Correlation Between Turkish Stock Market and Economy

Journal of Computers, 2017

News items have significant impact on stock markets but the ways is obscure. Many previous works have aimed at finding accurate stock market forecasting models. In this paper, we use text mining and sentiment analysis on Chinese online financial news, to predict Chinese stock tendency and stock prices based on support vector machine (SVM). Firstly, we collect 2,302,692 news items, which date from 1/1/2008 to 1/1/2015. Secondly, based on this dataset, a specific domain stop-word dictionary and a precise sentiment dictionary are formed. Thirdly, we propose a forecasting model using SVM. On the algorithm of SVM implementation, we also propose two parameter optimization algorithms to search for best initial parameter setting. Result shows that parameter G has the main effect, while parameter C's effect is not obvious. Furthermore, support vector regression (SVR) models for different Chinese stocks are similar whereas in support vector classification (SVC) models best parameters are quite differential. Series of contrast experiments show that: a) News has significant influence on stock market; b) Expansion input vector for additional situations when that day has no news data is better than normal input in SVR, yet is worse in SVC; c) SVR shows a fantastic degree of fitting in predicting stock fluctuation while such result has some time lag; d) News effect time lag for stock market is less than two days; e) In SVC, historic stock data has a most efficient time lag which is about 10 days, whereas in SVR this effect is not obvious. In addition, based on the special structure of the input vector, we also design a method to calculate the financial source impact factor. Result suggests that the news quality and audience number both have significant effect on the source impact factor. Besides, for Chinese investors, traditional media has more influence than digital media.

Journal of Physics: Conference Series, 2018

Stock market means the aggregation of all sellers and buyers of stocks representing their ownership claims on the business. To be completely absolute about the investment on these stocks, proper knowledge about them as well as their pricing, for both present and future is very essential. Large amount of data is collected and parsed to obtain this essential information regarding the fluctuations in the stock market. This data can be any news or public opinions in general. Recently, many methods have been used, especially big unstructured data methods to predict the stock market values. We introduce another method of focusing on deriving the best statistical learning model for predicting the future values. The data set used is very large unstructured data collected from an online social platform, commonly known as Quindl. The data from this platform is then linked to a csv fie and cleaned to obtain the essential information for stock market prediction. The method consists of carrying out the NLP (Natural Language Processing) of the data and then making it easier for the system to understand, finds and identifies the correlation in between this data and the stock market fluctuations. The model is implemented using Python Programming Language throughout the entire project to obtain flexibility and convenience of the system.

Journal of Economics, Business and Management, 2016

This paper presents a new method to predicting the change of stock prices by utilizing text mining news of the stock market. Term Frequency Inverse Document Frequency (TF-IDF) is one of the most useful and widely used concepts in information retrieval. The method can handle without difficulty unstructured news of Saudi stock market (Tadawul) through reading and analysis of the news and build a relationship between the contents of the news, and keywords (core phrases). This technique must identified by analysts and financial specialists that affect the direction of the share price up or down. The aim of this paper is to explore the possibility of using text mining to automate the identification of financial news articles. The empirical results show that the proposed techniques can predict the up and down on a stock price after the news announce or released. The proposed method presented in the study is straightforward, simple and valuable for the short-term investors. Index Terms-Stock market prediction, term frequency inverse document frequency (TF-IDF), text mining.

Communications in Computer and Information Science, 2017

This study focuses on the use of machine learning algorithms to construct a model that can predict the movements of Bursa Malaysia stock prices. In this research, we concentrate on linguistics terms from financial news that can contribute movements of the prices. Our aim is to develop a prototype that can classify sentiments towards financial news for investment decision. We experimented with five blue-chip companies from different industries of the top market constituents in Bursa Malaysia KLCI. A total of 14,992 finance articles were crawled and used as the dataset. Support Vector Machine algorithm was employed and the accuracy recorded was at 56%. The findings of this research can be used to assist investors in investment decision making.

