Insulin therapy: Difference between revisions

Latest revision as of 02:00, 22 January 2014

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.

News analytics are used in financial modeling, particularly in quantitative and algorithmic trading.^[1] Further, news analytics can be used to plot and characterize firm behaviors over time and thus yield important strategic insights about rival firms.^[2]^[3]

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 calculations are now routinely used by both buy-side and sell side in alpha generation, trading execution, risk management, and market surveillance and compliance. 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.^[4] Academic researchers have become interested in news analysis especially with regards to predicting stock price movements, volatility and traded volume.^[5]^[6]^[7] 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.^[8] 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 return 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, $S$ , of Company $X$ and Market $Y$ has moved beyond $+ 20$ . That is, $S_{X} - S_{Y}$ ≥ $20$ .

Action: Buy the stock on Company $X$ and short the future on Market $Y$ .

Exit Strategy: When the gap in the news sentiment scores for direction of Company $X$ and Market $Y$ has disappeared, $S_{X} - S_{Y}$ = $0$ , sell the stock on Company $X$ and go long the future on Market $Y$ to close the positions.

Example 2

Scenario: The news sentiment score for volatility of Company $X$ goes above $70$ out of $100$ 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 $X$ .

Exit Strategy: Keep the straddle on Company $X$ until expiry or until a certain profit target has been reached.

Relative Return Strategies

The objective of relative return 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 $X$ goes above $70$ out of $100$ .

Action: Buy the stock on Company $X$ .

Exit Strategy: When the news sentiment score for direction of Company $X$ falls below $60$ , sell the stock on Company $X$ to close the position.

Example 2

Scenario: The news sentiment score for direction of Sector $Z$ goes above $70$ out of $100$ .

Action: Include Sector $Z$ as a tactical bet in the asset allocation model.

Exit Strategy: When the news sentiment score for direction of Sector $Z$ falls below $60$ , remove the tactical bet for Sector $Z$ from the asset allocation model.

Financial Risk Management

The objective of financial risk management 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.

Algorithmic Order Execution

The objective of algorithmic order execution, which is part of the concept of algorithmic trading, 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 $X$ .

Action: Scale the daily volume distribution for Company $X$ 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 XML 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.^[9]^[10]

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.

References

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↑ http://www.sentimentnews.com
↑ http://www.newsanalytics.net
↑ http://www.eventstudytools.com/competitive-dynamics
↑ Tetlock, Paul C.,Does Public Financial News Resolve Asymmetric Information?(November 1, 2008). Available at SSRN: http://ssrn.com/abstract=1303612
↑ http://www.mccombs.utexas.edu/faculty/Paul.Tetlock/papers/Tetlock-Investor_Sentiment-11_07.pdf
↑ http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1153450
↑ http://www1.gsb.columbia.edu/mygsb/faculty/research/pubfiles/3096/More_Than_Words_tetlock.pdf
↑ http://www.northinfo.com/documents/313.pdf
↑ http://www.ravenpack.com/services/data.htm
↑ https://www.recordedfuture.com/news-analytics.html

[1] ttp://www.sentimentnews.com

[2] ttp://www.newsanalytics.net

[3] ttp://www.eventstudytools.com/competitive-dynamics

[4] Tetlock, Paul C.,Does Public Financial News Resolve Asymmetric Information?(November 1, 2008). Available at SSRN: http://ssrn.com/abstract=1303612

[5] ttp://www.mccombs.utexas.edu/faculty/Paul.Tetlock/papers/Tetlock-Investor_Sentiment-11_07.pdf

[6] ttp://papers.ssrn.com/sol3/papers.cfm?abstract_id=1153450

[7] ttp://www1.gsb.columbia.edu/mygsb/faculty/research/pubfiles/3096/More_Than_Words_tetlock.pdf

