News analytics
Encyclopedia
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 permits the manipulation of everyday information in a mathematical and statistical way.
News analytics are used in financial modeling
, particularly in quantitative and algorithmic trading
. They 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.
A large number of companies use news analysis to help them make better business decisions. Academic researchers have become interested in news analysis especially with regards to predicting stock price movements, volatility
and traded volume. Provided a set of values such as sentiment and relevance as well as the frequency of news arrivals, it is possible to construct news sentiment scores for multiple asset classes such as equities, Forex, fixed income
, and commodities. Sentiment scores can be constructed at various horizons to meet the different needs and objectives of high and low frequency trading strategies, whilst characteristics such as direction and volatility of asset returns as well as the traded volume may be addressed more directly via the construction of tailor-made sentiment scores. Scores are generally constructed as a range of values. For instance, values may range between 0 and 100, where values above and below 50 convey positive and negative sentiment, respectively. Based on such sentiment scores, it should be possible to generate a set of strategies useful for instance within investing, hedging, and order execution.
strategies is absolute (positive) returns regardless of the direction of the financial market. To meet this objective, such strategies typically involve opportunistic long and short positions in selected instruments with zero or limited market exposure. In statistical terms, absolute return strategies should have very low correlation
with the market return. Typically, hedge funds tend to employ absolute return strategies. Below, a few examples show how news analysis can be applied in the absolute return strategy space with the purpose to identify alpha opportunities applying a market neutral
strategy or based on volatility trading.
Example 1
Scenario: The gap between the news sentiment scores for direction,
, of Company 
and Market 
has moved beyond 
. That is, 
≥ 
.
Action: Buy the stock on Company
and short the future on Market 
.
Exit Strategy: When the gap in the news sentiment scores for direction of Company
and Market 
has disappeared, 
= 
, sell the stock on Company 
and go long the future on Market 
to close the positions.
Example 2
Scenario: The news sentiment score for volatility of Company
goes above 
out of 
indicating an expected volatility above the option implied volatility
.
Action: Buy a short-dated straddle (the purchase of both a put and a call) on the stock of Company
.
Exit Strategy: Keep the straddle on Company
until expiry or until a certain profit target has been reached.
strategies is to either replicate (passive management
) or outperform (active management
) a theoretical passive reference portfolio or benchmark. To meet these objectives such strategies typically involve long positions in selected instruments. In statistical terms, relative return strategies often have high correlation with the market return. Typically, mutual funds tend to employ relative return strategies. Below, a few examples show how news analysis can be applied in the relative return strategy space with the purpose to outperform the market applying a stock picking strategy and by making tactical tilts to ones asset allocation
model.
Example 1
Scenario: The news sentiment score for direction of Company
goes above 
out of 
.
Action: Buy the stock on Company
.
Exit Strategy: When the news sentiment score for direction of Company
falls below 
, sell the stock on Company 
to close the position.
Example 2
Scenario: The news sentiment score for direction of Sector
goes above 
out of 
.
Action: Include Sector
as a tactical bet in the asset allocation model.
Exit Strategy: When the news sentiment score for direction of Sector
falls below 
, remove the tactical bet for Sector 
from the asset allocation model.
is to create economic value in a firm or to maintain a certain risk profile of an investment portfolio by using financial instruments to manage risk exposures, particularly credit risk
and market risk
. Other types include Foreign exchange, Shape, Volatility, Sector, Liquidity, Inflation risks, etc. As a specialization of risk management, financial risk management focuses on when and how to hedge
using financial instruments to manage costly exposures to risk. Below, a few examples show how news analysis can be applied in the financial risk management space with the purpose to either arrive at better risk estimates in terms of Value at Risk
(VaR) or to manage the risk of a portfolio to meet ones portfolio mandate.
Example 1
Scenario: The bank operates a VaR model to manage the overall market risk of its portfolio.
Action: Estimate the portfolio covariance matrix taking into account the development of the news sentiment score for volume. Implement the relevant hedges to bring the VaR of the bank in line with the desired levels.
Example 2
Scenario: A portfolio manager operates his portfolio towards a certain desired risk profile.
Action: Estimate the portfolio covariance matrix
taking into account the development of the news sentiment score for volume. Scale the portfolio exposure according to the targeted risk profile.
