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1
Arreola Hernandez, Jose Arreola, Sang Hoon Kang, Zhuhua Jiang, and Seong-Min Yoon. "Spillover Network among Economic Sentiment and Economic Policy Uncertainty in Europe." Systems 10, no. 4 (2022): 93. http://dx.doi.org/10.3390/systems10040093.
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We explore the directional spillover network among economic sentiment indicators and the economic policy uncertainty (EPU) index from Europe. We derive our results by fitting the directional spillover index approach to the monthly frequency data of eleven European countries, economic sentiment indicators and the European EPU index, spanning from 1 January 1987, to 1 February 2019. The empirical results indicate that the economic sentiment indicators of the largest European economies (Germany, France, and Italy) spillover with each other the most. The economic sentiment indicators of Germany and France most strongly influence the EU and Euro area economic sentiment indicators. The economic sentiment indicators of France and Italy have the most influence on the European EPU index, while the latter has the strongest influence on the economic sentiment indicators of Germany and France.
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Malyshenko, Konstantin, Vadim Malyshenko, Marina Anashkina, and Dmitry Anashkin. "Implementation of Text Mining in Socio-Economic Research." International Journal of Business Data Communications and Networking 19, no. 1 (2024): 1–21. http://dx.doi.org/10.4018/ijbdcn.341263.
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This work aims to analyze insights from social networks for identification of population satisfaction with pay level in Russia using the text mining approach. For this, a sentiment analysis framework was developed, which integrates Twitter mining tools and a sentiment index. Sentiments were extracted using Twitter mining and then recoded and substituted into the sentiment formula. The results of sentiment analysis indicate low satisfaction with levels of pay among Russians. Twitter was chosen as the object of research, as one of the most active and independent networks in Russia. It is possible that some of the tweets belong to authors who are not living in Russia at the moment, but their number is not significant and their interest in this issue, in the authors' opinion, only enhances the relevance of the problem under study.
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Shakri, Irfan Haider, Jaime Yong, and Erwei Xiang. "IMPACT OF COVID-19 ON CRYPTOCURRENCIES: EVIDENCE ON INFORMATION TRANSMISSION THROUGH ECONOMIC AND FINANCIAL MARKET SENTIMENTS." Applied Finance Letters 10 (October 31, 2021): 103–13. http://dx.doi.org/10.24135/afl.v10i.429.
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This paper investigates the relationship between the COVID-19 crisis and the two leading cryptocurrencies, Bitcoin and Ethereum, from 31 December 2019 to 18 August 2020. We also use an economic news sentiment index and financial market sentiment index to explore the possible mechanisms through which COVID-19 impacts cryptocurrency. We employ a VAR Granger Causality framework and Wavelet Coherence Analysis and find the cryptocurrency market was impacted in the early phase of the sample period through economic news and financial market sentiments, but this effect diminished after June 2020.
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Bai, Sarula, Jaewon Jung, and Shun Li. "The Spillover Effects of Market Sentiments on Global Stock Market Volatility: A Multi-Country GJR-GARCH-MIDAS Approach." Journal of Risk and Financial Management 17, no. 12 (2024): 569. https://doi.org/10.3390/jrfm17120569.
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In behavioral economics, it has widely been documented that there might be a close relationship between overall market sentiment and economic performance, such as GDP per capita. In this paper, we investigate the effects of market sentiment on stock market volatility, which has widely been recognized as an important factor for economic sustainability. In particular, we aim to identify the existence of spillover effects of market sentiments on global stock market volatility. As a first attempt, we chose ten countries from major economic regions over the world (including America, Asia, Europe, and Oceania), and analyzed their interdependence and interconnectedness using a GJR-GARCH-MIDAS model. The results highlight that an individual country’s stock market volatility is significantly influenced not only by its own market sentiment (proxied by the consumer confidence index) but also by the overall market sentiments of other countries across the world. The results also highlight significant country-by-country heterogeneity in the time lags of the global spillover effects, which indicates substantial heterogeneity in the behavioral dynamics of individual countries.
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Kim, Eung Soo, Yoon a. Sung, and Sung Gu Jang. "A Study on the Effect of Consumer Sentiment on Housing Sales and Auction Market : Targeting the Apartment Market in Seoul." Korea Real Estate Society 42, no. 4 (2024): 53–84. https://doi.org/10.37407/kres.2024.42.4.53.
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This study attempted to analyze the impact of consumer sentiment on the housing sales market using vector error modification model for the housing sales market and auction market.As a result of the apartment sales market model analysis, the apartment sales price index was described by consumer sentiment index, and interest rates, and interest rates were shown in the order of aging index, and interest rates.In particular, consumer goods and interest rates increased, the higher the explanation of apartment sales prices increased. As a result of the apartment auction market model analysis, the change in apartment auction market, interest rates, interest rate, interest rate, and economic growth rate, and economic growth rate was analyzed as the next to the housing sales market consumer sentimentIn particular, economic growth rate shows high explanation power over long-term and interest rates, and interest rates increased by consumers and interest rates increased.As a result of the two models, the housing auction market, interest rate, interest rate, interest rate, interest rate, interest rate, economic growth rate, and economic growth rate, economic growth rate, and economic growth rate.Therefore, the direction of participating in the housing market is necessary to understanding of economic variables such as housing consumption sentiment, such as housing consumption psychological changes in housing market, such as housing consumption sentiment.
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Azkiya, Azka Al, Iliana Patricia Vega, M. Iqbal, Zahra Nurul Fatimah, and Utami Dyah Syafitri. "Kata Netizen tentang Kesetaraan Gender dalam Sentimen Warganet Twitter." Martabat: Jurnal Perempuan dan Anak 5, no. 2 (2021): 434–58. http://dx.doi.org/10.21274/martabat.2021.5.2.434-458.
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Abstract: Gender equality is one of the goals in the Sustainable Development Goals. However, until now Indonesia is still having difficulties in achieving this goal. According to the United Nations Development Program (UNDP) data, Indonesia's Gender Inequality Index (GII) is ranked 107 out of 189 countries. In addition, according to The Global Gender Gap Index 2021 data by the World Economic Forum (WEF), Indonesia is ranked 105th out of 153 countries. This shows that Indonesia is still lagging behind in terms of gender equality. Therefore, this study aims to analyze the sentiments of Indonesian twitter netizens regarding gender equality in 2018-2021 and its accuracy. Data was collected from primary data, scraping twitter data with the keywords #kesetaraan and #gender in Indonesian. The method used is Lexicon-based Sentiment Analysis with AFINN-111 dictionary translated into Indonesian. The results obtained are that the percentage of positive sentiments tends to decrease from year to year except for 2021. On the contrary, the negative sentiments of Twitter tend to increase. This is due to controversial articles in RKUHP, RUU Cipta Kerja, Covid-19 pandemic, and the online gender-based violence. This shows that the gender equality in Indonesia is still minimal and needs to be improved.
 Keywords: AFINN-111, gender equality, lexicon-based sentiment analysis, text mining, twitter
 Abstrak: Kesetaraan gender termasuk tujuan pada Sustainable Development Goals. Namun hingga saat ini Indonesia masih kesulitan dalam mencapai tujuan tersebut. Menurut data United Nations Development Programme (UNDP), nilai Gender Inequality Index (GII) Indonesia menempati peringkat 107 dari 189 negara. Selain itu, menurut data The Global Gender Gap Index 2021 dari World Economic Forum (WEF), Indonesia menempati posisi ke-105 dari total 153 negara. Hal ini membuktikan gender di Indonesia masih belum setara. Oleh karena itu, penelitian ini bertujuan untuk menganalisis sentiment netizen twitter Indonesia mengenai kesetaraan gender pada 2018-202i dan akurasinya. Data dikumpulkan dari data primer yaitu scraping data twitter dengan keyword #kesetaraangender dan #gender dalam Bahasa Indonesia. Metode yang digunakan adalah Lexicon-based Sentiment Analysis dengan bantuan kamus AFINN-111 yang diterjemahkan dalam Bahasa Indonesia pada software python. Hasil yang diperoleh adalah persentase sentimen positif netizen twitter cenderung menurun dari tahun ke tahun kecuali 2021, sebaliknya sentimen negatif netizen twitter cenderung meningkat setiap tahun. Hal ini dikarenakan adanya pasal yang mengandung kontroversi pada Rancangan Kitab Undang-undang Hukum Pidana (RKUHP), RUU Cipta Kerja, adanya pandemi Covid-19, dan maraknya kekerasan berbasis gender online. Hal ini menunjukkan bahwa tingkat kesetaraan gender di Indonesia masih minim dan perlu untuk ditingkatkan kedepannya.
