Journal articles: 'Sentiment Indicator'

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Silgoner, Maria Antoinette. "The Economic Sentiment Indicator." Journal of Business Cycle Measurement and Analysis 2007, no. 2 (March 10, 2008): 199–215. http://dx.doi.org/10.1787/jbcma-v2007-art11-en.

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Martináková, Radka, and Svatopluk Kapounek. "Economic sentiment indicator and its information capability in the Czech Republic." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 61, no. 7 (2013): 2491–98. http://dx.doi.org/10.11118/actaun201361072491.

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The paper focuses on the indicators of economic agents’ perceptions in the Czech Republic. We assume that these information are provided by economic sentiment indicator surveys based on the Joint Harmonised EU Programme. The aim of this paper is to offer the alternate methodology of qualitative data transformation (balance statistic data) in relation with the macroeconomic quantitative indicators. In the empirical analysis we distinguished between the indicators of confidence in industry, construction, retail and consumer confidence indicator. We found link between the aggregate economic sentiment indicator and economic activity. Especially, aggregate economic sentiment indicator copies the development of the GDP. However, partial indicators does not follow changes in the specific sectors of the economy. We also found that economic agents underestimate the intensity of the economic recession after the year 2007.Finally, we cannot recommend the economic sentiment indicator as the leading indicator of the future economic activity in the Czech Republic. Our methodological contribution is in quantifying of the consumer survey results by standardization.

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Baker, Malcolm, and Jeremy C. Stein. "Market liquidity as a sentiment indicator." Journal of Financial Markets 7, no. 3 (June 2004): 271–99. http://dx.doi.org/10.1016/j.finmar.2003.11.005.

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Huang, Robin, Na Liu, Mary Ann Nicdao, Mary Mikaheal, Tanya Baldacchino, Annabelle Albeos, Kathy Petoumenos, Kamal Sud, and Jinman Kim. "Emotion sharing in remote patient monitoring of patients with chronic kidney disease." Journal of the American Medical Informatics Association 27, no. 2 (October 21, 2019): 185–93. http://dx.doi.org/10.1093/jamia/ocz183.

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Abstract Objective To investigate the relationship between emotion sharing and technically troubled dialysis (TTD) in a remote patient monitoring (RPM) setting. Materials and Methods A custom software system was developed for home hemodialysis patients to use in an RPM setting, with focus on emoticon sharing and sentiment analysis of patients’ text data. We analyzed the outcome of emoticon and sentiment against TTD. Logistic regression was used to assess the relationship between patients’ emotions (emoticon and sentiment) and TTD. Results Usage data were collected from January 1, 2015 to June 1, 2018 from 156 patients that actively used the app system, with a total of 31 159 dialysis sessions recorded. Overall, 122 patients (78%) made use of the emoticon feature while 146 patients (94%) wrote at least 1 or more session notes for sentiment analysis. In total, 4087 (13%) sessions were classified as TTD. In the multivariate model, when compared to sessions with self-reported very happy emoticons, those with sad emoticons showed significantly higher associations to TTD (aOR 4.97; 95% CI 4.13–5.99; P = < .001). Similarly, negative sentiments also revealed significant associations to TTD (aOR 1.56; 95% CI 1.22–2; P = .003) when compared to positive sentiments. Discussion The distribution of emoticons varied greatly when compared to sentiment analysis outcomes due to the differences in the design features. The emoticon feature was generally easier to understand and quicker to input while the sentiment analysis required patients to manually input their personal thoughts. Conclusion Patients on home hemodialysis actively expressed their emotions during RPM. Negative emotions were found to have significant associations with TTD. The use of emoticons and sentimental analysis may be used as a predictive indicator for prolonged TTD.

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Altin, Mehmet, and Muzaffer Uysal. "Economic Sentiment Indicator as a Demand Determinant." Tourism Analysis 19, no. 5 (November 21, 2014): 581–97. http://dx.doi.org/10.3727/108354214x14116690097855.

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Sorić, Petar, Ivana Lolić, and Mirjana Čižmešija. "European economic sentiment indicator: an empirical reappraisal." Quality & Quantity 50, no. 5 (July 26, 2015): 2025–54. http://dx.doi.org/10.1007/s11135-015-0249-2.

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Meire, Matthijs, Kelly Hewett, Michel Ballings, V. Kumar, and Dirk Van den Poel. "The Role of Marketer-Generated Content in Customer Engagement Marketing." Journal of Marketing 83, no. 6 (September 9, 2019): 21–42. http://dx.doi.org/10.1177/0022242919873903.

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Despite the demonstrated importance of customer sentiment in social media for outcomes such as purchase behavior and of firms’ increasing use of customer engagement initiatives, surprisingly few studies have investigated firms’ ability to influence the sentiment of customers’ digital engagement. Many firms track buyers’ offline interactions, design online content to coincide with customers’ experiences, and face varied performance during events, enabling the modification of marketer-generated content to correspond to the event outcomes. This study examines the role of firms’ social media engagement initiatives surrounding customers’ experiential interaction events in influencing the sentiment of customers’ digital engagement. Results indicate that marketers can influence the sentiment of customers’ digital engagement beyond their performance during customers’ interactions, and for unfavorable event outcomes, informational marketer-generated content, more so than emotional content, can enhance customer sentiment. This study also highlights sentiment’s role as a leading indicator for customer lifetime value.

