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News Sentiment
In trading strategy, news analysis refers to the measurement of the various qualitative and quantitative attributes of textual (unstructured data) 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. News analytics are usually derived through automated text analysis and applied to digital texts using elements from natural language processing and machine learning such as latent semantic analysis, support vector machines, "bag of words" among other techniques. Applications and strategies The application of sophisticated linguistic analysis to news and social media has grown from an area of research to mature product solutions since 2007. News analytics and news sentiment ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Trading Strategy
In finance, a trading strategy is a fixed plan that is designed to achieve a profitable return by going long or short in markets. The difference between short trading and long-term investing is in the opposite approach and principles. Going short trading would mean to research and pick stocks for future fast trading activity on one's accounts with a rather speculative attitude. While going into long-term investing would mean contrasting activity to short one. Low turnover, principles of time-tested investment approaches, returns with risk-adjusted actions, and diversification are the key features of investing in a long-term manner. For every trading strategy one needs to define assets to trade, entry/exit points and money management rules. Bad money management can make a potentially profitable strategy unprofitable.Nekrasov, V. Knowledge rather than Hope: A Book for Retail Investors and Mathematical Finance Students''. 2014pages 24-26 '' Trading strategies are based on fundame ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Active Management
Active may refer to: Music * ''Active'' (album), a 1992 album by Casiopea * "Active" (song), a 2024 song by Asake and Travis Scott from Asake's album ''Lungu Boy'' * Active Records, a record label Ships * ''Active'' (ship), several commercial ships by that name * HMS ''Active'', the name of various ships of the British Royal Navy * USCS ''Active'', a US Coast Survey ship in commission from 1852 to 1861 * USCGC ''Active'', the name of various ships of the US Coast Guard * USRC ''Active'', the name of various ships of the US Revenue Cutter Service * USS ''Active'', the name of various ships of the US Navy Computers and electronics * Active Enterprises, a defunct video game developer * Sky Active, the brand name for interactive features on Sky Digital available in the UK and Ireland * Active (software), software used for open publishing by Indymedia; see Independent Media Center * The "live" circuit of mains power in countries observing AS/NZS 3112 electrical sta ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Unstructured Data
Unstructured data (or unstructured information) is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically plain text, text-heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in fielded form in databases or annotation, annotated (Tag (metadata), semantically tagged) in documents. In 1998, Merrill Lynch said "unstructured data comprises the vast majority of data found in an organization, some estimates run as high as 80%." It is unclear what the source of this number is, but nonetheless it is accepted by some. Other sources have reported similar or higher percentages of unstructured data. , International Data Corporation, IDC and Dell EMC project that data will grow to 40 zettabytes by 2020, resulting in a 50-fold growth from the beginnin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Trading The News
Trading the news is a technique to trade equities, currencies and other financial instruments on the financial markets. Trading news releases can be a significant tool for financial investors. Economic news reports often spur strong short-term moves in the markets, which may create trading opportunities for traders. Announcements about corporate profits, a change in management, rumors of a merger, are events that can cause a company's share price to move wildly up or down. Interest rates, unemployment and export rates, or the central bank's policy shifts, can cause a deep change of an exchange rate. Methods Manual Investors trading shares of a listed company know there are certain events that cause the share price to rise or fall — sudden changes in energy prices, a labor strike at a supplier, a poor month for the sales, for example. Trading the news is the technique of making a profit by trading financial instruments (stock, currency, etc.) just in time, and in accordance to ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Text Mining
Text mining, text data mining (TDM) or 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), there are three perspectives of text mining: information extraction, data mining, and knowledge discovery in databases (KDD). Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sentiment Analysis
Sentiment analysis (also known as opinion mining or emotion AI) 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.Hamborg, Felix; Donnay, Karsten (2021)"NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles" "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume" Simple cases * "Coron ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computational Linguistics
