Shifts In Sentiment Research Articles

Reclaiming a Revolutionary Past: War Veterans, Pensions, and the Struggle for Recognition

Reclaiming a Revolutionary PastWar Veterans, Pensions, and the Struggle for Recognition Michael A. McDonnell (bio) and Briony Neilson (bio) In March 1818, Congress passed into law a bill that seemed to mark a watershed moment in the contest over public memory of the Revolutionary War. The Pension Act finally recognized and rewarded the services of rank-and-file war veterans from the conflict that gave birth to the new nation. Introduced by President James Monroe—a former Continental Army officer himself—the Pension Act was hailed as "an auspicious circumstance," by one Congressional Representative. It was "gratifying evidence of the re-connexion of public feeling with the principles of the Revolution."1 Historians have generally seen the Pension Act of 1818 as a marker of a transformation in public sentiment over the memory of the Revolutionary War. In this telling, for many years after the conflict a mythic view of the conflict as a "people's war" had sidelined the achievements of those [End Page 467] who had served in the Continental Army in favor of the vigilant patriotism of the people and the near-universal contributions of the militia toward the war. Yet over time, as John P. Resch argued, the image of the suffering soldier "transformed perceptions of the Continental Army." Accelerated by the War of 1812, Americans came to lay aside a view of the Army as filled by the dregs of society and something to be feared and despised, and instead to see it as composed of brave, heroic, and patriotic citizens who came from all ranks of society. Ultimately, Congress ratified this new view of the Revolutionary War as the legislation "codified the image of the suffering soldier and the public's sentiment of gratitude to honor, reward, and aid Revolutionary War veterans." According to this view, the 1818 Pension Act, then, signified a shift toward a more inclusive understanding of military service in the conflict.2 While the legislation no doubt reflected a shift in sentiment, especially toward the "suffering soldiers" of the Continental Army, the inclusiveness of the legislation has been overstated. While the 1818 Act did extend benefits to an important group of soldiers, it nonetheless continued to exclude tens of thousands of veterans of the Revolutionary War who did not meet the requirements of the legislation. It excluded, for example, anyone who had not served in the Continental Army. It also targeted a particular type of veteran—one who had served at least nine months in the Army. Finally, recipients of a pension had to show they were in "reduced circumstances." To be sure, some congressmen argued for a more expansive pension system that would include all veterans, no matter how long they served and whether they were in the Continental Army, state troops, or militia. But their calls were undermined by concerns about the possible expense of such a plan. Congress had to make a decision about who was worthy of a pension. And in 1818, Representatives decided that only deserving professional soldiers should be rewarded. As Maryland Senator Robert Goldsborough put it, if anyone should receive a pension, it ought to be the "Half starved, half naked" soldiers who had suffered most during the Revolutionary War. Those [End Page 468] "most worthy of our gratitude," he argued, "are officers and soldiers of the Revolutionary Army." Wartime suffering, in other words, was explicitly understood to be an experience unique to the semi-professional troops who ended up serving for long periods in the Continental Army.3 We would argue, then, that while the Pension Act of 1818 was an important achievement, the final provisions of the Act still reflected a limited view and understanding of the Revolutionary War on the part of lawmakers. As Sarah Purcell, Alfred F. Young, Robert E. Cray, and others have argued, images of heroic wartime leaders—most often officers in the Continental Army—dominated the iconography and public memory of the Revolutionary War for most of the period up until 1818. That view had originated with the efforts of George Washington and his fellow Continental Army officers during the war, when they disparaged the efforts of the militia during the conflict in order to...

When the Going Gets Tough, The Tweets Get Going! An Exploratory Analysis of Tweets Sentiments in the Stock Market

Information artifacts in social media can have significant impact on domains and subject matter opinions. Asset prices, stock prices and volumes, and metrics are influenced by turbulence in their information ecosystems. Analyses of digital information networks and social media information events have shown information artifacts to affect stock performance without consistent correlation to fundamentals. Thus it becomes important to dispel ambiguity and gain insights into how the sentiments associated with information artifacts in Twitter (due to its extensive usage in relevant signaling), are associated with equity movements. Using an exploratory analysis to study tweet sentiment, we link stock price variations, and explore associated metrics. Our tweets analytics deploying textual analytics, identifies forms in tweet behavior and sentiment shifts.

Comparing automated text classification methods

Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.

