analysis techniques, employing the Wilcoxon Signed Rank Test approach with the statistical tool SPSS version 25.0. The results of the research indicate that Net Profit Margin (NPM), Return On Asset (ROA), and Return On Equity (ROE) before Covid-19 in 2019 were better compared to Net Profit Margin (NPM), Return On Asset (ROA), and Return On Equity (ROE) during the Covid-19 pandemic in 2020 for manufacturing companies in Indonesia.

ABSTRACT Changes in economic conditions can occur suddenly with drastic effects. However, economic statistics are published with significant lags, e.g. GDP, and more timely information about the economy is required. Nowcasting methods have become widely popular for providing up-to-date information about the current economic stance. This study adds a novel idea to the previous literature by nowcasting the nowcasting, i.e. the purchasing manager’s index (PMI) and the non-manufacturing survey index (NSI) of the ISM Business survey indicators with the weekly Google Trends data. We used two-dimension reduction methods: the principal component analysis (PCA) and partial least squares (PLS) to eliminate ‘the curse of dimensionality’. Pseudo-out-of-sample exercises performed with different Google Trends search categories indicated that Google Search data is able to generate useful information to nowcast the nowcasting. In particular, we contribute the existing literature that weekly Google Search data can nowcast the monthly PMI and NSI.

This article uses the GARCH-MIDAS model to analyze the impact of economic uncertainty on the volatility of the new energy vehicle market. Economic uncertainty includes economic policy uncertainty and macroeconomic uncertainty. Use EPU as the proxy variable for economic policy uncertainty, and the Manufacturing Purchasing Managers Index (PMI) as the proxy variable for macroeconomic uncertainty. The results indicate that economic policy uncertainty has a significant negative impact on the volatility of the new energy vehicle market. The PMI index's impact weight on the new energy vehicle sector has slowly decreased over the past 12 months, indicating that the macroeconomic indicators of the manufacturing industry have a relatively long impact on the new energy vehicle sector, which takes about a year to dissipate, The fluctuation of economic policy uncertainty on the new energy vehicle market is transmitted through the EPU index, and the impact of economic policy uncertainty presents a short-term effect. Policy shocks are absorbed within 2 months and will not have a long-term impact on the stock market. Among all economic uncertainty factors, economic operation has the most significant impact on long-term volatility. Overall, macroeconomic uncertainty does not have a significant impact on the volatility of the new energy vehicle market, and its contribution is limited.

Determinants of dry bulk shipping freight rates: Considering Chinese manufacturing industry and economic policy uncertainty