# X Zhou – D Boyer Match Prediction | 03-10-2019 01:00

In the MACD-HVIX histogram, the solid line represents the DIF-HVIX, the dotted line represents the DEA-HVIX, and the histogram represents the MACD-HVIX bar. According to the strategy described in Section 3, we buy the stock when the DIF-HVIX and DEA-HVIX are positive, the DIF-HVIX cuts the DEA-HVIX in an uptrend, and the divergence is positive, and we sell the stock when the DEA-HVIX cuts the DIF-HVIX in a downtrend, and the divergence is negative. Figure 4 shows the candlestick chart and MACD histogram of HVIX. As shown in Figure 4, we sell the stock on days 118 and 187 and buy the stock on days 222, 231, 241, 243, 292, 415, and 447. In the candlestick chart, the blue line represents the 12-d EMA-HVIX, and the red line represents the 26-d EMA-HVIX. The buy-and-sell signals in the candlestick chart and the MACD histogram are shown in Figure 5.

We sell the stock when the DEA cuts the DIF in a downtrend, and the divergence is negative. The buy-and-sell signals in the candlestick chart and the MACD histogram are shown in Figure 3. According to the strategy described in Section 3, we buy the stock when the DIF and DEA are positive, the DIF cuts the DEA in an uptrend, and the divergence is positive. As shown in Figure 2, we sell the stock on days 155 and 355 and buy the stock on days 212, 290, 310, 381, and 393. In the MACD histogram, the solid line represents the DIF, the dotted line represents the DEA, and the histogram represents the MACD bar.

Although the MACD oscillator is one of the most popular technical indicators, it is a lagging indicator. In Section 3, we propose an improved model called MACD-HVIX to deal with the lag factor. We also compare the prediction accuracy and cumulative return of the MACD-HVIX histogram with those of the MACD histogram. In Section 4, data for empirical research are described. Therefore, the trading strategy based on the MACD-HVIX index is useful for trading. We will introduce the concept of moving average convergence divergence (MACD) and help the readers understand its principle and application in Section 2. Finally, in Section 5, we develop a trading strategy using MACD-HVIX and employ actual market data to verify its validity and reliability. The performance of MACD-HVIX exceeds that of MACD. Section 6 presents our conclusion.

In da Silveiraet al.,40 we introduced the Cutoff Scanningalgorithm to extract distance patterns from protein structure graphsand summarized them into a signature vector. An alternative way of extracting relevant patterns from moleculargraphs is using the concept of structural signatures.

## Predicting Stock Price Trend Using MACD Optimized by Historical Volatility

Manyin silico approaches for predicting pharmacokinetic and toxicityproperties of compounds from their chemical structure have been developed,13 ranging from data-based approaches such as quantitativestructureactivity relationship (QSAR),14,15 similarity searches,16,17 and 3-dimensional QSAR,18 to structure-based methods such as ligandproteindocking19 and pharmacophore modeling.20 Many of these are unfortunately not freely available,which limits their utility for the scientific community.

In cases where previous methods exhibit a better correlation coefficientthan pkCSM, we observed that, after removing the outliers, pkCSM presenteda comparable performance and/or a lower standard error, such as thecase for the bloodbrain barrier permeability data set (BBB). Compounds were ranked basedon the absolute prediction error, andthe worst 10% were considered outliers for regression analysis purposes. It is interesting to note the increase in performance when 10% ofthe outliers are removed. For instance, pkCSM is able to achieve acorrelation of R2 = 0.779 in 90% of thedata for rat toxicity and R2 = 0.828 forCaco2 permeability, a significant improvement in comparison with thecorrelations for the whole data sets (R2 = 0.663 and R2 = 0.733, respectively). No distinguishable trends were identified in the analysis of physicochemicalproperties of outlier compounds in comparison with the remaining dataset.

