Cross stock trading volume in and returns autocorrelation this definition and with it 60 second read

Text expands the universe of data many-fold. See my monograph on text mining in finance at: In Finance, for example, text has become a major source of trading information, leading to a new field known as News Metrics. Some of these attributes are: Expressing news stories as numbers permits the manipulation of everyday information in a mathematical and statistical way.

In this chapter, I provide a framework for text analytics techniques that are in widespread use. I will discuss various text analytic methods and software, and then provide a set of metrics that may be used to assess the performance of analytics. Various directions for this field are discussed through the exposition. The techniques herein can aid in the valuation and trading of securities, facilitate investment decision making, meet regulatory requirements, provide marketing insights, or manage risk.

Further, news analytics can be used to plot and characterize firm behaviors over time and thus yield important strategic insights about rival firms. There are many reasons why text has business value. But this is a narrow view. Textual data provides a means of understanding all human behavior through a data-driven, analytical approach. As definitions go, it is often easier to enumerate various versions and nuances of an activity than to describe something in one single statement.

The R programming language is increasingly being used to download text from the web and then analyze it. The ease with which R may be used to scrape text from web site may be seen from the following simple command in R:. We have first invoked the library stringr that contains many string handling functions. In fact, we may also get the length of each line in the text vector by applying the function length to the entire text vector.

Some lines are very long and cross stock trading volume in and returns autocorrelation this definition and with it 60 second read the ones we are mainly interested in as they contain the bulk of the story, whereas many of the remaining lines that are shorter contain html formatting instructions. Thus, we may extract the top three lengthy lines with the following set of commands.

In short, text extraction can be exceedingly simple, though getting clean text is not as easy an operation. Removing html tags and other unnecessary elements in the file is also a fairly simple operation. We undertake the following steps that use generalized regular expressions i. This will generate one single paragraph of text, relatively clean of formatting characters.

The XML package in R also comes with many functions that aid in cleaning up text and dropping it mostly unformatted into a flat file or data frame. This may then be further processed. Here is some example code for this. Now we look at using regular expressions with the grep command to clean out text. I will read in my research page to process this. Take a look at the text now to see how cleaned up it is.

But there is a better way, i. Text mining involves applying functions to many text documents. A library of text documents irrespective of format is called a corpus. The essential and highly useful feature of text mining packages is the ability to operate on the entire set of documents at one go. The writeCorpus function in tm creates separate text files on the hard drive, and by default are names 1.

The simple program code above shows how text scraped off a web page and collapsed into a single character string for each document, may then be converted into a corpus of documents using the Corpus function.

Cross stock trading volume in and returns autocorrelation this definition and with it 60 second read extremeley important object in text analysis is the Term-Document Matrix. This allows us to store an entire library of text inside a single matrix.

This may then be used for analysis as well as searching documents. It forms the basis of search engines, topic analysis, and classification spam filtering. It is a table that provides the frequency count of every word term in each document. The number of rows in the TDM is equal to the number of unique terms, and the number of columns is equal to the number of documents.

This is a weighting scheme provided to sharpen the importance of rare words in a document, relative to the frequency of these words in the corpus. It is based on simple calculations and even though it does not have strong theoretical foundations, it is still very useful in practice.

Therefore it is a function of all these three, i. We usually normalize word frequency so that. Another form of normalization is known as double normalization and is as follows:.

Note that normalization is not necessary, but it tends to help shrink the difference between counts of words. Then these word weights may be used in further text analysis.

We may also directly use the weightTfIdf function in the tm package. This undertakes the following computation:. In this segment we will learn some popular functions on text that are used in practice. One of the first things we like to do is to find similar text or like sentences think of web search as one application. Since documents are vectors in the TDM, we may want to find the closest vectors or compute the distance between vectors. This gives the cosine of the angle between the two vectors and is zero for orthogonal vectors and 1 for identical vectors.

This package has a few additional functions that make the preceding ideas more streamlined to implement. The last function removes non-english characters, numbers, white spaces, brackets, punctuation. It also handles cases like abbreviation, contraction. It converts entire text to lower case. We now make TDMs for unigrams, bigrams, trigrams. Then, combine them all into one list for word prediction. Wordlcouds are interesting ways in which to represent text. They give an instant visual summary.

The wordcloud cross stock trading volume in and returns autocorrelation this definition and with it 60 second read in R may be used to create your own wordclouds. Stemming is the procedure by which a word is reduced to its root or stem. This is done so as to treat words from the one stem as the same word, rather than as separate words.

Regular expressions are syntax used to modify strings in an efficient manner. They are complicated but extremely effective. Here we will illustrate with a few examples, but you are encouraged to explore more on your own, as the variations are endless.

What you need to do will depend on the application at hand, and with some experience you will become better at using regular expressions. The initial use will however be somewhat confusing. We now explore some more complex regular expressions. One case that is common is cross stock trading volume in and returns autocorrelation this definition and with it 60 second read the search for special types of strings like telephone numbers.

Suppose we have a text array that may contain telephone numbers in different formats, we can use a single grep command to extract these numbers. Here is some code to illustrate this. We would proceed as in the following example. You get the idea. Using the functions gsubgrepregmatchesand gregexpryou can manage most fancy string handling that is needed.

The rvest package, written bu Hadley Wickham, is a powerful tool cross stock trading volume in and returns autocorrelation this definition and with it 60 second read extracting text from web pages. The package is best illustrated with some simple examples. The selector gadget ius a useful tool to be used in conjunction with the rvest package. It allows you to find the html tag in a web page that you need to pass to the program to parse the html page element you are interested in.

Here is some code to read in the slashdot web page and gather the stories currently on their headlines. Sometimes we need to read a table embedded in a web page and this is also a simple exercise, which is undertaken also with rvest. Note that this code extracted all the web tables in the Yahoo! Finance page and returned each one as a list item.

Here we take note of some Russian language sites where we want to extract forex quotes and store them in a data frame. We now look to getting text from the web and using various APIs from different services like Twitter, Facebook, etc. You will need to open free developer accounts to do this on each site.

You will also need the special R packages for each different source. First create a Twitter developer account to get the required credentials for accessing the API. This completes the handshaking with Twitter. Now we can access tweets using the functions in the twitteR package. This assumes you have a working twitter account and have already connected R to it using twitteR package.

Now we move on to using Facebook, which is a little less trouble than Twitter. Also the results may be used for creating interesting networks. The Harvard General Inquirer: Math dictionary, such as http: Medical dictionary, see http:

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