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The influence of social media on stock volatility
... In the similar vein, Siikanen et al. (2018) have found that the choices of purchase versus sell were linked with Facebook data, particularly for unreceptive households and nonprofit. However, Wu et al. (2017) revealed that the impact of different factors, where increased consideration of a stock's unpredictability, is more important than public opinion. ...
... Abu-Taleb and Nilsson (2021) discovered a significant positive correlation between company social media presence and portfolio allocation. Similarly, Wu et al. (2017) found social media data has a large but fading impact on the volatility on the next day. It may be attributed to the fact that capital market experts are also affected by social media (Andrzej Cwynar et al., 2017). ...
... However, this study defies some other studies (Tetlock, 2015;Ma & McGroarty, 2017;Kumari, 2019;Rani & Prerana, 2021;Wu et al., 2017). For example, studies conducted in the USA (Tetlock, 2015), India (Kumari, 2019), and China (Wu et al., 2017) found negative relationship between social media and investment decisions. ...
The growing pervasiveness and influence of social media in different spheres of life cannot be denied at all. In this light, this study aims to examine the relationship between different aspects of social media and investment decisions in the context of the Nepali stock market. The study sample included 384 retail investors, and the data was collected through structured questionnaires. Descriptive statistics, Pearson correlation, and standard multiple regression analyses were used to analyze the data. Although significantly positive relationships were found between all aspects of social media and investment decisions, content on social media has a stronger relationship with investment decisions relative to the online community behavior on social media and corporate image on social media. This is among the limited studies of its kind in the distinct socio-economic context of Nepal. Corporate managers may regularly update relevant information on their social media platforms for attracting potential investors and increase their firm value. Likewise, regulators may run investor education programs in order to protect investors, particularly immature retail investors, from the risk associated with potentially less reliable information on social media platforms. Future researchers may employ a mixed-method research design to uncover the comprehensive set of factors influencing investment decisions in the context of the Nepali stock market.
... These sentiments can sometimes affect the whole market's volatility. Similarly, Lehrer, Xie, and Zhang (2021) and Wu et al. (2017) show that social media sentiment significantly explains stocks' volatility. ...
... Therefore, in this study, we control for the impact of the six mega-cap stocks' prices. Wu et al. (2017) argue that trading volume has the biggest effect on the volatility of a stock. Therefore, we include the trading volume as a second control variable. ...
This paper summarizes the arguments and counterarguments within the scientific discussion on the issue of the impact of social media events on stock price volatility. The main purpose of the research is to examine the impact of the Reddit posts from January 2022 through July 2023 on the price volatility of the six U.S. mega-cap technology stocks. Unlike most of the previous studies that focus on Twitter, this study focuses on Reddit. This study not only examines how Reddit posts relate to volatility but also how trading volume and stock price relate to volatility. Therefore, while focusing on the impact of social media events on volatility, the study controls for the effects of trading volume and price. Based on the previous research on social media events on different platforms, it is expected that Reddit events significantly affect stock price volatility. Again, based on the previous research on social media events on different platforms, higher trading volume and higher stock prices are expected to have a positive relationship with stock price volatility (i.e. higher volumes and higher prices are associated with higher volatility). Overall, the findings in this paper support these expectations. First, the ANOVA test results reject the null hypothesis of no predictive relationship between the three independent variables (i.e. “Socialmedia”, “Price”, and “Volume”) and the stock price volatility of the six mega-cap stocks. For the whole group of firms, the regression analyses show that the positive Reddit events are associated with lower volatility when compared to negative Reddit events, and that higher trading volumes and prices are associated with higher volatility. Therefore, for the group of six mega-cap stocks, the results support our hypothesis. When individual regressions are performed for each stock, the results are mixed. The results for Alphabet (i.e. Google), Tesla, Meta, and Microsoft are more in line with the expectations, while the results for Apple and Nvidia are not. For Google and Tesla stocks, when there is a positive social media event, the volatility is lower. This finding indicates that a positive event calms the investors of these stocks. For Meta and Microsoft stocks, when there is a positive social media event, the volatility is higher. This finding may imply that increased volatility due to a positive event possibly stems from the extra demand for these stocks in a very short period. For Apple and Nvidia stocks, there is no significant relationship between social media events and volatility. Overall, we conclude that, a prospective investor who wants to invest in a pool of “mega-cap technology stocks”, social media events should be a factor when making an investment decision. On the other hand, a prospective investor who is a “stock picker”, needs to evaluate each individual regression result when making an investment decision.