 Kata kunci: AFINN-111, kesetaraan gender, lexicon-based sentiment analysis, text mining, twitter
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Marschner, Paulo Fernando, and Paulo Sergio Ceretta. "Investor sentiment, economic uncertainty, and monetary policy in Brazil,." Revista Contabilidade & Finanças 32, no. 87 (2021): 528–40. http://dx.doi.org/10.1590/1808-057x202113220.
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ABSTRACT The aim of this study is to analyze how economic uncertainty and monetary policy affect investor sentiment in Brazil. Investor sentiment is an important element in the finance, economics, and accounting literature and its impact on financial markets is widely documented. However, understanding the variables that affect it remains an important challenge, and this research seeks to explore this gap within the Brazilian context. The study provides initial evidence regarding the impact of economic uncertainty and monetary policy on investor sentiment in Brazil. The findings documented here provide theoretical, managerial, and social contributions, with a possible impact on the areas of finance, economics, and accounting. Monthly data were used relating to four mechanisms of transmission of economic uncertainty and of monetary policy (interest rate, exchange rate, inflation rate, economic uncertainty index) and to the consumer confidence index as a proxy for investor sentiment (covering the period from January of 2006 to March of 2020). An autoregressive distributed lag model was estimated to capture short- and long-term relationships between the variables. The results indicate that investor sentiment is affected by economic uncertainty and by the main mechanisms of transmission of monetary policy to different extents and in the different time horizons. The evidence suggests that investors, policymakers, and monetary authorities should consider sentiment as a signal, whether for altering investment portfolios or for anticipating economic trends. It also provides support for focusing on economic and monetary policy in the National Financial Education Strategy (Estratégia Nacional de Educação Financeira - ENEF) recently adopted in Brazil
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Huseynova, Arzu, Tarana Aliyeva, and Ulviyya Rzayeva. "Investigation of the relationship between the dynamics of GDP and economic sentiment index." Eastern-European Journal of Enterprise Technologies 5, no. 13 (119) (2022): 60–72. http://dx.doi.org/10.15587/1729-4061.2022.265656.
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The paper develops and presents an appropriate model toolkit that allows assessing the relationship between the calculated indices of economic sentiment and confidence for the main types of economic activity. The aim of the study is to experimentally substantiate the relevance of data on the opinions of technological economic agents and assess the value of this information for the statistical description and analysis of macroeconomic trends, including economic cycles and unforeseen and prolonged crises. The main hypothesis about the cyclical sensitivity of composite indices, in particular the index of economic sentiment in relation to the dynamics of the physical volume of GDP, is tested. The authors calculate a composite indicator of aggregate economic sentiment and, based on a consistent analysis of the relationship between the index of physical volume of GDP and the indicator of economic sentiment, identify aggregate empirical patterns and features of the cyclical development of technological enterprises. Accordingly, the turning points of the business cycle are discussed and the leading nature of the proposed index of economic sentiment is affirmed. The importance of the composite indicators in the economic analysis of entrepreneurial behavior in the implementation of technological innovations is shown. The nature of the calculated Economic Sentiment Index was established, and its predictive capabilities for monthly and annual real GDP growth rates using autoregression and error correction models were investigated. The stages of calculating and setting indices with the application of the DEMETRA+ statistical package were implemented.
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Arzu, Huseynova, Aliyeva Tarana, and Rzayeva Ulviyya. "Investigation of the relationship between the dynamics of GDP and economic sentiment index." Eastern-European Journal of Enterprise Technologies 5, no. 13 (119) (2022): 60–72. https://doi.org/10.15587/1729-4061.2022.265656.
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The paper develops and presents an appropriate model toolkit that allows assessing the relationship between the calculated indices of economic sentiment and confidence for the main types of economic activity. The aim of the study is to experimentally substantiate the relevance of data on the opinions of technological economic agents and assess the value of this information for the statistical description and analysis of macroeconomic trends, including economic cycles and unforeseen and prolonged crises. The main hypothesis about the cyclical sensitivity of composite indices, in particular the index of economic sentiment in relation to the dynamics of the physical volume of GDP, is tested. The authors calculate a composite indicator of aggregate economic sentiment and, based on a consistent analysis of the relationship between the index of physical volume of GDP and the indicator of economic sentiment, identify aggregate empirical patterns and features of the cyclical development of technological enterprises. Accordingly, the turning points of the business cycle are discussed and the leading nature of the proposed index of economic sentiment is affirmed. The importance of the composite indicators in the economic analysis of entrepreneurial behavior in the implementation of technological innovations is shown. The nature of the calculated Economic Sentiment Index was established, and its predictive capabilities for monthly and annual real GDP growth rates using autoregression and error correction models were investigated. The stages of calculating and setting indices with the application of the DEMETRA+ statistical package were implemented.
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ne, G. "Machine-Coded News-Based Sentiment Index for Turkey and its Impact on Exchange Rates." Ekonomik Yaklasim 33, no. 123 (2022): 147. http://dx.doi.org/10.5455/ey.22001.
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This paper, initially, develops an automated system that conducts a content analysis using newspaper coverage to generate a high frequency news-driven sentiment index. The system classifies the news as good, bad, or neutral depending on word frequencies. Then, the study investigates the relationship between the news-based sentiment index and exchange rates in Turkey after controlling for both domestic and foreign macroeconomic fundamentals. The results reveal that US economic announcements drive the value of Turkish Lira whereas the Turkish economic announcements and sentiment index are ineffective. When other financial variables are considered, there is strong evidence that the sentiment index affects the stock market returns. Turkish macroeconomic announcements are insignificant in the stock and bond market specifications. The US announcements affect medium to long-term bond yields.
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Zhang, Weiran, Xinmeng Zhang, and Yixin Chen. "Quantitative Statistical Study of Financial Market Sentiment on Economic Cycles: An Analysis Based on the FinBERT Model and TVP-VAR." Transactions on Economics, Business and Management Research 9 (August 21, 2024): 294–302. http://dx.doi.org/10.62051/c7vskc54.
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Amid global financial market turmoil, the relationship between market sentiment and macroeconomic cycles has garnered significant attention. This study leverages big data from financial markets to quantitatively analyze market sentiment using the FinBERT model and investigates its impact on macroeconomic cycles with the TVP-VAR method. Based on textual data from the Shanghai Stock Exchange Index forums and Baidu Index online engagement metrics, the study employs GIS technology to analyze regional emotional responses to financial market fluctuations and economic activity trends.The research reveals significant regional differences in China's financial sentiment index during 2022-2023, with hotspots in the eastern coastal regions and cold spots in the west. Economically developed areas exhibit higher sensitivity to market fluctuations. TVP-VAR analysis indicates that changes in market sentiment have a minor impact on macroeconomic cycle volatility, typically exerting a mild negative effect at year's end, though the effects are not significant. This study unveils the dynamic relationship between financial market sentiment and macroeconomics, demonstrating the potential of using social media and online data for macroeconomic analysis. It offers practical recommendations for policymakers on leveraging market sentiment data for forecasting and regulating the macroeconomy, fostering interdisciplinary development in economics and financial engineering.
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Bouteska, Ahmed. "Some evidence from a principal component approach to measure a new investor sentiment index in the Tunisian stock market." Managerial Finance 46, no. 3 (2019): 401–20. http://dx.doi.org/10.1108/mf-11-2018-0570.
