Impact Of Sentiment Research Articles

The Impact of Investor Sentiment on Corporate Financial Decision-making: Taking New Media Platforms as an Example

In accordance with the theory of asymmetry, this article studies the impact of investor sentiment on the proportion of corporate accounts receivable. During the research process, 2617 listed companies in the Shanghai and Shenzhen A-share markets from 2011 to 2022 were selected as samples. The article constructs a multiple regression model, selects investor sentiment as the core explanatory variable, accounts receivable ratio as the dependent variable, and adds revenue growth rate, largest shareholder shareholding ratio, price to book ratio, and cash flow ratio as control variables. Descriptive statistics, analysis of Correlation, test of collinearity, analysis of regression, test of heterogeneity, and analysis of robustness are used to explore the financial indicators and financial decisions of enterprises; Research has indicated that a negative correlation exists between investor sentiment and the proportion of corporate accounts receivable. Meanwhile, there are also certain differences between state-owned enterprises and non-state-owned enterprises. Based on this, this article proposes relevant improvement suggestions for enterprises, investors, and regulatory authorities, which will help facilitate the healthy and stable development of the capital market.

The Impact of Earning Press Releases Sentiment on Abnormal Stock Returns and Volatility

This dissertation investigates the impact of sentiment in earnings press releases on abnormal stock returns and volatility based on 150 countries from 1 January 2000 to 31 December 2016. Using Henry’s dictionary method for sentiment analysis, the study finds a negative association between optimistic tones in press releases and abnormal stock returns, implying that highly positive sentiment may trigger lower market reactions. Additionally, the results indicate that positive sentiment in press releases leads to reduced abnormal volatility both immediately and consistently over a period of 5 days, 10 days, and 30 days. These findings remain robust even after accounting for the impact of the 2008 financial crisis. By addressing both short-term and long-term effects, this research contributes to the literature on enhancing the understanding of how effectively investors incorporate sentiment from press releases into their trading behaviours.

Industry return volatility and spillover effects in stock market under climate risk perception

ABSTRACT Utilizing alternative dimension reduction methods, this study constructs three Climate Risk Perception Indices (CRPIs) to quantify the impact of investor climate change sentiment on industry return volatility in the Chinese stock market. There are several findings in this study including (1) the CRPIs effectively capture the trend of climate change in China over the past decade. (2) The investor’s attention to climate change positively influences short-term stock market returns, exhibiting seasonal characteristics and industry heterogeneity. (3) The attention serves as an information sender for stocks in Telecommunication, Oil & Petrochemicals, and Environmental Protection. (4) The total spillover effect among climate-sensitive industries is significantly higher during extreme climate events. These conclusions provide empirical evidence for the stock market to respond effectively to climate risks.

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

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.

Trust in scientific institutions and experts during the first 2 years of the COVID-19 pandemic in Finland.

To explore variations in trust in science and scientific institutions in Finland during the COVID-19 pandemic, and the factors influencing trust in experts of a key institution in the management of the pandemic, the Finnish Institute for Health and Welfare (THL). These results are contrasted with trust in the Ministry of Social Affairs and Health. Five surveys were conducted between April 2020 and March 2022 (n=5448). The changes were tested with the chi-square test. Predictors of trust in THL and the ministry was examined with binary logistic regression. Trust in science and key scientific institutions remained consistently high throughout this period. In the early pandemic, trust in the ministry declined. The most significant explanatory factors for trust in THL and the ministry were being a supporter of some other party than the right-wing Finns Party and belonging to the age groups over 50 years. Our findings indicate that the pandemic, marked by unpredictability, did not weaken trust in science and THL, whereas trust in the ministry responsible for managing the pandemic and university experts weakened. In increasingly politically polarised societies, the impact of political sentiments on health-related perceptions and choices should be analysed more in future public health studies.