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Liu, Yuan, Yan Shang, Jianming Shi, and Shouyang Wang. "A New Investor Sentiment Indicator Based on Return Decomposition." Journal of Systems Science and Information 4, no. 2 (April 25, 2016): 121–30. http://dx.doi.org/10.21078/jssi-2016-121-10.

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AbstractThis paper extends the DSSW model to accommodate rational arbitrageurs, optimistic investors and pessimistic investors. We model the price impact by using daily data and create a new methodology to calculate the optimistic and the pessimistic. The new sentiment indicator has high correlation with the other traditional ones, and as a proxy variable of individual share or financial market on daily, it could distinguish the optimistic and the pessimistic. In the empirical research, we develop a time-series model and a cross-section model respectively to explore the explanatory power of highly frequent investor sentiment to idiosyncratic volatility and capital asset mispricing. The results show that the new sentiment indicator can explain 21.31% of idiosyncratic volatility to individual share on average, and it has a great explanation of 36% to capital asset mispricing.

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Forss, Thomas, and Peter Sarlin. "News-sentiment networks as a company risk indicator." Journal Of Network Theory In Finance 4, no. 1 (2018): 65–86. http://dx.doi.org/10.21314/jntf.2018.039.

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Čižmešija, Mirjana, and Petar Sorič. "Assessing Croatian GDP Components Via Economic Sentiment Indicator." Economic Research-Ekonomska Istraživanja 23, no. 4 (January 2010): 1–10. http://dx.doi.org/10.1080/1331677x.2010.11517429.

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Aliouche, E. Hachemi, Nelson A. Barber, and Raymond J. Goodman. "Lodging Executives’ Sentiment As a Leading Economic Indicator." Cornell Hospitality Quarterly 54, no. 4 (July 19, 2013): 406–15. http://dx.doi.org/10.1177/1938965513492905.

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Braun, Johannes, Jochen Hausler, and Wolfgang Schäfers. "Artificial intelligence, news sentiment, and property market liquidity." Journal of Property Investment & Finance 38, no. 4 (November 29, 2019): 309–25. http://dx.doi.org/10.1108/jpif-08-2019-0100.

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Purpose The purpose of this paper is to use a text-based sentiment indicator to explain variations in direct property market liquidity in the USA. Design/methodology/approach By means of an artificial neural network, market sentiment is extracted from 66,070 US real estate market news articles from the S&P Global Market Intelligence database. For training of the network, a distant supervision approach utilizing 17,822 labeled investment ideas from the crowd-sourced investment advisory platform Seeking Alpha is applied. Findings According to the results of autoregressive distributed lag models including contemporary and lagged sentiment as independent variables, the derived textual sentiment indicator is not only significantly linked to the depth and resilience dimensions of market liquidity (proxied by Amihud’s (2002) price impact measure), but also to the breadth dimension (proxied by transaction volume). Practical implications These results suggest an intertemporal effect of sentiment on liquidity for the direct property market. Market participants should account for this effect in terms of their investment decisions, and also when assessing and pricing liquidity risk. Originality/value This paper not only extends the literature on text-based sentiment indicators in real estate, but is also the first to apply artificial intelligence for sentiment extraction from news articles in a market liquidity setting.

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He, Gang, Shuzhen Zhu, and Haifeng Gu. "A PLS Approach to Measuring Investor Sentiment in Chinese Stock Market." Discrete Dynamics in Nature and Society 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/2387543.

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We select five objective sentiment indicators and one subjective sentiment indicator to build investor sentiment composite index in Chinese stock market by using the partial least squares. The reason why we do that is to improve the shortcomings of the principal component analysis, which was adopted to build investor sentiment composite index in the pioneering research. Moreover, due to the large proportion of individual investors in Chinese stock market and the rapid change of investor sentiment, we innovatively use the weekly data with smaller information granularity and higher frequency. Through empirical tests for its reasonability and market’s predictive capability, we find that this index appears to fit the data better and improves prediction.

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Kunze, Frederik, Tobias Basse, Miguel Rodriguez Gonzalez, and Günter Vornholz. "Forward-looking financial risk management and the housing market in the United Kingdom: is there a role for sentiment indicators?" Journal of Risk Finance 21, no. 5 (September 21, 2020): 659–78. http://dx.doi.org/10.1108/jrf-10-2019-0191.

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Purpose In the current low-interest market environment, more and more asset managers have started to consider to invest in property markets. To implement adequate and forward-looking risk management procedures, this market should be analyzed in more detail. Therefore, this study aims to examine the housing market data from the UK. More specifically, sentiment data and house prices are examined, using techniques of time-series econometrics suggested by Toda and Yamamoto (1995). The monthly data used in this study is the RICS Housing Market Survey and the Nationwide House Price Index – covering the period from January 2000 to December 2018. Furthermore, the authors also analyze the stability of the implemented Granger causality tests. In sum, the authors found clear empirical evidence for unidirectional Granger causality from sentiment indicator to the house prices index. Consequently, the sentiment indicator can help to forecast property prices in the UK. Design/methodology/approach By investigating sentiment data for house prices using techniques of time-series econometrics (more specifically the procedure suggested by Toda and Yamamoto, 1995), the research question whether sentiment indicators can be helpful to predict property prices in the UK is analyzed empirically. Findings The empirical results show that the RICS Housing Market Survey can help to predict the house prices in the UK. Practical implications Given these findings, the information provided by property market sentiment indicators certainly should be used in a forward-looking early warning system for house prices in the UK. Originality/value To authors’ knowledge, this is the first paper that uses the procedure suggested by Toda and Yamaoto to search for suitable early warning indicators for investors in UK real estate assets.