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Purpose The purpose of this paper is to study a novel and direct measurement of investor sentiment index in the Tunisian stock market that overcomes the weaknesses of a well-known investor sentiment index by Baker and Wurgler (2006, 2007). Design/methodology/approach Based on the data of 43 firms of the Tunisian stock market index (Tunindex) over the period 2004–2016, the author constructs a monthly investor sentiment that reflects both the economic fundamentals and the investor sentiment components. Seven indirect indicators collected from investor sentiment literature and Tunisian stock exchange were analyzed. Specifically, after accounting to remove the sentiment component for macroeconomic factors, the author estimates each sentiment proxy with a number of controlling variables. The residual from the estimation is used to define the author’s measure of excessive investor sentiment. To determine the best timing of sentiment indicators, the author employs a factor sentiment series as the first principal component of these total seven sentiment proxies and their lags of a month. Furthermore, by capturing the highest saturations with the first factor analysis, the author regressed each selected indicator’s lead or one-month lag in a second linear principal component analysis to reach the author’s Tunisian market’s total sentiment index. Findings The results show that all employed indicators may reflect the investor sentiment on the Tunisian stock market. The findings also indicate significant evidence that the author’s sentiment index takes into consideration the political and economic events such as the Jasmine Revolution experienced by Tunisia during the period from January 2, 2004 to December 30, 2016. Moreover, investor sentiment index flow appears to be one leading mechanism for the performance of Tunindex. Originality/value Results found have clearly shown that the author’s seven indirect indicators can reflect investor sentiment in the Tunisian context. The various sentiment proxies are bullish indicators of investor sentiment. Brown and Cliff (2004) argue that the higher bull/bear ratio, the more investor sentiment is bullish. An important value of price–earnings ratio implies that the level of investor confidence as for change in market is also important. Liquidity measured by trading volume, market turnover ratio and liquidity ratio reflects individual investor sentiment. Otherwise, it seems that investors only invest when they are optimistic and reduce market liquidity once they became pessimistic. The monthly response rate to initial public offerings (IPOs) represents a bullish sentiment indicator. Indeed, the more optimistic investors are, the higher the response rate to IPOs. Investor satisfaction also reflects investor sentiment. In other words, a high level of satisfaction translates an important level of optimism. In addition, the author also recognizes that the authors’ Tunisian sentiment index follow general trend of stock market prices and appears to be an important determinant of Tunindex returns during the period of study, from January, 2004 to December, 2016. The author suggests investor sentiment can help predict Tunindex returns, distinguishing between turbulent and tranquil periods in the financial market. The graphical illustration of monthly investor sentiment index shows that it captures extreme events such as the Tunisian revolution of January, 2011, also known as the Jasmine revolution which marked the start of the Arab Spring and the consequences of economic and political turmoil in Tunisia that have disrupted economic activity in the next few years. Like all research work, the current research paper has certain limitations. The choice of control variables allowing the author to separate sentiment component of that fundamental might be criticized. Moreover, there is no unanimous number of control variables but they are chosen according to data availability. The author also believes that one of the study’s weaknesses is that the author has not examined the impact of investor sentiment on the Tunisian stock market. For future interesting avenues of research, the author proposes, first, to study the effect of investor sentiment on financial asset returns and check, second, if sentiment factor constitutes an additional source of business risk valued by the marketplace.
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Gui, Junxiao, Nathee Naktnasukanjn, Xi Yu, and Siva Shankar Ramasamy. "Research on the Impact of Economic Policy Uncertainty and Investor Sentiment on the Growth Enterprise Market Return in China—An Empirical Study Based on TVP-SV-VAR Model." International Journal of Financial Studies 12, no. 4 (2024): 108. http://dx.doi.org/10.3390/ijfs12040108.
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This study employs the economic policy uncertainty index to gauge the level of economic policy uncertainty in China. Utilizing textual data from the growth enterprise market internet community, we construct the growth enterprise market investor sentiment index by applying the deep learning ERNIE (Enhanced Representation through Knowledge Integration) model, thereby capturing investors’ sentiment within the growth enterprise market. The dynamic interplay between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is scrutinized via the TVP-SV-VAR (time-varying parameter stochastic volatility vector auto-regression) model, and the asymmetric response of different industries’ stock returns within the growth enterprise market to economic policy uncertainty and investor sentiment shock. The findings of this research are that economic policy uncertainty exerts a negative influence on both investor sentiment and returns of the growth enterprise market. While it may trigger a temporary decline in stock prices, the empirical evidence suggests that the impact is of short duration. The influence of investor sentiment on the growth enterprise market returns is characterized by a reversal effect, suggesting that improved sentiment may initially boost stock prices but could lead to a subsequent decline over the long term. The relationship between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is time-variant, with heightened sensitivity observed during bull markets. Lastly, the effects of economic policy uncertainty and investor sentiment on the returns of different industries within the growth enterprise market are found to be asymmetric. These conclusions contribute to the existing body of literature on the Chinese capital market, offering a deeper understanding of the complex dynamics and the factors influencing market behavior.
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Stander, Yolanda S. "A News Sentiment Index to Inform International Financial Reporting Standard 9 Impairments." Journal of Risk and Financial Management 17, no. 7 (2024): 282. http://dx.doi.org/10.3390/jrfm17070282.
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Economic and financial narratives inform market sentiment through the emotions that are triggered and the subjectivity that gets evoked. There is an important connection between narrative, sentiment, and human decision making. In this study, natural language processing is used to extract market sentiment from the narratives using FinBERT, a Python library that has been pretrained on a large financial corpus. A news sentiment index is constructed and shown to be a leading indicator of systemic risk. A rolling regression shows how the impact of news sentiment on systemic risk changes over time, with the importance of news sentiment increasing in more recent years. Monitoring systemic risk is an important tool used by central banks to proactively identify and manage emerging risks to the financial system; it is also a key input into the credit loss provision quantification at banks. Credit loss provision is a key focus area for auditors because of the risk of material misstatement, but finding appropriate sources of audit evidence is challenging. The causal relationship between news sentiment and systemic risk suggests that news sentiment could serve as an early warning signal of increasing credit risk and an effective indicator of the state of the economic cycle. The news sentiment index is shown to be useful as audit evidence when benchmarking trends in accounting provisions, thus informing financial disclosures and serving as an exogenous variable in econometric forecast models.
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Naidoo, Deevarshan, Peter Moores-Pitt, and Paul-Francois Muzindutsi. "The South African Fear and Greed Index and Its Connectedness to the U.S. Index." Journal of Risk and Financial Management 18, no. 7 (2025): 349. https://doi.org/10.3390/jrfm18070349.
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This study investigates the cross-country spillover effects of investor sentiment, specifically Fear and Greed, between the United States and South Africa, within the broader context of increasing global financial integration and behavioral finance. Using monthly data from June 2007 to June 2024, this research constructs and tests the validity of a South African Fear and Greed Index, adapted from CNN’s U.S.-centric index, to better capture the unique dynamics and contribute to an alternate sentiment index for an emerging market. Employing the Diebold and Yilmaz (DY) connectedness framework, this study quantifies both static and dynamic spillover effects via a vector autoregression-based variance decomposition model. The results reveal significant bidirectional sentiment transmission, with the U.S. acting as a dominant net transmitter and South Africa as a net receiver, along with notable cross-country effects closely linked to the global economic trend. Spillover intensity escalates during periods of global economic stress, such as the 2008 financial crisis and the COVID-19 pandemic. The findings highlight that the USA significantly influences South Africa and that the adapted SA Fear and Greed Index better accounts for South African market conditions.
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Giannarakis, Grigoris, Xanthi Partalidou, Eleni Zafeiriou, and Nikolaos Sariannidis. "An analysis of United States on Dow Jones Sustainability Index." Investment Management and Financial Innovations 13, no. 3 (2016): 353–61. http://dx.doi.org/10.21511/imfi.13(3-2).2016.07.
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This paper examines the effect of various economic and financial indicators on the Dow Jones Sustainability Index (DJSI) returns. In particular, four explanatory variables are employed, namely United States (US) 10 Year bond value, gold price, Trade Weighted U.S. Dollar Index and Consumer Sentiment Index calculated by Michigan University. A generalized autoregressive conditional heteroskedasticity (GARCH) model is applied over DJSI United States which incorporates socially responsible companies for the period August, 1999 to May, 2016 using monthly data. The empirical results indicate that the consumer sentiment and the bond market exert positive impact on the DJSI US, whereas the gold and currency market affects it negatively. In addition, the structural analysis of DJSI US returns volatility showed that the US trade balance has a stabilizing effect on the conditional variance of the DJSI US return series. JEL Classification: G1, F2, Q40, M21. Keywords: Dow Jones Sustainability Index, bond value, gold, exchange rate, consumer sentiment
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Ken, Wong Vui, and Saizal Pinjaman. "Economic Influences on Plantation Sector Stock Prices in Malaysia: A Quantile Regression Approach." Business and Economic Research 14, no. 4 (2024): 22. http://dx.doi.org/10.5296/ber.v14i4.22099.