SOCIAL MEDIA SENTIMENT AND STOCK MARKET VOLATILITY: EVIDENCE FROM THE US HI-TECH COMPANIES

Purpose: This study aims to investigate the effects of social media sentiment, measured by Twitter-based uncertainty index, on the stock market volatility of the US technology companies. Design/methodology/approach: Methodologically, we employ the quantile regression model. Our findings reveal that the volatility of Amazon, Apple, Google and IBM stocks are sensitive to the variations in twitter-based economic and market uncertainties (i.e., TEU and TMU indexes). Findings: We observe significant influences at both lower and upper quantiles. Thus, for both high and low volatility regimes, the information on twitter-based uncertainty indexes can be used to predict the market volatility of these leading hi-tech companies. Moreover, TEU and TMU indexes exert positive effects on the stock price implied volatility implying that the variance of these technology firms experiences an upward trend as the social media uncertainty rises. Originality/value: While numerous studies have focused on the influence of social media (e.g., Facebook, twitter etc.) on investment strategies, the impact of twitter sentiments on the risk linked to hi-tech firms remains understudied. Hence, investors participating in the technology sectors could use our findings for managing portfolio risk.

Navigating historiographical boundaries in the early Turkish Republican period: astrology, nationalism and Aydın Sayılı’s scholarship

This article explores two aspects of Aydın Sayılı’s scholarship: his focus on astrology and the impact of nationalist sentiments on his research. Sayılı, perhaps the first globally recognized PhD-holder in history of science, completed his degree at Harvard in the early 1940s and established Turkey’s first academic chair in the field at the University of Ankara. His works addressed both international and local audiences, advancing the history of mathematics and astronomy in Islamic societies; his scholarship on Islamic observatories is still highly esteemed today. This study highlights Sayılı’s integration of astrology into discussions in the history of science, especially with respect to Islamic observatories, a significant choice in an era when astrology and related disciplines received minimal attention in Turkish historiography. While his approach to this discipline diverges from that of prevalent twentieth-century Turkish scholarship, Sayılı’s motivations were deeply intertwined with the nationalist sentiments of the early Turkish Republic, emphasizing ‘Turkish contributions to science’. His scholarship invites further examination of the intricate relationship between the historiography of science and nationalism, as well as of twentieth-century historicizations of astrological studies.

Measuring Impact of Market Sentiment on Stock Prices

This study considers the impact of investor sentiment on stock market prices, it is when something beyond traditional economic factors, such as emotions or perceptions that play dominating role in driving psychological and real pricing. This behaviour in investor sentiment often leads to irrational decision making, always influenced by news, social media and the movements of markets. This will make stocks price more volatile. Through sentiment analysis, historical stock data and behavioural finance models the study examines times when there were significant movements in the market. These trends suggest that happy days can cause major prices to rise (along with the sun) and bad news can punch holes in a report even if business hasn't really looked any worse. The regulators and the investors should understand that sentiment can improve the efficiency of the stock price-prediction algorithms as well as bring anomalies to light in the market. The study of market sentiment and its effect on the stock price. In the given paper of research, it is easy to notice the impact of interaction between investor sentiment and stock price.

MFB: A Generalized Multimodal Fusion Approach for Bitcoin Price Prediction Using Time-Lagged Sentiment and Indicator Features

Bitcoin’s volatile nature has made its price prediction a sought-after mathematical model in the FinTech industry. Existing studies, however, need to look into the critical aspect of time-lagged sentiment in Bitcoin price forecasting. This omission is significant because time-lagged sentiment captures delayed market reactions that are not immediately apparent in price movements. Moreover, the correlation between time-lagged sentiment and technical indicators and the limitations of individual machine learning and deep learning models necessitates a comprehensive approach for accurate and reliable Bitcoin price predictions. This paper introduces the multimodal fusion Bitcoin (MFB), an innovative generalized multimodal fusion approach that effectively integrates BiLSTM and BiGRU layers for complex feature extraction. The model employs the BorutaShap algorithm for feature selection and utilizes attention mechanisms and spatial dropout for optimization and generalization. MFB’s training and validation use news and tweet data combined with Bitcoin technical indicators to explore the impact of time-lagged sentiment on price movements, leading to more accurate and timely market predictions. The MFB performs superior Bitcoin prediction performance, achieving 97.63% accuracy and an MAE of 0.0065. Experiments highlight MFB’s capability to outperform existing models, offering significant insights for investors in making informed decisions. MFB’s innovative methodology, particularly in next-hour Bitcoin price forecasting, marks an advancement in financial forecasting. By capturing the nuanced dynamics of market sentiment and its delayed effects, MFB is a pioneering multimodal fusion approach in the FinTech domain, revolutionizing Bitcoin price prediction.

