Sentiment Index Research Articles

Market sentiment in emerging economies: evidence from the South African property market

Purpose Market sentiment has shown to influence housing prices in the global north, but in emerging economies, the nexus is rare to chance on in the current state of science for policy direction. More importantly in the recent decade where policymakers are yet to conclude on the myriad of factors confronting the housing market in sub-Saharan Africa inhibiting affordability. This paper therefore examines the impact of market sentiment on house prices in South Africa. Design/methodology/approach The study used the Autoregressive Distributed Lag (ARDL) approach with quarterly data spanning from 2005Q1 to 2020Q4. Findings In all, it was established that market sentiment plays a minimal role in the property market in South Africa. But there was enough evidence of cointegration from the bound test between sentiment and house prices. Nevertheless, the lag values of sentiment pointed to a rise in house prices. Exchange rate volatilities and inflation had a statistically significant effect on prices in both the long and short term, respectively. Research limitations/implications Policymakers could still monitor market sentiment in the housing market due to the strong chemistry between house prices and sentiment, as evidenced from the bound test, but focus on economic fundamentals as the main policy tool for house price reduction. Originality/value The findings and the creation of the sentiment index make an invaluable contribution to the paper and add to the paucity of literature on the study of market sentiment in the housing market.

Data depth and core-based trend detection on blockchain transaction networks

Blockchains are significantly easing trade finance, with billions of dollars worth of assets being transacted daily. However, analyzing these networks remains challenging due to the sheer volume and complexity of the data. We introduce a method named InnerCore that detects market manipulators within blockchain-based networks and offers a sentiment indicator for these networks. This is achieved through data depth-based core decomposition and centered motif discovery, ensuring scalability. InnerCore is a computationally efficient, unsupervised approach suitable for analyzing large temporal graphs. We demonstrate its effectiveness by analyzing and detecting three recent real-world incidents from our datasets: the catastrophic collapse of LunaTerra, the Proof-of-Stake switch of Ethereum, and the temporary peg loss of USDC–while also verifying our results against external ground truth. Our experiments show that InnerCore can match the qualified analysis accurately without human involvement, automating blockchain analysis in a scalable manner, while being more effective and efficient than baselines and state-of-the-art attributed change detection approach in dynamic graphs.

Investor sentiment and stock returns: New evidence from Chinese carbon-neutral stock markets based on multi-source data

Combining internet text and stock trading data, we construct two investor sentiment proxies for 78 carbon-neutral stocks in Chinese stock markets based on the FinBERT model and principal component analysis. We then examine the effects of the two investor sentiment indices on carbon-neutral stock returns using dynamic panel models. We also investigate the mediating role of liquidity on the effects of investor sentiment. Our results reveal that both trading and internet sentiment negatively affect stock returns through the mediating effect of liquidity. This finding holds after robustness checks for dividing the sample into pre and post-epidemics. However, due to the effects of COVID-19, carbon-neutral stock returns are found to be more susceptible to investor sentiment.

Capturing Swiss economic confidence

Survey data can offer timely information on the current state of the economy and its short-term outlook. In this paper, we propose a “Swiss Economic Confidence Index” (SEC). This is a monthly indicator based on aggregating a selection of individual survey indicators, which we show to have favorable leading properties. Applying simple criteria, we select those surveys from a set of currently more than 250 sentiment indicators. We show that the SEC index provides useful signals on GDP growth in a number of real-time out-of-sample forecasting exercises.

The growth of the real estate corporate bond market in Vietnam: the role of investor sentiment

PurposeThis paper aims to examine the effects of investor sentiment on the development of the real estate corporate bond market in Vietnam.Design/methodology/approachThe research uses an autoregressive distributed lag (ARDL) model with quarterly data. Additionally, the study employs Google Trends search data (GVSI) related to topics such as “Real Estate” and “Corporate Bond” to construct a sentiment index.FindingsThe empirical outcomes reveal that real estate market sentiment improves the growth of the real estate corporate bond market, while stock market sentiment reduces it. Also, there is evidence of a long-run negative effect of corporate bond market sentiment on the total value of real estate bond issuance. Further empirical research evidences the short-term effect of sentiment and economic factors on corporate bond development in the real estate industry.Research limitations/implicationsDue to difficulties in collecting data, this paper has the limited sample of 54 valid quarterly observations. Moreover, the sentiment index based on Google search volume data only reflects the interest level of investors, not their attitudes.Practical implicationsThese results yield important implications for policymakers in respect of strengthening the corporate bond market platform and maintaining stability in macroeconomic and monetary policies in order to promote efficient and sustainable market development.Social implicationsThe study offers some suggestions for regulators and governments to improve the real estate corporate bond market.Originality/valueThis is the first quantitative study to examine the effect of sentiment factors on real estate corporate bond development in Vietnam.