Computational linguistics is an interdisciplinary field concerned with the computational modelling of natural language, as well as the study of appropriate computational approaches to linguistic questions. In general, computational linguistics draws upon linguistics, computer science, artificial intelligence, mathematics, logic, philosophy, cognitive science, cognitive psychology, psycholinguistics, anthropology and neuroscience, among others. Computational linguistics is closely related to mathematical linguistics. Origins The field overlapped with artificial intelligence since the efforts in the United States in the 1950s to use computers to automatically translate texts from foreign languages, particularly Russian scientific journals, into English. Since rule-based approaches were able to make arithmetic (systematic) calculations much faster and more accurately than humans, it was expected that lexicon, morphology, syntax and semantics can be learned using explicit rules, a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Opportunity Cost
In microeconomic theory, the opportunity cost of a choice is the value of the best alternative forgone where, given limited resources, a choice needs to be made between several mutually exclusive alternatives. Assuming the best choice is made, it is the "cost" incurred by not enjoying the ''benefit'' that would have been had if the second best available choice had been taken instead. The '' New Oxford American Dictionary'' defines it as "the loss of potential gain from other alternatives when one alternative is chosen". As a representation of the relationship between scarcity and choice, the objective of opportunity cost is to ensure efficient use of scarce resources. It incorporates all associated costs of a decision, both explicit and implicit. Thus, opportunity costs are not restricted to monetary or financial costs: the real cost of output forgone, lost time, pleasure, or any other benefit that provides utility should also be considered an opportunity cost. Types Expl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Algorithmic Trading
Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. This type of trading attempts to leverage the speed and computational resources of computers relative to human traders. In the twenty-first century, algorithmic trading has been gaining traction with both retail and institutional traders. A study in 2019 showed that around 92% of trading in the Forex market was performed by trading algorithms rather than humans. It is widely used by investment banks, pension funds, mutual funds, and hedge funds that may need to spread out the execution of a larger order or perform trades too fast for human traders to react to. However, it is also available to private traders using simple retail tools. The term algorithmic trading is often used synonymously with automated trading system. These encompass a variety of trading strategies, some of which are based on formulas and results ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Covariance Matrix
In probability theory and statistics, a covariance matrix (also known as auto-covariance matrix, dispersion matrix, variance matrix, or variance–covariance matrix) is a square matrix giving the covariance between each pair of elements of a given random vector. Intuitively, the covariance matrix generalizes the notion of variance to multiple dimensions. As an example, the variation in a collection of random points in two-dimensional space cannot be characterized fully by a single number, nor would the variances in the x and y directions contain all of the necessary information; a 2 \times 2 matrix would be necessary to fully characterize the two-dimensional variation. Any covariance matrix is symmetric and positive semi-definite and its main diagonal contains variances (i.e., the covariance of each element with itself). The covariance matrix of a random vector \mathbf is typically denoted by \operatorname_, \Sigma or S. Definition Throughout this article, boldfaced u ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Value At Risk
Value at risk (VaR) is a measure of the risk of loss of investment/capital. It estimates how much a set of investments might lose (with a given probability), given normal market conditions, in a set time period such as a day. VaR is typically used by firms and regulators in the financial industry to gauge the amount of assets needed to cover possible losses. For a given portfolio, time horizon, and probability ''p'', the ''p'' VaR can be defined informally as the maximum possible loss during that time after excluding all worse outcomes whose combined probability is at most ''p''. This assumes mark-to-market pricing, and no trading in the portfolio. For example, if a portfolio of stocks has a one-day 5% VaR of $1 million, that means that there is a 0.05 probability that the portfolio will fall in value by $1 million or more over a one-day period if there is no trading. Informally, a loss of $1 million or more on this portfolio is expected on 1 day out of 20 days (because of 5% p ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Market Risk
Market risk is the risk of losses in positions arising from movements in market variables like prices and volatility. There is no unique classification as each classification may refer to different aspects of market risk. Nevertheless, the most commonly used types of market risk are: * '' Equity risk'', the risk that stock or stock indices (e.g. Euro Stoxx 50, etc.) prices or their implied volatility will change. * ''Interest rate risk'', the risk that interest rates (e.g. Libor, Euribor, etc.) or their implied volatility will change. * '' Currency risk'', the risk that foreign exchange rates (e.g. EUR/USD, EUR/GBP, etc.) or their implied volatility will change. * '' Commodity risk'', the risk that commodity prices (e.g. corn, crude oil) or their implied volatility will change. * '' Margining risk'' results from uncertain future cash outflows due to margin calls covering adverse value changes of a given position. * '' Shape risk'' * '' Holding period risk'' * '' Basi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