The Effect of Consumer Sentiment on Consumption: Cross-Sectional Evidence from Elections

We seek to identify the causal effect of sentiment innovations on consumption. Using unique Australian consumer sentiment survey data, we show that, immediately after elections with a change of government, supporters of the winning party report substantially more optimistic beliefs about economic conditions than supporters of the losing party. We argue that this variation in beliefs is orthogonal to changes in fundamentals and find robust evidence that the shifts in sentiment affect spending intentions. Furthermore, using geographic variation in sentiment, vote shares, and automobile purchases, we find evidence that stated spending intentions are indicative of actual spending. (JEL E23, E24, E32, F44, L16)

Aggregation, Public Criticism, and the History of Reading Big Data

This essay traces the history of ideas behind the critical methods of distant reading and macroanalysis, modes of criticism enabled by the rise of the science, media, and technology of aggregation. I situate these methods in the intellectual shifts marked by the advent of modern polling practices, computational census technologies, post-1945 marketing strategies, and other methods of analyzing an aggregated public. Drawing on work by Sarah E. Igo, Mary Poovey, Bill Kovarik, and others, I demonstrate that the ideas legitimated through these shifts in technology and public sentiment are fundamental to the types of claims made in the “big data” digital humanities. This attention to intellectual history raises important problems and qualifications for big data methods like distant reading, particularly regarding their underlying assumptions about the publics of literary history.

Changing childhoods: Using queer theory and intersectional methods to reconsider the epistemic resources of children with gay and lesbian parents

This article explores contemporary childhoods through a lens of epistemic privileges and injustices in order to consider the experiences of children whose family models may not reflect the heterosexual norm. More than 14 million children in the United States have one or more gay parents. As the legal definition of marriage in the United States now recognizes same-sex partnerships, it is likely that this official number will increase. The experiences of children with gay and lesbian parents are often overlooked due to public sentiment toward gay partnerships and parenting, but the changing legal status of gay marriage around the world may indicate a shift in sentiment toward these family structures. For childhood studies researchers, this shift will provide opportunities to conduct studies with children whose voices largely were silenced or omitted from past and current scholarship. Particularly, young children with gay parents are in a unique position to describe the world since they must navigate between their homonormative private worlds and the heteronormative world of public institutions. Drawing on queer theory and incorporating the concept of intersectionality, I posit that applying Fricker’s concept of epistemic injustice to studies of childhood may reveal new ways to identify systemic and cultural biases including heteronormativity and adult–child power asymmetries. Examining issues of epistemic injustice through a queer lens and using intersectional methods may elucidate aspects of childhood culture that are misunderstood or absent from the scholarship.

Governance and Stakeholders

Economic models routinely assume firms maximize shareholder wealth; however common law legal systems only require that officers and directors pursue the interests of the corporation, leaving this ill-defined. Economic arguments for shareholder wealth maximization derived from shareholders’ status as residual claimants are vulnerable on several fronts. Share valuations fluctuate as sentiment shifts. Introductory finance casts firms as maximizing expected net present values, which are quasirents, expected earnings beyond expected costs of capital from investors, to which shareholders have no obvious claim. Other stakeholders – entrepreneurial founders or CEOs, employees, employees, customers, suppliers, communities or governments, having made firm-specific investments, may exert stronger claims than atomistic public shareholders have to shares of their firms’ quasirents. Consistent with this, their contractual claims are often augmented by residual claims and liabilities. Still, shareholder value maximization constitutes something of a bright line; whereas stakeholder welfare maximization is an ill-defined charge to assign boards that gives self-interested insiders broader scope for private benefits extraction. The common law concept of “the interests of the corporation” captures this ambiguity.

Investor Overconfidence, Margin Trade and Market Efficiency: Evidence Based on a Recent Financial Market Crash

We investigate the effect of investor overconfidence and margin trades on market efficiency around a market crash. We find that the price delay in a pre-crash period is about twice the price delay in a post-crash period. After a market crash, investors become more sensitive to market movements, resulting in small price delay and high price synchronicity. Margin traders not only trade on market trends but also put additional force on it, escalating the pyramiding and de-pyramiding effects caused by the shift in market sentiment. Finally, our results show that negative information travels slowly only when investors are overconfident.

Investor sentiment and US presidential elections

PurposeThe purpose of this paper is to examine the shifts in investor sentiment around the last seven US presidential elections (1988 through 2012).Design/methodology/approachInvestor sentiment is measured by changes in closed-end funds discounts, and the results are corroborated with three robustness tests, including an alternate measure of investor sentiment obtained from the survey conducted by the American Association of Individual Investors.FindingsClosed­end funds discounts are significantly diminished from two weeks before a US presidential election to a week before the election, and persist until the week after the election, suggesting an increase in investors’ optimism during that period, particularly when a Democrat is elected president. More than the particular party prevailing, however, investors appear to be more interested in avoiding the entrenchment of power since the results suggest that they become optimistic when a change in the ruling party takes place, but become pessimistic when there is power continuity in the White House. The increase in investor optimism that is observed around the time of US presidential elections is not replicated during non-election years, which seems to corroborate that the elections are indeed driving the results.Originality/valueThis paper is the first to formally examine the relation between investor sentiment and US presidential elections using closed-end funds discounts as the measure for sentiment.