... In the education sector, Nguyen et al. [3] emphasized the role of social media engagement in driving enrolment intentions, while Brown et al. [4] explored the potential of social media data in forecasting sports outcomes, using tweets to predict soccer match results. Other studies by scholars such as Wu et al. [5] and Giri et al. [6], have further demonstrated the relevance of social media data in predicting stock market volatility and garment sales, respectively. These studies collectively highlight the growing importance of social media as a rich resource for predictive analytics across various fields. ...
This study examines the extent to which incorporating social media data enhances the predictive accuracy of models forecasting international students’ arrivals. Private social media data collected from a public university, along with collected web traffic data and Google Trend data, were used in the forecasting models. Initially, a correlation analysis was conducted, revealing a strong relationship between the institution’s international student enrolment and the social media activity, as well as with the overall number of international students arriving in Australia. Building on these insights, features were derived from the collected data for use in the development of the forecasting models. Two XGBoost models were developed: one excluding social media’s features and one including them. The model incorporating social media data outperformed the one without it. Furthermore, a feature selection process was applied, resulting in even more accurate forecasts. These findings suggest that integrating social media data can significantly enhance the accuracy of forecasting models for international student arrivals.
... In essence, the Nigerian case underscores a broader challenge in managing misinformation's impact on financial markets. Enhancing media literacy and investor education, alongside regulatory reforms and technological solutions, is crucial to mitigating the risks posed by misinformation in Nigeria's stock market and ensuring more stable and informed market conditions during crises (Wu et al., 2017;Zhang et al., 2022). ...
This paper examines the economic consequences of misinformation on stock market volatility during the COVID-19 pandemic, highlighting how false information significantly disrupted financial markets. The analysis explores specific high-profile cases where misinformation about vaccines, lockdowns, and treatments led to increased market volatility, panic selling, and shifts in investor behaviour. The study delves into the effects on major indices such as the S&P 500 and Dow Jones, revealing the substantial financial losses experienced by retail and institutional investors. It also discusses the regulatory and institutional responses from financial authorities and social media platforms, as well as the challenges they face in curbing misinformation’s rapid spread. The paper concludes with recommendations for enhancing market resilience, emphasising the importance of media literacy, robust fact-checking, and proactive regulatory frameworks to mitigate the impact of misinformation in future crises. This study underscores the ongoing need for vigilant market practices and improved information governance to maintain economic stability.
... Another study found that online rumors had a short-term effect on stock market volatility within the Chinese stock market (Zhang et al., 2022). An additional study has proven that social media sentiment can be used to accurately predict short-term stock price volatility (Wu et al., 2017). This is in agreement with the potential impacts the results of this paper suggest. ...
This paper aims to identify the correlation between stock performance and social media traction during 2020. It analyzes data from three different investment-focused communities within the social media platform Reddit. This data is compared to the historical financial data of six stocks across six different industries in the year 2020. The number of posts with the name of the stock within the original post each week is compared to the absolute percent change between market open to close for that week. As a control, data from the same time frame on the SPDR S&P 500 ETF Trust is used for comparison with the experimental data. All of the stock price information was taken from Yahoo! Finance. This study reveals that there is a statistically significant positive correlation between stock price change and number of Reddit posts each week. The observed correlation between stock performance and social media patterns has a significant impact in determining future stock volatility and predicting short-term investor sentiment.
... Several studies have examined the effect of sentiment analysis on the volatility of the stock market. Social media data were utilized to forecast stock market volatility, and the results demonstrated that sentiment analysis is a useful method for predicting stock market volatility (Wu et al., 2017). In addition to social media, sentiment analysis has been used on other platforms to predict stock market behavior. ...