Firm-specific investor sentiment and earnings management: evidence from Brazil

PropósitoEl propósito de este estudio es investigar la influencia del sentimiento de los inversionistas a nivel de empresa en la manipulación contable de las empresas brasileñas entre 2010 y 2018. El documento pretende aportar una visión más profunda sobre la relación entre las expectativas de los inversores y la toma de decisiones de los gestores en un mercado emergente.Diseño/metodologia/enfoqueusamos el enfoque cuantitativo y aplicamos un modelo de regresión lineal múltiple para probar la relación entre las acumulaciones anormales, el índice de sentimiento de los inversores a nivel de empresa y las variables de control. La muestra final incluye datos de 175 empresas, entre 2010 y 2018.HallazgosLos resultados revelan una asociación negativa entre el sentimiento de los inversores a nivel de empresa y la manipulación contable basada em acumulaciones, lo que podría significar que la propensión al riesgo de los administradores a manipular las ganancias aumenta cuando enfrentan pérdidas conocidas en el mercado de capitales.Limitaciones/implicaciones de la investigaciónlos resultados de la investigación proporcionan una valiosa comprensión de cómo las expectativas de los mercados de capitales emergentes pueden influir en las decisiones de gestión, como la manipulación contable basada en acumulaciones. El área geográfica de estudio se limitó únicamente a Brasil y, en consecuencia, los hallazgos y conclusiones del estudio tuvieron sus límites.Originalidad/valorestudios anteriores sobre mercados desarrollados muestran que el sentimiento de los inversores a nivel de mercado influye positivamente en la manipulación contable. Sin embargo, el presente estudio muestra que el índice de sentimiento de los inversores a nivel de empresa tiene una relación significativa y negativa con la manipulación de las ganancias de las empresas brasileñas, mientras que el sentimiento del mercado indica una relación contradictoria en estudios anteriores en el país.

The ECB press conference statement: deriving a new sentiment indicator for the euro area

AbstractWe analyse the introductory statements of the ECB president and derive new sentiment indicators for the euro area based on a novel approach. To evaluate sentiment, we utilize a Large Language Model, namely FinBERT, which classifies the verbal sentiment of economics and finance‐related textual data. We find that the ECB's conveyed sentiment about monetary policy, which is influenced by the economic outlook and the state of the euro area macroeconomy as expressed in speeches, plays a significant role in shaping the content of press conferences following a governing council decision. In contrast, speech sentiment regarding financial stability does not significantly influence introductory statements.

The impact of gender inclusion on the profits of global public tourism companies

AbstractThe present paper tests the impact of women’s inclusion and general equity market sentiment on Global tourism companies’ return on equity (ROE). With data panel regressions of the historical ROE data of the members of Refinitiv Global Hotel, we estimated several models that used the leverage and market capitalization, sentiment indexes (such as Economic Policy news, geopolitical news, pandemic news, currency market, commodity market or social media uncertainties) and the level of inclusion measured with the percentage of women in the board of directors, the percentage of women top managers or the percentage of women in the company. Our results suggest that there is only an inverse relationship between top management inclusion and ROE in leisure and recreation companies. From a geographical perspective, this negative relationship holds only in Asia, Europe, and Northern America. In Australasia (Australia), there is a positive relationship between top management inclusion and ROE. As expected, the company size has a direct and significant relationship with ROE, and the leverage is negative. Also, only the pandemic news fear harms profits due to sales reduction, management decisions due to fear, or cost increase. Our results align with the position that women’s inclusion has a weak impact on profits, suggesting more women inclusion without return loss in the company. Also, they contribute to the discussion that women’s management is more conservative and is exposed to a crystal cliff once they overcome a crystal roof in the company.