Public sentiment shifts on peer‐to‐peer fundraising

Corporate Philanthropy ReportVolume 32, Issue 5 p. 1-12 News Public sentiment shifts on peer-to-peer fundraising Traditional events losing favor while new upstarts gain traction First published: 20 April 2017 https://doi.org/10.1002/cprt.30141Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume32, Issue5May 2017Pages 1-12 RelatedInformation

Can investor sentiment be a momentum time-series predictor? Evidence from China

This paper challenges the prevailing view that investor sentiment is a contrarian predictor of market returns at nearly all horizons. As an important piece of out-of-sample evidence, we document that investor sentiment in China is a reliable momentum signal at monthly frequency. The strong momentum predictability is robust under both single- and multi-regressor settings, and is statistically and economically significant both in and out of sample, enhancing portfolio performance as shown by our numerical examples. More importantly, we find a striking term structure that local sentiment shifts from a short-term momentum predictor to a contrarian predictor in the long run. Cross-sectional analysis reveals that sentiment is more of a small-firm effect. Finally, we confirm that global sentiment spills over to the local Chinese market, as it predicts negatively future returns over the longer horizons and in the cross section.

The Danger of Investor Overconfidence

We investigate how investor overconfidence and margin trades affect market efficiency around a market crash. We find that the price delay before a crash is about twice the price delay after a crash and that negative information travels slowly only when market sentiment is high because of investor overconfidence and attribution bias. After a market crash, constrained investors become more sensitive to market movements, resulting in high price synchronicity. In addition, margin traders not only trade on market trends but also generate additional momentum in prices, escalating the pyramiding and de-pyramiding effects caused by the shift in market sentiment.

Price Reaction of Ethically Screened Stocks: A Study of the Dow Jones Islamic Market World Index

This paper investigates the short-term effects on the price of the ethically screened stocks of the Dow Jones Islamic Market World Index (DJIMWI) quarterly revisions. Using a sample of 8,250 stocks from May 1999 through June 2012, we find a significant price reaction of the ethically screened stocks following additions and deletions. The results show that additions (deletions) from emerging stock markets tend to experience a greater and significantly positive (negative) price response than additions (deletions) from the developed markets. Further tests reveal that the price reactions following DJIMWI revisions are likely to be driven by shifts in investor sentiment rather than changes in firm fundamentals.

Does investor sentiment really matter?

We examine the role sentiment plays and its manifestation in the trading behavior of investors in the U.S. stock market. Our findings support the notion that sentiment-induced buying and selling is an important determinant of stock price variation. While ‘classical’ asset pricing categorizes investors who trade in ways not consistent with mean-variance optimization as ‘irrational,’ we show that this traditional view should not hastily be evoked to characterize sentiment-driven investing. We instead show that sentiment-driven investors can trade against the herd and sell when prices are overinflated as a result of over-bullishness and vice versa. The asset pricing implications of this paper are that sentiment is linked to shifts in risk tolerance and this triggers contrarian-type behavior. In sum, we uncover the following regarding the behavior of sentiment-driven investors; firstly, they are more apt to trade on survey-based indicators rather than market-based indicators. Secondly, they trade on the basis of information extracted from individual, rather than institutional, investor surveys. Thirdly, they respond asymmetrically to shifts in sentiment and trade more aggressively during periods of declining sentiment. Finally, there is asymmetry in the role of sentiment with respect to business conditions whereby such buying and selling is more pronounced during bear markets.

Predictable Sports Sentiment and Local Trading

We examine whether predictable outcomes of the National Basketball Association playoff games can generate increased trading of firms headquartered in the geographic area of the participating teams. We find statistically significant increased trading before games and this effect is more pronounced and persistent for games with more predictable outcomes, for predictable losses more than wins, and for more critical games. We also find that this effect is more pronounced for firms that are more vulnerable to shifts in investor sentiment and we find weak evidence that trading leads to price effects. The results are consistent with the hypothesis that predictable sentiment can drive investor behavior.

The University of the National Football League: How Technology, Injury Surveillance, and Health Care Have Improved the Safety of America's Game.