, is to reduce trading costs by optimizing on the timing of a given order. It is widely used by hedge funds, pension funds, mutual funds, and other institutional traders to divide up large trades into several smaller trades to manage market impact, opportunity cost
, and risk more effectively. The example below shows how news analysis can be applied in the algorithmic order execution space with the purpose to arrive at more efficient algorithmic trading systems.
Example 1
Scenario: A large order needs to be placed in the market for the stock on Company
.
Action: Scale the daily volume distribution for Company
applied in the algorithmic trading system, thus taking into account the news sentiment score for volume. This is followed by the creation of the desired trading distribution forcing greater market participation during the periods of the day when volume is expected to be heaviest.
documents, JSON
, or .csv files. They include numerical values, tags, and other properties that tend to represent underlying news stories. For testing purposes, historical information is often delivered via flat files, while live data for production is processed and delivered through direct data feeds or APIs.
Quantitative property
A quantitative property is one that exists in a range of magnitudes, and can therefore be measured with a number. Measurements of any particular quantitative property are expressed as a specific quantity, referred to as a unit, multiplied by a number. Examples of physical quantities are distance,...
attributes of textual (unstructured data
Unstructured data
Unstructured Data refers to information that either does not have a pre-defined data model and/or does not fit well into relational tables. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well...
) news stories. Some of these attributes are: sentiment, relevance, and novelty. Expressing news stories as numbers permits the manipulation of everyday information in a mathematical and statistical way.
News analytics are used in financial modeling
Financial modeling
Financial modeling is the task of building an abstract representation of a financial decision making situation. This is a mathematical model designed to represent the performance of a financial asset or a portfolio, of a business, a project, or any other investment...
, particularly in quantitative and algorithmic trading
Algorithmic trading
In electronic financial markets, algorithmic trading or automated trading, also known as algo trading, black-box trading or robo trading, is the use of electronic platforms for entering trading orders with an algorithm deciding on aspects of the order such as the timing, price, or quantity of the...
. They are usually derived through automated text analysis and applied to digital texts using elements from natural language processing
Natural language processing
Natural language processing is a field of computer science and linguistics concerned with the interactions between computers and human languages; it began as a branch of artificial intelligence....
and machine learning
Machine learning
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...
such as latent semantic analysis
Latent semantic analysis
Latent semantic analysis is a technique in natural language processing, in particular in vectorial semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close...
, support vector machines, "bag of words" among other techniques.
Applications & 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 calculations are now routinely used by both buy-side and sell side in alpha generation and trading execution applications. There is however a good deal of variation in the quality, effectiveness and completeness of currently available solutions.A large number of companies use news analysis to help them make better business decisions. Academic researchers have become interested in news analysis especially with regards to predicting stock price movements, volatility
Volatility (finance)
In finance, volatility is a measure for variation of price of a financial instrument over time. Historic volatility is derived from time series of past market prices...
and traded volume. Provided a set of values such as sentiment and relevance as well as the frequency of news arrivals, it is possible to construct news sentiment scores for multiple asset classes such as equities, Forex, fixed income
Fixed income
Fixed income refers to any type of investment that is not equity, which obligates the borrower/issuer to make payments on a fixed schedule, even if the number of the payments may be variable....
, and commodities. Sentiment scores can be constructed at various horizons to meet the different needs and objectives of high and low frequency trading strategies, whilst characteristics such as direction and volatility of asset returns as well as the traded volume may be addressed more directly via the construction of tailor-made sentiment scores. Scores are generally constructed as a range of values. For instance, values may range between 0 and 100, where values above and below 50 convey positive and negative sentiment, respectively. Based on such sentiment scores, it should be possible to generate a set of strategies useful for instance within investing, hedging, and order execution.
Absolute Return Strategies
The objective of absolute returnAbsolute return
The absolute return or simply return is a measure of the gain or loss on an investment portfolio expressed as a percentage of invested capital. The adjective absolute is used to stress the distinction with the relative return measures often used by long-only equity funds, i.e...
strategies is absolute (positive) returns regardless of the direction of the financial market. To meet this objective, such strategies typically involve opportunistic long and short positions in selected instruments with zero or limited market exposure. In statistical terms, absolute return strategies should have very low correlation
Correlation
In statistics, dependence refers to any statistical relationship between two random variables or two sets of data. Correlation refers to any of a broad class of statistical relationships involving dependence....
with the market return. Typically, hedge funds tend to employ absolute return strategies. Below, a few examples show how news analysis can be applied in the absolute return strategy space with the purpose to identify alpha opportunities applying a market neutral
Market neutral
An investment strategy or portfolio is considered market neutral if it seeks to entirely avoid some form of market risk, typically by hedging. In order to evaluate market neutrality, it is first necessary to specify the risk being avoided...
strategy or based on volatility trading.
