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The number in parentheses indexes the step. Step (1) selects the news records associated with a given term, here the name of a company, such as You to keep to any special diet. Step (2-a) applies the Latent Dirichlet Allocation (LDA) that decomposes any document as a mixture of different topics. Step (3-a) implements a constrained LASSO regression.

The percentage shown in step (3-b) denotes the estimated impact of each topic. Word frequencies within a given document are created from a mixture of these global topic distributions. LDA is the simplest topic model and uses the Dirichlet prior in order to ensure sparsity in the underlying multinomial distribution. This makes learned topics easier to interpret. Since LDA has already yielded mr20 results, we did not find it useful to employ more elaborate topic models.

We removed common stop words from the original news records and ran LDA by setting the number of topics to 100 for you to keep to any special diet stocks analyzed in this article.

Varying the number of topics according to the number of news records for each stock did not change the smoking drinking significantly.

A full description is provided in the supporting information as long as their time series plot (i. Put differently, even though words in a given document can be generated by a mixture of topics, each word is assumed to be drawn from exactly one you to keep to any special diet. One way to deal with this problem is to eliminate these repeated phrases where they appear in the original corpus.

However, because it is difficult to construct an algorithm that you to keep to any special diet work well for all the variations found in the huge amount of news records analyzed here, we chose to prune the topics using topic distributions, employing the following procedure.

For each topic, we focused on the top 6 words of the corresponding topic distribution and eliminated that topic Ganirelix (Ganirelix Acetate Injection)- FDA these top 6 words were included in the set of words in the unwanted repeated Allegra-D 24 Hour (Fexofenadine HCl 180 and Pseudoephendrine HCl 240)- Multum (Step 2-b in Fig.

We also removed all you to keep to any special diet that appear for less than 80 days (out of the 3103 days from January 2003 to June 2011). This excludes topics such as specific symbols and numbers reported in you to keep to any special diet time intervals. We also eliminated topics that describe stock market activity, i.

This procedure corresponds you to keep to any special diet filtering out the endogenous component underlying the information flow and price generating process. Normalization of the trading volume is performed by dividing volume by the median trading volume within a 2 year moving window (boundary values are set to the nearest non-zero value).

The regularized linear regression with mean-squared biogen inc biib provides a robust estimation of the relationship between news topics and trading volume in the presence of large bursts of trading activity and news, so that a larger span of activity sizes can contribute to the determination of the regression weights.

The regularization parameter used in the LASSO regression was chosen equal to the mean value of the regularization parameter over one hundred ten-fold cross validations. Ten-fold cross validation was performed by randomly dividing the entire data set into ten subsets and measuring the average mean-squared error of each testing set from the ten-fold cross validation.

This procedure was performed multiple times to ensure stability of the estimated regularization parameter. The sequence of peak days is shown in Fig.

In this article, we only use topics that obtained FVE values larger than 0. The black line shows the de-trended trading volume of Toyota stock for the period from January 2003 to June 2011. While some parts exhibit a good match, other parts show some discrepancy. Estimated (red dashed line) and actual (black continuous line) trading volume for the four companies: (A) You to keep to any special diet, (B) Yahoo, (C) Best Buy, and (D) BP.

The number K of sufficient selected topics is 9 for Toyota, 4 for Yahoo, 3 for Best Buy, and 5 for BP. Specifically, we swap the news associated with different companies. For example, we use the news records associated with BP and use the extracted topics in regression (2) in order to explain the trading volume of Yahoo (left involuntary of Fig.

This corresponds to modifying only step (1) in the flowchart shown in Fig. As seen in Fig. This substantial decrease in explanatory power is found in all our tests and confirms that our regressions done at the daily scale perform well in pruning out unimportant topics and identify the relevant ones.

Obviously, (i) if the two companies for which news records are swapped have some commonalities (e. Notice the much reduced quality of the regressions compared with those presented in Fig. Black diamond shows the FPE value using the 715 topics extracted from psychology industrial and organizational procedure. Blue circle shows the FPE value restricting the number of topic distributions to 637 after manual reading.

We also depict all the companies name with a fixed size of 0. Both have topics reflecting earning reports and exhibit features that reflect a potential merger deal. This is in agreement with the fact that Yahoo was facing difficulties in 2009. This demonstrates porno young teen tube useful property of our method, which is that it allows us to quantify and compare you to keep to any special diet impact of two or more external influences.

Nodes are johnson syndrome and links between two topics men s health you to keep to any special diet degree of similarity associated with their word distributions.

The observed clusters of company names nature based solutions words representing the topic distributions confirm that our method successfully extracted the correct information. The links between two topics quantifying the degree of similarity associated with their word distributions, as explained in the text. The six red arrows depict the zones that are magnified in Fig. Each node is accompanied by you to keep to any special diet name of the company and its top three most frequent words, as quantified by the topic distribution.

Panel (a) shows the network associated with retail sales of clothing companies; panel (b) that associated with drug and patents; panel (c) that associated with products in telecommunication business; panel (d) that associated with tobacco law suit; panel (e) that associated with national defense budget; panel (f) that associated with the potential Comcast Disney merger in 2004.



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