Sentiment and Stock Characteristics: Comprehensive Study of Individual Investor Influence on Returns, Volatility, and Trading Volumes

Abstract Background Traditional asset pricing models face challenges from financial anomalies, prompting exploration through behavioural finance theory. This study analyses the nuanced relationship between individual investor sentiment and key stock market variables. Objectives To assess the impact of individual investor sentiment on stock returns, volatilities, and trading volumes using the American Association of Individual Investors (AAII) sentiment index. Methods/Approach Using regression models, we examine the relationship between individual investor sentiment and various stock characteristics across 480 components of the Standard & Poor's 500 index. Results We find a positive relationship between the AAII sentiment index and stock returns and a negative relationship with volatility and trading volume. Conclusions Our study contributes to understanding the intricate role of individual investor sentiment in financial markets.

Investor sentiment and stock market anomalies: Evidence from Islamic countries

Studies of the Ramadan effect argue that higher stock returns in Muslim countries during Ramadan relate to higher investor sentiment. However, Islamic countries rank low on the Hofstede Individualism Index, a proxy for investor overconfidence. Therefore, this study examines the impact of investor sentiment on stock market anomalies in two advanced Islamic finance jurisdictions: Malaysia and Indonesia. It hypothesizes that stock market anomalies are stronger following high sentiment if investors in Malaysia and Indonesia are overconfident. The results show that the long and short legs of the stock market anomalies earn relatively low returns following high investor sentiment, indicating overpricing during high sentiment. Moreover, the short leg earns relatively lower returns than the long leg following high sentiment because the short leg is more overpriced than the long leg when sentiment is high. Therefore, consistent with the hypothesis, the long-short returns of anomalies are stronger following high investor sentiment because of the relatively lower returns of the short leg than the long leg.

Trading Trends: Unveiling The Impact of Sentiment, Behavior and Performance on Online Trading in Eastern Priangan

Pasar modal merupakan indikator kemajuan perekonomian suatu negara dimana pasar modal dapat menunjang ekonomi negara yang bersangkutan. Pasar Modal memiliki peran penting bagi perekonomian suatu negara karena pasar modal menjalankan dua fungsi, yaitu pertama, sebagai sarana bagi pendanaan usaha atau sebagai sarana bagi perusahaan untuk mendapatkan dana dari masyarakat pemodal atau investor. Banyak faktor yang berpengaruh terhadapnya tiga di antaranya adalah market sentimen, investor behavior dan stock market performance dan market sentimen merupakan aspek yang memiliki pengaruh besar terhadap terhadap intensitas penggunaan sistem perdagangan online oleh investor di Priangan Timur, begitu juga dengan investor behavior dan stock market performance,, sama sama memiliki pengaruh besar terhadap intensitas penggunaan sistem perdagangan online oleh investor. Metode yang digunakan adalah pendekatan kuantitatif dengan hasil menunjukkan bahwa market sentimen, investor behavior dan stock market performance berpengaruh besar terhadap intensitas penggunaan sistem perdagangan online oleh investor.