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Gelper, Sarah, and Christophe Croux. "On the Construction of the European Economic Sentiment Indicator*." Oxford Bulletin of Economics and Statistics 72, no. 1 (February 2010): 47–62. http://dx.doi.org/10.1111/j.1468-0084.2009.00574.x.

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Wilcox, James A. "The Home Purchase Sentiment Index: A New Housing Indicator." Business Economics 50, no. 4 (October 2015): 178–90. http://dx.doi.org/10.1057/be.2015.27.

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Cao Mai Phuong, Lai, and Vu Cam Nhung. "Investor sentiment measurement based on technical analysis indicators affecting stock returns: Empirical evidence on VN100." Investment Management and Financial Innovations 18, no. 4 (December 3, 2021): 297–308. http://dx.doi.org/10.21511/imfi.18(4).2021.25.

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The purpose of this study is to examine whether investor sentiment as measured by technical analysis indicators has an impact on stock returns. The research period is from 2015 to mid-2020. 1-year government bond yields, financial data, transaction data of 57 companies in the VN100 basket, and VNIndex are analyzed. The investor sentiment variable is measured by each technical analysis indicator (Relative Strength Index – RSI, Psychological Line Index – PLI), and the general sentiment variable is established based on extracting the principal component from individual indicators. The paper uses two regression methods – Fama-MacBeth and Generalized Least Square (GLS) – for five different research models. The results show that sentiment plays an important role in stock returns in the Vietnamese stock market. Even controlling the factors such as cash flow per share, firm size, market risk premium, and stock price volatility in the studied models, the impact of sentiment is significant in both the model using individual technical indicators and the model using the general sentiment variable. Furthermore, investor sentiment has a stronger power to explain excess stock returns than their trading behavior. The implication from the results shows that the Vietnamese stock market is inefficient, in which psychology is a very important issue and participants need to pay due attention to this factor. AcknowledgmentThis study was funded by the Industrial University of Ho Chi Minh City (IUH), Vietnam (grant number: 21/1TCNH03).

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Önder, Irem, Ulrich Gunter, and Arno Scharl. "Forecasting Tourist Arrivals with the Help of Web Sentiment: A Mixed-frequency Modeling Approach for Big Data." Tourism Analysis 24, no. 4 (November 13, 2019): 437–52. http://dx.doi.org/10.3727/108354219x15652651367442.

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Online news media coverage regarding a destination, a form of big data, can affect destination image and influence the number of tourist arrivals. Sentiment analysis extracts the valence of an author's perception about a topic by rating a segment of text as either positive or negative. The sentiment of online news media can be viewed as a leading indicator for actual tourism demand. The aim of this study is to examine if web sentiment of online news media coverage of four European cities (Berlin, Brussels, Paris, and Vienna) possesses information to predict actual tourist arrivals. This study is the first to use web sentiment for forecasting tourism demand. Automated semantic routines were conducted to analyze the sentiment of online news media coverage. Due to the differing data frequencies of tourist arrivals (monthly) and web sentiment indicators (daily), the MIxed-DAta Sampling (MIDAS) modeling approach was applied. Results indicate that MIDAS models including various web sentiment indicators outperform time-series and naive benchmarks in terms of typical accuracy measures. This study shows that utilizing online news media coverage as an indication of destination image can improve tourism demand forecasting. Because destination image is dynamic, the results can vary depending on time period of the analysis and the destination. A managerial implication of the forecast evaluation exercise is that destination management organizations (DMOs) should add models incorporating web sentiment data to their forecast modeling toolkit to further improve the accuracy of their tourism demand forecasts.

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Srivastava, Praveen Ranjan, Zuopeng (Justin) Zhang, and Prajwal Eachempati. "Deep Neural Network and Time Series Approach for Finance Systems." Journal of Organizational and End User Computing 33, no. 5 (September 2021): 204–26. http://dx.doi.org/10.4018/joeuc.20210901.oa10.

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The stock market is an aggregation of investor sentiment that affects daily changes in stock prices. Investor sentiment remained a mystery and challenge over time, inviting researchers to comprehend the market trends. The entry of behavioral scientists in and around the 1980s brought in the market trading's human dimensions. Shortly after that, due to the digitization of exchanges, the mix of traders changed as institutional traders started using algorithmic trading (AT) on computers. Nevertheless, the effects of investor sentiment did not disappear and continued to intrigue market researchers. Though market sentiment plays a significant role in timing investment decisions, classical finance models largely ignored the role of investor sentiment in asset pricing. For knowing if the market price is value-driven, the investor would isolate components of irrationality from the price, as reflected in the sentiment. Investor sentiment is an expression of irrational expectations of a stock's risk-return profile that is not justified by available information. In this context, the paper aims to predict the next-day trend in the index prices for the centralized Indian National Stock Exchange (NSE) deploying machine learning algorithms like support vector machine, random forest, gradient boosting, and deep neural networks. The training set is historical NSE closing price data from June 1st, 2013-June 30th, 2020. Additionally, the authors factor technical indicators like moving average (MA), moving average convergence-divergence (MACD), K (%) oscillator and corresponding three days moving average D (%), relative strength indicator (RSI) value, and the LW (R%) indicator for the same period. The predictive power of deep neural networks over other machine learning techniques is established in the paper, demonstrating the future scope of deep learning in multi-parameter time series prediction.