In recent years, there has been growing interest in using deep learning methods to improve the accuracy of stock price prediction, which has always been challenging due to the unpredictable nature of the market. This paper introduces two new hybrid deep learning-based models, named “En-Tweet-Deep-SMF” and “En-Tweet-Hib-SMF,” that combine effective strategies to enhance stock price prediction accuracy. These strategies involve enhancing Twitter sentiment scores using an enhanced model and utilizing potent technical indicators. The “En-Tweet-Deep-SMF” model employs a gated recurrent unit, while the “En-Tweet-Hib-SMF” model uses the convolutional neural network-bidirectional long-short term memory hybrid deep learning-based model. Additionally, kernel principal component analysis is utilized to reduce the dataset dimensionality. These models can capture both quantitative and qualitative factors that can influence stock prices, making them more accurate and robust than traditional methods. The proposed models have the potential to adapt and learn from new data and trends, providing traders, investors, and financial analysts with a valuable tool to make informed decisions and mitigate risks in the stock market. Experimental results indicate that these models outperform several state-of-the-art models, demonstrating their effectiveness and potential practical applications in the financial industry.
... ESN, such as Yammer and Jive, has been widely adopted by organizations to support team members in communicating and collaborating with each other [75]. The prevalence of ESN is not only changing workplace communication and coordination but also providing considerable opportunities and challenges in stress management and creativity development [26,45,84]. Thus, scholars have indicated that a team's various usage of ESN may influence the outcomes of team members perceiving different work stressors; however, such an effect may not be equal among members [15]. ...
Enterprise social networking (ESN) techniques have been widely adopted by organizations to provide a platform for public communication among employees. Based on the job demand–control–support model and communication visibility theory, this study investigates how the relationships between the perceived work stressors of employees (i.e., challenge and hindrance stressors) and their creativity are moderated by team task- and relationship-oriented ESN usage. We used the hierarchical linear model to test this multilevel model. Results of a survey of 260 employees in 72 groups indicate that two ESN usage types differentially moderate the relationship between work stressors and employee creativity. Specifically, task-oriented ESN usage positively moderates the effects of the two types of stressors on employee creativity, whereas relationship-oriented ESN usage negatively moderates the relationship between hindrance stressors and employee creativity. Theoretical and practical implications are also discussed.
... 1) What factors make important effects during the information processing? Identified factors of eWOM include quantity (Park et al., 2007), quality (Cheung & Lee, 2012;Park et al., 2007), credibility (Zhang et al., 2014;Wu, Wang, Ma & Ye, 2017), and the type of reviews (Floyd, Freling, Alhoqail, Cho, & Freling, 2014;Cheung & Thadani, 2012;Park & Kim, 2008). These factors are divided into central route and peripheral route to analyze the effect of eWOM on consumers' persuasion. ...
Previous studies have demonstrated that online reviews play an important role in the purchase decision process. Though the effects of positive and negative reviews to consumers’ purchase decisions have been analyzed, they were examined statically and separately. In reality, online review community allows everyone to express and receive opinions and individuals can reexamine their opinions after receiving messages from others. The goal of this paper is to study how potential customers form their opinions dynamically under the effects of both positive and negative reviews using a numerical simulation. The results show that consumers with different membership levels have different information sensitivities to online reviews. Consumers at low and medium membership levels are often persuaded by online reviews, regardless of their initial opinion about a product. On the other hand, online reviews have less effect on consumers at higher membership levels, who often make purchase decisions based on their initial impressions of a product.
The influence of social media on stock markets, both in India and worldwide, is a noteworthy and multifaceted occurrence in today's tech-driven society. The popularity of social media is gauged by user count and stock market engagement. The internet's growing prominence has elevated social media to a vital global platform for interpersonal connections. Advancements in technology have enabled global connectivity, removing geographical limitations for the stock market and investors. Limited research exists on the influence of social media on stock markets in Haryana, India. This study investigates the influence of social media on the stock market and potential investors in Haryana. The study employs primary and secondary data, gathered via a questionnaire and online sources like e-journals, e-magazines, and company reports. The study finds that social media is important for the stock market and helps investors in trading activities.
Mining relevant stocks given a trending topic/concept in capital markets is an application with significant economic and societal impacts. Previous concept stock recommendation system mines concept stocks only from public social media like financial news. On stock forums, investors discuss emerging concepts and stocks by using forum comments, which are unneglectable resources to capture trending concept stocks accurately and timely. However, the comment data from a single forum is insufficient to build a high-quality recommendation system. The forums are data silos protected by privacy regulations, and their comments are still underutilized. In this paper, we propose a federated concept stock recommendation baseline and an optimized method that both leverage the private forum comments and public social media without compromising privacy regulations. Our baseline,
i.e.
