K-Line Patterns’ Predictive Power Analysis Using the Methods of Similarity Match and Clustering

Lv, Tao; Hao, Yong; Shen, Chunfeng

doi:10.1155/2017/3096917

Cited by 6 publications

(3 citation statements)

References 16 publications

(30 reference statements)

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“…Candlestick charts are a form of technical analysis visualization that are created by plotting the opening, highest, lowest, and closing prices of each analysis period [15]. Figure 1 represents an example of a candlestick chart.…”

Section: Candlestick Patterns Analysismentioning

confidence: 99%

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Chen,

Hu,

Xue

2024

Electronics

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Accurately forecasting the trajectory of stock prices holds crucial significance for investors in mitigating investment risks and making informed decisions. Candlestick charts visually depict price information and the trends in stocks, harboring valuable insights for predicting stock price movements. Therefore, the challenge lies in efficiently harnessing candlestick patterns to forecast stock prices. Furthermore, the selection of hyperparameters in network models has a profound impact on the forecasting outcomes. Building upon this foundation, we propose a stock price prediction model SSA-CPBiGRU that integrates candlestick patterns and a sparrow search algorithm (SSA). The incorporation of candlestick patterns endows the input data with structural characteristics and time series relationships. Moreover, the hyperparameters of the CPBiGRU model are optimized using an SSA. Subsequently, the optimized hyperparameters are employed within the network model to conduct predictions. We selected six stocks from different industries in the Chinese stock market for experimentation. The experimental results demonstrate that the model proposed in this paper can effectively enhance the prediction accuracy and has universal applicability. In comparison to the LSTM model, the proposed model produces an average of 31.13%, 24.92%, and 30.42% less test loss in terms of MAPE, RMSE and MAE, respectively. Moreover, it achieves an average improvement of 2.05% in R2.

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Section: Candlestick Patterns Analysismentioning

confidence: 99%

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Chen,

Hu,

Xue

2024

Electronics

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“…Statistical results revealed that four patterns were profitable for the Taiwan stock market after transaction costs [14]. Lv et al testing the predictive power of the Three Inside Up pattern and Three Inside Down pattern with the testing dataset of the K-line series data of Shanghai 180 index component stocks over the latest 10 years [15].…”

Section: Introductionmentioning

confidence: 99%

Stock Trend Prediction Using Candlestick Charting and Ensemble Machine Learning Techniques With a Novelty Feature Engineering Scheme

et al. 2021

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Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric, complex and chaotic nature of the stock price time series. With a simple eight-trigram feature engineering scheme of the inter-day candlestick patterns, we construct a novel ensemble machine learning framework for daily stock pattern prediction, combining traditional candlestick charting with the latest artificial intelligence methods. Several machine learning techniques, including deep learning methods, are applied to stock data to predict the direction of the closing price. This framework can give a suitable machine learning prediction method for each pattern based on the trained results. The investment strategy is constructed according to the ensemble machine learning techniques. Empirical results for the period of 2000 to 2017 of China's stock market confirm that our feature engineering has effective predictive power, with a prediction accuracy of more than 60% for some trend patterns. Various measures such as big data, feature standardization, and elimination of abnormal data can effectively solve the problem of data noise. An investment strategy based on our forecasting framework excels in both individual stock and portfolio performance theoretically. Transaction costs have a significant impact on investment. Additional technical indicators can improve the forecast accuracy to varying degrees. Technical indicators, especially the momentum indicators, can improve forecasting accuracy in most cases.

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“…In [19], the author used multiresolution analysis techniques to predict the interest rate next-day variation. Using K-line patterns' predictive power analysis, