Novel and topical business news and their impact on stock market activities

We propose an indicator to measure the degree to which a particular news article is novel, as well as an indicator to measure the degree to which a particular news item attracts attention from investors. The novelty measure is obtained by comparing the extent to which a particular news article is similar to earlier news articles, and an article is regarded as novel if there was no similar article before it. On the other hand, we say a news item receives a lot of attention and thus is highly topical if it is simultaneously reported by many news agencies and read by many investors who receive news from those agencies. The topicality measure for a news item is obtained by counting the number of news articles whose content is similar to an original news article but which are delivered by other news agencies. To check the performance of the indicators, we empirically examine how these indicators are correlated with intraday financial market indicators such as the number of transactions and price volatility. Specifically, we use a dataset consisting of over 90 million business news articles reported in English and a dataset consisting of minute-by-minute stock prices on the New York Stock Exchange and the NASDAQ Stock Market from 2003 to 2014, and show that stock prices and transaction volumes exhibited a significant response to a news article when it is novel and topical.Comment: 8 pages, 6 figures, 2 table

Information Outlook, October 2004

Volume 8, Issue 10https://scholarworks.sjsu.edu/sla_io_2004/1009/thumbnail.jp

The Stages of Scandal and the Roles of General Counsel

This Essay examines the roles of a general counsel, as the corporation’s chief legal officer, in responding to scandals when they happen and in developing and enforcing internal preventive practices prior to the occurrence of any particular scandal. The Essay differentiates between scandals and crises more generally, emphasizing the integral connection between scandal and jeopardy to reputation and tracing the interrelationships between a corporation’s reputation and that of its general counsel. The Essay argues that risks associated with scandal may strengthen general counsel’s power within the senior management team, in particular in general counsel’s relationship with the corporation’s CEO. Although general counsel’s position as a member of the senior management team may imperil counsel’s ability to bring detached judgment to bear, counsel’s position within the corporation is a critical component of effectiveness in anticipating and addressing scandals

Toward a process theory of entrepreneurship: revisiting opportunity identification and entrepreneurial actions

This dissertation studies the early development of new ventures and small business and the entrepreneurship process from initial ideas to viable ventures. I unpack the micro-foundations of entrepreneurial actions and new ventures’ investor communications through quality signals to finance their growth path. This dissertation includes two qualitative papers and one quantitative study. The qualitative papers employ an inductive multiple-case approach and include seven medical equipment manufacturers (new ventures) in a nascent market context (the mobile health industry) across six U.S. states and a secondary data analysis to understand the emergence of opportunities and the early development of new ventures. The quantitative research chapter includes 770 IPOs in the manufacturing industries in the U.S. and investigates the legitimation strategies of young ventures to gain resources from targeted resource-holders.Open Acces

Cross market monitoring on financial markets.

by Lee Yue, Wefield.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 105-111).Abstracts in English and Chinese.Abstract --- p.IAbstract (Chinese) --- p.IIAcknowledgement --- p.IIITable of Content --- p.IVList of Figures --- p.VIIList of Tables --- p.VIIIChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Motivation --- p.2Chapter 1.3 --- Organization --- p.4Chapter 2 --- Literature Review --- p.5Chapter 2.1 --- Market Monitoring --- p.5Chapter 2.1.1 --- Regulatory Framework --- p.5Chapter 2.1.2 --- Surveillance Technology --- p.6Chapter 2.2 --- Cross Market Relationship --- p.7Chapter 2.3 --- Knowledge Management --- p.9Chapter 2.3.1 --- From Data and Information to Knowledge --- p.9Chapter 2.3.2 --- From Knowledge to Knowledge Management --- p.10Chapter 3 --- Market Activities and Market Surveillance --- p.13Chapter 3.1 --- Overview of Market Structure --- p.13Chapter 3.1.1 --- Monetary Market --- p.13Chapter 3.1.2 --- Stock and its Derivatives Market --- p.14Chapter 3.1.3 --- Futures --- p.19Chapter 3.2 --- Cross-Market Activities and Manipulation --- p.20Chapter 3.3 --- Monitoring and Surveillance --- p.22Chapter 3.4 --- Stock Monitoring Systems --- p.23Chapter 4 --- Financial Knowledge Management (FKM) Model --- p.27Chapter 4.1 --- Introduction --- p.27Chapter 4.2 --- Knowledge Management cycle --- p.28Chapter 4.2.1 --- Information Collection --- p.29Chapter 4.2.2 --- Information Storage --- p.29Chapter 4.2.3 --- Knowledge Generation --- p.30Chapter 4.2.4 --- Knowledge Dissemination --- p.30Chapter 4.3 --- The 4 levels of FKM --- p.31Chapter 5 --- Level 1: Range Detection --- p.32Chapter 5.1 --- Basic idea --- p.32Chapter 5.2 --- Detection cycle --- p.32Chapter 5.3 --- Mathematical Model --- p.32Chapter 5.4 --- Knowledge generation --- p.34Chapter 6 --- Level 2: Momentum Detection --- p.36Chapter 6.1 --- Basic idea --- p.36Chapter 6.2 --- Detection cycle --- p.36Chapter 6.3 --- Mathematical Model --- p.37Chapter 6.4 --- Knowledge generation --- p.38Chapter 7 --- Level 3: Case Detection --- p.40Chapter 7.1 --- Basic Idea --- p.40Chapter 7.2 --- Technical Analysis --- p.40Chapter 7.3 --- Details and Characteristics of Chart Patterns --- p.41Chapter 7.3.1 --- Continuation and Reversal Patterns --- p.41Chapter 7.3.2 --- Bar Charts --- p.42Chapter 7.3.3 --- Different Patterns --- p.42Chapter 7.4 --- Mathematical Model --- p.54Chapter 7.4.1 --- Smoothing of Data 一 Exponential Smoothing --- p.55Chapter 7.4.2 --- Recognition of Different Patterns --- p.57Chapter 7.4.3 --- Detection Cycle --- p.59Chapter 7.5 --- Knowledge generation --- p.60Chapter 8 --- Level 4: Scenario Detection --- p.62Chapter 8.1 --- Basic idea --- p.62Chapter 8.2 --- Detection cycle --- p.65Chapter 8.2.1 --- RETRIEVE --- p.66Chapter 8.2.2 --- REUSE --- p.75Chapter 8.2.3 --- REVISE --- p.76Chapter 8.2.4 --- RETAIN --- p.82Chapter 8.3 --- Knowledge Generation --- p.82Chapter 9 --- Experiments and Research Findings --- p.85Chapter 9.1 --- Experiments on Monitoring and Detection --- p.85Chapter 9.1.1 --- Precision and Recall --- p.85Chapter 9.1.2 --- Architecture of FKM --- p.86Chapter 9.1.3 --- Experiment and Result Analysis --- p.88Chapter 9.2 --- Evaluation of Knowledge Management --- p.89Chapter 9.2.1 --- Evaluation Design --- p.90Chapter 9.2.2 --- Result Analysis --- p.91Chapter 10 --- Conclusion and Future Work --- p.94Chapter 10.1 --- Conclusion --- p.94Chapter 10.2 --- Future Direction --- p.95Appendix I A Survey on Investors of Hong Kong --- p.96Appendix II Theories on Cross-Market Relation --- p.99Appendix III Mathematical Model for Patterns --- p.102Bibliography --- p.10