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Moon, Hye-Jung. "Construction of an Economic Sentiment Indicator for the Korean Economy." Korean Journal of Applied Statistics 24, no. 5 (October 31, 2011): 745–58. http://dx.doi.org/10.5351/kjas.2011.24.5.745.

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Lukac, Zrinka, and Mirjana Cizmesija. "(Re)Constructing the European Economic Sentiment Indicator: An Optimization Approach." Social Indicators Research 155, no. 3 (February 9, 2021): 939–58. http://dx.doi.org/10.1007/s11205-020-02602-6.

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Fernandez, Raul, Brenda Palma Guizar, and Caterina Rho. "A sentiment-based risk indicator for the Mexican financial sector." Latin American Journal of Central Banking 2, no. 3 (September 2021): 100036. http://dx.doi.org/10.1016/j.latcb.2021.100036.

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Ryu, Doojin, Karam Kim, and Heejin Yang. "The Impact of Credit Rating Change on Investor Sentiment." Journal of Derivatives and Quantitative Studies 27, no. 1 (February 28, 2019): 85–111. http://dx.doi.org/10.1108/jdqs-01-2019-b0003.

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The behavioral finance literature focuses on the effect of investor sentiment on the fundamental values of individual stocks. This study constructs a firm-level investor sentiment indicator based on transaction and price data for individual firms and shows that credit rating changes affect investor sentiment. We find the following empirical results. First, the response of investor sentiment to upgrades (downgrades) is significantly positive (negative). Second, the greater the magnitude of the downgrade is, the more negative the investor sentiment reaction is, although we do not find a similar result for upgrades. Third, cumulative abnormal returns around the event day are affected by cumulative abnormal sentiment before that day. This result suggests that the market reaction is affected by a combination of credit rating downgrades and investor sentiment.

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Phuong, Lai Cao Mai. "Investor Sentiment by Money Flow Index and Stock Return." International Journal of Financial Research 12, no. 4 (March 18, 2021): 33. http://dx.doi.org/10.5430/ijfr.v12n4p33.

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Factors affecting stock prices have been studied by many scholars on different stock markets. However, the number of empirical studies applying technical analysis indicators to measure investor sentiment is quite limited. To explore this interesting topic, this study uses the Money Flow Index (MFI) indicator to measure an investor's sentiment by various thresholds and to test its effect on the excess return on Vietnam stock market. Data series including market, interest rate, finance and transaction data of 138 companies listed on the Ho Chi Minh City Stock Exchange from 2015 to June 2020 are used in the equations Regression. The study's findings show that, after controlling for market factors, individual characteristics and liquidity of each company, investor sentiment as measured by the MFI indicator still has a significant impact on the return of stocks at all thresholds. In addition, when the MFI value area is near the starting and ending point of the scale (less than 20, greater than 80), the regression coefficients of these two thresholds and control variables both increase compared to the remaining models, return and significant effect to the excess return of the securities.

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Anastasiou, Dimitris, and Athanassios Petralias. "On the Construction of a Leading Indicator Based on News Headlines for Predicting Greek Deposit Outflows." International Journal of Business Management and Finance Research 4, no. 1 (September 10, 2021): 1–11. http://dx.doi.org/10.53935/2641-5313.v4i1.57.

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Employing data in a monthly frequency, with a sample period spanning from 2002 to 2018, the purpose of this study is twofold. First, we construct a novel leading indicator based on news headlines drawn from Bloomberg, and second, examine whether this leading indicator able to capture agents’ sentiment affects Greek bank deposit flows’ trajectory. Employing alternative econometric methodologies, we find that this index proxies for depositors’ crisis sentiment and the higher this index becomes, the higher the depositors’ negative sentiment becomes, leading them to withdraw their bank deposits. Overall, in this work, we show that the last decade’s advances in internet technology, which permit us to have direct access to a vast amount of information such as news headlines, offers the possibility of forecasting critical measures in the economy’s banking system, such as the number of bank deposits, which are of crucial importance. Monetary poly authorities or macroprudential regulators could adapt our model to assess the resilience of a bank or the whole banking sector.

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Wang, Bingkun, Yongfeng Huang, Xian Wu, and Xing Li. "A Fuzzy Computing Model for Identifying Polarity of Chinese Sentiment Words." Computational Intelligence and Neuroscience 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/525437.

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With the spurt of online user-generated contents on web, sentiment analysis has become a very active research issue in data mining and natural language processing. As the most important indicator of sentiment, sentiment words which convey positive and negative polarity are quite instrumental for sentiment analysis. However, most of the existing methods for identifying polarity of sentiment words only consider the positive and negative polarity by the Cantor set, and no attention is paid to the fuzziness of the polarity intensity of sentiment words. In order to improve the performance, we propose a fuzzy computing model to identify the polarity of Chinese sentiment words in this paper. There are three major contributions in this paper. Firstly, we propose a method to compute polarity intensity of sentiment morphemes and sentiment words. Secondly, we construct a fuzzy sentiment classifier and propose two different methods to compute the parameter of the fuzzy classifier. Thirdly, we conduct extensive experiments on four sentiment words datasets and three review datasets, and the experimental results indicate that our model performs better than the state-of-the-art methods.

