Sentiment Index Research Articles

Insights from the Geopolitical Sentiment Index made with Google Trends

Insights from the Geopolitical Sentiment Index made with Google Trends

How does the Bitcoin Sentiment Index of Fear & Greed affect Bitcoin returns?

The efficient market hypothesis encounters scrutiny from behavioral finance insights, highlighting the pronounced influence of investor emotions on market dynamics, a phenomenon especially evident in the tumultuous cryptocurrency markets. This investigation utilizes the autoregressive distributed lag (ARDL) model and the error correction model (ECM) to examine the impact of the Bitcoin Sentiment Index (BSI), also known as the Crypto Fear & Greed Index (CFGI), on Bitcoin returns, leveraging monthly data spanning from 2016 to 2021. The ARDL analysis identifies a positive and statistically significant correlation between BSI and Bitcoin returns, indicating that strong sentiment may beneficially affect Bitcoin’s long-term returns. Concurrently, the ECM analysis reveals that fluctuations in the BSI positively influence the changes in Bitcoin returns in the short term. The error correction term demonstrates a significantly negative value, signifying an expedient adjustment toward long-term equilibrium following transient disturbances. These findings remain robust upon the integration of additional macroeconomic control variables. Unlike prior studies centered on singular sentiment indicators or limited temporal analyses, this research employs an extensive sentiment measure over an extended duration. The integrated application of ARDL and ECM methodologies facilitates a thorough and rigorous examination of short-term fluctuations alongside long-term equilibrium dynamics.

Quantitative Trading Timing Strategy Based on Investor Sentiment Index

Quantitative Trading Timing Strategy Based on Investor Sentiment Index

Investor sentiment at the initial stage of public emergency : Evidence from Shanghai stock market

The paper collects the daily data from January 20th, 2020 to September 30th, 2020 and refers to the BW index model of Baker and Wurgler (2006) to construct the investor sentiment index of Shanghai stock market at the initial stage of COVID-19. Differ from the previous research, after excluding the influence of macroeconomics, the paper found that the investor sentiment shows a significantly negative relation with the SSE A-share Index.

Homogenous influence of heterogeneous emotions on investor behavior

The use of text readability indicators and sentiment factors to asset pricing is practical in investment risk management yield pursuit. We analyzed the polarity of textual sentiment of Management Discussion and Analysis (MD&A) in 10-Ks for firms in the U.S. by constructing Sentiment Entropy, a new sentiment indicator based on L&M Dictionary, which captures the same characteristics of positive and negative emotions from the perspective of polarity. The empirical results show that the polarity of sentiments increases investment risk measured by return volatility, which differs from the traditional one-way emotion indicator. We found through a mediator effect test that emotional polarity significantly increases investment risk by stimulating large changes in trading volume, which is consistent with conventional wisdom. Challenging conventional findings, we find that positive and negative emotions in disclosures may have homogenous effects on the market, providing inspirations for understanding the role of textual sentiment on investor behavior.

Exploring the Impact of Digital Shared Services in Finance on Corporate Treasurers’ Job Anxiety

Abstract It has become a trend for enterprises to establish financial shared service centers for digital financial transformation, but the emotional impact of this transformation on the original financial staff of enterprises has been neglected. In this paper, we collect relevant intranet texts of enterprises, mine the emotions of the collected texts through the LDA theme algorithm, use the improved LSTM model to classify and identify the emotions of the collected texts, and construct the LDA-BiLSTM sentiment analysis model to analyze the anxiety condition of the financial staff of enterprises. The efficiency of LDA can be improved by using the weighted median to deal with outliers. Two LSTM networks are combined to form a BiLSTM network to improve the problem of inaccurate judgment of sentiment indicators by a single LSTM. Finally, the model is used to empirically analyze the emotions of financial staff before and after the transformation of an enterprise’s financial digital service. Negative feelings that are linked to anxiety have an accuracy rate of 95.23%. The frequency of separation after the transition was as high as 0.5, and the frequency of dismissal was as high as 0.423. Overall sentiment scores were lower than 0.7. After the transition, the number of people worried about negative feelings related to anxiety rose from 32% to 64%. Finance professionals are experiencing a significant increase in anxiety due to the post-transition of the enterprise.

