Stock Exchange Frequent Topics @NYSE

The utilization of Twitter content to predict the market is necessary since the fundamental perception related to the activities of the market influenced by the opinion in the market space. Therefore this research is utilizing the tweet of @NYSE and compare the research finding @IDX_BEI the stock exchange office in Indonesia. There is 2069 tweet extracted from the period January until June 2018. Further analysis using the Exploratory Factor Analysis conducted, and show that @NYSE delivering more varieties of information related to the stock market. It is not only information related to the IPO, but also an opinion from the recognized economist and analyst. The findings show that Twitter could be improved for further utilization and reduce asymmetric information related to the market Keywords: @NYSE, Frequent Topics, Unstructured Dataset DOI: 10.7176/RJFA/10-16-10 Publication date: August 31st 201

Behavioral finance in fintech : biases & opinions

Fintech has become a worldwide and continuously growing phenomenon, yet fintech adoption has still not reached its full potential. The customer value proposition is now the core of development of any platform, and it is necessary to implement tools and measures that improve the experience. As such, the present thesis proposes the introduction of Behavioral Finance into modern fintechs as a provider of an enhanced customer engagement and increased value. The behavioral finance tool is described as an abstract algorithm, based on the concepts and methodologies of the subject, then tested via a two-part survey. The first part aims to understand the impact of two drivers behind the adoption of fintechs, namely behavioral biases and pre-conceived opinions. It is found that, on average, opinions have a positive impact on the likelihood of fintech adoption, whereas behavioral biases, despite present in the population, are not statistically significant in the engagement decision. In addition, past usage had a positive influence on the future usage, and this expected use had a positive influence in the future recommendation of the technology. The second part aims to study whether the introduction of the behavioral finance tool impacts the decision of adoption. It is found that, on average, future usage and future recommendation increase with the insertion of the algorithm, but the increase is not statistically significant. Furthermore, market perception of adoption is above 70%, indicating a possible opportunity.Fintech tornou-se num fenómeno mundial, e de crescimento constante, mas a sua adoção ainda não atingiu o seu potencial. Hoje em dia, o valor para o cliente é o centro de desenvolvimento de qualquer plataforma, tomando-se necessário implementar ferramentas e medidas que melhorem a sua experiência. Como tal, a presente tese propõe a introdução de Finanças Comportamentais nas fintechs modernas como uma ferramenta que permite fornecer uma experiência superior e adaptada ao cliente. A ferramenta de finanças comportamentais é descrita como um algoritmo abstrato baseado nos conceitos e metodologias da disciplina, e seguidamente testada, através de um questionário de duas partes. A primeira parte tem como objetivo estudar o impacto de dois fatores determinantes para a adoção das fintech, nomeadamente, os comportamentais irracionais e as opiniões pré-concebidas. Constatou-se que, em média, as opiniões têm um impacto positivo na probabilidade de adoção das fintech, embora os vieses, apesar de presentes na população, não são estatisticamente significativos na decisão de utilização. A utilização passada tem uma influência positiva no uso futuro, sendo que o último influencia positivamente a recomendação futura da tecnologia. A segunda parte visa analisar se a introdução da ferramenta afeta a decisão de adoção da tecnologia. Verifica-se que, em média, o uso futuro e a recomendação futura aumentam com a inserção do algoritmo, mas o aumento não é estatisticamente significativo. Ademais, a perceção de adoção do mercado supera os 70%, indicando uma possível oportunidade

Doctor of Philosophy

dissertationDue to the popularity of Web 2.0 and Social Media in the last decade, the percolation of user generated content (UGC) has rapidly increased. In the financial realm, this results in the emergence of virtual investing communities (VIC) to the investing public. There is an on-going debate among scholars and practitioners on whether such UGC contain valuable investing information or mainly noise. I investigate two major studies in my dissertation. First I examine the relationship between peer influence and information quality in the context of individual characteristics in stock microblogging. Surprisingly, I discover that the set of individual characteristics that relate to peer influence is not synonymous with those that relate to high information quality. In relating to information quality, influentials who are frequently mentioned by peers due to their name value are likely to possess higher information quality while those who are better at diffusing information via retweets are likely to associate with lower information quality. Second I propose a study to explore predictability of stock microblog dimensions and features over stock price directional movements using data mining classification techniques. I find that author-ticker-day dimension produces the highest predictive accuracy inferring that this dimension is able to capture both relevant author and ticker information as compared to author-day and ticker-day. In addition to these two studies, I also explore two topics: network structure of co-tweeted tickers and sentiment annotation via crowdsourcing. I do this in order to understand and uncover new features as well as new outcome indicators with the objective of improving predictive accuracy of the classification or saliency of the explanatory models. My dissertation work extends the frontier in understanding the relationship between financial UGC, specifically stock microblogging with relevant phenomena as well as predictive outcomes