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Zeng, Hongjun. "Volatility Modelling of Chinese Stock Market Monthly Return and Investor Sentiment Using Multivariate GARCH Models." International Journal of Accounting & Finance Review 5, no. 1 (June 22, 2020): 123–33. http://dx.doi.org/10.46281/ijafr.v5i1.635.

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This article examines the linkage and volatility spillover among Chinese Stock Market Monthly Return and Investor Sentiment, investigating the effect dynamic links of various investor sentiment indicators and Chinese stock market return volatility. Employing the DCC and BEKK GARCH, we find investor sentiment is to some extent linked to the yield fluctuations of the Chinese stock market, but the volatility spillover is relatively weak. In the test period (2005-2020), we observe that several indicators do not explain their linkage effects with CSI 300 index of return fluctuations and volatility spillovers well, with no indicators can reflect both of these effects. Most indicators are linkage with the CSI 300 index, especially consumer confidence index (CCI), new investor account openings last month (NIA) and the volume of transactions last month (TURN) have significant linkage effects with the CSI 300 index. We also find that only the CCI index has a one-way volatility spillover on the CSI 300 index, and the CSI 300 index has no volatility spillover on any indicator.

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Zeng, Hongjun. "Volatility Modelling of Chinese Stock Market Monthly Return and Investor Sentiment Using Multivariate GARCH Models." International Journal of Accounting & Finance Review 5, no. 1 (June 27, 2020): 123–33. http://dx.doi.org/10.46281/ijafr.v5i1.643.

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This article examines the linkage and volatility spillover among Chinese Stock Market Monthly Return and Investor Sentiment, investigating the effect dynamic links of various investor sentiment indicators and Chinese stock market return volatility. Employing the DCC and BEKK GARCH, we find investor sentiment is to some extent linked to the yield fluctuations of the Chinese stock market, but the volatility spillover is relatively weak. In the test period (2005-2020), we observe that several indicators do not explain their linkage effects with CSI 300 index of return fluctuations and volatility spillovers well, with no indicators can reflect both of these effects. Most indicators are linkage with the CSI 300 index, especially consumer confidence index (CCI), new investor account openings last month (NIA) and the volume of transactions last month (TURN) have significant linkage effects with the CSI 300 index. We also find that only the CCI index has a one-way volatility spillover on the CSI 300 index, and the CSI 300 index has no volatility spillover on any indicator.

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Teplova, T. V., T. V. Sokolova, A. F. Tomtosov, D. V. Buchko, and D. D. Nikulin. "The sentiment of private investors in explaining the differences in the trade characteristics of the Russian market stocks." Journal of the New Economic Association 53, no. 1 (2022): 53–84. http://dx.doi.org/10.31737/2221-2264-2022-53-1-3.

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In our paper, for the first time, we examine the influence of the sentiment of private investors in social networks on the trade characteristics of stocks in the Russian market. Monthly return rates and trading volumes are analyzed under the control of financial indicators and indicators of the quality of corporate governance of stock issuers, as well as the changing external environment in the period from 2013 to 2020. The sample for various sentiment metrics is based on unique data: messages in the Telegram and mfd.ru platforms. The tonality of messages is diagnosed according to the authors’ method using artificial intelligence (neural network). The main conclusion is: the sentiment can be seen as an explanatory factor in pricing and trading activity. The influence of sentiment is non-linear. The author’s HYPE indicator of sentiment is proposed and compared in terms of explanatory ability of the trade characteristics with a wide range of proxy variables. The explanatory ability to identify differences is realized through regression constructions on panel data. It is shown that trade characteristics are more sensitive to the growth of negative messages, which is consistent with the postulates of behavioral finance. An increase in messages’ number of both positive and negative sentiment contributes to the growth of trading activity. An important practical conclusion is: following the crowd when the company is most intensely discussed will not result in high returns to an investor.

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王, 帅. "Performance-Related Research of Sentiment Indicator and the Closed-End Fund." Emergence and Transfer of Wealth 02, no. 02 (2012): 7–13. http://dx.doi.org/10.12677/etw.2012.22008.

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Lu, Hengzhen, Xiaoyu Zhu, Jianli Wang, and Ho Yin Yick. "Share pledge transactions as an investor sentiment indicator - Evidence from China." Quarterly Review of Economics and Finance 82 (November 2021): 230–38. http://dx.doi.org/10.1016/j.qref.2021.09.011.

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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 (December 16, 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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Puhachova, M. V. "Using International Ranks and Business Activity Indicators for Economic Development Forecasting." Statistics of Ukraine 83, no. 4 (December 17, 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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Wassan, Sobia, Tian Shen, Chen Xi, Kamal Gulati, Danish Vasan, and Beenish Suhail. "Customer Experience towards the Product during a Coronavirus Outbreak." Behavioural Neurology 2022 (February 2, 2022): 1–18. http://dx.doi.org/10.1155/2022/4279346.

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Nowadays, sentimental analysis of consumers’ review is becoming much crucial in the marketing world. It is not just giving ideas to the firms that how consumers like their product or service, but it would also help them make their service better. In this article, the statistical method identifies the relationship of many factors in consumer feedback. It introduces a deep-based learning method called DSC (deep sentiment classifier) to determine whether or not to recommend the reviewed product thoroughly. Our suggested method also investigates the effect sizes of the feedback, such as positives, negatives, and neutrals. We used the women’s clothing review dataset containing 22,642 records after preprocessing of the results. Experimental studies show that the recommendations are an excellent positive sentiment indicator. In comparison, ratings become fuzzy performance metrics in product reviews. The 10-fold cross-validation analysis shows that the recommended form has the top F1 score (93.56%) in the sentimental classification on average and the recommended classification (88.32%) on average. A comparative description of other classifiers focused on machine learning, for example, KNN, random forest, logistic regression, decision tree, support vector machine multilayer perceptron, and naïve Bayes, also demonstrates that DSC gives the best possible result. We have tested DSC on the dataset IMDB (Internet Video Database), which includes the sentiment of the 50,000 movie reviews (25000 for training and 25000 for testing). In comparison to other baseline methods, DSC obtained an excellent classification score for this experiment.