Optimal profit-making strategies in stock market with algorithmic trading

<p>Machine learning (ML) techniques are being increasingly applied to financial markets for analyzing trends and predicting stock prices. In this study, we compared the price prediction and profit-making performance of various ML algorithms embedded into stock trading strategies. The dataset comprised daily data from the CSI 300 Index of the China stock market spanning approximately 17 years (2006–2023). We incorporated investor sentiment indicators and relevant financial elements as features. Our trained models included support vector machines (SVMs), logistic regression, and random forest. The results show that the SVM model outperforms the others, achieving an impressive 60.52% excess return in backtesting. Furthermore, our research compared standard prediction models (such as LASSO and LSTM) with the proposed approach, providing valuable insights for users selecting ML algorithms in quantitative trading strategies. Ultimately, this work serves as a foundation for informed algorithm choice in future financial applications.</p>

Implementation of Long Short-Term Memory (LSTM) Networks for Stock Price Prediction

In this research, we explore the potential of Long Short-Term Memory (LSTM) networks for predicting stock prices. Due to the complexities of the financial markets and the inherent volatility of stock prices, accurate forecasting is now crucial for investors and financial specialists. It has been shown that LSTM, a type of recurrent neural network (RNN), can recognise temporal correlations and patterns in serial data. Training and assessing LSTM models in this work involves analysing stock price data, relevant financial measures, and market sentiment indicators. We looked into other ideas, hyper parameters, and preprocessing methods to see if we might boost the networks' performance. To further improve the model's generalizability, we utilise series normalisation and removal to reduce overfitting. The outcomes demonstrate that the LSTM network outperforms more standard series temporal prediction methods in capturing and anticipating shifts in action pricing. We also conduct extensive back testing and evaluation, using measures like mean squared error (MSE) and mean absolute error (MAE), to assess the model's accuracy and resilience. The results of this study shed light on how deep learning techniques, in particular LSTM networks, can be applied to the prediction of stock prices, potentially assisting traders, investors, and other financial decision-makers in navigating complex and volatile financial markets.

Business Confidence in the Sustainable Manufacturing Sector in the Context of Production, Production Prices, and Interest Rates

Business confidence can be a very important predictor of future development and sustainability in a selected industry. It is one of the economic sentiment indicators belonging to the most important indicators of the health of the economy. This paper examines the relationships between the Business Confidence Indicator (BCI) of the manufacturing sector and the production, production prices, and interest rates of two neighbouring countries, the Slovak Republic and Hungary. These relationships are examined within the manufacturing sector because it has long been the driving force of sustainable economic development and has significant spill-over effects on the other sectors of the economy. A cluster analysis is performed with the aim of positioning the selected countries into clusters within the OECD countries in relation to the examined variables. Multiple regression models were used to verify the mentioned relationships between variables, separately for each of the two analysed countries. The coefficients of determination are not too high (Slovakia 0.5 and Hungary 0.38), but in the case of sentiment, they reach higher values than in comparable research. Data from the freely available OECD database was used. It can be concluded that the results show positive relationships between the BCI and the production and long-term interest rates, and negative relationships between the BCI and the Producer Prices Index (PPI) and short-term interest rates. The relationship with the short-term interest rates cannot be confirmed in the case of Hungary. Other differences are also shown in that a higher PPI can negatively affect the BCI more intensively in Hungary (regression coefficient 0.18), and that in the case of the Slovak Republic, it appears that the level of influence of the long-term interest rates on the BCI is higher than that in Hungary (the difference between the regression coefficients is 0.27). Practical implications are stated for relevant institutions in the country in three areas: (1) in support of increasing production; (2) in the regulation and subsidization of input production costs; (3) in the stabilization of the lending behaviour of financial institutions.

Modeling crude oil volatility using economic sentiment analysis and opinion mining of investors via deep learning and machine learning models

Investor emotions can significantly impact the perceived value of financial assets, and sentiment analysis technology can accurately model daily responses and views of investors. In this study, we analyze investor remarks from one of the most popular web economic areas to investigate the effects of investor emotions on the price of crude oil in China. We construct investor emotions indices for China's crude oil futures based on specific economic factors and employ five machine learning technologies, including the AR approach, SVR approach, MLP method, neural networks with a recurrent units model, a recurrent unit with a gate approach, and LSTM model, to predict one-day-ahead prices. Our research offers insights into the effectiveness of different machine learning technologies and how specific economic factors contribute to sentiment analysis accuracy. We discuss the natural language processing techniques and tools used to extract sentiment from financial forum comments, along with the measures taken to ensure the accuracy and reliability of sentiment scoring. Our study highlights the advantages of the LSTM model, paired with the compound sentiment index, in the context of crude oil price forecasting and specifies the accuracy levels achieved by this model in comparison to others. We also discuss potential limitations and challenges in the long-term application of sentiment analysis for price forecasting and propose possible solutions. Our findings have practical implications for investors, financial analysts, and policymakers involved in China's crude oil market. We explore patterns or trends in investor emotions in our analysis and discuss their correlation with major economic or geopolitical events in China and globally. Our study contributes to a deeper understanding of investor emotions' impact on crude oil prices in China and provides valuable insights for various stakeholders in the dynamic and speculative energy oil industry.