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This study aims to examine the relationship between microeconomic, sentiment, and macroeconomic variables across three quantiles (q25, q50, and q75) of stock prices in the Malaysian plantation sector. This study used Earnings per share (EPS) and return on equity (ROE) as the proxy for microeconomic variables, consumer sentiment index (CSI), and business condition index (BCI) as the proxy for sentiment variables, while inflation (INF which proxy from consumer price index) and exchange rate (ER, RM/USD) as the proxy for macroeconomic variables. Using a panel dataset covering 32 listed companies in plantation sector from 2008Q3 to 2023Q3. Result of quantile regression indicates that EPS, BCI, INF have a consistently positive and statistically significant relationship with stock prices across all quantiles. ROE has a mixed impact, with a positive relationship at the 25th and 50th quantile, but with a negative relationship at the 75th quantile. Furthermore, CSI and ER have a consistently negative and statistically significant relationship with stock prices across all quantiles.
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Huang, Lingui. "The Impact of China Economic Policy Uncertainty on CSI 300: An Analysis of the Mediating Effect of Investor Sentiment." Advances in Economics, Management and Political Sciences 51, no. 1 (2023): 41–49. http://dx.doi.org/10.54254/2754-1169/51/20230608.
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This paper explores the mediating effect of investor sentiment on the relationship between China's economic policy uncertainty and the CSI 300 stock market returns. The study employs Principal Component Analysis (PCA) to construct the investor sentiment index, integrating six proxy variables. Additionally, the bootstrap analysis method is utilized to examine whether investor sentiment acts as an intermediary in determining the impact of economic policy uncertainty on stock market performance. The findings reveal that a significant portion (87.0%) of the total effect of economic policy uncertainty on stock returns is mediated through investor sentiment. The study underscores the pivotal role of investor sentiment in financial market behavior and emphasizes the need for policymakers to consider its influence during economic adjustments. Furthermore, it provides crucial recommendations for individual investors, promoting informed and rational decision-making in the dynamic financial landscape.
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Pavan, Luca. "Sentiment Analysis of Italian and English Corpora of Internet News: A Comparison with Some Economic Trends." International Journal of Linguistics, Literature and Translation 5, no. 5 (2022): 136–41. http://dx.doi.org/10.32996/ijllt.2022.5.5.17.
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In this article, the sentiment analysis of several large Internet corpora made of Italian and English news is performed using a software written by the author, showing a possible connection with some economic trends. In this research, the news includes different topics (not necessarily financial news), and they are extrapolated from a large number of Internet newspapers. The software, already used in a previous article by the same author, is lexicon-based and makes use of scale points ranging from 0 to 100 to calculate an index of positivity in a text. The variation of sentiment tendency in the news corpora, calculated for a time period of several years, is later compared with some graphs showing some parameters of some economic trends, including the gross domestic product (GDP). It is found that the sentiment tendency of the news seems to have a relationship with the tendency of some economic trends that span the same time period. Positive growth of the economy per year seems connected with a positive variation in the index of positivity. Inversely, for a negative trend in the economy, the variation in the index of positivity is also negative. The article shows that, for various news topics, sentiment analysis can be useful to better understand some economic trends. For financial news, many studies show the possibility of predicting GDP growth through sentiment analysis. In this article, it is hypothesized that a prediction based on large news corpora including various topics could also be possible.
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Wang, Lei, and Hongwei Tan. "Agricultural Economic Risk Forecast Based on Data Mining Technology." Computational Intelligence and Neuroscience 2022 (April 27, 2022): 1–9. http://dx.doi.org/10.1155/2022/3684736.
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In order to improve the effect of agricultural economic risk forecast, this paper studies the agricultural economic risk forecast combined with data mining technology and builds an intelligent agricultural economic risk forecast system. Moreover, this paper employs a dynamic factor model to estimate common factors that drive changes in target topics. In order to construct a sentiment index that can reflect the overall operating situation of the macroeconomy, this paper improves the agricultural economic risk mining algorithm and standardizes the sentiment value corresponding to the target theme. In addition, this article analyzes the sentiment changes of its individual topics one by one in combination with the specific economic environment. The simulation study shows that the agricultural economic risk forecast system based on data mining technology proposed in this paper has a good effect.
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Kim, Byungchan, and Sol Kim. "The Impact of Investor Sentiment on Risk Neutral Skewness: Around Financial Crisis." Journal of Derivatives and Quantitative Studies 23, no. 4 (2015): 475–516. http://dx.doi.org/10.1108/jdqs-04-2015-b0001.
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We examine the relation between investor sentiment proxies and the risk neutral skewness of S&P 500 index option. The risk neutral skewness is estimated by the method of Bakshi, Kapadia and Madan (2003), which is non-parametric method, and the interpolation-extrapolation method and trapezoidal rule is used. We use four sentiment proxies: Michigan Consumer Sentiment Index, non-commercial trader's net position of S&P 500 futures market, Baker and Wurgler (2006)'s sentiment index, and bull-bear survey of American Association of Individual Investors. We firstly conduct the regression to find the general relations of two variables, and then examine the lead-lag relation between investor sentiment proxies and risk neutral skewness through VAR analysis. Contrary to the previous studies, we observe that sentiment proxies show different signs by the economic conditions. Overall, the sentiment proxies explain the three-dimension moment better in the crisis in U.S, and especially non-commercial trader's net position of S&P 500 futures market explains bet among the proxies.
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Lukauskas, Mantas, Vaida Pilinkienė, Jurgita Bruneckienė, Alina Stundžienė, Andrius Grybauskas, and Tomas Ruzgas. "Economic Activity Forecasting Based on the Sentiment Analysis of News." Mathematics 10, no. 19 (2022): 3461. http://dx.doi.org/10.3390/math10193461.
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The outbreak of war and the earlier and ongoing COVID-19 pandemic determined the need for real-time monitoring of economic activity. The economic activity of a country can be defined in different ways. Most often, the country’s economic activity is characterized by various indicators such as the gross domestic product, the level of employment or unemployment of the population, the price level in the country, inflation, and other frequently used economic indicators. The most popular were the gross domestic product (GDP) and industrial production. However, such traditional tools have started to decline in modern times (as the timely knowledge of information becomes a critical factor in decision making in a rapidly changing environment) as they are published with significant delays. This work aims to use the information in the Lithuanian mass media and machine learning methods to assess whether these data can be used to assess economic activity. The aim of using these data is to determine the correlation between the usual indicators of economic activity assessment and media sentiments and to forecast traditional indicators. When evaluating consumer confidence, it is observed that the forecasting of this economic activity indicator is better based on the general index of negative sentiment (comparisons with univariate time series). In this case, the average absolute percentage error is 1.3% lower. However, if all sentiments are included in the forecasting instead of the best one, the forecasting is worse and in this case the MAPE is 5.9% higher. It is noticeable that forecasting the monthly and annual inflation rate is thus best when the overall negative sentiment is used. The MAPE of the monthly inflation rate is as much as8.5% lower, while the MAPE of the annual inflation rate is 1.5% lower.
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Khan, Badal, and Salman Ahmed Shaikh. "The Effect of Investor Sentiment on Portfolio Returns: Evidence from the Pakistan Stock Exchange." Qlantic Journal of Social Sciences 5, no. 2 (2024): 433–51. http://dx.doi.org/10.55737/qjss.743519357.
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Economic literature presents numerous factors for pricing stocks following the normative assumptions. However, a perfect model for elaborating the returns has not been developed. Therefore, the theory proposes behavioural aspects for determining portfolio returns. This study tests a behavioural attribute investor sentiment on portfolio returns of 682 listed stocks on the Pakistan Stock Exchange from 2001-2021 over 245 months. The results show that the sentiment index mimics the stock index during the sampling period. The OLS and Newey-West standard error regressions, verify that investor sentiment can explain size and book-to-market sorted portfolio returns. Furthermore, the sentiment also explains the decile portfolio returns of selected anomalies. The results reveal that sentiment is an independent risk factor. The findings of this enquiry are helpful for investors and analysts who may consider investor sentiment when making investment and portfolio formation decisions.
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Krishnan, CNV, Jiemin Yang, and Xiyao Tan. "Analyzing Changing “Investor Exuberance”: The Determinants of S&P Composite Index Total Return CAPE Changes." Journal of Finance Issues 22, no. 3 (2024): 1–25. https://doi.org/10.58886/jfi.v22i3.8634.
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We analyze the determinants of changes in S&P Composite Index Total Return Cyclically Adjusted Price-to-Earnings ratio (TR CAPE), to better understand changing “investor exuberance”. We use three different methods - linear regression using PCA, Lasso, and Ridge regression techniques – and a large number of explanatory variables, to compare and contrast the significant determinants. Different methods yield different results. Across all methods, we find that monthly changes in Michigan sentiment index is significantly associated with monthly changes in TR CAPE. When we cross check the results using annual changes (rather than monthly changes), across all methods, annual changes in Michigan sentiment index and changes in core inflation are significantly associated with annual changes in TR CAPE. Overall, changes in the Michigan Sentiment Index appears to have significant association with changes in investor exuberance. Michigan Sentiment Index is a measure of consumer sentiment, when high, typically reflects optimism about future economic growth, leading to increased consumer spending and higher corporate earnings expectations. This positive outlook can boost investor confidence, driving up stock prices and, consequently, increasing the TR CAPE ratio as markets anticipate stronger future earnings. In contrast, the financial crisis of 2008, for example, led to a sharp decline in the Michigan Sentiment Index as consumer confidence plummeted due to fears of a prolonged recession.
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Xu, Tao, Yingying Zhao, and Jie Yu. "A Real Estate Price Index Forecasting Scheme Based on Online News Sentiment Analysis." Systems 13, no. 1 (2025): 42. https://doi.org/10.3390/systems13010042.
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The real estate price index serves as a crucial indicator reflecting the operational status of the real estate market in China. However, it often lags until mid-next month, hindering stakeholders from grasping market trends in real time. Moreover, the real estate market has an extremely complex operating mechanism, which makes it difficult to accurately assess the impact of various policy and economic factors on the real estate price index. Therefore, we hope, from the perspective of data science, to explore the emotional fluctuations of the public towards the real estate market and to reveal the dynamic relationship between the real estate price index and online news sentiment. Leveraging massive online news data, we propose a forecasting scheme for the real estate price index that abandons complex policy and economic data dependence and is solely based on common and easily obtainable online news data. This scheme involves crawling historical online real estate news data in China, employing a BERT-based sentiment analysis model to identify news sentiment, and subsequently aggregating the monthly Real Estate Sentiment (RES) index for Chinese cities. Furthermore, we construct a Vector Autoregression (VAR) model using the historical RES index and housing price index to forecast future housing price indices. Extensive empirical research has been conducted in Beijing, Shanghai, Guangzhou, and Shenzhen, China, to explore the dynamic interaction between the RES index and both the new housing price index and the second-hand housing price index. Experimental results showcase the unique features of the proposed RES index in various cities and demonstrate the effectiveness and utility of our proposed forecasting scheme for the real estate price index.
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Bai, Yuchen. "Investor Sentiment Measurement and Time Series Analysis." SHS Web of Conferences 163 (2023): 01002. http://dx.doi.org/10.1051/shsconf/202316301002.
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Investor sentiment is one of the destabilizing factors in the stock market. In the past, some scholars have proposed models for the measurement of emotions. On this basis, this paper selects 9 factors (closed-end fund discount rate, trading volume of the previous month, market turnover rate of the previous month, number of IPOs, first-day yield of IPOs, number of new investors opened accounts in the last month, Consumers Confidence index, Advance/Decline Line, Business Index of Macro-economic), and constructs the investor sentiment index through principal component analysis. Through statistical analysis of the index, it is found that it satisfies the conditions of a stationary time series, and a related formula is put forward. At the same time, the relationship between index and stock market is discussed.
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Chen, Wen-Yi, and Mei-Ping Chen. "Twitter’s daily happiness sentiment, economic policy uncertainty, and stock index fluctuations." North American Journal of Economics and Finance 62 (November 2022): 101784. http://dx.doi.org/10.1016/j.najef.2022.101784.
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Seki, Kazuhiro, Yusuke Ikuta, and Yoichi Matsubayashi. "News-based business sentiment and its properties as an economic index." Information Processing & Management 59, no. 2 (2022): 102795. http://dx.doi.org/10.1016/j.ipm.2021.102795.
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Harper, Alan, and Zhenhu Jin. "Examining the Relationship Between Consumer Sentiment and Stock Price Returns." Archives of Business Research 12, no. 10 (2024): 102–11. http://dx.doi.org/10.14738/abr.1210.17707.
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This study centers on exploring the dynamic relationship between the University of Michigan Consumer Sentiment Index (UMCSI) and the S&P 500 Index, employing both a bivariate regression model and Granger Causality analysis. The aim is to provide deeper insights into how fluctuations in consumer sentiment, as reflected by the UMCSI, impact and are impacted by movements in the S&P 500 Index. Furthermore, Granger Causality analysis is employed to discern the directionality of this relationship. The results indicate that the S&P 500 Index Granger Causes the UMCSI, implying that past values of the S&P 500 Index contain predictive information about future changes in consumer sentiment. This finding suggests that stock market movements can potentially serve as leading indicators of shifts in consumer confidence, offering predictive insights into broader economic trends and consumer behavior.
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Sudaryanti, Dwiyani, and Mohamad Bastomi. "From pandemic uncertainty to economic recovery: Does investor sentiment still matter for stock returns?" JEMA: Jurnal Ilmiah Bidang Akuntansi dan Manajemen 20, no. 1 (2023): 66–90. http://dx.doi.org/10.31106/jema.v20i1.19153.
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The relationship between investor sentiment and market dynamics is a highly intriguing research topic for both academics and the financial industry. By using Spearman rank correlation analysis, this paper aims to explore investors’ decision-making behavior (rational and irrational) on stock returns during and after the COVID-19 outbreak. While irrational factors of the study were measured by the Google Search Volume Index and trading volume, rational factors were measured by profitability and size. The study used three different characteristics of subsectors manufacturing industry namely Food and Beverage, Pharmacy, and Cigarettes that are listed in the Indonesia Stock Market. To the best of our knowledge, our study is the first to examine and compare the level of rationality of investors in a wide range of industries and sectors during and after the COVID-19 pandemic. Our finding supports the notion that both sentiments have an effect on stock returns indicating that cognitions, emotions, and the noise of traders still have an impact on the market. While overall rational sentiment has a more significant correlation with stock returns during the economic recovery phase, there was a highly significant correlation between irrational factors and stock returns during pandemic uncertainty conditions. Moreover, investors tend to be irrational and overreact when making investment decisions in Cigarette sectors during the COVID-19 pandemic. In contrast, after the pandemic, the correlation of rational sentiment of investors toward the Pharmacy industry is still higher than others.
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Singh, Pranav. "A Study of Investor Sentiment and Market Volatility During the Covid – 19 Pandemic." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04108.
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Abstract: This study investigates the influence of investor sentiment on stock market volatility in India during the COVID-19 pandemic. Using data from the NIFTY 50 index and Google Trends from March 2020 to December 2021, we analyze correlations between public mood and market fluctuations. The findings indicate that negative sentiment—driven by lockdowns, rising case numbers, and economic uncertainty— was strongly associated with increased market volatility. Furthermore, the study reveals that positive news, such as vaccine developments and easing restrictions, had a stabilizing effect on the markets, underscoring the importance of tracking sentiment. The paper suggests that integrating sentiment analysis into risk assessment strategies may improve investor decision-making and market resilience by providing a more comprehensive understanding of behavioral drivers influencing market behavior. Future research could further explore the impact of media coverage and government interventions on investor sentiment to develop more sophisticated predictive models. Additionally, understanding how investor sentiment interacts with global economic conditions and local market factors can help refine strategies to mitigate risk during future crises. Keywords : Investor Sentiment , COVID-19 Pandemic , Sentiment Analysis , Market Fluctuations
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Kowerski, Mieczysław. "The Economic Sentiment and Dividend Policy in Poland." Barometr Regionalny. Analizy i Prognozy, no. 1 (27) (May 17, 2012): 13–27. http://dx.doi.org/10.56583/br.1265.
