Project description:Many different time-series methods have been widely used in forecast stock prices for earning a profit. However, there are still some problems in the previous time series models. To overcome the problems, this paper proposes a hybrid time-series model based on a feature selection method for forecasting the leading industry stock prices. In the proposed model, stepwise regression is first adopted, and multivariate adaptive regression splines and kernel ridge regression are then used to select the key features. Second, this study constructs the forecasting model by a genetic algorithm to optimize the parameters of support vector regression. To evaluate the forecasting performance of the proposed models, this study collects five leading enterprise datasets in different industries from 2003 to 2012. The collected stock prices are employed to verify the proposed model under accuracy. The results show that proposed model is better accuracy than the other listed models, and provide persuasive investment guidance to investors.
Project description:We investigate the impact of information on biopharmaceutical stock prices via an event study encompassing 503,107 news releases from 1,012 companies. We distinguish between pharmaceutical and biotechnology companies, and apply three asset pricing models to estimate their abnormal returns. Acquisition-related news yields the highest positive return, while drug-development setbacks trigger significant negative returns. We also find that biotechnology companies have larger means and standard deviations of abnormal returns, while the abnormal returns of pharmaceutical companies are influenced by more general financial news. To better understand the empirical properties of price movement dynamics, we regress abnormal returns on market capitalization and a sub-industry indicator variable to distinguish biotechnology and pharmaceutical companies, and find that biopharma companies with larger capitalization generally experience lower magnitude of abnormal returns in response to events. Using longer event windows, we show that news related to acquisitions and clinical trials are the sources of potential news leakage. We expect this study to provide valuable insights into how diverse news types affect market perceptions and stock valuations, particularly in the volatile and information-sensitive biopharmaceutical sector, thus aiding stakeholders in making informed investment and strategic decisions.
Project description:The stock market is very complex and volatile. It is impacted by positive and negative sentiments which are based on media releases. The scope of the stock price analysis relies upon ability to recognise the stock movements. It is based on technical fundamentals and understanding the hidden trends which the market follows. Stock price prediction has consistently been an extremely dynamic field of exploration and research work. However, arriving at the ideal degree of precision is still an enticing challenge. In this paper, we are proposing a combined effort of using efficient machine learning techniques coupled with a deep learning technique-Long Short Term Memory (LSTM)-to use them to predict the stock prices with a high level of accuracy. Sentiments derived by users from news headlines have a tremendous effect on the buying and selling patterns of the traders as they easily get influenced by what they read. Hence, fusing one more dimension of sentiments along with technical analysis should improve the prediction accuracy. LSTM networks have proved to be a very useful tool to learn and predict temporal data having long term dependencies. In our work, the LSTM model uses historical stock data along with sentiments from news items to create a better predictive model.
Project description:Behavioral finance studies reveal that investor sentiment affects investment decisions and may therefore affect stock pricing. This paper examines whether the geographic proximity of information disseminated by the 2014–2016 Ebola outbreak events combined with intense media coverage affected stock prices in the U.S. We find that the Ebola outbreak event effect is the strongest for the stocks of companies with exposure of their operations to the West African countries (WAC) and the U.S. and for the events located in the WAC and the U.S. This result suggests that the information about Ebola outbreak events is more relevant for companies that are geographically closer to both the birthplace of the Ebola outbreak events and the financial markets. The results also show that the effect is more pronounced for small and more volatile stocks, stocks of specific industry, and for the stocks exposed to the intense media coverage. The event effect is also followed by the elevated perceived risk; that is, the implied volatility increases after the Ebola outbreak events.
Project description:Transfer Entropy was applied to analyze the correlations and flow of information between 200,500 tweets and 23 of the largest capitalized companies during 6 years along the period 2013-2018. The set of tweets were obtained applying a text mining algorithm and classified according to daily date and company mentioned. We proposed the construction of a Sentiment Index applying a Natural Processing Language algorithm and structuring the sentiment polarity for each data set. Bootstrapped Simulations of Transfer Entropy were performed between stock prices and Sentiment Indexes. The results of the Transfer Entropy simulations show a clear information flux between general public opinion and companies' stock prices. There is a considerable amount of information flowing from general opinion to stock prices, even between different Sentiment Indexes. Our results suggest a deep relationship between general public opinion and stock prices. This is important for trading strategies and the information release policies for each company.
Project description:We examined whether press reports on the collective mood of investors can predict changes in stock prices. We collected data on the use of emotion words in newspaper reports on traders' affect, coded these emotion words according to their location on an affective circumplex in terms of pleasantness and activation level, and created indices of collective mood for each trading day. Then, by using time series analyses, we examined whether these mood indices, depicting investors' emotion on a given trading day, could predict the next day's opening price of the stock market. The strongest findings showed that activated pleasant mood predicted increases in NASDAQ prices, while activated unpleasant mood predicted decreases in NASDAQ prices. We conclude that both valence and activation levels of collective mood are important in predicting trend continuation in stock prices.
Project description:The continuous rise of the world's population has made food security a major point of the global agenda, with fisheries providing a key source of nutrition, especially in developing countries. Ensuring their health is key to maintain the availability of the resource, but its effect over accessibility is yet unclear. In this paper, we discuss the relevance of stock health for ensuring the price accessibility of the resource. A Least Square Dummy Variable panel model is proposed for bluefin tuna prices, with a biological explanatory component, and dummy variables reflecting changes in fishing trends. Both have proven to be significant to explain annual price variations, with improvements in stock health achieving price reductions.
Project description:This data presented in this article is specifically employed from the Asian region based on the top position in the list of oil exporting and oil-importing countries around the world. Asia as the biggest continent on the earth had high consumption of energy [1]. Here we employed the daily prices of crude oil and seven oil trading countries, out of which three are oil exporting (Saudi Arabia, United Arab Emirates, Iraq) and four are oil-importing countries (China, Japan, South Korea, India), from the time period of 1-09-2009 to 31-08-2018. The data is collected from an authentic database Bloomberg. This data is related to the research paper "Volatility spillover impact of world oil prices on leading Asian energy exporting and importing economies' stock returns. Energy, 188 (2019), 116002, https://doi.org/10.1016/j.energy.2019.116002 [2]". This data is useful to compare the oil prices impact on the leading oil trading countries and also compare a set of countries affected most by oil prices' fluctuations, oil-exporting countries or oil-importing countries. Since this data covers the period of latest oil-crisis, so the impact of oil-crisis could also be analysed.
Project description:This paper examines whether American banks' exposure to the oil industry could lead to instability in both oil and financial markets. To address this issue, we investigate volatility spillovers between oil prices and the stock prices of the four major American banks involved in the oil industry by employing the vector autoregressive fractionally integrated moving average framework. We use high-frequency data from January 3, 2006, to June 30, 2016. Our results support the existence of such volatility spillovers, as evidenced by the significant volatility responses of oil price (banks' stock price) to a shock in banks' stock price (oil price). These responses, more pronounced following the banks' exposure to the shale industry, mainly reflect the financial fragility of shale companies and their high indebtedness levels. Thus, this paper emphasises how the shale oil industry could trigger turmoil in both oil and financial markets.