Market sentiment and SPACs

ABSTRACT This study investigates how market sentiment affects Special Purpose Acquisition Companies (SPACs). SPACs are blank-check, no-operation companies, which go public and raise money with the intention of acquiring and taking a private company public. Using hand-collected SPAC data, we find that SPAC activities grow significantly in volume and size, especially during the pandemic period of 2020–2021. Based on two market sentiment indexes, the investor sentiment (IS) and consumer confidence index (CCI), we find that SPAC activities are positively associated with market sentiment. The results remain strong and consistent even after controlling for macroeconomic conditions. Overall, we attribute our findings to investors’ willingness to invest in more speculative securities during times of high market sentiment.

How congruent sentiment indices are to real investor beliefs?

ABSTRACT We use survey data to investigate the relationship between widely used sentiment indices and real investor beliefs. We find a strikingly low congruency between sentiment indices and investor expectations of future stock returns. More importantly, the sentiment indices are found to have poor abilities to capture investor overoptimism, attenuating the nexus between sentiment indices and mispricing which was employed extensively by previous literature. By cautiously examining the relations between sentiment indices (survey expectations) and model-based expected returns, we are keenly aware that sentiment indices and survey expectations probably correspond to distinct classes of sentimental investors.

Economic sentiment and foreign portfolio flows: Evidence from Türkiye

The notable surge in capital flows in recent years has emerged as a key factor shaping the dynamics of international financial markets and influencing economic performance of emerging economies. Even though macroeconomic fundamentals of an economy can explain some of the patterns in international capital flows, behavioral factors also seem to be essential for positioning capital flows across countries. In this study, we aim to examine whether overall economic sentiment towards Turkish economy plays a significant role on net portfolio flows to Türkiye. To this end, we first construct a novel text-based sentiment index called "Turkish Economic Sentiment Index (TESI)", to capture the behavioral tendencies of international investors and media towards Türkiye. Our subsequent step integrates TESI into autoregressive distributed lag models (ARDL) alongside major pull-push determinants to assess whether market sentiment holds discernible influence on capital influx into Turkey. The results reveal that the TESI and VIX stand out as pivotal determinants influencing international portfolio flows. The TESI has a positive impact on portfolio flow dynamics, whereas the degree of global risk aversion inversely affects these flows. These findings align with the contention that a favorable sentiment can boost portfolio inflows to emerging markets. Conversely, heightened volatility expectations in global markets can prompt outflows from these economies.

STOCK MARKET ANALYSIS

The abstract aims to address the correlation between stock market movements and public sentiments expressed on Twitter. It delves into the utilization of sentiment analysis and supervised machine learning techniques to explore this connection. The study leverages Word2vec for textual representation, examining how shifts in stock prices align with sentiments expressed in tweets about specific companies. The investigation underscores the potential impact of positive news and social media sentiments on stock prices, emphasizing a demonstrated correlation between fluctuations in stock prices and sentiments conveyed in Twitter.- Keywords: Hashtag Collection, Data Collection, Real-Time Stock History Data, Positive Keywords, Negative Keywords, Polarity Computation, Sentiment Analysis , Sentiment Index Computation, Sentiment Discrepancy Index, Price Prediction, Yahoo Finance API

Forecasting GDP in Europe with textual data

SummaryWe evaluate the informational content of news‐based sentiment indicators for forecasting gross domestic product (GDP) and other macroeconomic variables of the five major European economies. Our dataset includes over 27 million articles for 26 major newspapers in five different languages. The evidence indicates that these sentiment indicators are significant predictors to forecast macroeconomic variables and their predictive content is robust to controlling for other indicators available to forecasters in real time.

Exploring the association between the built environment and positive sentiments of tourists in traditional villages in Fuzhou, China

