Towards Automated Event Studies Using High Frequency News and Trading Data

Bohn, Nicolai; Rabhi, Fethi A.; Kundisch, Dennis; Yao, Lawrence; Mutter, Tobias

doi:10.1007/978-3-642-36219-4_2

Cited by 7 publications

(5 citation statements)

References 45 publications

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“…A surprising event, on the other hand, leads to abnormal returns. This is the case because such an event is unknown, and no estimate has been made, which results in risk that must be addressed [10].…”

Section: Literature Surveymentioning

confidence: 99%

Building a Calendar of Events Database by Analyzing Financial Spikes

et al. 2021

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An event is a piece of news that triggers a change in stock prices. Here, an event study is undertaken to capture the effect of abnormal returns due to an event. The event can affect the stock market in the long term or short term. Event research is relevant to both the efficient market hypothesis and behavioral finance. In this study, we collected data from websites that manage financial and economic data, performed a sentiment analysis, and correlated news article data with changes in a particular company's stock prices in the stock market. Data were collected from two well-known financial news websites. We observed a correlation between stock prices and news items. An event period of one day was considered for the study. The regression equation determined the relationship between stock returns and polarity and subjectivity. Bayesian model averaging was performed to identify the effects of polarity and subjectivity on stock returns. Time-series data were decomposed into components and detrended via regression. Prominent keywords and their polarity values for a particular day were plotted. An event enhanced some stock returns while adversely affecting other stocks. We found a variation range of -400 to 200 for different company stocks for the selected period.

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Section: Literature Surveymentioning

confidence: 99%

Building a Calendar of Events Database by Analyzing Financial Spikes

et al. 2021

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“…The FC_PARAM entity represents a given financial context and is linked to entities in the market data model through the following relationships (see • CtxBenchmark: This relationship defines a benchmark that is offered as a product in the Product entity, usually an Index event. Defining benchmark entities is a method used in many event data evaluation studies (Bohn et al, 2012).…”

Section: Defining the Cpd Modelmentioning

confidence: 99%

“…• Intraday Price Jumps: measures the volatilities in stock price timeseries data. The measure used based on the method proposed in (Lee & Mykland, 2007) and used in (Bohn et al, 2012), which is capable of capturing the timing and size of price jumps. The measure applies a threshold over the stock prices observations and a price jump is recorded if an observation breaks through the threshold.…”

Section: Figure 48 Defining Impact Measures Parametersmentioning

confidence: 99%

News Sentiment Impact Analysis (NSIA) Framework

Qudah

Rabhi

2017

Lecture Notes in Business Information Processing

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The huge increase in online content has rapidly stepped up the challenges facing various entities such as organisations, businesses, governments. Dealing with this immense increase in information is beyond any human ability, as it requires massive efforts to go through huge volumes of posts/news to analyse and understand their content. In responding to this challenge, research has picked up in recent years to automatically analyse, and extract opinions/sentiment from online content. This thesis investigated the area of sentiment analysis and its impact on financial market entities. The thesis includes a review of the sentiment analysis processes, , an overview of sentiment analysis studies and their application to the financial markets domain. The literature shows a gap in defining systematic and reusable evaluation processes, for users to automatically conduct impact analysis of sentiment datasets in different financial contexts. In addressing the research gap the thesis proposes a framework called News Sentiment Impact Analysis (NSIA) which consists of three components which are a conceptual data model, a software architecture, and a set of use cases. The key component which is the conceptual data model captures three sets of parameters: the financial context parameters, the sentiment related parameters, and the impact measure parameters. The CPD model is supported by a software architecture, which consists of a GUI, Business, and Data layers. The main use case allows the analyst to define the CPD parameters using the provided GUI layer and trigger the impact analysis process. To evaluate the proposed framework, a prototype is implemented. Three case studies have been conducted to evaluate the different aspects of the proposed framework. ORIGINALITY STATEMENT 'I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.'

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“…When a market has to process surprising new information or reassessments of stock value, sudden discrete price jumps may occur, resulting in abnormal returns. Various methods have been developed to detect jumps in high‐frequency data . For example, the Lee and Mykland method uses a rolling window of past observations to estimate a measure called instantaneous volatility, and a jump is detected if the instantaneous volatility exceeds some predetermined threshold.…”

Section: Case Study: Financial Researchmentioning

confidence: 99%

“…Bohn et al . have proposed a typical event studies analysis process that can be used to analyse impacts of a predefined event on stock prices or alternatively find effects of impact and then look for events that may explain the impact effects. The analysis process essentially involves first finding abnormal returns in a stock price time series and then attempting to correlate it with news events to see if the disclosure of new information may have led to the abnormal returns.…”

Section: Case Study: Financial Researchmentioning

confidence: 99%

Building architectures for data‐intensive science using the ADAGE framework

Yao

Rabhi

2014

Concurrency and Computation

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SUMMARYOne of the main activities in data-intensive science is data analysis. Although there are many popular technologies that can assist scientists in various isolated aspects of data analysis, supporting analysis processes in holistic ways that promote system interoperability, integration and automation, as well as scientific reproducibility and efficient data handling, presents many challenges. A common solution to address these challenges is to find efficient ways of integrating various existing technologies together to meet the analysis needs of scientists (which is similar to the idea behind science gateways). We believe that this solution is essentially an exercise in software design; and in many situations, these challenges should be tackled from a software design perspective. Consequently, this paper reviews different architectural design approaches that can be used to address these challenges and proposes a service-oriented framework called the Ad Hoc Data Grid Environment, which consists of an architectural pattern and its associated operational guidelines. The guidelines prescribe a number of activities based on an iterative decomposition approach to produce and evolve software architectures according to constantly changing user needs. The framework is demonstrated on a case study involving analysis processes required for conducting financial event studies.

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Towards Automated Event Studies Using High Frequency News and Trading Data

Cited by 7 publications

References 45 publications

Building a Calendar of Events Database by Analyzing Financial Spikes

Building a Calendar of Events Database by Analyzing Financial Spikes

News Sentiment Impact Analysis (NSIA) Framework

Building architectures for data‐intensive science using the ADAGE framework

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