In finance, technical analysis is an analysis methodology for analysing and forecasting the direction of prices through the study of past market data, primarily price and volume. Behavioral economics and quantitative analysis use many of the same tools of technical analysis, which, being an aspect of active management, stands in contradiction to much of modern portfolio theory. The efficacy of both technical and fundamental analysis is disputed by the efficient-market hypothesis, which states that stock market prices are essentially unpredictable, and research on whether technical analysis offers any benefit has produced mixed results.
Business intelligence (BI) comprises the strategies and technologies used by enterprises for the data analysis and management of business information. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.
Text mining, also referred to as text data mining, similar to text analytics, is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning. According to Hotho et al. (2005) we can distinguish between three different perspectives of text mining: information extraction, data mining, and a KDD process. Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interest. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.
A decision support system (DSS) is an information system that supports business or organizational decision-making activities. DSSs serve the management, operations and planning levels of an organization and help people make decisions about problems that may be rapidly changing and not easily specified in advance—i.e. unstructured and semi-structured decision problems. Decision support systems can be either fully computerized or human-powered, or a combination of both.
Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns toward effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.
Market timing is the strategy of making buying or selling decisions of financial assets by attempting to predict future market price movements. The prediction may be based on an outlook of market or economic conditions resulting from technical or fundamental analysis. This is an investment strategy based on the outlook for an aggregate market rather than for a particular financial asset.
A marketing decision support system is a decision support system for marketing activity. The system is used to help businesses explore different scenarios by manipulating already collected data from the past events. It consists of information technology, marketing data, systems tools, and modeling capabilities that enable it to provide predicted outcomes from different scenario and marketing strategies. MKDSS assists decision makers in different scenarios and can be a very helpful tool for a business to take over their competitors.
A market anomaly in a financial market is predictability that seems to be inconsistent with theories of asset prices. Standard theories include the capital asset pricing model and the Fama-French Three Factor Model, but a lack of agreement among academics about the proper theory leads many to refer to anomalies without a reference to a benchmark theory. Indeed, many academics simply refer to anomalies as "return predictors", avoiding the problem of defining a benchmark theory.
Market sentiment, also known as investor attention, is the general prevailing attitude of investors as to anticipated price development in a market. This attitude is the accumulation of a variety of fundamental and technical factors, including price history, economic reports, seasonal factors, and national and world events. If investors expect upward price movement in the stock market, the sentiment is said to be bullish. On the contrary, if the market sentiment is bearish, most investors expect downward price movement. Market participants who maintain a static sentiment, regardless of market conditions, are described as permabulls and permabears respectively. Market sentiment is usually considered as a contrarian indicator: what most people expect is a good thing to bet against. Market sentiment is used because it is believed to be a good predictor of market moves, especially when it is more extreme. Very bearish sentiment is usually followed by the market going up more than normal, and vice versa. A bull market refers to a sustained period of either realized or expected price rises, whereas a bear market is used to describe when an index or stock has fallen 20% or more from a recent high for a sustained length of time.
Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly.
Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit. The efficient-market hypothesis suggests that stock prices reflect all currently available information and any price changes that are not based on newly revealed information thus are inherently unpredictable. Others disagree and those with this viewpoint possess myriad methods and technologies which purportedly allow them to gain future price information.
Social data analysis is the data-driven analysis of how people interact in social contexts, often with data obtained from social networking services. The goal may be to simply understand human behavior or even to propagate a story of interest to the target audience. Techniques may involve understanding how data flows within a network, identifying influential nodes, or discovering trending topics.
Customer analytics is a process by which data from customer behavior is used to help make key business decisions via market segmentation and predictive analytics. This information is used by businesses for direct marketing, site selection, and customer relationship management. Marketing provides services in order to satisfy customers. With that in mind, the productive system is considered from its beginning at the production level, to the end of the cycle at the consumer. Customer analytics plays an important role in the prediction of customer behavior.
In trading strategy, news analysis refers to the measurement of the various qualitative and quantitative attributes of textual news stories. Some of these attributes are: sentiment, relevance, and novelty. Expressing news stories as numbers and metadata permits the manipulation of everyday information in a mathematical and statistical way. This data is often used in financial markets as part of a trading strategy or by businesses to judge market sentiment and make better business decisions.
Arizona Financial Text System (AZFinText) is a textual-based quantitative financial prediction system written by Robert P. Schumaker of University of Texas at Tyler and Hsinchun Chen of the University of Arizona.
Derwent Capital Markets was a pioneer in the use of social media sentiment analysis to trade financial derivatives. The company was founded in 2008 by co-owner Paul Hawtin. Derwent Capital Market's registered office is in London.
Active users is a measurement metric that is commonly used to measure the level of engagement for a particular product or object, by quantifying the number of active interactions from visitors within a relevant range of time . The metric has many uses in both commerce and academia, such as on social networking services, online games, or mobile apps. Although having extensive uses in digital behavioural learning, prediction and reporting, it also has impacts on the privacy and security, and ethical factors should be considered thoroughly. Like any metric, active users may have limitations and criticisms. Active Users is relatively new or neologistic in nature, that became important with the rise of the commercialised internet, with uses in communication and social-networking. It measures how many users visit or interact with the product or service over a given interval or period. This metric is commonly assessed per month as monthly active users (MAU), per week as weekly active users (WAU), per day as daily active users (DAU) or peak concurrent users (PCU).
Collaborative decision-making (CDM) software is a software application or module that helps to coordinate and disseminate data and reach consensus among work groups.
Johan Lambert Trudo Maria Bollen is a scientist investigating complex systems and networks, the relation between social media and a variety of socio-economic phenomena such as the financial markets, public health, and social well-being, as well as Science of Science with a focus on impact metrics derived from usage data. He presently works as associate professor at the Indiana University School of Informatics of Indiana University Bloomington and a fellow at the SparcS Institute of Wageningen University and Research Centre in the Netherlands. He is best known for his work on scholarly impact metrics, measuring public well-being from large-scale social media data, and correlating Twitter mood to stock market prices. He has taught courses on data mining, information retrieval, and digital libraries. His research has been funded by The Andrew W. Mellon Foundation, National Science Foundation, Library of Congress, National Aeronautics and Space Administration and the Los Alamos National Laboratory. In his free time, he DJs at the Root Cellar Lounge in Bloomington, Indiana. He specializes in Deep House and Techno.
Jason Goepfert is an American researcher and columnist focused on the development of behavioral finance. Prior to founding Sundial Capital Research, he was the manager of back office operations for Deephaven Capital Management, a Minnesota-based hedge fund, and Wells Fargo's online brokerage unit.