, Federated Meta Embedding (FedME), is built upon the federated learning framework and learns a concept-stock embedding jointly from private and public data. Our optimized method, Federated Graph Meta Embedding (FedGME), improves FedME by using a graph to combine two sources of embeddings and additional human experts' concept-stock knowledge. Empirically, the experiments on two concept stock datasets show that FedME and FedGME substantially improve the performance of recommendation. Our methods provide practical guidance on privacy-preserving FinTech applications.
In the current technological driven society, the role and impact of social media has versatile significant in different fields / sectors at gross in all over the world. The impact of social media on stock markets of India as well as world is one of these fields / sectors. The social media impact can be measured by no of persons using social media and how many of them are interested in stock market. The increase rate in use of internet leads the popularity of social media. Social Media is one of the important media which can connect the each and every people of India as well as whole World. Today stock market and the investors are not restricted to geographical boundaries of a country. Today, we can say the whole world is market as well as a stock market. It is only possible for the development of technology and use of technology by the most of the people of the world. A lot study has been undertaken by various researchers, academician, and scholars on the impact of social media on stock market in India as well as the whole World. But, no remarkable study has been undertaken on the impact of social media on stock market of Odisha. So, this is an attempt to study impact of social media on stock market of India and potential investors of Odisha. The present study based on both primary and secondary data. The primary data has been collected through a well-defined questionnaire which is designed for this specific purpose. The secondary data have collected through a well-designed strategy, and these have been collected from various e-journals, e-magazines, e-annual reports of companies, and various reputed websites. There are various statistical tools, i.e., percentage calculations; correlation, and chi-square test have used for analysis and interpretation of results. The present study concluded that the social media has vital role and impact on stock market. The social media is also helping stock market and investors in their trading in the current market scenario.
In financial markets, there are situations where investors have the future stock prices according to the experts’ evaluations rather than historical data. Thus, the estimations of the stock prices contain much subjective imprecision instead of randomness. This paper discusses a portfolio investment with options in such a kind of situation. Treating the stock index price as an uncertain variable, we build an uncertain mean-chance portfolio model based on uncertainty theory and provide the equivalent form of the model. Furthermore, we make a comparison of the optimal expected return between portfolio investment with options and without options. An important conclusion is reached: The portfolio investment with options produces a no less expected return than that without options. In addition, we make sensitivity analysis and get two vital corresponding results. As an illustration, a numerical example is presented as well. The numerical results reveal that the options should be considered in portfolio investment. And the call option with maximum exercise price is most valuable per premium cost with the same exercise date.
In this paper, text mining and statistical models are deployed to explore the relationship between the Shanghai Stock Exchange Composite Index (SSECI) and the collective emotions of individual investors. The emotions of individual investors are quantified by extracting and aggregating investor online posts that contain finance-related keywords. To identify a set of finance-related keywords, three years of blogs from a famous financial blog site are segmented by an automatic text segmentation method; meanwhile, in the literature of social media, people typically select keywords manually. Posts that discuss the keywords are extracted out of all types of topics from Sina Weibo, the largest microblog platform in China. Statistical results reveal the relationship between daily posts and daily opening prices with a one-day lag, which indicates the existence of information (news) propagation lag. This study contributes to the existing literature by demonstrating that the microblog sentiment level reports can be quantitatively incorporated as a proxy to provide valuable support to portfolio decision making.
In the past, enterprises used time-consuming questionnaire surveys and statistical analysis to formulate consumer profiles. However, explosive growth in social media had produced enormous quantities of texts, images, and videos, which is sometimes referred to as a digital footprint. This provides an alternative channel for enterprises seeking to gain an objective understanding of their target consumers. Facilitating the analysis of data used in the formulation of a marketing strategy based on digital footprints from online social media is crucial for enterprises seeking to enhance their competitive advantage in today’s markets. This study develops an approach for predicting consumer decision-making styles by analyzing digital footprints on Facebook to assist enterprises in rapidly and correctly mastering the consumption profile of consumers, thereby reducing marketing costs and promoting customer satisfaction. This objective can be achieved by performing the following tasks: (i) designing a process for predicting consumer decision-making styles based on the analysis of digital footprints on Facebook, (ii) developing techniques related to consumer decision-making style prediction, and (iii) implementing and evaluating a consumer decision-making style prediction mechanism. In the practical experiment, we obtained questionnaires and various digital footprint contents (including “Likes,” “Status,” and “Photo/Video”) from 3304 participants in 2018, 2644 of which were randomly selected as a training dataset, with the remaining 660 participants forming a testing dataset. The experimental results indicated that the accuracy increased to 75.88% and proved that the approach proposed in this study can effectively predict consumers’ decision-making styles.