2011

This work is focused on the relationship between the news articles (breaking news) and stock prices. The student will design and develop methods to analyze how and when the news articles influence the stock market. News articles about Norwegian oil related companies and stock prices from "BW Offshore Limited" (BWO), "DNO International" (DNO), "Frontline" (FRO), "Petroleum Geo-Services" (PGS), "Seadrill" (SDRL), "Sevan Marine" (SEVAN), "Siem Offshore" (SIOFF), "Statoil" (STL) and "TGS-NOPEC Geophysical Company" (TGS) will be crawled, preprocessed and the important features in the text will be extracted to effectively represent the news in a form that allows the application of computational techniques. This data will then be used to train text sense classifiers. A prototype system that employs such classifiers will be developed to support the trader in taking sell/buy decisions. Methods will be developed for automaticall sense-labeling of news that are informed by the correlation between the changes in the stock prices and the breaking news. Performance of the prototype decision support system will be compared with a chosen baseline method for trade-related decision making.

international journal for research in applied science and engineering technology ijraset, 2020

The task point is to break down stock forecast based on news stories and this should be possible by nostalgic examination by having three parameters and this undertaking will give a brief of any news (feelings). The parameters on which our venture will produce results are Positive, Negative or Neutral. This will help the proficiency of any association in light of the fact that previous manual methodology was taken a stab at utilizing a human to peruse the articles and grouping the notion, yet this framework will make a programmed approach for advertise developments (forecast). In this undertaking, we will utilize two innovations for example AI and Natural Language Processing. Because of the unpredictability of the securities exchange, value variances dependent on opinion and news reports are normal. Brokers draw upon a wide assortment of freely accessible data to advise their market choices. For this task, we concentrated on the examination of freely accessible news reports with the utilization of PCs to give guidance to stock exchanging. We execute a model that predicts the following days' stock development by fusing pertinent monetary data, for example, late stock value development and income shock, and literary data from these money related reports. We exhibit that the model which incorporates printed data performs altogether better than a model with budgetary data alone. Index Terms: Stock news prediction, NLP modeling, Profitable Stock analysis, financial market, news articles, sentiment analysi

… and Engineering Systems, 2007

In this paper, we analyze about the relation between stock price returns and Headline News. Headline News is very important sources of information in asset management, and is sent in large quantities every day. We study the effect of more than 13,000 Headline News sent from JIJI PRESS. We classify Headline News using Text Categorization and analyze the reaction of a stock price return for every type of News. From our research, we figure out following issues; 1) we make the Text Categorization System that has about 80% of classification accuracy, 2) this system can extract effective information to stock price returns from Headline News.

International Journal of Knowledge Based Computer Systems, 2018

Short-term trading, specifically day-trading involves a high risk concerning monetary resources. Investing money becomes risky without adequate knowledge and understanding of factors governing the market, some of which include public sentiment, commodity prices, political stability, etc. News is a common way via which people get updates about the latest happenings around the world and hence form opinions about industries, companies, stocks, etc. This affects their trading decisions; substantially impacting their chances of making a profit. Our research focused on building software models that could analyze general news during trading hours and predict the probable stock index closing trend for the end of that day. We used top 25 articles from the Reddit World News Channel and tried to correlate their impact on the DJIA in this study. Using two approaches to the process the text and further deploying machine learning models, we achieved fairly acceptable prediction accuracies.

Managing Global Transitions, 2017

Stock market analysis is one of the biggest areas of interest for text mining. Many researchers proposed different approaches that use text information for predicting the movement of stock market indices. Many of these approaches focus either on maximising the predictive accuracy of the model or on devising alternative methods for model evaluation. In this paper, we propose a more descriptive approach focusing on the models themselves, trying to identify the individual words in the text that most affect the movement of stock market indices. We use data from two sources (for the past eight years): the daily data for the Dow Jones Industrial Average index ('open' and 'close' values for each trading day) and the headlines of the most voted 25 news on the Reddit WorldNews Channel for the previous 'trading days. ' By applying machine learning algorithms on these data and analysing individual words that appear in the final predictive models, we find that the words gay, propaganda and massacre are typically associated with a daily increase of the stock index, while the word iran mostly coincide with its decrease. While this work presents a first step towards qualitative analysis of stock market models, there is still plenty of room for improvements.

Using News Articles to Predict Stock Movements Based on Different Forecasting Techniques Statistical, Regression and Text Mining