Stock News Research Articles

News Sentiment and the Risk of a Stock Price Crash Risk: Based on Financial Dictionary Combined BERT‐DCA

This study combines a financial knowledge dictionary and pretraining method based on BERT (Bidirectional Encoder Representation from Transformers) to construct a deep learning model for identifying stock news sentiments. The study then calculates the sentiment metrics of all stocks and analyzes the impact of news sentiment on the risk of a stock price crash and its heterogeneity. The results show that stocks with more positive sentiment metrics have a higher risk of crash in the following year. We also investigate the information intermediation and investor sentiment channels by which news sentiment affects the risk of a crash. The results show that more net insider sales, lower information transparency, and less analyst coverage amplify the impact of news sentiment on future crash risk, which is consistent with the information intermediation channel. Additionally, more retail investor positions, more active investor sentiment, and divergence between analysts’ opinions and news amplify the impact of news sentiment on the risk of a future stock price crash, which is consistent with the investor sentiment channel.

Stock Price Prediction by Using BLSTM (Bidirectional Long Short Term Memory)

Currently, many researchers are working on stock price prediction system by using deep learning algorithms. Stock market is completely random, and there is no pattern. Even though, a pattern in stock market could be found, it will not be last for a long time because the stock market will adopt a new situation and the strategy is no longer available on already changed stock market. There are many auto trading programs such as a trading bot on stock market. However, they are literally trade stocks based on human’s direction or rules. It will not affect any changes, and it keeps working as what rules are set up from the initial status on the stock market. Stock price depends on volume of total sales, stock news, revenue, total asset, big buyer’s position and so on. There are many aspects for affecting stock price, and it changes all the time. Therefore, it keeps monitoring stock market and makes a decision whether buy or sell at the right time for earning profits. This research uses Bidirectional Long Short-Term Memory (BLSTM) to predict stock price in the near future. BLSTM is more accurate than LSTM which is one directional. In addition, stock market is like a living creature. Data to manipulate stock price must be inputted and analyzed consistently. Therefore, stock price can be predicted by consistent monitoring with BLSTM.

Research on the Influence of Heterogeneous Beliefs and Media Reports on Investor Behavior from the Perspective of Informationization

Based on the background of informatization, the development and progress of science and technology has brought an immeasurable impact on the securities market. Therefore, this article uses python technology to crawl the number of individual stock news and study the influence of media attention on investor behavior under the constraint of heterogeneous beliefs. It is found that there is a significant positive correlation between the degree of media attention and the degree of investors’ heterogeneous beliefs, and investors are more likely to choose stocks that are frequently reported by the media; further research has found that media reports intensify the degree of investor’s heterogeneous beliefs, leading to stock transactions. The amount has risen abnormally. Therefore, while enjoying informatization, it is necessary to guard against the risks it bring.

A study on stock price prediction system based on text mining method using LSTM and stock market news

The stock price reflects people's psychology, and factors affecting the entire stock market include economic growth rate, economic rate, interest rate, trade balance, exchange rate, and currency. The domestic stock market is heavily influenced by the stock index of the United States and neighboring countries on the previous day, and the representative stock indexes are the Dow index, NASDAQ, and S & P500. Recently, research on stock price analysis using stock news has been actively conducted, and research is underway to predict the future based on past time series data through artificial intelligence-based analysis. However, even if the stock market is hit for a short period of time by the forecasting system, the market will no longer move according to the short-term strategy, and it will have to change anew. Therefore, this model monitored Samsung Electronics' stock data and news information through text mining, and presented a predictable model by showing the analyzed results.

A Cluster based Non-Linear Regression Framework for Periodic Multi-Stock Trend Prediction on Real Time Stock Market Data

Trend prediction is and has been one of the very important tasks in the stock market since day one. For a sophisticated trend prediction using real time stock market data, stock sentiment news and technical analysis plays a vital role. While predicting the trend in the conventional way, technical indicators are delayed due to temporal data and less historic data. All the conventional stock trend predicting methods sustained without sentiment scores, technical scores and time periods for trend prediction. Considering the fact that all the previous conventional methods of stock trend predictions are bound to take single stock for trend prediction due to high computational memory and time, this prototype of highly functioning algorithms focus on trend prediction with multi stock data breaking all the conventional rules. This multi stock trend prediction model commissions and implements the effectively programmed algorithms on real time stock market data set. In this multi-stock trend prediction model, a new stock technical indicator and new stock sentiment score are proposed in order to improve the stock feature selection for trend prediction. In order to find the best real time feature selection model, a technical feature selection measure and stock news sentiment score are developed and incorporated. We used integrated stock market data to make a hybrid clustered model to find the relational multi stocks. Giving a final verdict, this is a cluster based nonlinear regression multi stock framework in order to predict the time-based trend prediction. The multi stock trend regression accuracy is bettered by 12% and recall by 11% while we cross check the experimental outcomes, henceforth making this model more accurate and precision furnished.

