scholarly journals Application of the committee machine method to analysis of stock market technical indicators

2019 ◽  
Vol 13 (4) ◽  
pp. 73-86
Author(s):  
Nikolay Chernavin
Keyword(s):  
Stock Market ◽  
Machine Method ◽  
Committee Machine ◽  
Technical Indicators

scholarly journals Application of the committee machine method to forecast the movement of exchange rates and oil prices

2017 ◽  
Vol 23 (46) ◽  
pp. 2746-2761
Author(s):  
V.V. Akberdina ◽  
◽  
N.P. Chernavin ◽  
F.P. Chernavin ◽  
◽  
...  
Keyword(s):  
Exchange Rates ◽  
Oil Prices ◽  
Machine Method ◽  
Committee Machine

scholarly journals Predicting Stock Price Trend Using MACD Optimized by Historical Volatility

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jian Wang ◽  
Junseok Kim
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Moving Average ◽  
Rapid Development ◽  
Price Trend ◽  
Technical Indicators ◽  
Volatility Index ◽  
Fixed Weight ◽  
The Stability

With the rapid development of the financial market, many professional traders use technical indicators to analyze the stock market. As one of these technical indicators, moving average convergence divergence (MACD) is widely applied by many investors. 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). Traders find the analysis of 12- and 26-day EMA very useful and insightful for determining buy-and-sell points. The purpose of this study is to develop an effective method for predicting the stock price trend. Typically, the traditional EMA is calculated using a fixed weight; however, in this study, we use a changing weight based on the historical volatility. We denote the historical volatility index as HVIX and the new MACD as MACD-HVIX. We test the stability of MACD-HVIX and compare it with that of MACD. 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. 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. We found that the new indicator is more stable. Therefore, the improved stock price forecasting model can predict the trend of stock prices and help investors augment their return in the stock market.

The Association Analysis of Technical Indicators of Shanghai Stock Market

2014 ◽  
Vol 651-653 ◽  
pp. 1651-1654
Author(s):  
Rui Zhong Wang
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Stock Market ◽  
Success Rate ◽  
Association Analysis ◽  
Association Rules ◽  
Technical Indicators ◽  
Use Of Data ◽  
Stock Market Investors ◽  
The Relationship

This paper selected as part of a number of technical indicators, the main use of data mining software for different technical indicators signal given trading technical analysis of association rules. By studying the resulting characteristics of the relationship between the rules and give the stock market investors a certain decision support, to enable investors to operate with a higher success rate.

scholarly journals A RNN-LSTM based Predictive Modelling Framework for Stock Market Prediction using Technical Indicators

2021 ◽  
Vol 7 (1) ◽  
pp. 0-0
Keyword(s):  
Stock Market ◽  
Short Term Memory ◽  
Predictive Modelling ◽  
Future Price ◽  
Data Set ◽  
Technical Indicators ◽  
Modelling Framework ◽  
Successful Prediction ◽  
Input Dataset ◽  
Two Phases

The successful prediction of the stocks’ future price would produce substantial profit to the investor. In this paper, we propose a framework with the help of various technical indicators of the stock market to predict the future prices of the stock using Recurrent Neural Network based Long Short-Term Memory (LSTM) algorithm. The historical transactional data set is amalgamated with the technical indicators to create a more effective input dataset. The historical data is taken from 2010-2019 ten years in total. The dataset is divided into 80% training set and 20% test set. The experiment is carried out in two phases first without the technical indicators and after adding technical indicators. In the experimental setup, it has been observed the LSTM with technical indicators have significantly reduced the error value by 2.42% and improved the overall performance of the system as compared to other machine learning frameworks that are not accounting the effect of technical indicators.

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