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In previous research on determinants of company dividend-policy, a much higher significance was given to micro-economic factors describing the economic and financial situation of companies rather than to macro-economic factors. However, there is no analysis of the impact of economic sentiment on the dividend policy of companies. Moreover, companies do not operate in ‘a vacuum’. The economic situation in a certain country and even the global economic situation and its perception by entrepreneurs has a tremendous impact on their activities and decisions. To verify the hypothesis about the impact of economic sentiment on dividend policies of the companies listed on the Warsaw Stock Exchange in 1996–2009, logit pooled-regression models were applied. The dependent variable takes a value 1 if the company paid a cash dividend in year t and value 0 otherwise. As explanatory variables, we adopted the most common ones in this type of study, namely those describing the profitability, size, maturity, risk and investment opportunities as well as the dividend policy of the company in the year t – 1. Economic sentiment was measured using the Economic Sentiment Index, computed by the European Commission at monthly intervals. This allowed us to determine the period in which the changes in sentiment have the highest influence on dividend decisions. The estimated models allowed us to draw conclusions that apart from the economic and financial situation of a company in the year t – 1, dividend decisions made in year t are also affected by economic sentiment found in the Polish economy at the turn of the second quarter of year t. According to the Polish Code of Commercial Companies, it is understandable that the company should decide on the distribution of its profit within six months after the end of the business year. The research demonstrates, when making decisions, the boards of companies and shareholders take into account not only profits achieved in the previous year, but also the recent dividend and investment policy, and the current economic sentiment.
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Yürük, Muhammed Fatih. "Economic Uncertainty on Social Media: The Impact of X Posts on Economic and Financial Indicators." International Journal of Business and Economic Studies 7, no. 1 (2025): 56–69. https://doi.org/10.54821/uiecd.1634814.
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This study explores the influence of posts on the social media platform X (formerly Twitter) concerning economic uncertainty on key economic and financial indicators, including GDP, commercial bank loans, and the New York Stock Exchange (NYSE). The analysis focuses on the United States due to its pivotal role in global financial markets and the significant presence of U.S. users, who account for 50% of English-speaking X users, offering a rich dataset for studying social media-driven economic sentiment. Variables such as the USA Gross Domestic Product Index, Commercial Bank Loans, New York Stock Exchange Composite, and X-based Economic Uncertainty Index (TEU) were analyzed using monthly data from June 2011 to April 2023. Employing a Vector Autoregressive (VAR) model, the study finds that fluctuations in commercial bank loans and the NYSE Composite significantly impact GDP, while posts reflecting economic uncertainty, as captured by the TEU, primarily respond to changes in bank loans. The results reveal a bidirectional relationship between GDP and commercial bank loans, where loans can drive economic growth through increased consumer spending and investment, though excessive borrowing may lead to instability and crises. Furthermore, the TEU is influenced solely by variations in commercial bank loans, highlighting social media sentiment’s sensitivity to credit dynamics in the U.S. economy.
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Yu, Yong-Hao, Ru Wang, Hong-Ran Li, Inryeong Jin, and Winjang Ri. "The Impact of Volatility of MSCI Global Index and Korean Stock Index on Korean Trade." Northeast Asia Economic Association Of Korea 36, no. 1 (2024): 23–44. http://dx.doi.org/10.52819/jnes.2024.36.1.23.
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This study aims to analyse the impact of the stock market on Korea’s import and export trade, mainly through the MSCI Global and KOSPI indices as stock market proxies, and Korea’s total imports, total exports, import price indices, economic sentiment indices, and business surveys indices as proxies for import and export trade. Using a time-varying vector autoregression (TVP-VAR) model, it was found that the MSCI Global and KOSPI indices have a short-term positive effect on Korea’s total imports and total exports, but the KOSPI has a negative effect on total exports. For price indices, the MSCI Global Index has a positive effect on the import price index, while the KOSPI has a negative effect at a 1-month lag. For the export price index, the MSCI Global Index has a negative impact at 1-month lag but a positive impact at 3-month lag. For the Economic Sentiment Index, both indices have a negative impact at the 1-month lag. For the business survey index, both have a positive impact. In addition, the study analyses data from three different points in time: the US subprime crisis in 2008, the European credit crisis in 2015 and the New Crown epidemic in 2020. The results show that in all three financial events, the MSCI Global Index had a positive impact on Korea’s total exports and imports in the short term, while the KOSPI had a positive impact on total imports and a negative impact on total exports. For the import price index, the MSCI Global Index has a short-term positive impact while the KOSPI has a negative impact. For the export price index, both indices have a negative impact during financial events. For the economic sentiment index, the MSCI Global Index has a negative impact in the first lag and turns to a positive impact in the second lag, while the opposite is true for the KOSPI. Finally, in all three financial events, both indices had a short-term positive impact on the business survey index.
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Wang, Shuo. "Machine Learning Approaches to Stock Index Prediction." Advances in Economics, Management and Political Sciences 176, no. 1 (2025): 135–40. https://doi.org/10.54254/2754-1169/2025.22100.
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In response to the stock market's volatile nature, this research examines stock index forecasting evolution from traditional econometric models to advanced machine learning techniques. Market volatility, influenced by economic conditions, investor sentiment, and market interconnectedness, often renders linear models inadequate. While fundamental, conventional methods like multiple regression and ARMA face limitations with non-linear, noisy data, prompting development of machine learning approaches such as BP neural networks, SVM, and attention-enhanced CNN-LSTM models. These advanced techniques better capture data complexity, significantly improving prediction accuracy. The study explores both macro factors (economic linkages) and micro elements (herding behavior, loss aversion), alongside innovations like social media sentiment analysis that incorporate emotional and behavioral insights. Despite progress, challenges remain in balancing model complexity with accuracy and overcoming traditional statistical constraints in non-linear environments. This review emphasizes the necessity for integrated, fuzzy prediction models that consider multiple influences, with potential applications extending to other time series like commodity prices. These findings underscore the need for flexible, accurate forecasting methodologies to help authorities and investors navigate the unpredictable financial landscape.
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Unger, Stephan. "The Role of Country-pair-related News Sentiment in Foreign Exchange." Athens Journal of Business & Economics 9, no. 3 (2023): 327–44. http://dx.doi.org/10.30958/ajbe.9-3-5.
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This article explores the relative, explanatory contribution of country-pair-related political and financial news to foreign exchange rates. Contributing political factors are measured through the sentiment scores of published news while contributing financial factors are measured through various economic indicators such as price and volume of USD and CNY oil futures, the Russian IMOEX Index, and corresponding interest differentials. The results show that news sentiment plays a minor role in exchange rate determination while other factors such as prices and traded volumes in oil future contracts and interest differentials are significant contributing factors to the exchange rate determination. Nevertheless, the quality and quantity of news coverage of geo-political or economic events seems to play an important role when it comes to the impact of news on exchange rates. Among the sentiment-analyzed currency pairs, EUR/USD exhibits by far the highest sensitivity to political and economic news, followed by EUR/RUB, RUB/CNY, EUR/CNY, USD/CNY, and USD/RUB. Keywords: foreign exchange, news sentiment analysis, text mining, geo-political sentiment JEL-Codes: F31, E71
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Celler, Jan. "Readability and Sentiment Analysis of Central Bank Communication in Central and Eastern Europe." Journal of Advanced Computational Intelligence and Intelligent Informatics 28, no. 4 (2024): 1018–33. http://dx.doi.org/10.20965/jaciii.2024.p1018.
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This study analyzes the readability and sentiment of central bank communications across six Central and Eastern European countries. It reveals considerable variability in readability, with Moldova being the most accessible and Serbia the most complex. Notably, readability declined during the 2020 COVID-19 pandemic, reflecting the urgent and complex nature of economic communication. The study finds no direct correlation between readability and sentiment; however, the net hawkishness index significantly correlates with business cycle phases, suggesting its potential to forecast monetary policy shifts. This study underscores the intricate relationship between central bank communication, readability, sentiment, and economic conditions, advocating for enhanced clarity in central bank communication. It also highlights the importance of domain-specific sentiment analysis for interpreting and predicting the implications of monetary policy communication, providing valuable insights for policymakers and market participants.
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Meylianingrum, Kurniawati, Kholilah Kholilah, and Tiara Juliana Jaya. "Google Trends Analysis on Investor Sentiment on Stock Return Jakarta Islamic Index." Ekonomi Bisnis 28, no. 1 (2024): 14. https://doi.org/10.17977/um042v28i1p14-20.
