ABSTRACT
In recent years, special attention has been paid to climate change assessment and research into our changing environment. The greatest economic losses worldwide are due to the negative effects of drought stress and extreme temperature on the plants' morphological, physiological, and biochemical properties which limit crop growth and productivity. Sweet basil (Ocimum basilicum L.) is one of the most popular plants widely grown around the world as a spice, as well a medicinal, aromatic plant. The seedlings with 5-6 true leaves were divided into two groups, and one group of seedlings was sprayed with 10 mM potassium bicarbonate (KHCO3). Three days after potassium bicarbonate treatment, half of the plants from each group were subjected to a water deficit (drought stress), and the other half were grown under stress-free conditions (well-watered). The present study aimed to evaluate the effect of potassium bicarbonate (KHCO3) on morphophysiological parameters, phenolics content and the antioxidant activity of basil under drought conditions. The application of potassium bicarbonate to drought stressed plants significantly increased the chlorophyll content, fresh and dry weight, phenolics content in the two of tested cultivars, and antioxidant activity, determined by DPPH and ABTS methods. Principal component analysis showed that the first factor was highly and positively related to all the investigated parameters. Hierarchical clustering analysis showed that the first cluster was formed by being well-watered, well-watered and sprayed with potassium bicarbonate, and grown under drought conditions and sprayed with potassium bicarbonate basil cultivars, while the second cluster was formed by all the tested cultivars grown under drought conditions.
ABSTRACT
Prediction and quantification of future volatility and returns play an important role in financial modeling, both in portfolio optimisation and risk management. Natural language processing today allows one to process news and social media comments to detect signals of investors' confidence. We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. We investigated the strength of the correlation between sentiment measures on a given day and market volatility and returns observed the next day. We found that there is evidence of correlation between sentiment and stock market movements. Moreover, the sentiment captured from news headlines could be used as a signal to predict market returns; we also found that the same does not apply for volatility. However, for the sentiment found in Twitter comments we obtained, in a surprising finding, a correlation coefficient of -0.7 (p < 0.05), which indicates a strong negative correlation between negative sentiment captured from the tweets on a given day and the volatility observed the next day. It is important to keep in mind that stock volatility rises greatly when the market collapses but not symmetrically so when it goes up (the so-called leverage effect). We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information by deploying topic modeling, based on Latent Dirichlet Allocation, in order to extract feature vectors from a collection of tweets and financial news. The obtained features were used as additional input to the classifier. Thanks to the combination of sentiment and topic modeling even on modest (essentially personal) architecture our classifier achieved a directional prediction accuracy for volatility of 63%.