Social media-based forecasting has received significant attention from academia and industries in recent years. With a focus on Twitter, this paper investigates whether sentiments of the tweets regarding the 7 largest US financial service companies in U.S. dollars are related to the stock price changes of these companies. The authors' findings indicate, in the financial services context, negative sentiments predict firms' future stock prices. However, the number of and the positive sentiment of tweets are not correlated with stock prices. The findings of this paper suggest the possible predictive value of social media data on stock prices at the company level.
In this paper, we develop a simple model to study and analyze communication dynamics in the blogosphere and use these dynamics to determine interesting correlations with stock market movement. This work can drive targeted advertising on the web as well as facilitate understanding community evolution in the blogosphere. We describe the communication dynamics by several simple contextual properties of communication, e.g. the number of posts, the number of comments, the length and response time of comments, strength of comments and the different information roles that can be acquired by people (early responders / late trailers, loyals / outliers). We study a "technology-savvy" community called Engadget ( http://www.engadget.com ). There are two key contributions in this paper: (a) we identify information roles and the contextual properties for four technology companies, and (b) we model them as a regression problem in a Support Vector Machine framework and train the model with stock movements of the companies. It is interestingly observed that the communication activity on the blogosphere has considerable correlations with stock market movement. These correlation measures are further cross-validated against two baseline methods. Our results are promising yielding about 78% accuracy in predicting the magnitude of movement and 87% for the direction of movement.
Microblogging today has become a very popular communication tool among Internet users. Millions of users share opinions on different aspects of life everyday. Therefore microblogging web-sites are rich sources of data for opinion mining and sentiment analysis. Because microblogging has appeared relatively recently, there are a few research works that were devoted to this topic. In our paper, we focus on using Twitter, the most popular microblogging platform, for the task of sentiment analysis. We show how to automatically collect a corpus for sentiment analysis and opinion mining purposes. We perform linguistic analysis of the collected corpus and explain discovered phenomena. Using the corpus, we build a sentiment classifier , that is able to determine positive, negative and neutral se ntiments for a document. Experimental evaluations show that our proposed techniques are efficient and performs better than previousl y proposed methods. In our research, we worked with English, however, the proposed technique can be used with any other language.
Extracting sentiment from text is a hard semantic problem. We develop a methodology for extracting small investor sentiment from stock message boards. The algorithm comprises dierent classifier algorithms coupled together by a voting scheme. Accuracy levels are similar to widely used Bayes classifiers, but false positives are lower and sentiment accuracy higher. Time series and cross-sectional aggregation of message information improves the quality of the resultant sentiment index, particularly in the presence of slang and ambiguity. Empirical applications evidence a relationship with stock values - aggregate tech sector sentiment is found to predict stock index levels, but not at the individual stock level. The algorithms may be used to assess the impact on investor opinion of management announcements, press releases, third-party news, and regulatory changes.
Our research examines a predictive machine learning approach for financial news articles analysis using several different textual representations: Bag of Words, Noun Phrases, and Named Entities. Through this approach, we investigated 9,211 financial news articles and 10,259,042 stock quotes covering the S&P 500 stocks during a five week period. We applied our analysis to estimate a discrete stock price twenty minutes after a news article was released. Using a Support Vector Machine (SVM) derivative specially tailored for discrete numeric prediction and models containing different stock-specific variables, we show that the model containing both article terms and stock price at the time of article release had the best performance in closeness to the actual future stock price (MSE 0.04261), the same direction of price movement as the future price (57.1% directional accuracy) and the highest return using a simulated trading engine (2.06% return). We further investigated the different textual representations and found that a Proper Noun scheme performs better than the de facto standard of Bag of Words in all three metrics.
This paper proposes a new tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute and cut-point choice while splitting a tree node. In the extreme case, it builds totally randomized trees whose structures are independent of the output values of the learning sample. The strength of the randomization can be tuned to problem specifics by the appropriate choice of a parameter. We evaluate the robustness of the default choice of this parameter, and we also provide insight on how to adjust it in particular situations. Besides accuracy, the main strength of the resulting algorithm is computational efficiency. A bias/variance analysis of the Extra-Trees algorithm is also provided as well as a geometrical and a kernel characterization of the models induced.
In recent years, social media has become ubiquitous and important for social
networking and content sharing. And yet, the content that is generated from
these websites remains largely untapped. In this paper, we demonstrate how
social media content can be used to predict real-world outcomes. In particular,
we use the chatter from Twitter.com to forecast box-office revenues for movies.
We show that a simple model built from the rate at which tweets are created
about particular topics can outperform market-based predictors. We further
demonstrate how sentiments extracted from Twitter can be further utilized to
improve the forecasting power of social media.
Innovation is crucial for firms to gain competition advantages. Social media provides firms a new platform to facilitate innovation because consumers are getting more involved in the whole innovation process with the help of Web 2.0 technologies. Social media has been used as an important tool by firms to influence consumers’ attitude and behavior. Influence occurs in social media-based brand communities because community members have the control of discovering, producing, sharing, and distributing information and because the spread out of their experiences and opinions in the format of electronic word-of-mouth forms brand images. Because social media has the ability of global reach, cross-border factors should not be neglected in studying the influence process. As such, this paper adopts national cultural dimensions identified by Hofstede (1984), individualism/collectivism and power distance particularly, the index of cultural distance, and the social influence theory to explore how culture impacts the opinion influence occurring in social media-based brand communities. Furthermore, this paper predicts that the use of social media facilitate firms’ innovation due to consumers’ active involvement.
In this paper, we propose a robust methodology to assess the value of microblogging data to forecast stock market variables: returns, volatility and trading volume of diverse indices and portfolios. The methodology uses sentiment and attention indicators extracted from microblogs (a large Twitter dataset is adopted) and survey indices (AAII and II, USMC and Sentix), diverse forms to daily aggregate these indicators, usage of a Kalman Filter to merge microblog and survey sources, a realistic rolling windows evaluation, several Machine Learning methods and the Diebold-Mariano test to validate if the sentiment and attention based predictions are valuable when compared with an autoregressive baseline. We found that Twitter sentiment and posting volume were relevant for the forecasting of returns of S&P 500 index, portfolios of lower market capitalization and some industries. Additionally, KF sentiment was informative for the forecasting of returns. Moreover, Twitter and KF sentiment indicators were useful for the prediction of some survey sentiment indicators. These results confirm the usefulness of microblogging data for financial expert systems, allowing to predict stock market behavior and providing a valuable alternative for existing survey measures with advantages (e.g., fast and cheap creation, daily frequency).
A feature selection algorithm based on random forest (RFFS) is proposed. This algorithm adopts random forest algorithm as the basic tool, the classification accuracy as the criterion function. The sequential backward selection and generalized sequential backward selection methods are employed for feature selection. The experimental results on UCI datasets show that the RFFS algorithm has better performance in classification accuracy and feature selection subset than the other methods in literatures.
The anecdotal evidence is growing that postings in Internet financial forums affect stock prices, either because the postings contain new information or because they represent successful attempts to manipulate stock prices. From an investment perspective, knowing whether this phenomenon is pervasive is important. We examined the relationship between Internet message board activity and abnormal stock returns and trading volume in the period from mid-April 1999 to mid-February 2000. Our study focused on the RagingBull.com discussion forum, an extremely popular site whose format permits the construction of an objective measure of investor opinions. For stocks in the Internet service sector, we found that on days with abnormally high message activity, changes in investor opinion correlated with abnormal industry-adjusted returns. These event days also coincided with abnormally high trading volume, which persisted for a second day. However, we found that message board activity did not predict industry-adjusted returns or abnormal trading volume, which is consistent with market efficiency.