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Investor sentiment arises when there is an event or event that affects people's lives such as pandemics, terrorism, wars, and economic conditions. The Covid 19 pandemic is an outbreak that has emerged and its spread is very fast around the world, so it requires people to stay at home to reduce the rate of transmission. This research was conducted to find out the positive and negative sentiment of investors towards the return of Jakarta Islamic Index shares. Positive and negative sentiment is induced from medical terms that appear on the google trends page. The method carried out is to use quantitative with a descriptive approach. The results obtained in this study are that investor sentiment, both positive and negative, partially and simultaneously has a significant positive effect on stock returns on the Jakarta Islamic Index. This shows that if there is a trust issue that is happening such as covid-19, war, data breaches, it can make investor sentiment change in choosing companies that issue stocks with data evidence taken from Google Trends to analyze the popularity of queries with keywords entered into search engines.
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Neves, Maria Elisabete Duarte, Luís Miguel Aragão Duarte Gonçalves, Mario Joaquim Silva Ribeiro, Paulo Jorge Santiago Feiteira, and Clara Margarida Pisco Viseu. "The unidirectional relationship between consumer confidence and PSI-20 returns - The influence of the economic cycle." Revista Contabilidade & Finanças 27, no. 72 (2016): 363–77. http://dx.doi.org/10.1590/1808-057x201602280.
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ABSTRACT The aim of this paper is to determine the relationship between market sentiment and rates of return on the main Portuguese benchmark and verify whether this relationship is influenced by different economic cycles. Given the subjectivity inherent to the use of variables capturing investor sentiment, the Consumer Confidence Index (CCI) was used as a benchmark. To achieve the proposed objective, an analysis of time series stationarity, Pearson correlation, and Granger causality using the autoregressive vectors model was carried out, followed by the Least Squares Method with macroeconomic variables. The results obtained suggest a one-way relationship between stock market returns and the sentiment variable. In fact, in times of recession, investor pessimism induces linear behavior and the sentiment-return relationship is more evident. This article will thus be of interest both to the academic community, in providing a basis for future investigations, and to managers and investors, with regards to the perception that the predictability of returns will be easier in periods of recession.
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Galena, Marcelena Vicky, Sediono Sediono, M. Fariz Fadillah Mardianto, and Elly Pusporani. "Prediction Analysis of Jakarta Composite Index Movement Using Support Vector Regression Method." G-Tech: Jurnal Teknologi Terapan 9, no. 1 (2025): 68–77. https://doi.org/10.70609/gtech.v9i1.5879.
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The JCI is an important indicator that reflects the performance of the Indonesian stock market. In recent times, the JCI has faced significant fluctuations due to complex factors, including global economic conditions and market sentiment, which make predicting its movements challenging. Good prediction is needed to support market stability and sustainable economic development as per SDGs point 8. This study applies a modern nonparametric regression method, namely Support Vector Regression (SVR), to predict a dataset in the form of weekly JCI data from the period April 2022 to October 2024 obtained from the investing.com website. The analysis shows that the SVR model with RBF kernel function provides the best performance, with MAPE of 1.43%, RMSE of 121.6196, and MAE of 104.65. The findings also reveal that the fluctuation pattern of the JCI cannot be fully explained based solely on historical data. External variables, such as global economic conditions and market sentiment, have a significant influence on the prediction results. Therefore, the SVR method can be utilized to optimize portfolio allocation based on weekly JCI predictions. In addition, the results of this study provide guidance for policymakers in designing proactive economic policies to mitigate market volatility and increase investor confidence.
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Su, Chi-Wei, Yuan Xi, Ran Tao, and Muhammad Umar. "CAN BITCOIN BE A SAFE HAVEN IN FEAR SENTIMENT?" Technological and Economic Development of Economy 28, no. 2 (2022): 268–89. http://dx.doi.org/10.3846/tede.2022.15502.
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This paper explores how fear sentiment affects the price of Bitcoin by employing the rolling-window Granger causality tests. The analysis reveals negative influences from the volatility index (VIX) to Bitcoin price (BTC), which ascertains that Bitcoin can not be considered a haven in fear sentiment. Due to the liquidity in economic downside risks, BTC may decrease with high VIX to hedge losses, increasing during low VIX periods. The empirical results conflict with the intertemporal capital asset pricing model, which underlines that the increasing VIX can promote the price of Bitcoin. In turn, BTC positively impacts VIX, which shows that Bitcoin price can be treated as the main indicator for a more comprehensive analysis of the fear index. Under severe global uncertainty and changeable fluctuation of market sentiment, investors can optimize investment decisions based on market fear sentiment. The government can also consider VIX to grasp the trend of BTC to participate in cryptocurrency speculation effectively.
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Dementieva, I. N. "Public sentiment of the population in the new social reality: a municipal cross-section." Vestnik Universiteta, no. 10 (December 2, 2021): 161–69. http://dx.doi.org/10.26425/1816-4277-2021-10-161-169.
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The article analyses the dynamics of public sentiment of residents of the Vologda Region. The author’s methodology for index analysis of public sentiment of the region, using the results of sociological monitoring, has been presented. At the same time, the main emphasis has been made on assessing the peculiarities of public sentiment of residents of municipalities – the cities of Vologda and Cherepovets in the context of changing social reality. The results of the study showed that the analysis of public sentiment in the monitoring regime makes it possible to obtain important information about the quality of state and municipal administration and determine the areas for improving their effectiveness, which is particularly relevant in the context of the socio-economic and epidemiological crisis.
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Liu, Yibo. "Stock price prediction for Google based on LSTM model with sentiment analysis." Applied and Computational Engineering 54, no. 1 (2024): 90–97. http://dx.doi.org/10.54254/2755-2721/54/20241395.
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Data analytics is increasingly widely used in economic and financial fields, with one of the more important applications being the prediction of stock price changes. However, the prediction of stock price changes is challenging because stock price changes are often uncertain and affected by multiple factors. This study is designed to use the LSTM model to predict stock price changes, and in the construction of the model to consider the psychological and emotional changes of investors, adding a sentiment analysis, combined with the sentiment index obtained from the sentiment analysis and the original stock price data as the input data for the prediction model. During the experiment, a comparison experiment was set up, i.e., only using the basic LSTM prediction model to predict stock price changes and the improved LSTM prediction model with the sentiment index obtained from the added sentiment analysis to predict stock price changes. After the comparison, the prediction results obtained by the LSTM model with the addition of sentiment analysis are more accurate, which on the one hand indicates that the change of investors' psychological sentiment will have an impact on the stock price change, and indicates that the prediction results obtained by the prediction model that considers the change of investors' sentiment are more accurate. The improved LSTM prediction model can help investors to effectively avoid possible risks when investing in stocks and thus gain more profit.
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Wang, Shizhen, Chyi Lin Lee, and Yan Song. "The COVID-19 Sentiment and Office Markets: Evidence from China." Buildings 12, no. 12 (2022): 2100. http://dx.doi.org/10.3390/buildings12122100.
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This study examines the impact of COVID-19 sentiment on office building rents and vacancy rates in China with a COVID-19 sentiment index constructed based on Baidu search queries on COVID-19-related keywords. We analyzed the data of office buildings and economic data from 2013 Q3 to 2022 Q2 in seven major Chinese cities with a two-stage Error Correction Model framework. We found that a heightened level of COVID-19 sentiment significantly and adversely affects the Chinese office buildings market. Specifically, office building rents decrease more than 8% if a city is exposed to an increase of one unit of COVID-19 sentiment for an entire quarter. The interaction terms model further reveals that the COVID-19 sentiment has a more substantial impact on office building rents where office vacancy is higher, reflecting an asymmetric effect. The findings here support the fear sentiment hypothesis. The findings suggest that a heightened level of investors’ COVID-19 sentiment resulted in a deterioration of office rents, reinforcing the role of investors’ sentiment in the pricing of office buildings. The findings suggest that investors should consider investor sentiment, particularly COVID-19 sentiment, in their decision-making.
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Khan, Mehwish, and Eatzaz Ahmad. "Measurement of Investor Sentiment and Its Bi-Directional Contemporaneous and Lead–Lag Relationship with Returns: Evidence from Pakistan." Sustainability 11, no. 1 (2018): 94. http://dx.doi.org/10.3390/su11010094.