American football has become one of the most popular sports in the United States. Despite the millions of players at all levels of competition who gain the physical, social, and psychological rewards that football provides, many interested stakeholders continue to ask, "Is football safe?" Although there are only approximately 1,700 players on National Football League (NFL) rosters, the injuries they sustain have garnered the most attention-and criticism-from the national media. Increased public awareness of the injury potential football possesses has led to an open debate and a major shift in public sentiment over the past 5 years. Although no sport is perfectly safe, the question is whether it can be made relatively safe and if the long-term consequences are worth the risk. This article reviews the methods by which one sports league-the NFL-has used advances in medical technology and injury surveillance to improve the health and safety of its players.

Publicly Available Online Tool Facilitates Real-Time Monitoring Of Vaccine Conversations And Sentiments.

Real-time monitoring of mainstream and social media can inform public health practitioners and policy makers about vaccine sentiment and hesitancy. We describe a publicly available platform for monitoring vaccination-related content, called the Vaccine Sentimeter. With automated data collection from 100,000 mainstream media sources and Twitter, natural-language processing for automated filtering, and manual curation to ensure accuracy, the Vaccine Sentimeter offers a global real-time view of vaccination conversations online. To assess the system's utility, we followed two events: polio vaccination in Pakistan after a news story about a Central Intelligence Agency vaccination ruse and subsequent attacks on health care workers, and a controversial episode in a television program about adverse events following human papillomavirus vaccination. For both events, increased online activity was detected and characterized. For the first event, Twitter response to the attacks on health care workers decreased drastically after the first attack, in contrast to mainstream media coverage. For the second event, the mainstream and social media response was largely positive about the HPV vaccine, but antivaccine conversations persisted longer than the provaccine reaction. Using the Vaccine Sentimeter could enable public health professionals to detect increased online activity or sudden shifts in sentiment that could affect vaccination uptake.

Emoticon-Based Ambivalent Expression: A Hidden Indicator for Unusual Behaviors in Weibo.

Recent decades have witnessed online social media being a big-data window for testifying conventional social theories quantitatively and exploring much detailed human behavioral patterns. In this paper, by tracing the emoticon use in Weibo, a group of hidden “ambivalent users” are disclosed for frequently posting ambivalent tweets containing both positive and negative emotions. Further investigation reveals that this ambivalent expression could be a novel indicator of many unusual social behaviors. For instance, ambivalent users with the female as the majority like to make a sound in midnights and at weekends. They mention their close friends frequently in ambivalent tweets, which attract more replies and serve as a more private communication way. Ambivalent users also respond differently to public affairs from others and demonstrate more interests in entertainment and sports events. Moreover, the sentiment shift in ambivalent tweets is more evident than usual and exhibits a clear “negative to positive” pattern. The above observations, though being promiscuous seemingly, actually point to the self-regulation of negative mood in Weibo, which could find its basis from the traditional emotion management theories in sociology but makes an important extension to the online environment in this study. Finally, as an interesting corollary, ambivalent users are found connected with compulsive buyers and turn out to be perfect targets for online marketing.

RELATIONSHIP BETWEEN INVESTORS SENTIMENT INDEX WITH FIRST AND SECOND MARKET INDEXES IN TEHRAN STOCK EXCHANGE (TSE)

http://dx.doi.org/10.5902/2179460X19424The process of being affected of the investors in decision making from their sentiment has been considered in many recent researches in the field of the financial assets pricing. Some authors suggest that shifts in investor sentiment may in some instances better explain shortterm movement in asset prices than any other set of fundamental factors. The present study tries to investigate the relation between the investors sentiment index and First and Second Market Indexes in Tehran Stock Exchange Market. This paper, an index was used in order to measure the available data on sentiment of the investors in facing the risks in a 142-month period (2001 – 2014) in Tehran Stock Exchange. The used index can reveal and present the situation of the Iranian capital Market in terms of the Investors Sentiments. The results obtained from the present study reveal a positive and significant relation between the investors’ sentiment Index of the Tehran Exchange First and Second Market Indexes; and also by using from Granger Causality Test, it was revealed that there is a mutual causality relation between the investors’ sentiment Indexes of the TEFMIX and TESMIX.

Searching for Gambles: Investor Attention, Gambling Sentiment, and Stock Market Outcomes

This paper shows that changes in gambling attitudes affect asset prices and corporate decisions. Using the Internet search volume for lottery-related keywords to capture gambling sentiment shifts, we show that when the overall gambling sentiment is high, investor demand for lottery-like stocks increases, stocks with lottery-like characteristics earn positive abnormal returns in the short-run, managers are more likely to announce stock splits to cater to the increased demand for low-priced lottery stocks, and IPOs perceived as lotteries earn higher first-day returns. Further, the sentiment-return relation is stronger among low institutional-ownership firms and in regions where gambling is more acceptable.