NgramPOS: a bigram-based linguistic and statistical feature process model for unstructured text classification

Research in financial domain has shown that sentiment aspects of stock news have a profound impact on volume trades, volatility, stock prices and firm earnings. In-depth analysis of stock news is now sourced from financial reviews by various social networking and marketing sites to help improve decision making. Nonetheless, such reviews are in the form of unstructured text, which requires natural language processing (NLP) in order to extract the sentiments. Accordingly, in this study we investigate the use of NLP tasks in effort to improve the performance of sentiment classification in evaluating the information content of financial news as an instrument in investment decision support system. At present, feature extraction approach is mainly based on the occurrence frequency of words. Therefore low-frequency linguistic features that could be critical in sentiment classification are typically ignored. In this research, we attempt to improve current sentiment analysis approaches for financial news classification by focusing on low-frequency but informative linguistic expressions. Our proposed combination of low and high-frequency linguistic expressions contributes a novel set of features for sentiment classification. The experimental results show that an optimal Ngram feature selection (combination of optimal unigram and bigram features) enhances sentiment classification accuracy as compared to other types of feature sets.

'Deja Vol' Revisited: Survey Forecasts of Macroeconomic Variables Predict Volatility in the Cross-Section of Industry Portfolios

We investigate the question whether macroeconomic variables contain information about future stock volatility beyond that contained in past volatility. We show that forecasts of GDP and industrial production growth from the Federal Reserve's Survey of Professional Forecasters predict volatility in a cross-section of 49 industry portfolios. The expectation of higher growth rates is associated with lower stock volatility. Inflation forecasts predict higher or lower stock volatility depending on the state of the economy and the stance of monetary policy. Forecasts of higher unemployment rates are good news for stocks during expansions and go along with lower stock volatility. Our results hold in- as well as out-of-sample and pass various robustness checks.

Stock Price Jumps and News Sentiment: A Case of Investor Overreaction

The research paper develops an understanding on how news based sentiment capture investor behaviour reflected in price jumps in stock markets. It compares the impact on two models of stock price jumps; the non-parametric model proposed by BNS and the wavelet based method. The study is also a perspective on the semi strong form of market efficiencyUsing the high frequency data from the stock and options market along with the actual high frequency news data from Bloomberg, the two alternative methodologies of jumps have been tested. In addition, options trades have been simulated to see whether profits can be earned from the news sentiment captured by jumps.Methodologically, jumps based on wavelets were found to be better related with the news sentiment compared to the BNS method. Also, the news sentiment based jumps were found to present opportunities in the simulated trades that could be exploited for earning profits suggesting that investors overreact.The paper uses an innovative method for computation of the news based sentiment. To the best of our knowledge, the paper is the first to evaluate jumps and news sentiment using the actual news data. A perspective on the semi strong form of market efficiency is presented, that too by departing from the event study based models.

Textual Sentiment and Sector Specific Reaction

News move markets and contains incremental information about stock reactions. Future trading volumes, volatility and returns are a ected by sentiments of texts and opinions expressed in articles. Earlier work of sentiment distillation of stock news suggests that risk prole reactions might differ across sectors. Conventional asset pricing theory recognizes the role of a sector and its risk uniqueness that differs from market or rm specic risk. Our research assesses whether incorporating the sentiment distilled from sector specic news carries information about risk proles. Textual analytics applied to about 600K articles leads us with lexical projection and machine learning to classication of sentiment polarities. The texts are scraped from offcial NASDAQ web pages and with Natural Language Processing (NLP) techniques, such as tokenization, lemmatization, a sector specic sentiment is extracted using a lexical approach and a nancial phrase bank. Predicted sentence-level polarities are aggregated into a bullishness measure on a daily basis and fed into a panel regression analysis with sector indicators. Supervised learning with hinge or logistic loss and regularization yields good prediction results of polarity. Compared with standard lexical projections, the supervised learning approach yields superior predictions of sentiment, leading to highly sector specic sentiment reactions. The Consumer Staples, Health Care and Materials sectors show strong risk prole reactions to negative polarity.