Suositteluverkostot pääomasijoittajan hankevirran lähteenä

VCs with better networks outperform their peers, get to invest in the most prestigious syndicates, and have more investment opportunities in general. Networks are crucial for many VCs since they can source up to 80% of their deal flow through referrals from their networks. Academia has only covered dyadic relations the referee has and the number of referrals without forming a comprehensive network perspective on referrals. While many VCs rely heavily on referrals, their referral networks are composed very differently, and they are highly curious about how other VCs are managing their referral networks. The research question for the study is: How do VCs’ referral networks for deal sourcing operate? This question is answered with the help of sub-questions to understand motives for making referrals, motives for choosing a referral recipient, importance of referrals and their share of investments, main characteristics of referral networks, and tools VCs use to build and maintain referral networks. This study is as an inductive case study with mostly exploratory focus and some explanatory elements. The basis of the study is a systematic literature review that guided the data collection. The primary data source was eleven semi-structured interviews with VCs from Helsinki and London, and relevant internal documents from their VC firms supported the interviews. Referees have different motives for making referrals, namely financial incentives, investment syndication, creating future options to invest, and altruistic help with possible future reciprocity. The referee’s choice for choosing the referral recipient is affected by VC firm and personal brand, knowledge of VC’s focus, how VC treats referrals, the strength of a relationship, VC’s encouragement for referrals, and possible vested interest. Referrals account for a significant share of VCs’ deal flow, and their share is even higher in investments indicating higher quality compared to other deal flow sources. The main characteristics of referral networks are size and activity of referral network, centrality and roles of connections, and strength of relationships with them. VCs’ referral networks are created by previous work experience, investing and working with companies, explicitly building and maintaining a network, and being reciprocal. The most notable theoretical contribution of the study is the identification and high-level explanation of referral networks in venture capital. In addition to explaining the referral phenomenon and its importance, the study offers several other managerial implications for VCs including tracking deal flow and referral sources accurately, encouraging referrals from portfolio founders, and treating referrals as well as possible. For founders, the message is simple: get a referral to a VC instead of approaching cold.Pääomasijoittajat, joilla on parempi verkosto, menestyvät paremmin, pääsevät mukaan kilpailtuihin sijoituksiin ja saavat enemmän sijoitusmahdollisuuksia. Verkostot ovat elintärkeitä pääomasijoittajille, sillä he voivat löytää jopa 80% sijoituksistaan verkoston suosittelemina. Akatemia on tutkinut vain kahdenvälisiä suhteita, joita suosittelijalla on sekä suositteluiden määrää muodostamatta kokonaisvaltaista kuvaa suositteluverkostoista. Monet pääomasijoittajat luottavat paljon suositteluihin, heidän suositteluverkostonsa ovat hyvin erilaisia, ja he ovat erittäin kiinnostuneita siitä, miten muut pääomasijoittajat rakentavat suositteluverkostojaan. Diplomityön tutkimuskysymyksenä on: Kuinka pääomasijoittajien suositteluverkostot toimivat sijoitusmahdollisuuksien löytämisessä? Tähän vastataan apukysymysten avulla, jotta ymmärretään motiivit suositteluiden tekemiselle, motiivit suosittelun saajan valinnalle, suositteluiden tärkeys ja osuus sijoituksista, suositteluverkoston pääominaisuudet ja työkalut, joita pääomasijoittajat käyttävät suositteluverkostojen rakentamiseen sekä ylläpitoon. Tämä tutkimus on induktiivinen case-tutkimus, jossa on kuvaileva ote, mutta myös selittäviä piirteitä. Tutkimuksen pohjan muodostaa systemaattinen kirjallisuuskatsaus, joka ohjasi aineiston keräystä. Aineiston päälähteenä oli yksitoista puolistrukturoitua haastattelua helsinkiläisille ja lontoolaisille pääomasijoittajille. Haastattelujen tukena käytettiin pääomasijoittajien sisäisiä dokumentteja. Suosittelijoilla on eri motiiveja suositteluihin: taloudelliset kannustimet, yhteissijoitukset, tulevien sijoitusoptioiden luominen ja epäitsekäs auttaminen mahdollisella tulevalla vastavuoroisuudella. Suosittelun saajan valintaan vaikuttaa pääomasijoittajan henkilökohtainen ja yrityksen brändi, tieto pääomasijoittajan sijoituskriteereistä, pääomasijoittajan käytös suositteluita kohtaan, suhteen vahvuus, pääomasijoittajan aktiivisuus suositteluiden pyytämisessä ja mahdollinen taloudellinen intressi. Suositukset ovat merkittävä osa pääomasijoittajan hankevirrasta, ja niiden osuus sijoituksista on vielä suurempi, mikä viittaa suositusten olevan muita parempi hankevirran lähde. Suositteluverkoston pääominaisuudet ovat koko ja aktiivisuus, kontaktien keskeisyys ja roolit sekä suhteen vahvuus. Pääomasijoittajien suositteluverkostoon vaikuttaa aikaisempi työkokemus, sijoittaminen ja työskentely yritysten kanssa, verkoston rakentaminen ja ylläpitäminen sekä vastavuoroisuus. Tutkimuksen teoreettinen pääkontribuutio on suositteluverkostojen tunnistaminen ja ylätason selittäminen. Suositteluiden ymmärtämisen lisäksi ohjeet pääomasijoittajille ovat hankevirran tarkka seuraaminen, suositteluiden pyytäminen yrittäjiltä ja suositeltujen yrittäjien hyvä kohtelu. Yrittäjille viesti on yksinkertainen: etsi suosittelu pääomasijoittajalle kylmän yhteydenoton sijaan

