Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Essays on the New Blockchain-Based Digital Financial Market : Risks and Opportunities

This doctoral thesis consists of five original essays on the risks and opportunities of the new blockchain-based digital financial market. The purpose of this dissertation is to analyze, identify, and, if possible, predict some of the major risks in the market for blockchain-based digital assets. It analyzes how crypto-specific characteristics are associated with solvency risk, sustainability risk, seclusion risk, and sentiment risk. On top of that, it also sheds light on the opportunity side of this financial innovation. The first essay of this dissertation specifically focuses on cryptocurrency for solvency risks. To forecast potential cryptocurrency default at an early stage, this study focuses on variables that are part of the information set of the investor 1 month at most after the start of trading for a cryptocurrency. The results of this research show that bankruptcies among cryptocurrencies are predictable. The second essay explores energy risk as a fundamental market-driving force for the pricing of cryptocurrency. Cryptocurrencies using a high-energy-consumption consensus protocol are riskier than others because their mining costs are more exposed to changes in energy price. Surprisingly, the study finds that energy consumption does not seem to play a role in pricing cryptocurrency. The third essay hypothesizes that privacy coins form a distinct submarket in the cryptocurrency market, shedding light on seclusion risk. It shows that privacy coins and non-privacy coins are two distinct asset markets within the cryptocurrency market. The fourth essay is about news media sentiment risk. It explores whether news media sentiments have an impact on Bitcoin volatility. It also differentiates financial sentiment and psychological sentiment and finds that financially optimistic investors are driving the Bitcoin market. On the other hand, the fifth essay in this dissertation analyzes opportunities, especially the funding opportunity in the widely known category of new digital assets defined as crypto tokens. It analyzes the determinants of the success of initial coin offerings and finds that initial-coin-offering investors are largely guided by their emotions when making investment decisions. Surprisingly, regulatory framework has not yet become a priority among policymakers. Therefore, this doctoral dissertation not only facilitates future research, but also helps regulators in shaping the future of blockchain-based financial technologies.Tämä väitöskirja koostuu viidestä esseestä, jotka käsittelevät uuden lohkoketjupohjaisen digitaalisen rahoitusmarkkinan riskejä ja mahdollisuuksia. Väitöskirjan tarkoituksena on analysoida, tunnistaa ja mahdollisuuksien mukaan ennustaa joitakin lohkoketjupohjaisten digitaalisten varojen markkinoiden suurimpia riskejä. Siinä analysoidaan, miten kryptovaluuttakohtaiset ominaisuudet liittyvät vakavaraisuusriskiin, kestävyysriskiin, eristäytymisriskiin ja sentimenttiriskiin. Tämän lisäksi se valottaa myös tämän rahoitusinnovaation mahdollisuuksia. Tämän väitöskirjan ensimmäisessä esseessä keskitytään erityisesti kryptovaluuttaan maksukyvyttömyysriskinä. Tässä tutkimuksessa keskitytään muuttujiin, jotka ovat sijoittajan saatavilla korkeintaan 1 kuukausi sen jälkeen, kun kaupankäynti kryptovaluutalla on alkanut. Tämän tutkimuksen tulokset osoittavat, että kryptovaluuttojen konkurssit ovat ennustettavissa. Toisessa esseessä tutkitaan energiariskiä markkinoita ohjaavana voimana kryptovaluutan hinnoittelussa. Kryptovaluutat, jotka käyttävät paljon energiaa kuluttavaa konsensusprotokollaa, ovat muita riskialttiimpia, koska niiden louhintakustannukset ovat alttiimpia energian hinnan muutoksille. Yllättäen tutkimuksessa todetaan, että energiankulutuksella ei näytä olevan merkitystä kryptovaluuttojen hinnoittelussa. Kolmannessa esseessä hypoteesina on, että yksityisyyskolikot muodostavat erillisen alamarkkinan kryptovaluuttamarkkinoilla, ja tutkimus tarkastelee näiden eristäytymisriskiä. Siinä osoitetaan, että yksityisyyskolikot ja ei-yksityisyyskolikot ovat kaksi erillistä omaisuuserämarkkinaa kryptovaluuttamarkkinoilla. Neljäs essee käsittelee uutismedian sentimenttiriskiä. Siinä tutkitaan, vaikuttaako uutismedian sentimentti Bitcoinin volatiliteettiin. Siinä myös erotetaan toisistaan taloudellinen sentimentti ja psykologinen sentimentti ja todetaan, että taloudellisesti optimistiset sijoittajat ohjaavat Bitcoin-markkinoita. Väitöskirjan viidennessä esseessä analysoidaan mahdollisuuksia, erityisesti rahoitusmahdollisuuksi, liittyen laajalti tunnettuihin digitaalisiin tokeneihin. Siinä havaitaan, että näihin omaisuuseriin sijoittavat sijoittajat toimivat pitkälti tunteidensa ohjaamina sijoituspäätöksiä tehdessään. Yllättävää kyllä, sääntelykehyksestä ei ole vielä tullut poliittisten päättäjien prioriteettia. Siksi tämä väitöskirja ei ainoastaan tue tulevaa tutkimusta, vaan auttaa myös viranomaisia lohkoketjupohjaisten rahoitusteknologioiden tulevaisuuden määrittelyssä.fi=vertaisarvioitu|en=peerReviewed

Exploring the determinants of the user experience in P2P payment systems in Spain: a text mining approach

