business economics by sankaran pdf to word

( 2014) applied Naive Bayes to words extracted from analyst reports to analyze the market's reaction to analyst reports. Thus, Several studies applied text mining to analyst reports. Although analyst report contains useful information and unique information, it is difficult for investors, who manage many stocks, to read all analyst reports because many reports are issued during a period of the earnings announcements. It also includes analysts' forecasts of future earnings and stock price performance and objective information which analysts think is worth including. Analyst report includes facts, such as financial results, stock prices, and information announced by the company.

Analyst reports are written by analysts affiliated with securities companies that evaluate each stock, taking into account earnings guidance, valuation of stock price, press releases, macroeconomy trends, etc. In this situation, analyst reports have been attracting attention. It is not easy to gather useful information for investment decisions. However, IR information and news articles are limited to factual information, and social networking services (SNS) and message boards are not highly reliable. The trends of individual investors can be seen on social networking sites and message boards. A newspaper or a news site provides stock price charts and new information about companies. A company's website also provides investor relation (IR) information on its business performance and financial information. A search engine can provide a variety of information about a company. Investors need to search for market information such as stock prices and fundamental information such as companies' sales, earnings, and business conditions. However, classifying analyst reports into opinion and non-opinion sentences is insignificant for the forecasts. Consequently, we obtain an indication that the analyst profile effectively improves the model forecasts. As analyst profiles, we used the name of an analyst, the securities company to which the analyst belongs, the sector which the analyst covers, and the analyst ranking. In addition to analyst reports, we input analyst profiles to the networks. Then, we employ the proposed method to forecast the movements of analysts' estimated net income and stock price by inputting the opinion and non-opinion sentences into separate neural networks. First, we apply the proposed method to extract opinion sentences from the analyst report while classifying the remaining parts as non-opinion sentences.

Our methodology is based on applying natural language processing and neural networks in the context of analyst reports. This article proposes a methodology to forecast the movements of analysts' estimated net income and stock prices using analyst profiles.

Business economics by sankaran pdf to word