Example 1
Scenario: The gap between the news sentiment scores for direction,
, of Company and Market has moved beyond . That is, ≥ .Action: Buy the stock on Company
and short the future on Market .Exit Strategy: When the gap in the news sentiment scores for direction of Company
and Market has disappeared, = , sell the stock on Company and go long the future on Market to close the positions.Example 2
Scenario: The news sentiment score for volatility of Company
goes above out of indicating an expected volatility above the option implied volatilityImplied volatility
In financial mathematics, the implied volatility of an option contract is the volatility of the price of the underlying security that is implied by the market price of the option based on an option pricing model. In other words, it is the volatility that, when used in a particular pricing model,...
.
Action: Buy a short-dated straddle (the purchase of both a put and a call) on the stock of Company
.Exit Strategy: Keep the straddle on Company
until expiry or until a certain profit target has been reached.Relative Return Strategies
The objective of relative returnRelative return
Relative return is a measure of the return of an investment portfolio relative to a theoretical passive reference portfolio or benchmark.In active portfolio management, the aim is to maximize the relative return...
strategies is to either replicate (passive management
Passive management
Passive management is a financial strategy in which an investor invests in accordance with a pre-determined strategy that doesn't entail any forecasting...
) or outperform (active management
Active management
Active management refers to a portfolio management strategy where the manager makes specific investments with the goal of outperforming an investment benchmark index...
) a theoretical passive reference portfolio or benchmark. To meet these objectives such strategies typically involve long positions in selected instruments. In statistical terms, relative return strategies often have high correlation with the market return. Typically, mutual funds tend to employ relative return strategies. Below, a few examples show how news analysis can be applied in the relative return strategy space with the purpose to outperform the market applying a stock picking strategy and by making tactical tilts to ones asset allocation
Asset allocation
Asset allocation is an investment strategy that attempts to balance risk versus reward by adjusting the percentage of each asset in an investment portfolio according to the investors risk tolerance, goals and investment time frame.-Description:...
model.
Example 1
Scenario: The news sentiment score for direction of Company
goes above out of .Action: Buy the stock on Company
.Exit Strategy: When the news sentiment score for direction of Company
falls below , sell the stock on Company to close the position.Example 2
Scenario: The news sentiment score for direction of Sector
goes above out of .Action: Include Sector
as a tactical bet in the asset allocation model.Exit Strategy: When the news sentiment score for direction of Sector
falls below , remove the tactical bet for Sector from the asset allocation model.Financial Risk Management
The objective of financial risk managementFinancial risk management
Financial risk management is the practice of creating economic value in a firm by using financial instruments to manage exposure to risk, particularly credit risk and market risk. Other types include Foreign exchange, Shape, Volatility, Sector, Liquidity, Inflation risks, etc...
is to create economic value in a firm or to maintain a certain risk profile of an investment portfolio by using financial instruments to manage risk exposures, particularly credit risk
Credit risk
Credit risk is an investor's risk of loss arising from a borrower who does not make payments as promised. Such an event is called a default. Other terms for credit risk are default risk and counterparty risk....
and market risk
Market risk
Market risk is the risk that the value of a portfolio, either an investment portfolio or a trading portfolio, will decrease due to the change in value of the market risk factors. The four standard market risk factors are stock prices, interest rates, foreign exchange rates, and commodity prices...
. Other types include Foreign exchange, Shape, Volatility, Sector, Liquidity, Inflation risks, etc. As a specialization of risk management, financial risk management focuses on when and how to hedge
Hedge
Hedge may refer to:* Hedge or hedgerow, line of closely spaced shrubs planted to act as a barrier* Hedge , investment made to limit loss* Hedge , intentionally non-committal or ambiguous sentence fragments-See also:...
using financial instruments to manage costly exposures to risk. Below, a few examples show how news analysis can be applied in the financial risk management space with the purpose to either arrive at better risk estimates in terms of Value at Risk
Value at risk
In financial mathematics and financial risk management, Value at Risk is a widely used risk measure of the risk of loss on a specific portfolio of financial assets...
