ANALISIS PERGANTIAN KANTOR AKUNTAN PUBLIK DAN KETEPATAN WAKTU DALAM PELAPORAN LAPORAN KEUANGAN TERHADAP REAKSI PASAR (Studi empiris pada perusahaan manufaktur yang terdaftar di BEI)

CThis research aims to analyse the effect of auditor switching and timelines on the investor behavior which is indicated by market reaction of the 50 companies listed on the Indonesian Stock Exchange on the date of reporting at the Financial Service Authority (OJK) inthe period 2009-2012. The study used a measuring tool cumulative abnormal returns around the date of auditor switchinglisted on the Stock Exchange and the date of the financial statement reporting to the Financial Services Authority. This study also adds control variables are firm size, tenure, and ROA. The result of the analysis show that there is a markets’ reaction, which is indicated by negative cumulative abnormal stock return around the date of auditor- switching and a significant positive to the timelines. Based on the result, I conclude that there is an information content of auditor-switching, and investor percieved that as a bad news. The result also show us that the companies which submit timely the report to the public in accordance with the rules, is the company that the performances better than the companies that are not in time

Disclosure measurement in the empirical accounting literature: A review article

This is the first study to provide an extensive and critical review of different techniques used in the empirical accounting literature to measure disclosure. The purpose is to help future researchers to identify exemplars and to select suitable techniques or to develop their own techniques. It also provides in depth discussion of current measurement issues related to disclosure and identifies gaps in the current literature which future research may aim to cover

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

Measuring Impact: The Art, Science and Mystery of Nonprofit News

This report seeks to answer the two-pronged question, "What is 'impact,' and how can it be measured consistently across nonprofit newsrooms?" A review of recent, relevant literature and our informal conversations with experts in the field reveal growing ambitions toward the goal of developing a common framework for assessing journalism's impact, yet few definitive conclusions about how exactly to reach that framework. This is especially the case when journalism's "impact" is defined by its ultimate social outcomes -- not merely the familiar metrics of audience reach and website traffic. As with all journalism, the frame defines the story, and audience is all-important. Defining "impact" as a social outcome proves a complicated proposition that generally evolves according to the constituency attempting to define it. Because various stakeholders have their own reasons for wanting to measure the impact of news, understanding those interests is an essential step in crafting measurement tools and interpreting the metrics they produce. Limitations of impact assessment arise from several sources: the assumptions invariably made about the product and its outcome; the divergent and overlapping categories into which nonprofit journalism falls in the digital age; and the intractable problem of attempting to quantify "quality." These formidable challenges, though, don't seem to deter people from posing and attempting to find answers to the impact question. Various models for assessing impact are continually being tinkered with, and lessons from similar efforts in other fields offer useful insight for this journalistic endeavor. And past research has pointed to specific needs and suggestions for ways to advance the effort. From all of this collective wisdom, several principles emerge as the cornerstones upon which to build a common framework for impact assessment

Defining and Measuring The Creation of Quality Jobs

Our research is intended to support our peers in the Community Development Financial Institution (CDFI) industry who, through their financing, have served low-income and other disadvantaged communities for two decades.  While the CDFI industry has been instrumental in supporting job creation across the U.S., we believe that now is the time to focus greater attention on the quality of the jobs created in order to combat rising income and wealth inequality.Through a better understanding of what defines a quality job and a set of practical methods for measuring the quality of jobs created, we believe CDFIs and others in the impact investing community will be better positioned to make more effective investments that support good jobs for workers, businesses, and communities

Getting Local: How Nonprofit News Ventures Seek Sustainability

Examines eight nonprofit news ventures' mission, audience, reach, and social impact; revenue generation and diversification; and organizational adaptability, innovation, and resource allocation as critical elements of long-term sustainability

Measuring causality between volatility and returns with high-frequency data

We use high-frequency data to study the dynamic relationship between volatility and equity returns. We provide evidence on two alternative mechanisms of interaction between returns and volatilities: the leverage effect and the volatility feedback effect. The leverage hypothesis asserts that return shocks lead to changes in conditional volatility, while the volatility feedback effect theory assumes that return shocks can be caused by changes in conditional volatility through a time-varying risk premium. On observing that a central difference between these alternative explanations lies in the direction of causality, we consider vector autoregressive models of returns and realized volatility and we measure these effects along with the time lags involved through short-run and long-run causality measures proposed in Dufour and Taamouti (2008), as opposed to simple correlations. We analyze 5-minute observations on S&P 500 Index futures contracts, the associated realized volatilities (before and after filtering jumps through the bispectrum) and implied volatilities. Using only returns and realized volatility, we find a weak dynamic leverage effect for the first four hours at the hourly frequency and a strong dynamic leverage effect for the first three days at the daily frequency. The volatility feedback effect appears to be negligible at all horizons. By contrast, when implied volatility is considered, a volatility feedback becomes apparent, whereas the leverage effect is almost the same. We interpret these results as evidence that implied volatility contains important information on future volatility, through its nonlinear relation with option prices which are themselves forwardlooking. In addition, we study the dynamic impact of news on returns and volatility, again through causality measures. First, to detect possible dynamic asymmetry, we separate good from bad return news and find a much stronger impact of bad return news (as opposed to good return news) on volatility. Second, we introduce a concept of news based on the difference between implied and realized volatilities (the variance risk premium) and we find that a positive variance risk premium (an anticipated increase in variance) has more impact on returns than a negative variance risk premium

Statistical Inferences for Polarity Identification in Natural Language

Information forms the basis for all human behavior, including the ubiquitous decision-making that people constantly perform in their every day lives. It is thus the mission of researchers to understand how humans process information to reach decisions. In order to facilitate this task, this work proposes a novel method of studying the reception of granular expressions in natural language. The approach utilizes LASSO regularization as a statistical tool to extract decisive words from textual content and draw statistical inferences based on the correspondence between the occurrences of words and an exogenous response variable. Accordingly, the method immediately suggests significant implications for social sciences and Information Systems research: everyone can now identify text segments and word choices that are statistically relevant to authors or readers and, based on this knowledge, test hypotheses from behavioral research. We demonstrate the contribution of our method by examining how authors communicate subjective information through narrative materials. This allows us to answer the question of which words to choose when communicating negative information. On the other hand, we show that investors trade not only upon facts in financial disclosures but are distracted by filler words and non-informative language. Practitioners - for example those in the fields of investor communications or marketing - can exploit our insights to enhance their writings based on the true perception of word choice