Promoting positive emotional experiences for tourists is crucial for sustaining development in rural areas. However, existing research has limited focus on the rural built environment, particularly in developing a framework to evaluate environmental sentiment on a small to medium scale with detailed indicators. This study addresses this gap by examining the impact of the rural built environment on tourists' emotions. Natural Language Processing (NLP) technologies are employed to analyze web text data and determine the average sentiment index for traditional villages in Fuzhou, China. Additionally, data on the built environment were acquired through the HRnet segmentation model and Matlab. To assess the association between environmental indicators and the sentiment index, we used eXtreme Gradient Boosting (XGBoost), the SHapley Additive exPlanation (SHAP) model, and ArcMap software. The study demonstrated that (1) the spatial distribution of the average sentiment index was significant. Houfu Village (9.91), Qianhu Village (9.88), and Ximen Village (9.75) had the highest scores, while Doukui Village (−0.85), Jiji Village (0.2), and Qiaodong Village (0.55) had the lowest. (2) The indicators that have the most significant impact on sentiment are Openness, Greenness, and Color Complexity, with a contribution value above 0.7—followed by Enclosure, Visual Entropy, and Ground Exposure, with a contribution between 0.5 and 0.7. Furthermore, analyzing the interaction mechanism of the indicators showed a non-linear relationship. The environmental characteristics associated with high emotional index scores are openness in the range of 0.2 to 0.5, greenness in the range of 0.4 to 0.6, and color complexity in the range of 0.3 to 0.5. This study provides observations pertinent to the sustainable development of traditional village environments. The findings contribute to an understanding of how these environmental elements might be effectively designed to improve tourists' sentiment in rural settings.

Analysis and Study of Candlestick Indicators in Stock Trading

Candlestick analysis is an invaluable tool in technical analysis for understanding stock price movements. Candlesticks provide a clear visual representation of changes in stock prices over a certain period of time. The color and shape of the candlesticks provide a direct indication of buyer and seller sentiment. A series of candlesticks with continuously light colored bodies indicates an uptrend, while continuously dark colored bodies indicate a downtrend. This helps traders identify the direction of overall price movement. Candlestick patterns, such as doji, engulfing, harami, and others, provide signals about potential changes in price direction or trend continuation. Traders can use these patterns in conjunction with other factors to make better trading decisions. The length of the candlestick's shadow or wick gives an indication of market volatility. A long shadow indicates high volatility or significant movement during that period. Although candlestick analysis provides valuable insight, wise risk management remains the key to success in stock trading. Trading decisions should be based on comprehensive analysis and a deep understanding of market conditions. By combining candlestick analysis with other analytical tools, as well as implementing good risk management, traders can increase their chances of success and make more informed decisions in the dynamic stock market.

Consumer sentiments across G7 and BRICS economies: Are they related?

This paper utilizes fractional integration and cointegration techniques to investigate the stochastic properties of the bilateral linkages between the Consumer Sentiment Index (CSI) of eight developed economies, Australia, Canada, France, Germany, Italy, Japan, the UK and the US and five emerging economies comprising Brazil, Russia, India, China and South Africa, for the time period from 15th January 2010 to 15th July 2019. The univariate results support fractional integration with mean reverting behaviour, with many of the series displaying orders of integration in the interval (0, 1), which connotes that shocks to consumer sentiment have significant long-lasting though reverting effects. From the covariate results and testing for cointegration, we found evidence of cointegration for Australia versus Italy, and France versus Italy. For the BRICS, the only evidence of fractional cointegration is found between Russia and India. Some policy implications of the results obtained are also mentioned at the end of the article.

Investor sentiment and stock price crash risk: The mediating role of analyst herding

This study analyzes the impact of investor sentiment on firm's stock price crash risk by using Chinese A-Share firms data this study assesses the potency and existence of a relationship between crash risk and investor sentiment in the Chinese stock market and introduces analyst herding as a mediating variable for explaining the relationship between crash risk and investor sentiment. By utilizing a large data set of A-share listed firms on Chinese stock exchanges, comprising of 19,371 firm-year observations for the period of 2004–2019, an investor sentiment index is constructed. Results point towards a positive significant relation between stock price crash risk and investor sentiment. Furthermore, stock price crash is positively correlated with analyst herding i.e. it significantly mediates between stock price crash risk and investor sentiment. By measuring the relationship between crash risk, investor sentiment, and analyst herding this study provides systematic support on the mediating role of analyst herding in deepening the market sentiment which results in crash risk. These findings are robust by utilizing alternate proxies and controlling for firm specific variables, economy-wide shocks, and time trends year fixed effects.