Ranking of Evaluation Targets Based on Complex Sequential Data

This paper proposes a method that arranges evaluation targets based on their complex sequential data. The data is composed of numerical one and text one. This method focuses on both change ratios of the numerical sequential data and the occupation ratios of the text sequential data. It generates a ranking model of the evaluation targets. The model can extract important evaluation targets. This paper applies the method to the data composed of stock price information and news articles. The former one corresponds to the numerical sequential data and the latter one corresponds to the text sequential data. Lastly, this paper compares the method with a method based on random selection and shows the effect of the proposed method.

Monetary Policy and Stock Prices: Does the “Fed Put” Work When It Is Most Needed?

Most studies of the effect of monetary policy on asset prices use the event study methodology with daily data. The resulting estimates suffer from bias due to omitted variables and endogeneity of policy decisions. We provide evidence that this bias becomes so large during the 2007–2008 financial crisis that it reverses the sign of the estimated stock market response to monetary news, leading to an erroneous conclusion that interest rate cuts are bad news for stocks. We also examine the stock market reaction to monetary policy during the zero lower bound period. The results show a significant bias in daily event study estimates of the stock market response to news about the future path of monetary policy. © 2016 Wiley Periodicals, Inc. Jrl Fut Mark 36:1210–1230, 2016

Translating Conceptual Metaphors in Chinese Stock News Report: A Web-Based Study

Previous studies of stock news report mainly focused on the identification of conceptual metaphors in Chinese or English stock news report. Few of them further analyzed the translation strategies of conceptual metaphors between English and Chinese. Basing on the analysis of Chinese stock news report on FORBESCHINA.COM, this paper investigates conceptual metaphors of UP and DOWN, LOCOMOTION and WAR, and discusses their translation strategies: maintaining original conceptual metaphor, substituting original conceptual metaphor, and deleting the original conceptual metaphor with meaning-kept.

Algorithm for Text to Graph Conversion and Summarizing Using NLP: A New Approach for Business Solutions

Text can be analysed by splitting the text and extracting the keywords .These may be represented as summaries, tabular representation, graphical forms, and images. In order to provide a solution to large amount of information present in textual format led to a research of extracting the text and transforming the unstructured form to a structured format. The paper presents the importance of Natural Language Processing (NLP) and its two interesting applications in Python Language: 1. Automatic text summarization [Domain: Newspaper Articles] 2. Text to Graph Conversion [Domain: Stock news]. The main challenge in NLP is natural language understanding i.e. deriving meaning from human or natural language input which is done using regular expressions, artificial intelligence and database concepts. Automatic Summarization tool converts the newspaper articles into summary on the basis of frequency of words in the text. Text to Graph Converter takes in the input as stock article, tokenize them on various index (points and percent) and time and then tokens are mapped to graph. This paper proposes a business solution for users for effective time management.

OAR Algorithm Technology Based on Opinion Mining Utilizing Stock News Contents

현재 인터넷 온라인상의 비정형 텍스트 데이터의 증가로 인하여 국내외 기업들은 사용자의 인터넷 텍스트코멘트를 활용한 평판 분석을 진행하고 있으며, 오피니언마이닝 연구를 통해 기업의 운영 및 비즈니스 마케팅에 잠재적인 효과를 미치고 있다. 국외 감성분석의 경우 고유국가의 영문 언어 규칙으로 인해 적합한 맞춤형 분석이 가능한 반면 국내 감성분석의 경우에는 한국어 어휘 형태 문법에 적합한 감성분석 처리 기법의 부재로 텍스트 분석의 정확도가 미흡한 상황이다. 이에 따라 본 논문은 온라인 주식 뉴스 텍스트에 한국어 역접관계를 고려한 오피니언 반의법 규칙(OAR) 알고리즘을 제안하고자 한다. 또한 본 연구에서는 주식 관련 뉴스 데이터를 기반으로 오피니언 반의법 규칙 알고리즘을 적용하여 주식 데이터에 대한 새로운 감성사전을 도출하고, 코스피지수를 활용한 평판 분석을 통하여 감성사전에 대한 정확도를 검증한다.