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Blin, Olivier, Florian Ielpo, Joan Lee, and Jérôme Teiletche. "Alternative Risk Premia Timing: A Point-in-Time Macro, Sentiment, Valuation Analysis." Journal of Systematic Investing 1, no. 1 (February 23, 2021): 52–72. http://dx.doi.org/10.52354/jsi.1.1.iv.

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We investigate the question of dynamic allocation across a diversified range of alternative risk premia. By using a set of point-in-time indicators across macro, sentiment and valuation dimensions, we find that a majority of indicators deliver a positive information ratio for a majority of alternative risk premia over the period 2005–2020. In our empirical simulations, the macro dimension seems to have worked well, notably during recession periods. Sentiment (based on market stress and momentum) struggled during recovery periods, but added value elsewhere. Valuation has worked well from 2005 to 2013 and lost part of its appeal since then. The combination of indicators allows to deliver a higher information ratio thanks to the low correlation among them. Our research also finds that point-in-time macroeconomic variables (“nowcasters”) can add value over traditional indicators, while this improvement is not significant in the case of the market stress indicator.

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Myšková, Renáta, and Petr Hájek. "Sustainability and Corporate Social Responsibility in the Text of Annual Reports—The Case of the IT Services Industry." Sustainability 10, no. 11 (November 9, 2018): 4119. http://dx.doi.org/10.3390/su10114119.

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Annual reports are an important source of qualitative information about a company’s strategic areas, including sustainability. However, previous work on sustainability assessment has been limited to quantitative indicators that are difficult to obtain. Here, we address this issue by analyzing a company’s strategic documents, with the specific aim of demonstrating the role of sustainability and social responsibility in the text of the annual reports of companies in the IT services industry. We demonstrate that this information is a significant determinant of future economic outcomes. Specifically, here we evaluate sentiment in managerial communication in the area of sustainable business by using collocation analysis of topic and sentiment word lists. Several domain-specific word lists were used for each category monitored. Specifically, Loughran and McDonald’s word list was used to measure sentiment in the context of corporate social responsibility and sustainability. The word list that was developed by Pencle and Malaescu was used for CSR, while novel word lists are proposed for sustainability topics. The results of experiments show that the sentiment of sustainability topics (environmental and social in particular) in the annual reports may be a significant indicator of future profitability and thus represent an important information for corporate stakeholders.

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Fassas and Hourvouliades. "VIX Futures as a Market Timing Indicator." Journal of Risk and Financial Management 12, no. 3 (July 1, 2019): 113. http://dx.doi.org/10.3390/jrfm12030113.

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Our work relates to the literature supporting that the VIX also mirrors investor sentiment and, thus, contains useful information regarding future S&P500 returns. The objective of this empirical analysis is to verify if the shape of the volatility futures term structure has signaling effects regarding future equity price movements, as several investors believe. Our findings generally support the hypothesis that the VIX term structure can be employed as a contrarian market timing indicator. The empirical analysis of this study has important practical implications for financial market practitioners, as it shows that they can use the VIX futures term structure not only as a proxy of market expectations on forward volatility, but also as a stock market timing tool.

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Beracha, Eli, Marcel Lang, and Jochen Hausler. "On the Relationship between Market Sentiment and Commercial Real Estate Performance—A Textual Analysis Examination." Journal of Real Estate Research 41, no. 4 (October 2019): 605–37. http://dx.doi.org/10.22300/0896-5803.41.4.605.

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We examine whether and the extent to which news-based sentiment, captured by textual analysis, can predict the performance of the private commercial real estate market in the United States. Our results show that sentiment reflected in news abstracts of The Wall Street Journal predicts returns of commercial real estate up to four quarters in advance. These findings are statistically significant and persist even when controlling for other related factors. This suggests that news-based sentiment can serve as an early market indicator. We are the first to examine the bidirectional relationship between sentiment, measured by textual analysis, and the performance of the private U.S. commercial real estate market. The findings contribute to the academic literature, and carry practical implications for real estate professionals.

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Nistor, Sergiu Cosmin, Mircea Moca, Darie Moldovan, Delia Beatrice Oprean, and Răzvan Liviu Nistor. "Building a Twitter Sentiment Analysis System with Recurrent Neural Networks." Sensors 21, no. 7 (March 24, 2021): 2266. http://dx.doi.org/10.3390/s21072266.

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This paper presents a sentiment analysis solution on tweets using Recurrent Neural Networks (RNNs). The method is can classifying tweets with an 80.74% accuracy rate, considering a binary task, after experimenting with 20 different design approaches. The solution integrates an attention mechanism aiming to enhance the network, with a two-way localization system: at memory cell level and at network level. We present an in-depth literature review for Twitter sentiment analysis and the building blocks that grounded the design decisions of our solution, employed as a core classification component within a sentiment indicator of the SynergyCrowds platform.