COVID-19 media sentiment and IPO underpricing

Based on media coverage of the COVID-19 epidemic during 2020–2022, we construct media sentiment indicators to investigate their impact on IPO underpricing in China. We find that positive (negative) COVID-19 media sentiment significantly decreases (increases) IPO underpricing, and negative sentiment has a greater effect. In addition, the results indicate that COVID-19 media sentiment has significant effects on the IPO underpricing of private firms but not on that of state-owned enterprises.

Investor sentiment and the NFT hype index: to buy or not to buy?

PurposeThe primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype index was constructed as well as several approaches of XAI were employed to interpret Black Box models and assess the magnitude and direction of the impact of the features used.Design/methodology/approachThe research paper involved the construction of a sentiment index termed the NFT hype index, which aims to measure the influence of market actors within the NFT industry. This index was created by analyzing written content posted by 62 high-profile individuals and opinion leaders on the social media platform Twitter. The authors collected posts from the Twitter accounts that were afterward classified by tonality with a help of natural language processing model VADER. Then the machine learning methods and XAI approaches (feature importance, permutation importance and SHAP) were applied to explain the obtained results.FindingsThe built index was subjected to rigorous analysis using the gradient boosting regressor model and explainable AI techniques, which confirmed its significant explanatory power. Remarkably, the NFT hype index exhibited a higher degree of predictive accuracy compared to the well-known sentiment indices.Practical implicationsThe NFT hype index, constructed from Twitter textual data, functions as an innovative, sentiment-based indicator for investment decision-making in the NFT market. It offers investors unique insights into the market sentiment that can be used alongside conventional financial analysis techniques to enhance risk management, portfolio optimization and overall investment outcomes within the rapidly evolving NFT ecosystem. Thus, the index plays a crucial role in facilitating well-informed, data-driven investment decisions and ensuring a competitive edge in the digital assets market.Originality/valueThe authors developed a novel index of investor interest for NFT assets (NFT hype index) based on text messages posted by market influencers and compared it to conventional sentiment indices in terms of their explanatory power. With the application of explainable AI, it was shown that sentiment indices may perform as significant predictors for NFT sales and that the NFT hype index works best among all sentiment indices considered.

Research on the Effect of Investor Sentiment on the Stock Volatility in China Market Based on Asymmetric GARCH-VAR Model

Based on the information processing deviation in Behavioral Finance Theory, this paper, taking China stock market as the research object, discusses the effect of the overall emotional state of Chinese investors affected by information processing deviation on the stock volatility in the composite market of Shanghai and Shenzhen A-shares (SSAS) and the Science and Technology Innovation Board (STIB). Taking the current situation of China stock market into account, this paper selects the monthly comprehensive index of investor sentiment (CICSI_SDR) from March 2013 to March 2023, as well as the monthly stock returns in the composite market of SSAS and STIB. Then the volatility of stock returns is measured by the asymmetric GARCH model, then further combined with the comprehensive index of investor sentiment (CICSI_SDR), the asymmetric GARCH-VAR model is established. The research results found that there is a positive weak correlation between two variables, that is, the investor sentiment would drive the prosperity of financial market if that in the past upsurged and vice versa. Therefore, to better control market anomalies caused by investor sentiment, this paper suggests that the market should establish an evaluation mechanism for investor sentiment and gradually make the investor structure of China stock market be more reasonable.

Last Digit Tendency: Lucky Numbers and Psychological Rounding in Mobile Transactions

The distribution of digits in numbers obtained from different sources reveals interesting patterns. The well-known Benford’s law states that the first digits in many real-life numerical data sets have an asymmetric, logarithmic distribution in which small digits are more common; this asymmetry diminishes for subsequent digits, and the last digit tends to be uniformly distributed. In this paper, we investigate the digit distribution of numbers in a large mobile transaction data set with 835 million mobile transactions and payments made by approximately 460,000 users in more than 300 cities. Although the first digits of the numbers in these mobile transactions follow Benford’s law, the last digit has a strong tendency to be a lucky number or be influenced by psychological rounding. This lucky number tendency is more significant in transactions that are more strongly connected to social interactions, such as money sent as gifts or as “red envelopes” (a traditional method of gift-giving during Chinese holidays), and in transactions by individuals with potentially greater emotional needs, such as during COVID-19 outbreaks and natural disasters. This psychological rounding tendency is more common in online e-commerce payments, in-store purchases, and money transfers between individuals. These findings are key for understanding the last digit tendency and its psychological and emotional mechanisms, which could be used as an indicator of public sentiment or in methods of detecting fraudulent business activity.

Exploring the sentiment in Borsa Istanbul with deep learning

Sentiment analysis holds immense importance in finance and economics, addressing crucial issues such as principal–agent dynamics and information imbalances. The rise of natural language processing signifies a groundbreaking era in sentiment analysis, enabling the effective extraction of insights from textual data. Our research investigates the impact of qualitative financial data on firm valuation, utilizing sentiment extracted from annual financial disclosures, focusing on companies listed on the Borsa Istanbul Stock Exchange from 1998 to 2022. Employing a pre-trained transformer model, we develop sentiment indices and integrate textual data using a system-generalized method of moments. Our study aims to uncover how sentiment expressed in financial disclosures aids in mitigating challenges related to asymmetric information.