Purpose: This study aims to identify which determinants are responsible for impact ing the user experience of three peer-to-peer (P2P) payment services in the Spanish market. Design/methodology/approach: A sample of all online reviews (n=16,048) pub lished in Google Play of three paytech apps—Bizum, Twyp, and Verse—was analyzed using text mining and sentiment analysis. Findings: A holistic interpretation of the seed terms included in each aspect allowed to label them based on the preferences expressed by paytech app users in their reviews. Six latent aspects were identified: ease of use, usefulness, perceived value, per formance expectancy, perceived quality, and user experience. In addition, the results of the analysis suggest a positivity bias in the online reviews of fintech P2P app users. Our results also show that online reviews of apps associated with banks or financial institutions, such as Bizum (to a greater extent) or Twyp, show more negative emotions, whereas independent apps (Verse) show more positive emotions. Moreover, the most critical users are those of unidentified gender, while women remain in a more neutral position, and men tend to express their opinions more positively regarding P2P pay ment apps. Practical implications: Paytech providers should analyze the problems faced by users immediately after an encounter. By applying text mining analysis, service providers can gain efficiency in understanding user sentiments and emotions without tedious and time-consuming reviews. Originality/value: This is a pioneering study on peer-to-peer (P2P) mobile payment systems from the user’s perspective because it investigates the emotions and senti ments that users convey through bank reviews

Prosocial Antitrust

Antitrust law is at the center of today’s public debate. It has even emerged as a rare unifying force, with bipartisan promises to combat the concentration of economic power. Meanwhile, the business community is grappling with mounting systematic risks arising from climate change, income inequality, and the COVID-19 pandemic. Unexpectedly, the largest asset managers in the world find themselves on the front lines of these battles. Due to the rise of index investing, these “universal owners” manage portfolios that are so large and diversified, their holdings mirror the entire economy. Their diversification protects them against idiosyncratic risk, but greatly exposes them to systematic risks. The universal owners are keenly aware of their exposure to these risks. They are turning to their portfolio companies and increasing demands on directors and managers to “serve a social purpose” and reduce their negative externalities. Public-regarding pronouncements from CEOs of Wall Street’s biggest firms ring hollow to many shareholder primacy loyalists. But the skeptics downplay the economic logic underlying this paradigm shift—diversified shareholders do not want companies to externalize their negative impacts onto the rest of the investors’ portfolios. Many companies are rising to the challenge and making bold commitments. Some are recognizing that, to overcome pervasive social and environmental challenges, they must collaborate with their competitors. This Article reveals that current antitrust law is a barrier to this collaboration and offers a policy proposal for aligning antitrust law with the demands upon the prosocial corporation. The COVID-19 pandemic has reminded us that we are all interconnected. Climate change will continue to deepen that understanding. The problems we face are difficult, but they are not insurmountable. To solve them, however, antitrust law must empower more collaboration

A Fair Price and a Fair Deal: On the Future of \u27Entire Fairness\u27 in Freezeouts

Controlling shareholders can compel the sale of minorities’ shares in freezeouts, potentially to their financial detriment. To limit controllers’ opportunism and support the value of minorities’ investments, the Delaware supreme court has endorsed strong minority shareholder protections under the rubric of \u27Entire Fairness\u27 – the governing standard for cash-out mergers. However, the court of chancery has refused to apply Entire Fairness to tender offer freezeouts, and is advocating unifying freezeout doctrine around a looser, deferential standard of review. The influence of popular and Congressional concern over excess plaintiff lawyers’ fees and discovery costs is likely making itself felt, although the true extent of these litigation agency costs is unknown and likely overstated. This influence is evident in three recent court of chancery cases analyzed herein (Pure, Cysive and Cox), which advocate lesser scrutiny of controllers’ transactions. There are several problems with the court of chancery’s proposed reforms, including that they conflict with Delaware supreme court precedent. A fair price duty is crucial to minorities’ bargaining leverage with controllers, and controllers’ power financially to oppress minorities if their freezeouts are thwarted (\u27inherent coercion\u27) remains a genuine concern for equity. Lack of minority consent is still a problem that equity should be responsive to. This Article presents the case for applying Entire Fairness review to cash-out mergers and tender offer freezeouts. The sole exception should be when a controller authorized an independent committee to conduct an auction or market check and agreed to sell if a substantially higher offer for the company surfaced

NgramPOS: A Bigram-based Linguistic and Statistical Feature Process Model for Unstructured Text Classification

Research in financial domain has shown that sentiment aspects of stock news have a profound impact on volume trades, volatility, stock prices and firm earnings. With the ever growing social inetworking and online marketing sites, the reviews obtained from those, act as an important source for further analysis and improved decision making. These reviews are mostly unstructured by nature and thus, need processing like clustering or classification to provide different polarity categories such as positive and negative in order to extract a meaningful information for future uses. Accordingly, in this study we investigate the use of Natural Language processing (NLP) in a way to improve the sentiment classification performance to evaluate the information content of financial news as an instrument for using in investmentdecisions system.Since the proposed feature extraction approach is based on the occurrence frequency of words, low-frequency linguist features that could be critical in sentiment classification are typically ignored. In this research, therefore, we attempt to improve current sentiment analysis approaches for financial news classification in consideration of low-frequency, informative, linguistic expressions. Our proposed combination of low and high-frequency linguistic expressions contributes a novel set of features for text sentiment analysis and classification. The experimental results show that an optimal Ngram feature selection (combination of optimal unigram and bigram features) enhances sentiment classification accuracy than other types feature sets