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OHNO, HISASHI, MADOKA MOGAKI, AKIKO MIYOSHI, and KAE UCHIJIMA. "Fulfillment Sentiment and Inclusive Identity: A Multiple-Indicator Multiple-Cause (MIMIC) Model." Japanese Journal of Educational Psychology 52, no. 3 (2004): 320–30. http://dx.doi.org/10.5926/jjep1953.52.3_320.

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Petrovska, Magdalena, Aneta Krstevska, and Nikola Naumovski. "Forecasting Macedonian Business Cycle Turning Points Using Qual Var Model." Journal of Central Banking Theory and Practice 5, no. 3 (September 1, 2016): 61–78. http://dx.doi.org/10.1515/jcbtp-2016-0020.

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Abstract This paper aims at assessing the usefulness of leading indicators in business cycle research and forecast. Initially we test the predictive power of the economic sentiment indicator (ESI) within a static probit model as a leading indicator, commonly perceived to be able to provide a reliable summary of the current economic conditions. We further proceed analyzing how well an extended set of indicators performs in forecasting turning points of the Macedonian business cycle by employing the Qual VAR approach of Dueker (2005). In continuation, we evaluate the quality of the selected indicators in pseudo-out-of-sample context. The results show that the use of survey-based indicators as a complement to macroeconomic data work satisfactory well in capturing the business cycle developments in Macedonia.

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Myšková, Renáta, and Petr Hájek. "MINING RISK-RELATED SENTIMENT IN CORPORATE ANNUAL REPORTS AND ITS EFFECT ON FINANCIAL PERFORMANCE." Technological and Economic Development of Economy 26, no. 6 (November 19, 2020): 1422–43. http://dx.doi.org/10.3846/tede.2020.13758.

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Models that predict corporate financial risk are important early-warning systems for corporate stakeholders. Most models to date have been developed using financial indicators. However, in financial decision-making, increasing attention is being paid to the role of textual information, which may provide additional insight into managerial opinions and intentions and which has recently been used to more effectively predict corporate financial performance. Previous approaches in this regard have predominantly focused on sentiment analysis of managerial communication. However, the role of context-related sentiment remains poorly understood in the financial risk domain. Here, we investigate how risk-related sentiment in verbal managerial communication might predict corporate financial performance, including indebtedness, profitability, market value and bankruptcy risk. To ensure deductive content validity, we propose specific word lists for each type of corporate financial risk and assign each word with positive / negative labels. Our findings provide evidence for a major role of risk-related sentiment as an indicator of corporate performance in terms of financial risks. Notably, using novel risk-related word lists in regression models, we show that a proactive and opportunity-seeking risk management has a significantly positive impact on financial performance, implying that stakeholders should carefully consider the risk-related managerial communication in corporate annual reports.

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Lang, Stephan, and Wolfgang Schaefers. "Examining the sentiment-return relationship in European real estate stock markets." Journal of European Real Estate Research 8, no. 1 (May 5, 2015): 24–45. http://dx.doi.org/10.1108/jerer-10-2014-0036.

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Purpose – Recent studies in the field of behavioral finance have highlighted the importance of investor sentiment in the return-generating process for general equities. By employing an asset pricing framework, this paper aims to evaluate the performance of European real estate equities, based on their degree of sentiment sensitivity. Design/methodology/approach – Using a pan-European data set, we classify all real estate equities according to their sentiment sensitivity, which is measured relative to the Economic Sentiment Indicator (ESI) of the European Commission. Based on their individual sentiment responsiveness, we form both a high- and low-sensitivity portfolio, whose returns are included in the difference test of the liquidity-augmented asset pricing model. In this context, we analyze the performance of sentiment-sensitive and sentiment-insensitive real estate equities with a risk-adjusted perspective over the period July 1995 to June 2012. Findings – While high-sensitivity real estate equities yield significantly higher raw returns than those with low-sensitivity, we find no evidence of risk-adjusted outperformance. This indicates that allegedly sentiment-driven return behavior is in fact merely compensation for taking higher fundamental risks. In this context, we find that sentiment-sensitive real estate equities are exposed to significantly higher market risks than sentiment-insensitive ones. Based on these findings, we conclude that a sentiment-based investment strategy, consisting of a long-position in the high-sensitivity portfolio and a short-position in the low-sensitivity one, does not generate a risk-adjusted profit. Research limitations/implications – Although this study sheds some light on investor sentiment in European real estate stock markets, further research could usefully concentrate on alternative sentiment proxies. Originality/value – This is the first study to disentangle the relationship between investor sentiment and European real estate stock returns.

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Shen, Xiaohong, Gaoshan Wang, and Yue Wang. "The Influence of Research Reports on Stock Returns: The Mediating Effect of Machine-Learning-Based Investor Sentiment." Discrete Dynamics in Nature and Society 2021 (December 31, 2021): 1–14. http://dx.doi.org/10.1155/2021/5049179.

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This paper investigates whether and how the research reports issued by securities companies affect stock returns from the perspective of investor sentiment in China. By collecting research reports and investor comments from a popular Chinese investor community, i.e., East Money, we derive two indices that represent the information contained in research reports: one is the attention of research reports and the other is the average stock rating given by research reports; then we develop an investor sentiment indicator using the machine learning method. Based on behavioral finance theory, we hypothesize that research reports have a significant effect on stock returns and investor sentiment plays a mediating role in it. The empirical analysis results confirm the above hypotheses. Specifically, the average stock rating given by research reports can better predict future stock returns, and investor sentiment plays a partial mediating role in the relationship between stock rating and stock returns.

