5/5/2023 0 Comments Stock indicators jstock![]() For example, the MACD (moving average convergence divergence) utilizes the difference between the long term and short-term moving average to represent the convergence and divergence of the moving average values. Most of the existing stock price prediction technical analyses have based the input features on the moving average (MA) stock price, which can effectively express the recent trends of price fluctuations. (2006) concluded that trading performance can be additionally improved by training and utilizing independent predictors for different stock price patterns. A number of studies building intelligent trading systems have also been conducted based on the results of these AI price forecasting techniques ( Lin, Yang & Song, 2011 O et al., 2004 Song, Lee & Lee, 2020). ![]() However, the field of AI (artificial intelligence) has recently reported price forecasting techniques with the application of various machine learning techniques that show a significant level of statistical confidence ( Hsu, 2011 Hadavandi, Shavandi & Ghanbari, 2010 Armano, Marchesi & Murru, 2005 Ding et al., 2015 Jiang, 2021). There is an ongoing debate about whether or not it is possible to predict stock prices and, if it is possible, how much these predictions can outperform the market. Predicting stock prices has long been of interest to many related fields including economics, mathematics, physics, and computer science. ![]() Also, the results of this study can help investors who fail to invest in stocks due to the information gap. The significance of this study is the development of a stock price prediction model that exceeds the market index to help overcome the continued freezing of interest rates in Korea. Using data from the KOSPI and KOSDAQ markets, It was found that that the proposed pattern-based trading system can achieve better trading performances than domestic and overseas stock indices. The training of the neural network was conducted with stock data filtered in three patterns and trading signals were generated using the prediction results of those neural networks. The implications of each pattern are briefly analyzed using chart examples. Three highly volatile price patterns containing at least a record of the price hitting the daily ceiling in the recent trading days are defined. This paper proposes a pattern-based stock trading system using ANN-based deep learning and utilizing the results to analyze and forecast highly volatile stock price patterns.
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