Transparency and trust in the public sector: Target and benchmarks to ensure macroeconomic stability

The article is devoted to the study of the relationship between a country's macroeconomic stability and the level of transparency and public trust in the financial sector and public authorities. Canonical analysis and structural modeling served as methodological tools of the research. The study examined the data from eight EU countries (Austria, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, Hungary, Czech Republic, and Italy) over the 2011-2021period. Eight indicators of public sector transparency and one indicator of the degree of public trust (Consumer Sentiment Index) were chosen to establish the relationship between the components. The results of structural modeling proved that public trust has a much greater impact on macroeconomic stability than indicators of public sector transparency. A 1-point increase in public trust leads the GDP to increase by 0.018% and the stability of the currency exchange rate – by 0.352%. Meanwhile the same effect from a 1-point increase in the level of public sector transparency amounts to 0.061% and 0.021% increases, respectively.

The Impact of China Economic Policy Uncertainty on CSI 300: An Analysis of the Mediating Effect of Investor Sentiment

This paper explores the mediating effect of investor sentiment on the relationship between China's economic policy uncertainty and the CSI 300 stock market returns. The study employs Principal Component Analysis (PCA) to construct the investor sentiment index, integrating six proxy variables. Additionally, the bootstrap analysis method is utilized to examine whether investor sentiment acts as an intermediary in determining the impact of economic policy uncertainty on stock market performance. The findings reveal that a significant portion (87.0%) of the total effect of economic policy uncertainty on stock returns is mediated through investor sentiment. The study underscores the pivotal role of investor sentiment in financial market behavior and emphasizes the need for policymakers to consider its influence during economic adjustments. Furthermore, it provides crucial recommendations for individual investors, promoting informed and rational decision-making in the dynamic financial landscape.

Sensitivity of Chinese stock markets to individual investor sentiment: An analysis of Sina Weibo mood related to COVID-19

This research explores the impact of individual investor sentiment derived from social networks on stock market returns. Using keyword-based techniques, we collect and analyze Sina Weibo posts related to COVID-19, extracting daily influential weighted sentiment indexes from a dataset of over 2.4 million posts in 2020. Empirical tests utilizing a sentiment-augmented three-factor model reveal that individual investor sentiment exerts an independent influence on Chinese financial markets, after controlling for market risk, size, and value effects. We further find that negative sentiment carries a stronger impact on stock returns, which is in line with the loss-averse behavior commonly observed among individual investors. We also find an asymmetric pattern in the sentiment-return relation across different industry types. While positive sentiment affects both types of industries that suffer or benefit from COVID-19, negative sentiment affects only the industries that suffer from the pandemic. Overall, our empirical results provide robust support for the significance of individual investor sentiment in explaining the behavior of the Chinese financial markets.

A structured multi-head attention prediction method based on heterogeneous financial data

The diverse characteristics of heterogeneous data pose challenges in analyzing combined price and volume data. Therefore, appropriately handling heterogeneous financial data is crucial for accurate stock prediction. This article proposes a model that applies customized data processing methods tailored to the characteristics of different types of heterogeneous financial data, enabling finer granularity and improved feature extraction. By utilizing the structured multi-head attention mechanism, the model captures the impact of heterogeneous financial data on stock price trends by extracting data information from technical, financial, and sentiment indicators separately. Experimental results conducted on four representative individual stocks in China’s A-share market demonstrate the effectiveness of the proposed method. The model achieves an average MAPE of 1.378%, which is 0.429% lower than the benchmark algorithm. Moreover, the backtesting return rate exhibits an average increase of 28.56%. These results validate that the customized preprocessing method and structured multi-head attention mechanism can enhance prediction accuracy by attending to different types of heterogeneous data individually.

The Influence of Investor Sentiment on Scale Effect of the A-Share Market: An Empirical Analysis of Principal Components

Scale effect in the stock market refers to the negative correlation between company size and expected return, which is a common anomaly in the financial market. Investor sentiment is the opinion that investors create based on their expectations of the potential future cash flow from their assets and the inherent dangers of making investments. The initial goals of this paper were to develop an investor sentiment index system and investigate how it relates to the market scale impact. In this paper, principal component analysis and the CICSI index, which can better measure Chinese investor sentiment, are combined to construct investors More than 3000 sample stocks are grouped by size, the average return rate of each group is calculated, and the scale effect is tested. Through the verification of the scale effect under the optimistic, neutral, and pessimistic sentiment index, the applicability of generated sentiment index to the scale effect is confirmed.