Bid-ask spread, along with profit, also encompass the impact of asymmetric information cost and order processing cost. Asymmetric information influences stock prices with varying degree of investors’ perception. Estimation of asymmetric information cost and its determinants have been explored significantly under low-frequency trading. The literature hardly attempts to study asymmetric information cost under high-frequency trading (HFT). Asymmetric information cost significantly influences bid-ask spread, and hence the nature of its impact under different market conditions needs to be analyzed under HFT. The study attempts to estimate asymmetric information cost in HFT and analyze its determinants under different industry sectors and market conditions. The study followed Affleck-Graves et al. (1994 , The Journal of Finance, 49(4), 1471–1488) model to estimate the asymmetric information cost using 5 minutes interval data for a period of 82 trading days. Information gets reflected in equity through the movement in price, variation in trading volume, and return volatility. The study has found share price, traded volume, return volatility and trading frequency as the major determinants of asymmetric information cost in different market conditions. The findings of the study have significant implications for market microstructure for trading, lowering information asymmetry in market and enhancing market quality.
In recent times, intelligent predictive systems are showing greater levels of accuracy and effectiveness in early detection of the critical diseases of cancer in the liver, lungs, etc. Predictive models assist medical practitioners to identify the diseases based on symptoms and health indicators like hormones, enzymes, age, blood counts, etc. This article focuses on proposing an optimal classification model to detect chronic liver disease by enhancing the prediction accuracy through cutting-edge analytics. The article proposes an enhanced framework on the original study by Ramana et al. It uses measures like precision and balanced accuracy to choose the most efficient classification algorithm in Indian and USA patient datasets using various factors like enzymes, age, etc. Using Youden's index, individual thresholds for each model were identified to increase the power of sensitivity and specificity, respectively. The study proposes a framework for highly accurate automated disease detection in the medical industry and helps in strategizing preventive measures for patients.
determinants of the bid-ask spread to understand sector-specific influences. It also explores the influence of up and down market, settlement cycles, opening and closing intervals, and price and market-cap on determinants. The findings have significance regarding influence on the market microstructure for trading, designing of trading liquidity, and reduction of transaction cost.
Intelligent predictive systems are showing a greater level of accuracy and effectiveness in early detection of critical diseases like cancer and liver and lung disease.Predictive models assist medical practitioners in identifying the diseases based on symptoms and health indicators like hormone,enzymes,age,bloodcounts,etc.This study proposes a framework to use classification models to accurately detect chronic liver disease by enhancing the prediction accuracy through cutting-edge analytics techniques.The article proposes an enhanced framework on the original study by Ramana et al. (2011).It uses evaluation measures like Precision and Balanced Accuracy to choose the most efficient classification algorithm in INDIA and USA patient datasets using various factors like enzymes,age,etc.Using Youden’s Index, individual thresholds for each model were identified to increase the power of sensitivity and specificity.A framework is proposed for highly accurate automated disease detection in the medical industry,and it helps in strategizing preventive measures for patients with liver diseases.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.