Using peer-to-peer (P2P) lending as an example, we show that learning by doing plays an important role in alleviating the information asymmetry between market players. Although the P2P platform (Prosper.com) discloses part of borrowers’ credit histories, lenders face serious information problems because the market is new and subject to adverse selection relative to offline markets. We find that early lenders did not fully understand the market risk but lender learning is effective in reducing the risk over time. As a result, the market excludes more and more sub-prime borrowers and evolves towards the population served by traditional credit markets.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
This study examines whether user-generated content (UGC) is related to stock market performance, which metric of UGC has the strongest relationship, and what the dynamics of the relationship are. We aggregate UGC from multiple websites over a four-year period across 6 markets and 15 firms. We derive multiple metrics of UGC and use multivariate time-series models to assess the relationship between UGC and stock market performance.
Volume of chatter significantly leads abnormal returns by a few days (supported by Granger causality tests). Of all the metrics of UGC, volume of chatter has the strongest positive effect on abnormal returns and trading volume. The effect of negative and positive metrics of UGC on abnormal returns is asymmetric. Whereas negative UGC has a significant negative effect on abnormal returns with a short “wear-in” and long “wear-out,” positive UGC has no significant effect on these metrics. The volume of chatter and negative chatter have a significant positive effect on trading volume. Idiosyncratic risk increases significantly with negative information in UGC. Positive information does not have much influence on the risk of the firm. An increase in off-line advertising significantly increases the volume of chatter and decreases negative chatter. These results have important implications for managers and investors.
By reaching a broad population of investors, mass media can alleviate informational frictions and affect security pricing even if it does not supply genuine news. We investigate this hypothesis by studying the cross-sectional relation between media coverage and expected stock returns. We find that stocks with no media coverage earn higher returns than stocks with high media coverage even after controlling for well-known risk factors. These results are more pronounced among small stocks and stocks with high individual ownership, low analyst following, and high idiosyncratic volatility. Our findings suggest that the breadth of information dissemination affects stock returns. Copyright (c) 2009 the American Finance Association.
We describe an approach to object retrieval which searches for and localizes all the occurrences of an object in a video, given a query image of the object. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject those that are unstable. Efficient retrieval is achieved by employing methods from statistical text retrieval, including inverted file systems, and text and document frequency weightings. This requires a visual analogy of a word which is provided here by vector quantizing the region descriptors. The final ranking also depends on the spatial layout of the regions. The result is that retrieval is immediate, returning a ranked list of shots in the manner of Google. We report results for object retrieval on the full length feature films 'Groundhog Day', 'Casablanca' and 'Run Lola Run', including searches from within the movie and specified by external images downloaded from the Internet. We investigate retrieval performance with respect to different quantizations of region descriptors and compare the performance of several ranking measures.
We examine the problem of discrete stock price prediction using a synthesis of linguistic, financial and statistical techniques to create the Arizona Financial Text System (AZFinText).The research within this paper seeks to contribute to the AZFinText system by comparing AZFinText’s predictions against existing quantitative funds and human stock pricing experts. We approach this line of research using textual representation and statistical machine learning methods on financial news articles partitioned by similar industry and sector groupings. Through our research, we discovered that stocks partitioned by Sectors were most predictable in measures of Closeness, Mean Squared Error (MSE) score of 0.1954, predicted Directional Accuracy of 71.18% and a Simulated Trading return of 8.50% (compared to 5.62% for the S&P 500 index). In direct comparisons to existing market experts and quantitative mutual funds, our system’s trading return of 8.50% outperformed well-known trading experts. Our system also performed well against the top 10 quantitative mutual funds of 2005, where our system would have placed fifth. When comparing AZFinText against only those quantitative funds that monitor the same securities, AZFinText had a 2% higher return than the best performing quant fund.
Behavioral economics tells us that emotions can profoundly affect individual
behavior and decision-making. Does this also apply to societies at large, i.e.,
can societies experience mood states that affect their collective decision
making? By extension is the public mood correlated or even predictive of
economic indicators? Here we investigate whether measurements of collective
mood states derived from large-scale Twitter feeds are correlated to the value
of the Dow Jones Industrial Average (DJIA) over time. We analyze the text
content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder
that measures positive vs. negative mood and Google-Profile of Mood States
(GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital,
Kind, and Happy). We cross-validate the resulting mood time series by comparing
their ability to detect the public's response to the presidential election and
Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing
Fuzzy Neural Network are then used to investigate the hypothesis that public
mood states, as measured by the OpinionFinder and GPOMS mood time series, are
predictive of changes in DJIA closing values. Our results indicate that the
accuracy of DJIA predictions can be significantly improved by the inclusion of
specific public mood dimensions but not others. We find an accuracy of 87.6% in
predicting the daily up and down changes in the closing values of the DJIA and
a reduction of the Mean Average Percentage Error by more than 6%.