The impact of the Russia-Ukraine war on the competitiveness of European airlines

Purpose – The research identifies the impact of the Russia-Ukraine war on the stock prices of six publicly traded European airlines and evaluates their ability to adapt to this situation. Research methodology – The dependence of the development of airline stock prices on the level of public and institutional stress due to the war was measured using GoogleTrends and is analysed on the basis of a Vector Autoregression model (VAR). Findings – A short-term negative relationship was confirmed between the development of stock prices and GoogleTrends; the impact of the stress related to the war was negligible about 5 months after the outbreak thereof. Those companies that were the fastest to adapt to the shock of the war in terms of share prices are identified. Research limitations – The link between GoogleTrends, as an input variable reflecting market sentiment, and the stock prices of European airlines, is considered a limitation. Practical implications – The impact of investor sentiment on the returns on the stocks of airlines is a thing of the past; which is an important finding for financial market participants and airlines alike. Originality/value – The ability of the specific airlines to adapt to the shock of war creates a competitive advantage.

Investor Sentiment and Corporate Governance: The Role of Behavioral Biases in Stock Price Volatility and Managerial Decisions

Behavioural biases influence investor sentiment and behaviour, which in turn influence stock returns, stock price volatility and corporate governance structures. This paper focuses on the role of three common behavioural biases (overconfidence, herding and loss aversion) on investor behaviour, and how these biases drive inefficiencies in financial markets. Using worldwide market data from 1970 to 2019 and focusing on key events such as the 2008 financial crisis and the dot-com bubble, we investigate how these behavioural biases cause market volatility. This analysis further reinforces the importance of strong corporate governance structures such as independent boards, executive pay structures and risk management protocols that help reduce the impact of investor sentiment. The role of speculative bubbles, market overreaction and volatility clustering is also examined further, and corporate governance mechanisms are proposed to help mitigate the effects of behavioural biases on stock prices. The findings demonstrate the importance of adopting long-term corporate goals, transparency and strategies to help better manage market sentiment and safeguard shareholder value.

Analyzing the impact of investor sentiment on S&P 500 prices using deep learning models

Purpose This study aims to investigate to what extent the predictability of the standard and poor’s 500 (S&P 500) price levels is enhanced by investors’ sentiments extracted from social media content, specifically platform X. Design/methodology/approach Two recurrent neural network (RNN) models are developed. The first RNN model is merely based on historical records and technical indicators. In addition to the variables included in the first RNN model, the second RNN model comprises the outputs of the sentiment analysis, performed using the TextBlob library. The study was conducted between December 28, 2011, and December 30, 2021, over 10 years, to obtain better results by feeding the RNN models with a significant quantity of data by extending the period and capturing an extensive timespan. Findings Comparing the performance of both models reveals that the second model, with sentiment analysis inputs, yields superior outcomes. The mean absolute error (MAE) of the second model registered 72.44, approximately 50% lower than the MAE of the technical model, its percentage value, the mean absolute percentage error, recorded 2.16%, and finally, the median absolute percentage error reached a value of 1.30%. This underscores the significant influence of digital platforms in influencing the behavior of certain assets like the S&P 500, emphasizing the relevance of sentiment analysis from social media in financial forecasting. Originality/value This study contributes to the growing body of literature by highlighting the enhanced predictive power of deep learning models that incorporate investor sentiment from social media, thereby advancing the application of behavioral finance in financial forecasting.

Carbon market design and market sentiment

Concerns about systematic price distortions in the EU Emission Trading System (ETS) have risen in recent years. This paper shows how carbon-market design affects the impact of market sentiment, i.e. systematic deviations of price expectations from fundamentals by at least some market participants, on equilibrium prices. The Market Stability Reserve (MSR) of the EU ETS that adjusts supply based on past allowance banking undermines self-stabilization of the carbon market by discouraging rational intertemporal arbitrage. The MSR increases vulnerability of the EU ETS to market sentiments. Initially, the allowance price responds more to distorted expectations while the MSR (partially) prevents the distortion to spread across periods. Making the MSR more responsive to banking decisions increases the likelihood that distorted expectations turn out to be correct ex post. In contrast, a mechanism that adjust the cap based on the current allowance price does not face a trade off between stabilizing prices in the present and the future.