An intelligent stock trading system using comprehensive features

The aim of this study is to predict automatic trading decisions in stock markets. Comprehensive features (CF) for predicting future trend are very difficult to generate in a complex environment, especially in stock markets. According to related work, the relevant stock information can help investors formulate objects that may result in better profits. With this in mind, we present a framework of an intelligent stock trading system using comprehensive features (ISTSCF) to predict future stock trading decisions. The ISTSCF consists of stock information extraction, prediction model learning and stock trading decision. We apply three different methods to generate comprehensive features, including sentiment analysis (SA) that provides sensitive market events from stock news articles for sentiment indices (SI), technical analysis (TA) that yields effective trading rules based on trading information on the stock exchange for technical indices (TI), as well as the trend-based segmentation method (TBSM) that raises trading decisions from stock price for trading signals (TS). Experiments on the Taiwan stock market show that the results of employing comprehensive features are significantly better than traditional methods using numeric features alone (without textual sentiment features).

Time-Varying Price Discovery in the 18th Century

This paper investigates how quickly news is reflected in prices and the information shares for two of the great moneyed companies, the Bank of England and the East India Company, during the eighteenth century. These two English companies were cross-listed on the Amsterdam stock exchange and news between the capitals flowed mainly via the use of boats that transported mail. We examine in detail the historical context surrounding the defining events of the period, and use these as a guide to how the data should be analysed. We show that both trading venues contributed to price discovery, but the London venue was significantly more important for both stocks, although its information share gradually declined over time.

Can the Fed Talk the Hind Legs Off the Stock Market?

This paper analyzes the impact of US central bank communication on individual stock returns. We find a strong conditional effect of communication on stocks. The response of equities to central bank talk depends critically on the business cycle. In bad times, monetary policy communication inducing an upward revision of the path of future policy is good news for stocks. During an expansion the effect is weaker and on average negative. The impact of central bank communication on stock prices displays similar cross-sectional variation as central bank actions. Cyclical industries are found to be more sensitive to central bank communication. In our sample of S&P 500 companies we find that the stock prices of firms with low cash ows, low returns to assets or equity, very high or low debt levels, small size or using more trade credit to be affected more by central bank communication. Our evidence suggests that central bank communication by the FOMC has an impact on stocks and provides additional evidence for the demand and the credit channel.

웹 뉴스의 양과 주가의 관계에 관한 연구

In the stock market, the investors rely on stock information to trade. Good information may stimulate buying, raising the stock prices and the bad information may result in selling, decreasing the stock prices. In terms of the relationship between information and stock prices, stock prices can be viewed as reaction of investors to all the information flowing into the market. The significant increase of web stock news volume is often associated with the significant changes of stock prices. When the web stock news volume for a firm increases significantly, the stock price movement is often oscillatory. This paper attempts to investigate the relationship between volumes of information from Korean web IT and stock prices in Korean stock market. This research shows that when the web stock news volume increases significantly, volatility, trading volumes and rate of returns are increase too. The results of the study provide us with the new clues to the microstructure of the stock market from the perspective of the web news.

Can the Fed Talk the Hind Legs Off the Stock Market?

This paper analyzes the impact of US central bank communication on individual stock returns. We find a strong conditional effect of communication on stocks. The response of equities to central bank talk depends critically on the business cycle. In bad times, monetary policy communication inducing an upward revision of the path of future policy is good news for stocks. During an expansion the effect is weaker and on average negative. The impact of central bank communication on stock prices displays similar cross-sectional variation as central bank actions. Cyclical industries are found to be more sensitive to central bank communication. In our sample of S&P 500 companies we find that the stock prices of firms with low cash ows, low returns to assets or equity, very high or low debt levels, small size or using more trade credit to be affected more by central bank communication. Our evidence suggests that central bank communication by the FOMC has an impact on stocks and provides additional evidence for the demand and the credit channel.

Financial Markets in Motion: Visualising stock price and news interactions during the 2008 global financial crisis

Abstract This paper introduces a visualisation technique to help analyse large sets of financial data. The dataset used consists of price and news headlines for the Dow Jones Industrial Average (DJIA) and related companies for several weeks prior to the height of the Global Financial Crisis (GFC). Our visualisation can help analysts to uncover relationships between news headlines that can convey meaning about trader sentiment, and their influence on stock prices. This information is displayed visually in an interactive and animated Microsoft Excel dashboard consisting of three charts. Improvements in scalability and performance over previous techniques enables concurrent analysis of stock prices and news headlines from large datasets. A video screen capture is available to showcase our system [1] .