This study aims to predict the direction of US stock prices by integrating time-varying effective transfer entropy (ETE) and various machine learning algorithms. At first, we explore that the ETE based on 3 and 6 months moving windows can be regarded as the market explanatory variable by analyzing the association between the financial crises and Granger-causal relationships among the stocks. Then, we discover that the prediction performance on the stock price direction can be improved when the ETE driven variable is integrated as a new feature in the logistic regression, multilayer perceptron, random forest, XGBoost, and long short-term memory network. Meanwhile, we suggest utilizing the adjusted accuracy derived from the risk-adjusted return in finance as a prediction performance measure. Lastly, we confirm that the multilayer perceptron and long short-term memory network are more suitable for stock price prediction. This study is the first attempt to predict the stock price direction using ETE, which can be conveniently applied to the practical field. INDEX TERMS Econophysics, Effective transfer entropy, Feature engineering, Information entropy, Machine learning, Prediction algorithms, Stock markets, Time series analysis 19 characteristics of the system using random matrix theory 20 and network analysis [7]-[9]. Since then, the studies have 21 discovered that a linear model such as the Pearson correlation 22 is not sufficient enough to quantify the relationships among 23 the stocks. More importantly, the causal relationship is not 24 directly linked to the presence of correlation. In this context, 25 the Granger-causality [10] has been introduced to define 26 the causal relationship between time series. However, its 27 function is limited to express the existence of information 28 flow based on a linear relationship rather than measuring the 29 amount of information flow. 30 To overcome such limitations of a simple linear model of a 31 Granger-causal relationship, the concept of transfer entropy 32 (TE), proposed by Schreiber [11], has been suggested instead 33 to measure the amount of information flow. TE is a non-34 parametric measure of the amount of information transfer 35 from a variable to a variable based on the Shannon entropy 36

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BackgroundPostoperative nausea and vomiting (PONV) are common complications after anesthesia and surgery. This study was designed to compare the effects of palonosetron and ondansetron in preventing PONV in high-risk patients receiving intravenous opioid-based patient-controlled analgesia (IV-PCA) after gynecological laparoscopic surgery.MethodsOne hundred non-smoking female patients scheduled for gynecological laparoscopic surgery were randomly assigned into the palonosetron group (n = 50) or the ondansetron group (n = 50). Palonosetron 0.075 mg was injected as a bolus in the palonosetron group. Ondansetron 8 mg was injected as a bolus and 16 mg was added to the IV-PCA in the ondansetron group. The incidences of nausea, vomiting and side effects was recorded at 2 h, 24 h, 48 h and 72 h, postoperatively.ResultsThere were no significant differences between the groups in the incidence of PONV during 72 h after operation. However, the incidence of vomiting was lower in the palonosetron group than in the ondansetron group (18% vs. 4%, P = 0.025). No differences were observed in use of antiemetics and the side effects between the groups.ConclusionsThe effects of palonosetron and ondansetron in preventing PONV were similar in high-risk patients undergoing gynecological laparoscopic surgery and receiving opioid-based IV-PCA.