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It is widely acknowledged that stock price prediction is a job full of challenges due to the highly unpredictable existence of financial markets. Many market participants or analysts, however, attempt to predict stock prices using different mathematical, econometric, or even neural network models in order to make money or understand the nature of the equity market. In the past few years, a lot of models based on deep learning have been gaining popularity for predicting the volatility of the stock market prices. In this paper, the outcomes of many classical deep learning models such as LSTMs, GRUs, CNNs, and their several common variants are contrasted with two distinct stock price prediction targets: absolute stock price and volatility. The aim of the comparative study is to find out which model is the best fit for stock market prediction. We also attempt to research the relationship between news and stock trends, believing that news stories have an impact on the stock market by incorporating sentiment analysis into our model. Our methodology was to scrape news articles of a particular stock and use the corpus gathered to generate a sentiment score which is further used as an input to the model.
It is widely acknowledged that stock price prediction is a job full of challenges due to the highly unpredictable existence of financial markets. Many market participants or analysts, however, attempt to predict stock prices using different mathematical, econometric, or even neural network models in order to make money or understand the nature of the equity market. In the past few years, a lot of models based on deep learning have been gaining popularity for predicting the volatility of the stock market prices. In this paper, the outcomes of many classical deep learning models such as LSTMs, GRUs, CNNs, and their several common variants are contrasted with two distinct stock price prediction targets: absolute stock price and volatility. The aim of the comparative study is to find out which model is the best fit for stock market prediction. We also attempt to research the relationship between news and stock trends, believing that news stories have an impact on the stock market by incorporating sentiment analysis into our model. Our methodology was to scrape news articles of a particular stock and use the corpus gathered to generate a sentiment score which is further used as an input to the model.
In this paper, the informative value of firm capital structure is analyzed. In the first part, a theoretical background regarding capital structure theories is presented. In the second (empirical) part, the Ohlson (1995) valuation framework is used in order to analyze the informative value of firm capital structure on a sample of data for the Czech (non-financial) companies. A contextual approach is adopted and the value relevance of debt is analyzed considering the signalling and the optimal capital structure theories. According to the results and in accordance with the optimal capital structure theory, debt is more penalized in case of the companies that deviate from the target debt level. Moreover, debt proves to be a positive signal for the firms with a higher earnings growth potential. This, in turn, is consistent with the signalling theory.
Climate change has an effect human living in a variety of ways, such as health and food security. This study presents a prediction of crop yields and production risks during the years 2020–2029 in northern Thailand using the coupling of a 1 km resolution regional climate model, which is downscaled using a conservative remapping method, and the Decision Support System for the Transfer of Agrotechnology (DSSAT) modeling system. The accuracy of the climate and agricultural model was appropriate compared with the observations, with an Index of Agreement (IOA) in the range of 0.65–0.89. The results reveal the negative effects of climate change on rice and maize production in northern Thailand. We show that, in northern Thailand, rainfed rice and maize production may be reduced by 5% for rice and 4% for maize. Moreover, rice and maize production risk analysis showed that maize production is at a high risk of low production, while rice production is at a low risk. Additional irrigation, crop diversification, the selection of appropriate planting dates and methods of conservation are promising adaptation strategies in northern Thailand that may improve crop production.
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