(VaR) or to manage the risk of a portfolio to meet ones portfolio mandate.
Example 1
Scenario: The bank operates a VaR model to manage the overall market risk of its portfolio.
Action: Estimate the portfolio covariance matrix taking into account the development of the news sentiment score for volume. Implement the relevant hedges to bring the VaR of the bank in line with the desired levels.
Example 2
Scenario: A portfolio manager operates his portfolio towards a certain desired risk profile.
Action: Estimate the portfolio covariance matrix
Covariance matrix
In probability theory and statistics, a covariance matrix is a matrix whose element in the i, j position is the covariance between the i th and j th elements of a random vector...
taking into account the development of the news sentiment score for volume. Scale the portfolio exposure according to the targeted risk profile.
Algorithmic Order Execution
The objective of algorithmic order execution, which is part of the concept of algorithmic tradingAlgorithmic trading
In electronic financial markets, algorithmic trading or automated trading, also known as algo trading, black-box trading or robo trading, is the use of electronic platforms for entering trading orders with an algorithm deciding on aspects of the order such as the timing, price, or quantity of the...
, is to reduce trading costs by optimizing on the timing of a given order. It is widely used by hedge funds, pension funds, mutual funds, and other institutional traders to divide up large trades into several smaller trades to manage market impact, opportunity cost
Opportunity cost
Opportunity cost is the cost of any activity measured in terms of the value of the best alternative that is not chosen . It is the sacrifice related to the second best choice available to someone, or group, who has picked among several mutually exclusive choices. The opportunity cost is also the...
, and risk more effectively. The example below shows how news analysis can be applied in the algorithmic order execution space with the purpose to arrive at more efficient algorithmic trading systems.
Example 1
Scenario: A large order needs to be placed in the market for the stock on Company
.Action: Scale the daily volume distribution for Company
applied in the algorithmic trading system, thus taking into account the news sentiment score for volume. This is followed by the creation of the desired trading distribution forcing greater market participation during the periods of the day when volume is expected to be heaviest.Delivery & Data Formats
News analytics are delivered in a variety of formats, often as machine readable XMLXML
Extensible Markup Language is a set of rules for encoding documents in machine-readable form. It is defined in the XML 1.0 Specification produced by the W3C, and several other related specifications, all gratis open standards....
documents, JSON
JSON
JSON , or JavaScript Object Notation, is a lightweight text-based open standard designed for human-readable data interchange. It is derived from the JavaScript scripting language for representing simple data structures and associative arrays, called objects...
, or .csv files. They include numerical values, tags, and other properties that tend to represent underlying news stories. For testing purposes, historical information is often delivered via flat files, while live data for production is processed and delivered through direct data feeds or APIs.
Effects
Being able to express news stories as numbers permits the manipulation of everyday information in a statistical way that allows computers not only to make decisions once made only by humans, but to do so more efficiently. Since market participants are always looking for an edge, the speed of computer connections and the delivery of news analysis, measured in milliseconds, have become essential.See also
- Computational linguisticsComputational linguisticsComputational linguistics is an interdisciplinary field dealing with the statistical or rule-based modeling of natural language from a computational perspective....
- Sentiment analysisSentiment analysisSentiment analysis or opinion mining refers to the application of natural language processing, computational linguistics, and text analytics to identify and extract subjective information in source materials....
- text miningText miningText mining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as...
- unstructured dataUnstructured dataUnstructured Data refers to information that either does not have a pre-defined data model and/or does not fit well into relational tables. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well...
- Text analyticsText analyticsThe term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation. The term is roughly synonymous with text mining;...
- Natural language processingNatural language processingNatural language processing is a field of computer science and linguistics concerned with the interactions between computers and human languages; it began as a branch of artificial intelligence....
- Information asymmetryInformation asymmetryIn economics and contract theory, information asymmetry deals with the study of decisions in transactions where one party has more or better information than the other. This creates an imbalance of power in transactions which can sometimes cause the transactions to go awry, a kind of market failure...
- Algorithmic tradingAlgorithmic tradingIn electronic financial markets, algorithmic trading or automated trading, also known as algo trading, black-box trading or robo trading, is the use of electronic platforms for entering trading orders with an algorithm deciding on aspects of the order such as the timing, price, or quantity of the...