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The present study examines bi-directional contemporaneous and lead–lag relationships between investor sentiment and market returns in the emerging market of Pakistan over the period of 2006 to 2016. To measure investor sentiment, the study employs a direct proxy namely Google search volume index (GSVI) and nine other indirect proxies. Besides conventional regression and VAR model, the study applies Geweke’s (1982) tests to investigate the nature of relationships between sentiment and returns. Thus, the study adds to existing literature by providing latest and thorough statistical evidence on the role of investor sentiment in influencing market returns. The study finds sufficient evidence regarding irrational behavior of investors in the thin market of Pakistan. In particular, the results indicate substantive role of sentiment in dragging stock market away from its sustainable path as implied by economic fundamentals.
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Kang, Hyunsoo. "The Impact of Exchange Rate and Economic Sentiment Index (ESI) on Price Level." Journal of Korea Research Association of International Commerce 24, no. 5 (2024): 17–33. http://dx.doi.org/10.29331/jkraic.2024.10.24.5.17.
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Rahmanto, Karina Cindy, Lailatul Hasanah, Arfian Kurniawan Ramadhan, and Fitri Kartiasih. "NEXUS BETWEEN THRIFTING AND GDP GROWTH IN INDONESIA’S TEXTILE AND WEARING APPAREL MANUFACTURING: ARDL AND SENTIMENT ANALYSIS APPROACH." Jurnal Ekonomi dan Bisnis Airlangga 35, no. 1 (2025): 1–16. https://doi.org/10.20473/jeba.v35i12025.1-16.
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Introduction: The rise of used clothing imports has sparked concerns about its economic impact, particularly on Indonesia’s textile and apparel industry. Methods: This study employs a mixed-methods approach, combining quantitative (ARDL model) and qualitative (sentiment analysis) methods. It analyzes GDP, Google Trend Index (GTI), and used clothing import data from 2018–2023 to assess the economic impact of thrifting. Results: This study analyzes the impact of thrifting on the GDP of Indonesia's textile and apparel industry subsector. The findings indicate that thrifting has a significant negative effect on the sector's GDP, while sentiment analysis reveals that 81.90 percent of public sentiment on Twitter expresses positive views toward thrifting. Conclusion and suggestion: This study concludes that thrifting harms the GDP of Indonesia's textile and apparel industry subsector. This finding is reinforced by the high public interest in thrifting, as reflected in the predominantly positive sentiment on Twitter. In response, policymakers and industry stakeholders should strengthen the enforcement of existing regulations and focus on enhancing the competitiveness of local products.
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Puhachova, M. V. "Using International Ranks and Business Activity Indicators for Economic Development Forecasting." Statistics of Ukraine 83, no. 4 (2018): 34–43. http://dx.doi.org/10.31767/su.4(83)2018.04.04.
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The majority of countries use several well-known integral indicators for economic forecasting purposes, on which basis these countries’ ranks in the global economic community are computed. Apart from allowing investors to make investment decisions, such indicators and ranks help in forecasting economic development in forthcoming periods. The best known of them is Ease of Doing Business Index and Global Competitiveness Index.
 The less known ones are indicators of Business Tendency Surveys (BTS), computed on the basis of data obtained from questionings of enterprise managers in various economic sectors and from studies of consumer sentiments. Yet, specialists tend to use the data from these conjuncture surveys to analyze the current economic situation in a country (total or by industry) and build short-term forecasts.
 Apart from the survey indicators depicting quite clearly a situation in the economy, the most popular ones in Europe are Confidence Indicators for enterprises by economic activity, and Economic Sentiment Indicator incorporating the indicators from BTS of enterprises and consumer sentiments. These indicators are computed by the European Commission for EU member countries on monthly basis.
 The article shows changes in Doing Business ranks for selected EU member countries and Ukraine. BTS indicators for industrial enterprises (estimate of production capacities; estimate of change in the production orders; estimate of the competitive position of enterprises at the internal market) are analyzed for some of these countries. A comparison of the dynamics of production capacities utilization, business confidence indicators and Doing Business rank is made for Ukraine and Bulgaria. The prognosticating capacities of BTS indicators compared with Doing Business indicator are analyzed.
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Liu, Chengxi, and Yanting Shen. "The Influence of Public Sentiment on FDI is Analyzed by Extending Payphone’s Gravity Model." Highlights in Business, Economics and Management 47 (February 8, 2025): 289–94. https://doi.org/10.54097/tgkp9k96.
Full textAbstract:
Since Trump initiated the trade war against China, the impact of politics on the economy has significantly increased. In recent years, as China's economic power has grown, diplomatic conflicts between China and Japan, as well as China and South Korea, have become more frequent and intense. From the diplomatic level, to expand the interests of countries, foreign trade is an indispensable part of every country's diplomacy. A very important index of foreign trade is foreign direct investment. Foreign direct investment (FDI) has been crucial to China’s rapid economic growth since the 1980s. As China’s key trade partners in East Asia, Japan and South Korea have invested over $1.5 trillion in FDI in China. National sentiment reflects people's views on politics from the perspective of the people and can also reflect political changes to a certain extent. Therefore, this study reflects the change of political change to economic development to a certain extent through the impact of national sentiment on economy. This article uses the gravity model to estimate how national sentiment affects the inward FDI flows from Japan and South Korea. This study highlights the national sentiment in both Japan and South Korea. While research shows a positive correlation between public sentiment towards China in these two countries, the coefficient results indicate that the overall impact of national sentiment on FDI is limited. This article mainly analyzes the relationship between national sentiment and FDI in South Korea and Japan and subdivides the different foreign policies of these two countries. Additionally, countries demonstrate different levels of sensitivity to sentiment, suggesting further-segmented policies should be taken into consideration. Investigating these different sensitivities is useful for China's different national policies towards its own country.
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Kim, SungSin. "A Study on the Effects of Investors' Irrational Sentiment Index and Rational Sentiment Index on Long-term Performance for Listed Companies with Special Technology Cases." Asia Europe Perspective Association 19, no. 4 (2022): 1–26. https://doi.org/10.31203/aepa.2022.19.4.001.
Full textAbstract:
The study analyzed whether abnormalities in stock prices that occur after initial public offering can be explained by investor sentiment indexes for technology-specific listed companies and general listed companies. In this regard, the investor sentiment index was classified into an unexplained irrational part and a rational part based on economic fundamental. The investment sentiment index was orthogonalized with the relative strength index, psychological line index, adjusted turnover rate, and trading volume using Yang and Zhou (2015) methodology, and then new variables were calculated through principal analysis. According to Seok et al. (2019), a new variable was used as a dependent variable, and the macroeconomic environmental factors such as excess return on the stock market, term spread, credit spread, firm size, book to market ratio, and price to earnings per share ratio were analyzed. The estimated residuals and forecasts were used as substitutes for the irrational investment sentiment index and the rational investment sentiment index, respectively. The results of the empirical analysis of this study are as follows. First, according to the test results of the average difference between major variables for technology-specific listed companies, the return on total assets was relatively low, and the debt ratio, public offering amount, one-year turnover rate, and buy-sell imbalance of individual investors for one and two years were high. In terms of long-term performance, the industrial adjusted return for two years was relatively low. Second, according to the results of the generalized least squares regression analysis on long-term performance for one year after IPO, the turnover rate showed a negative (-) relationship except for some models. The public offering price adjustment rate showed a positive (+) relationship with long-term performance in some models, and the stock price fell when individual investors bought excessively. Irrational investor sentiment showed a positive (+) relationship with long-term performance in all models. In the emerging market, it can be seen that the price error phenomenon is not properly corrected due to the lack of smooth arbitrage and short selling. Rational investor sentiment showed a positive (+) relationship with long-term performance because it reflected expectations for the market outlook and information on the company's fundamental. Third, even when it was expanded to long-term performance for two years, irrational investor sentiment maintained a positive (+) relationship with long-term performance and was not confirmed in rational investor sentiment. Return on total assets showed a positive (+) relationship with long-term performance in some models, and investors seem to focus on the company's actual business performance after realizing the wrong belief in listed companies. Looking at the long-term performance of technology-specific listed companies and general listed companies for two years, the decline in the long-term performance of technology-specific listed companies was more serious.