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TAŞ, Oktay, and Özgüç AKDAĞ. "Trading Volume Trend As the Investor s Sentiment Indicator in Istanbul Stock Exchange." Doğuş Üniversitesi Dergisi 2, no. 13 (July 27, 2012): 290–300. http://dx.doi.org/10.31671/dogus.2018.132.

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Prachyachuwong, Kittisak, and Peerapon Vateekul. "Stock Trend Prediction Using Deep Learning Approach on Technical Indicator and Industrial Specific Information." Information 12, no. 6 (June 15, 2021): 250. http://dx.doi.org/10.3390/info12060250.

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A stock trend prediction has been in the spotlight from the past to the present. Fortunately, there is an enormous amount of information available nowadays. There were prior attempts that have tried to forecast the trend using textual information; however, it can be further improved since they relied on fixed word embedding, and it depends on the sentiment of the whole market. In this paper, we propose a deep learning model to predict the Thailand Futures Exchange (TFEX) with the ability to analyze both numerical and textual information. We have used Thai economic news headlines from various online sources. To obtain better news sentiment, we have divided the headlines into industry-specific indexes (also called “sectors”) to reflect the movement of securities of the same fundamental. The proposed method consists of Long Short-Term Memory Network (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) architectures to predict daily stock market activity. We have evaluated model performance by considering predictive accuracy and the returns obtained from the simulation of buying and selling. The experimental results demonstrate that enhancing both numerical and textual information of each sector can improve prediction performance and outperform all baselines.

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Vieira, Elisabete F. Simões. "Investor sentiment and share returns: evidence on family firms." Academia Revista Latinoamericana de Administración 29, no. 1 (March 7, 2016): 65–83. http://dx.doi.org/10.1108/arla-08-2015-0234.

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Purpose The purpose of this paper is to examine the effect of investor sentiment on share returns, exploring whether this effect is different for public family and non-family firms. Design/methodology/approach The author uses the European Economic Sentiment Indicator data, from Directorate General for Economic and Financial Affairs as a proxy for investor sentiment and focused on the share returns of family and non-family firms, using panel data methodology. Findings Using data from listed family and non-family firms for the period between 1999 and 2011, in accordance with behavioural finance theory, the results indicate that there is a negative relationship between sentiment and share returns. In addition, the author found no difference between family and non-family firms in what concerns the effect of sentiment on share returns. The evidence also suggests that young, large and medium growth firms are most affected by sentiment. Finally, the results suggest that the evidence concerning the relationship between sentiment and returns is sensitive to the proxy used to measure the sentiment. Research limitations/implications A limitation of this study is the small size of the sample, which is due to the small size of the Portuguese stock market, the Euronext Lisbon. Originality/value This paper offers some insights into the effect of investor sentiment on the share returns in the context of public family firms, a strand of finance that is scarcely developed. It also contributes to the analysis of a small European country, with a high concentration of equity ownership.

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Lewis, Clifford, and Michael Mehmet. "Does the NPS® reflect consumer sentiment? A qualitative examination of the NPS using a sentiment analysis approach." International Journal of Market Research 62, no. 1 (July 22, 2019): 9–17. http://dx.doi.org/10.1177/1470785319863623.

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The Net Promoter Score (NPS®) is extensively used as a key performance indicator in practice. Although the scale was initially considered to be a predictor of growth, the literature has disproved this assertion. Despite this, it is argued here that the NPS could be used as a measure of brand health if it provided an effective representation of consumer sentiment toward the brand. This research took a respondent perspective to examine if the NPS effectively captured the consumer’s sentiment. Using a questionnaire design, participants were asked to provide a response on an NPS scale, followed by which they were asked to explain why they gave that score. Therein, a sentiment analysis approach was applied and the open-ended responses were coded based on the type and strength of the attitude. The results indicate that at an overall level, the NPS captures the sentiment participants feel toward a brand. However, caution should be used when classifying participants into detractors, passives, and promoters.

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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 (January 18, 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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Jiang, Weina, Qi Yong, Ning Liu, and Yuze Luo. "RAPOT: An Adaptive Multifactor Risk Assessment Framework on Public Opinion for Trial Management." Wireless Communications and Mobile Computing 2021 (May 15, 2021): 1–11. http://dx.doi.org/10.1155/2021/5514003.

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Since public opinion from social media has a growing impact and supervision on trial, risk assessment on public opinion is increasingly important in refined trial management. However, the tremendous amount of public opinion and the insufficient historical logs of trial procedures bring challenges to risk assessment on public opinion. To address this, we propose an adaptive multifactor risk assessment framework on public opinion with fuzzy numbers. Initially, we establish a multilayer indicator model for assessing the risk of public opinion (POR) with multilayer analysis and decision methods. Then, we explore the association rules hidden in the process logs to update the indicator model periodically. Moreover, we design a public opinion analysis module for indicator evaluation, including analysis in public opinion sentiment, hot search, and social media coverage to deal with big data on social media. Especially, the public opinion sentiment is classified by topic-based BiLSTM (T-BiLSTM), which is more accurate. Finally, the fuzzy number similarity is employed to determine POR’s level in the nine-level risk system. Experimental results validate the efficiency of our framework when assessing the POR.

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Journal articles on the topic 'Sentiment Indicator'

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