Asymmetric dynamic linkage between consumer sentiment, inflation expectations, and international energy prices: Evidence from time-frequency wavelet and nonlinear analysis.

This study investigates the relationship between consumer sentiment (CONS), inflation expectations (INEX) and international energy prices, drawing on principles from behavioral. We focus on Brent crude oil price and Henry Hub natural gas prices as key indicators of energy market dynamics. Based on the monthly data from January 2003 to March 2023, three wavelet methods are applied to examine the time-frequency linkage, while the nonlinear distributed lag model (NARDL) is used to verify the asymmetric impact of two factors on energy prices. The results highlight a substantial connection between consumer sentiment, inflation expectations and international energy prices, with the former in the short term and the latter in the medium to long term. Especially, these correlations are particularly pronounced during the financial crisis and global health emergencies, such as the COVID-19 epidemic. Furthermore, we detect short-term asymmetric effects of consumer sentiment and inflation expectations on Brent crude oil price, with the negative shocks dominating. The positive effects of these factors on oil prices contribute to observed long-term asymmetry. In contrast, inflation expectations have short-term and long-run asymmetric effects on natural gas price, and both are dominated by reverse shocks, while the impact of consumer sentiment on natural gas prices appears to be less asymmetric. This study could enrich current theories on the interaction between the international energy market and serve as a supplement to current literature.

Research on Impact of Investor Sentiment on Chinese Stock Market: Review and Prospect

Over time, investor behavior has become more complicated due to the unpredictability of the Chinese stock market. Major research has found that investor sentiment swings have a huge impact on the market. Therefore, investors need to observe and understand how investor sentiment affects the market in the right way. Much of the past literature has used textual data mining and event analysis to measure the impact of investor sentiment and its impact on stock market returns, pricing, and volatility. Several articles have concluded that investor sentiment is highly responsive to the Chinese stock market. Another phenomenon that has emerged on various forums and social media is that people's fear of the stock market is spreading. The results of event analysis can prove that the impact of events such as the lifting of the ban on restricted shares and the listing of companies will be very obvious and have an impact on investor sentiment. An important implication of this study is that in addition to the dominant role of Granger causality in the causality that produces negative returns, negative investor sentiment is the main factor determining the overall behavior of the stock market. When bad news breaks, investors release their pent-up emotions for a short period, resulting in a violent reaction. In addition, market sentiment can also affect trading volumes and stock prices. This has put pressure on the market. In the end, this paper expands the future research direction from the two aspects of communication mechanism and quantitative analysis.

Fund Flows and Fund Performance on Lottery Funds

This study introduces a new indicator to measure the lottery-like degree of financial products by considering their idiosyncratic volatility and skewness. The findings indicate that investors tend to purchase funds that hold lottery-like stocks, while investors prefer lottery-like funds when the market sentiment is high. However, funds holding lottery-like stocks and those exhibiting lottery traits experience relatively lower adjusted returns in the next quarter. Interestingly, when the market sentiment is high, investors can obtain better performance only from funds that hold lottery-like stocks, indicating that investors may be better off selecting lottery-holding funds rather than lottery-like funds during periods of high sentiment. Overall, our study sheds light on investor behavior and the impact of market sentiment on fund flows and performance in lottery-like funds.

A study on the impact of investor sentiment on individual stocks based on text mining

This paper investigates the influence of investor sentiment on individual stock prices through text mining. By reviewing relevant theories and literature, we utilized web crawler technology to collect comment data from the Eastmoney Stock Bar, constructing a comprehensive index of investor sentiment and an index of investor sentiment for individual stocks. Employing the VAR model and Granger causality test, we analyzed the correlation and dynamic relationship between investor sentiment and stock price fluctuations. The study found that stocks of different types of financial institutions react differently to investor sentiment, and that investor sentiment significantly affects stock prices. This paper puts forward corresponding policy recommendations, which can help market investors make scientific decisions and promote the healthy development of the market.