Machine learningData miningStock price predictionSchrodinger equationThe Black鈥揝choles Option pricing model (BSOPM) has long been in use for valuation of equity options to find the price of stocks. In this work, using BSOPM, we have come up with a comparative analytical approach and numerical...
All the experiments are carried out using 10 years of historical data of two stocks Reliance Industries and Infosys Ltd. and two indices S&P BSE Sensex and CNX Nifty. Both stocks and indices are highly voluminous and vehemently traded in and so they reflect Indian economy as a whole. The ...
Machine learning algorithmsMarket participants use a wide set of information before they decide to invest in risk assets, such as stocks. Investors often follow the news to collect the information that will help them decide which strategy to follow. In this study, we analyze how public news and...
Empirical study is conducted on the levelhigh‐frequency data of 1271 stocks in the Shenzhen Stock Exchange of China to validate our approach. The result provides initial evidence of the predictability of jump arrivals and jump directions using levelstock data as well as the effectiveness of using...
Figure 7b clearly demonstrates the efficiency of using GCN, which integrates interaction between events due to construction specificity. There is an especially tangible impact on the results for the Positive class. We provide a fuller comparison of different machine learning models, namely GCN, GB, ...
Three days before inoculation, blood samples were collected from one third of the flock in order to confirm seronegativity against IBDV using a viral neutralization assay, as previously described [22]. Viral inocula were prepared by diluting viral stocks in PBS supplemented with penicillin (200 ...
Second, the supplier reviews planned supply capacities and stocks and allocates them against the order request. Third, the supplier confirms the order if possible. After that, allocations from the second step will rerun daily to adapt to supply chain dynamics. A customer can adjust quantities or...
captures local patterns through CNN and effectively models long-term dependencies with GRU thus aiming to accurately classify either "buy" or "sell" positions of stocks. The model is assessed using classification and financial evaluation metrics involving accuracy, precision, recall, f1-score, annualize...
Most of the raw material supply for in-house production is decoupled via storage with suitable safety stocks. Further, in-house production is set-up to be flexible so that potential delays in raw material supply can be partially compensated. In contrast, most of the finished components are ...
Machine learning models are employed to predict the price direction of clean energy stocks because compared to standard regression models, machine learning models can achieve higher prediction accuracy when the relationships between the predictor and features are complex (Hastie et al., 2009, James et...