Techniques for Stock Market Prediction: A Review

Stock market forecasting has long been viewed as a vital real-life topic in economics world. There are many challenges in stock market prediction systems such as the Efficient Market Hypothesis (EMH), Nonlinearity, complex, diverse datasets, and parameter optimization. A stock's value on the stock market fluctuates due to many factors like previous trends of the stock, the current news, twitter feeds, any online customer feedbacks etc. In this paper, the literature is critically analysed on approaches used for stock market prediction in terms of stock datasets, features used, evaluation metrics used, statistical, machine learning and deep learning techniques along with the directions for the future. The focus of this review is on trend and value prediction for stocks. Overall, 68 research papers have been considered for review from years 1998-2023. From the review, Indian stock market datasets are found to be most frequently used datasets. Evaluation metrics used commonly are accuracy and Mean Absolute Percentage Error. ARIMA is reported as the most used frequently statistical technique for stick market prediction. Long-Short Term Memory and Support Vector Machine are the commonly used algorithms in stock market prediction. The advantages and disadvantages of frequently used evaluation metrics, machine learning, deep learning and statistical approaches are also included in this survey

Distance matrix of enhanced power graphs of finite groups

The enhanced power graph of a group $G$ is the graph $\mathcal{G}_E(G)$ with vertex set $G$ and edge set \{(u,v): u, v \in \langle w \rangle,~\mbox{for some}~ w \in G\}. In this paper, we compute the spectrum of the distance matrix of the enhanced power graph of non-abelian groups of order $pq$, dihedral groups, dicyclic groups, elementary abelian groups \El(p^n) and the non-cyclic abelian groups \El(p^n)\times \El(q^m) and \El(p^n)\times \mathbb{Z}_m, where $p$ and $q$ are distinct primes. For the non-cyclic abelian group \El(p^n)\times \El(q^m), we also compute the spectrum of the adjacency matrix of its enhanced power graph and the spectrum of the adjacency and the distance matrix of its power graph

Role of color doppler in the diagnosis of intra uterine growth restriction (IUGR)

Background: The purpose of our study was to evaluate the diagnostic efficacy of the pulsatility index (PI) and resistive index (RI) in uterine artery, umbilical artery and middle cerebral artery in the diagnosis of IUGR and prediction of adverse perinatal outcome.Methods: A total of 100 clinically suspected IUGR cases were enrolled in the study. A detailed history and examination was done, color doppler carried out serially every three weeks starting from 30 weeks till delivery, subsequently confirmation of fetal growth restriction (FGR) was done by assessing the newborn parameters for growth restriction.Results: Doppler measurement for uterine artery showed higher efficacy as compared to umbilical artery and middle cerebral artery findings. The uterine artery RI was found to be 84.6% sensitive and 82.9% specific even at 30 weeks. Uterine artery PI too showed a good diagnostic efficacy with an accuracy of 79%, a sensitivity of 76.9%, a specificity of 82.9%.Both PI and RI for uterine artery showed a relatively higher specificity.Conclusion: Here we concluded that once IUGR is suspected, Doppler velocimetry may be useful as a part of evaluation and uterine artery analysis identifies a subgroup with an increased risk for developing IUGR.