SME Access to Finance: An exploration into the demand and supply contraints around SME access to finance

In March 2011, the Newcastle Business School, Northumbria University was commissioned by North East Access to Finance (NEA2F) to undertake a major piece of independent academic research to explore both the demand and supply sides of SME access to finance in the North East of England. The aim of the research was to gain insight and understanding into the challenges faced not only by the SME sector but also by the key suppliers of finance to that community, specifically the banking sector and Business Angels. Thus we do not take a position on what we think is right or what a best practice approach might be but rather reflect, as accurately as possible, the information that was shared with us. The research project commenced in May 2011 and was completed at the end of March 2012

Helsinki Startup City Guide

This thesis is a product-oriented thesis. The aim of the thesis is to create valuable and quality content for the commissioning party, StartUs. The content is educational and takes form as a guide. The guide was done with the support of Haaga-Helia StartUp School. The theoretical framework consists of startup and content marketing theory, and is essential in providing the reader of this thesis the understanding of why the guide is important for the commissioning party and its target audience. Defining a startup can be tricky as there are several different definitions to what is a startup. In addition, not all startups are alike as there are multiple types of startups each of its own goals and definition of success. All of the startup types can benefit from incorporating lean startup methodologies. Some of the core principles of the lean startup methodology are Customer Development and the build-measure-learn loop both of which ultimately allow startups to avoid failing and creating a greater demand for their product or service through the understanding of the true needs of the customers. Nowadays, content marketing is imperative for a company in order to gain visibility and reach the right audience. In order to stand out from competitors and achieve the attention of consumers and fulfill the company’s business goals, relevant and valuable content must be produced and distributed. The product of this thesis is the Helsinki Startup City Guide. It is a practical guide for startup enthusiasts and everyone interested and considering to establish a startup company in Helsinki. The guide lists the most relevant information one must know about the startup ecosystem of Helsinki. This thesis and the product were produced within three weeks due to limited time constraints. Both the author and the commissioning parties are pleased with the end result of the product

Expert Stock Picker: The Wisdom of (Experts in) Crowds

The phrase the wisdom of crowds suggests that good verdicts can be achieved by averaging the opinions and insights of large, diverse groups of people who possess varied types of information. Online user-generated content enables researchers to view the opinions of large numbers of users publicly. These opinions, in the form of reviews and votes, can be used to automatically generate remarkably accurate verdicts-collective estimations of future performance-about companies, products, and people on the Web to resolve very tough problems. The wealth and richness of user-generated content may enable firms and individuals to aggregate consumer-think for better business understanding. Our main contribution, here applied to user-generated stock pick votes from a widely used online financial newsletter, is a genetic algorithm approach that can be used to identify the appropriate vote weights for users based on their prior individual voting success. Our method allows us to identify and rank experts within the crowd, enabling better stock pick decisions than the S&P 500. We show that the online crowd performs better, on average, than the S&P 500 for two test time periods, 2008 and 2009, in terms of both overall returns and risk-adjusted returns, as measured by the Sharpe ratio. Furthermore, we show that giving more weight to the votes of the experts in the crowds increases the accuracy of the verdicts, yielding an even greater return in the same time periods. We test our approach by utilizing more than three years of publicly available stock pick data. We compare our method to approaches derived from both the computer science and finance literature. We believe that our approach can be generalized to other domains where user opinions are publicly available early and where those opinions can be evaluated. For example, YouTube video ratings may be used to predict downloads, or online reviewer ratings on Digg may be used to predict the success or popularity of a story

STOCK PREDICTION VIA SENTIMENT AND ONLINE SOCIAL STATUS

Studies of stock market prediction show that stock movements are related to the sentiment of social media. However, few studies have investigated the role of online social relations in predicting stock movements. This paper aims at constructing features that capture users’ online social status and incorporating these into stock prediction models. Online opinions are often developed through interactions and are weaker in their early stages. We developed a feature-enhancing procedure motivated by statistical surveillance approaches to strengthen the ability to capture emerging trends. We evaluated our feature-enhancing procedure by developing models to predict stock returns in the following 20-minute period. A comparison of experimental results with baseline models shows that our feature-enhancing design helped to predict stock movements. The model (SE_CUSUM) that adopted features enhanced by cumulative sum (CUSUM), a statistical surveillance approach, performed better than baseline models in terms of directional accuracy, balanced error rate, root mean square error, and mean absolute error. Our simulated trading also showed that SE_CUSUM realized a higher profit than the baseline approaches. These results suggest that incorporating online social status and our feature-enhancing procedure improve high frequency stock prediction performance