Receiver-operating characteristic (ROC) analysis was originally developed during World War II to analyze classification accuracy in differentiating signal from noise in radar detection.1 Recently, the methodology has been adapted to several clinical areas heavily dependent on screening and diagnostic tests,2–4 in particular, laboratory testing,5 epidemiology,6 radiology,7–9 and bioinformatics.10
ROC analysis is a useful tool for evaluating the performance of diagnostic tests and more generally for evaluating the accuracy of a statistical model (eg, logistic regression, linear discriminant analysis) that classifies subjects into 1 of 2 categories, diseased or nondiseased. Its function as a simple graphical tool for displaying the accuracy of a medical diagnostic test is one of the most well-known applications of ROC curve analysis. In Circulation from January 1, 1995, through December 5, 2005, 309 articles were published with the key phrase “receiver operating characteristic.” In cardiology, diagnostic testing plays a fundamental role in clinical practice (eg, serum markers of myocardial necrosis, cardiac imaging tests). Predictive modeling to estimate expected outcomes such as mortality or adverse cardiac events based on patient risk characteristics also is common in cardiovascular research. ROC analysis is a useful tool in both of these situations.
In this article, we begin by reviewing the measures of accuracy—sensitivity, specificity, and area under the curve (AUC)—that use the ROC curve. We also illustrate how these measures can be applied using the evaluation of a hypothetical new diagnostic test as an example.
A diagnostic classification test typically yields binary, ordinal, or continuous outcomes. The simplest type, binary outcomes, arises from a screening test indicating whether the patient is nondiseased (Dx=0) or diseased (Dx=1). The screening test indicates whether the patient is likely to be diseased or not. When >2 categories are used, the test data can be on an ordinal rating …
We analyze the impact of a Web-based trading channel on the trading activity in two corporate 401(k) plans. Using detailed data on about 100,000 participants, we compare trading growth in these firms to growth for a sample of firms without a Web channel. After 18 months of access, the inferred Web effect is very large: trading frequency doubles, and portfolio turnover rises by over 50 percent. We also document several patterns of Web-trading behavior. Young, male, and wealthy participants are more likely to try the Web channel. Frequent traders (before Web introduction) are less likely to try the Web. Participants who try the Web tend to stick with it. Web trades tend to be smaller than phone trades both in dollars and as a fraction of portfolio. "Short-term" trades make up a higher proportion of phone trades than of Web trades.
We examine whether a simple quantitative measure of language can be used to predict individual firms' accounting earnings and stock returns. Our three main findings are: (1) the fraction of negative words in firm-specific news stories forecasts low firm earnings; (2) firms' stock prices briefly underreact to the information embedded in negative words; and (3) the earnings and return predictability from negative words is largest for the stories that focus on fundamentals. Together these findings suggest that linguistic media content captures otherwise hard-to-quantify aspects of firms' fundamentals, which investors quickly incorporate into stock prices. Copyright (c) 2008 by The American Finance Association.
I quantitatively measure the interactions between the media and the stock market using daily content from a popular "Wall Street Journal" column. I find that high media pessimism predicts downward pressure on market prices followed by a reversion to fundamentals, and unusually high or low pessimism predicts high market trading volume. These and similar results are consistent with theoretical models of noise and liquidity traders, and are inconsistent with theories of media content as a proxy for new information about fundamental asset values, as a proxy for market volatility, or as a sideshow with no relationship to asset markets. Copyright 2007 by The American Finance Association.
Financial press reports claim that Internet stock message boards can move markets. We study the effect of more than 1.5 million messages posted on Yahoo! Finance and Raging Bull about the 45 companies in the Dow Jones Industrial Average and the Dow Jones Internet Index. Bullishness is measured using computational linguistics methods. "Wall Street Journal" news stories are used as controls. We find that stock messages help predict market volatility. Their effect on stock returns is statistically significant but economically small. Consistent with Harris and Raviv (1993) , disagreement among the posted messages is associated with increased trading volume. Copyright 2004 by The American Finance Association.
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