A STOCK PRICE PREDICTION MODEL BASED ON INVESTOR SENTIMENT AND OPTIMIZED DEEP LEARNING

Reducing investing risks and increasing rewards are possible with an accurate stock price forecast. This study builds the MS-SSA-LSTM model by combining the multi-source data influencing stock prices and using deep learning, swarm intelligence algorithms, and sentiment analysis. In order to create a distinct sentiment dictionary and determine the sentiment index, we first scan the East Money forum postings. Next, the hyperparameters of the Long and Short-Term Memory networks (LSTM) are optimized using the Sparrow Search Algorithm (SSA). Lastly, LSTM is utilized to predict future stock prices by integrating the sentiment index with fundamental trading data. Tests show that the MS-SSA-LSTM model performs better than the others and has a high degree of generalizability. The average improvement in R2 between MS-SSALSTM and regular LSTM is 10.74%. We discovered that: 1) The prediction performance of the model may be improved by including the sentiment index. 2) SSA is used to adjust the hyperparameters of the LSTM, improving the prediction impact and providing an objective explanation of the model parameter values. 3) The Chinese financial market's extreme volatility makes it more suited for short-term forecasting.

Sentiment Analysis as an Innovation in Inflation Forecasting in Romania

Romania faced the highest inflation rate in the European Union at the beginning of 2024, but progress has been made compared to that in 2023 due to the increasing interest rate. This inflation stemmed from a combination of global and domestic factors (global factors such as the Russia-Ukraine war, supply chain disruptions caused by the COVID-19 pandemic and war, rising commodity prices, domestic factors such as wage and pension increases, tax and charge hikes, and a strategy of gradual increase in the monetary policy interest rate). The National Bank of Romania (NBR) uses a combination of monetary policy instruments to target inflation and provides quarterly forecasts. However, under uncertain conditions, numerical forecasts are less reliable, and the inclusion of sentiment analysis in forecasts might lead to innovation in the field by improving the prediction accuracy. Sentiment analysis has become increasingly important in the field of economics, offering valuable insights and potentially improving economic forecasting and decision-making due to rapid technological progress. Sentiment analysis can identify potential changes in consumer behaviour and business decisions before they are translated into actual economic data, providing an early warning system for economic trends and potential crises. The methodological background relies on natural language processing to extract sentiment indices for large amounts of texts in Inflation Reports provided by NBR. Moreover, the sentiment indices calculated by IntelliDocker are incorporated into autoregressive distributed lag (ARDL) models to provide quarterly inflation forecasts. This type of econometric model has the advantage of addressing endogeneity. Moreover, the unemployment rate is considered an inflation predictor since tensions in the labour market might impact inflation. This paper contributes to empirical forecasting by proposing sentiment forecasts that are more accurate than NBR numerical forecasts corresponding to the 2006: Q1-2023: Q4 horizon. The new forecasting method might be used to make inflation predictions for the next quarters. More accurate forecasts would be valuable for businesses, the central bank, policymakers, and the general public. However, while sentiment analysis offers valuable insights, it is important to remember that human judgment and expertise remain essential for interpreting the data and making informed economic decisions.

Prediction of Bitcoin Prices Based on Blockchain Information: A Deep Reinforcement Learning Approach

Bitcoin, the first decentralized cryptocurrency, has attracted significant attention from investors and researchers alike due to its volatile and unpredictable price movements. However, predicting the price of Bitcoin remains a challenging task. This paper presents a detailed literature review on previous studies that have attempted to predict the price of Bitcoin. It discusses the main drivers of Bitcoin prices, including its attractiveness, macroeconomic and financial factors with a particular focus on the use of Blockchain information. We apply time series to daily data for the period from 28/04/2013 to 28/01/2023. We used Python and TensorFlow library version 2.11.0 and propose a deep multimodal reinforcement learning policy combining Convolutional Neural Network (CNN) and Long ShortTerm Memory (LSTM) neural network for cryptocurrencies’ prices prediction. Also, this study attempts to predict the price of Bitcoin using a special type of deep neural networks, a Deep Autoencoders. Two results are worth noting: Autoencoders turns out to be the best method of predicting Bitcoin prices, and Bitcoin-specific Blockchain information is the most important variable in predicting Bitcoin prices. This study highlights the potential utility of incorporating Blockchain factors in price prediction models. Also, our findings show that sentiment indicator, Ethereum, XRP and Doge Coin prices, global currency ratio, macroeconomic factors, and Blockchain information of Ethereum did not contribute significantly toward predicting Bitcoin prices. These conclusions provide decision support for investors and a reference for the governments to design better regulatory policies.