[8] ttp://www.northinfo.com/documents/313.pdf

[9] ttp://www.ravenpack.com/services/data.htm

[10] ttps://www.recordedfuture.com/news-analytics.html

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+'''News analysis''' refers to the measurement of the various [[Qualitative data|qualitative]] and [[quantitative property|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.
+News analytics are used in [[financial modeling]], particularly in quantitative and [[algorithmic trading]].<ref>http://www.sentimentnews.com</ref> Further, news analytics can be used to plot and characterize firm behaviors over time and thus yield important strategic insights about rival firms.<ref>http://www.newsanalytics.net</ref><ref>http://www.eventstudytools.com/competitive-dynamics</ref>
-Surely the second option would be more beneficial for any website. The next step is to visit your Word - Press blog dashboard. The Word - Press Dashboard : an administrative management tool that supports FTP content upload  2. Word - Press also provides protection against spamming, as security is a measure issue. All this is very simple, and the best thing is that it is totally free, and you don't need a domain name or web hosting. <br><br>Creating a website from scratch can be such a pain.  If you have any sort of questions pertaining to where and exactly how to make use of [http://aorta.in/BackupPlugin849263 wordpress dropbox backup], you can contact us at our own internet site. When you write a new post, you'll see a small bar that goes across the text input area. Which is perfect for building a mobile site for business use. They provide many such popular products which you can buy for your baby. That's a total of 180$ for each Wordpress theme if you sell 3 links and keep the designer link for your own website, or 240$ if you sell all links. <br><br>But before choosing any one of these, let's compare between the two. To sum up, ensure that the tactics are aiming to increase the ranking and attracting the maximum intended traffic in the major search engines. For a much deeper understanding of simple wordpress themes", check out  Upon browsing such, you'll be able to know valuable facts. Provide the best and updated information to the web searchers and make use of these wonderful free themes and create beautiful websites. Converting HTML to Word - Press theme for your website can allow you to enjoy the varied Word - Press features that aid in consistent growth your online business. <br><br>If all else fails, please leave a comment on this post with the issue(s) you're having and help will be on the way. In case you need to hire PHP developers or hire Offshore Code - Igniter development services or you are looking for Word - Press development experts then Mindfire Solutions would be the right choice for a Software Development partner. The templates are designed to be stand alone pages that have a different look and feel from the rest of your website. Can you imagine where you would be now if someone in your family bought an original painting from van Gogh during his lifetime. This includes enriching the content with proper key words, tactfully defining the tags and URL. <br><br>There is no denying that Magento is an ideal platform for building ecommerce websites, as it comes with an astounding number of options that can help your online business do extremely well. An ease of use which pertains to both internet site back-end and front-end users alike. As a result, it is really crucial to just take aid of some experience when searching for superior quality totally free Word - Press themes, Word - Press Premium Themes for your web site. If this is not possible you still have the choice of the default theme that is Word - Press 3. Definitely when you wake up from the slumber, you can be sure that you will be lagging behind and getting on track would be a tall order.
+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 calculations are now routinely used by both buy-side and sell side in alpha generation, trading execution, risk management, and market surveillance and compliance. 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.<ref>Tetlock, Paul C.,Does Public Financial News Resolve Asymmetric Information?(November 1, 2008). Available at SSRN: http://ssrn.com/abstract=1303612</ref> Academic researchers have become interested in news analysis especially with regards to predicting stock price movements, [[Volatility (finance)|volatility]] and traded volume.<ref>http://www.mccombs.utexas.edu/faculty/Paul.Tetlock/papers/Tetlock-Investor_Sentiment-11_07.pdf</ref><ref>http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1153450</ref><ref>http://www1.gsb.columbia.edu/mygsb/faculty/research/pubfiles/3096/More_Than_Words_tetlock.pdf</ref>  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.<ref>http://www.northinfo.com/documents/313.pdf</ref> Based on such sentiment scores, it should be possible to generate a set of strategies useful for instance within investing, [[hedge (finance)|hedging]], and order execution.
+=== Absolute Return Strategies ===
+The objective of [[absolute return]] 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, <math>S</math>, of Company <math>X</math> and Market <math>Y</math> has moved beyond <math>+20</math>. That is, <math>S_X-S_Y</math> ≥ <math>20</math>.
+'''Action:''' Buy the stock on Company <math>X</math> and short the future on Market <math>Y</math>.
+'''Exit Strategy:''' When the gap in the news sentiment scores for direction of Company <math>X</math> and Market <math>Y</math> has disappeared, <math>S_X-S_Y</math> = <math>0</math>, sell the stock on Company <math>X</math> and go long the future on Market <math>Y</math> to close the positions.
+'''Example 2'''
+'''Scenario:''' The news sentiment score for volatility of Company <math>X</math> goes above <math>70</math> out of <math>100</math> 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
+<math>X</math>.
+'''Exit Strategy:''' Keep the straddle on Company <math>X</math> until expiry or until a certain profit target has been reached.
+=== Relative Return Strategies===
+The objective of [[relative return]] 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 <math>X</math> goes above <math>70</math> out of <math>100</math>.
+'''Action:''' Buy the stock on Company <math>X</math>.
+'''Exit Strategy:''' When the news sentiment score for direction of Company <math>X</math> falls below <math>60</math>, sell the stock on Company <math>X</math> to close the position.
+'''Example 2'''
+'''Scenario:''' The news sentiment score for direction of Sector <math>Z</math> goes above <math>70</math> out of <math>100</math>.
+'''Action:''' Include Sector <math>Z</math> as a tactical bet in the asset allocation model.
+'''Exit Strategy:''' When the news sentiment score for direction of Sector <math>Z</math> falls below <math>60</math>, remove the tactical bet for Sector <math>Z</math> from the asset allocation model.
+=== Financial Risk Management===
+The objective of [[financial risk management]] 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.
+=== Algorithmic Order Execution===
+The objective of algorithmic order execution, which is part of the concept of [[algorithmic trading]], 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 <math>X</math>.
+'''Action:''' Scale the daily volume distribution for Company <math>X</math> 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 [[XML]] 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.<ref>http://www.ravenpack.com/services/data.htm</ref><ref>https://www.recordedfuture.com/news-analytics.html</ref>
+==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 linguistics]]
+* [[Sentiment analysis]]
+* [[text mining]]
+* [[unstructured data]]
+* [[Text analytics]]
+* [[Natural language processing]]
+* [[Information asymmetry]]
+* [[Algorithmic trading]]
+==References==
+{{Reflist}}
+[[Category:Natural language processing]]

Insulin therapy: Difference between revisions

Latest revision as of 02:00, 22 January 2014

Contents

Applications and strategies

Absolute Return Strategies

Relative Return Strategies

Financial Risk Management

Algorithmic Order Execution

Delivery & Data Formats

Effects

See also

References

Navigation menu

Insulin therapy: Difference between revisions

Latest revision as of 02:00, 22 January 2014

Applications and strategies

Absolute Return Strategies

Relative Return Strategies

Financial Risk Management

Algorithmic Order Execution

Delivery & Data Formats

Effects

See also

References

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