Typically, the traditional EMA is calculated using a fixed weight; however, in this study, we use a changing weight based on the historical volatility. As one of these technical indicators, moving average convergence divergence (MACD) is widely applied by many investors. We test the stability of MACD-HVIX and compare it with that of MACD. The purpose of this study is to develop an effective method for predicting the stock price trend. Traders find the analysis of 12- and 26-day EMA very useful and insightful for determining buy-and-sell points. When we use the buy-and-hold strategy for 5 and 10 days, the prediction accuracy of MACD-HVIX is 33.33% and 12% higher than that of the traditional MACD strategy, respectively. Furthermore, the validity of the MACD-HVIX index is tested by using the trend recognition accuracy. We compare the accuracy between a MACD histogram and a MACD-HVIX histogram and find that the accuracy of using MACD-HVIX histogram is 55.55% higher than that of the MACD histogram when we use the buy-and-sell strategy. MACD is a momentum indicator derived from the exponential moving average (EMA) or exponentially weighted moving average (EWMA), which reacts more significantly to recent price changes than the simple moving average (SMA). We denote the historical volatility index as HVIX and the new MACD as MACD-HVIX. With the rapid development of the financial market, many professional traders use technical indicators to analyze the stock market. Therefore, the improved stock price forecasting model can predict the trend of stock prices and help investors augment their return in the stock market. We found that the new indicator is more stable.

While optimal binding properties of a new drug to thetherapeutic target are crucial, ensuring that it can reach the targetsite in sufficient concentrations to produce the physiological effectsafely is essential for the introduction into the clinic. The interactionbetween pharmacokinetics, toxicity, and potencyis crucial for effective drugs. The pharmacokinetic profile of a compounddefines its absorption, distribution, metabolism, and excretion (ADME)properties.

It is a common indicator in stock analysis. The weight number is a fixed value equal to . The standard MACD is the 12-day EMA subtracted by the 26-day EMA, which is also called the DIF. Similar to the MACD, the MACD histogram is an oscillator that fluctuates above and below the zero line. The MACD histogram, which was developed by T. The analysis process of the cross and deviation strategy of DIF and DEA includes the following three steps. The number of the MACD histogram is usually called the MACD bar or OSC. Aspray in 1986, measures the signed distance between the MACD and its signal line calculated using the 9-day EMA of the MACD, which is called the DEA. The construction formula is as follows: where , , and . MACD evolved from the exponential moving average (EMA), which was proposed by Gerald Appel in the 1970s.

## Using Bookmaker Odds to Predict the Final Result of Football Matches

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and D.B.A.];Conselho Nacional de Desenvolvimento Cientfico e Tecnolgico(CNPq), and Centro de Pesquisas Ren Rachou (CPqRR/FIOCRUZMinas), Brazil [to D.E.V.P.]; NHMRC CJ Martin Fellowship [APP1072476to D.B.A.]; University of Cambridge and The Wellcome Trust for facilitiesand support [to T.L.B.]. Newton Fund RCUK-CONFAPgrant awarded by The Medical ResearchCouncil (MRC) and Fundao de Amparo Pesquisado Estado de Minas Gerais (FAPEMIG) [to D.E.V.P., T.L.B,. Funding for open access charge: The WellcomeTrust.

We consider all the edges to have unitaryweight. The second major component are distance-based patterns,representedas a cumulative distribution function, encoded in a small-moleculegraph-based signature, which was adapted from the Cutoff Scanningalgorithm.41,42 This way, each dimension of thesignature denotes the number of atoms (categorized by pharmacophoretype) within a certain distance in the molecular graph. The distancebetween any two nodes of the graph is given by the cost of their shortestpath, calculated by Johnsons algorithm.48 The cost of a shortest path is the sum of the weights ofthe edges on this path. Thus, the cost of the shortest path is the number of edgesin it.

pkCSM outperformed well established tools. The performance for the classification models can be found in Table 2. Table 1 shows the comparative predictionperformance for the regression models. Forexample, pkCSM AMES test achieved an accuracy of 83.8% compared toToxTree49 (which achieved an accuracy of75.8%). Further information on thedata sets used, number of data points, reference, and their validationprocedure (i.e., cross-validation, external test set) can also befound in Supporting Information (Table S2).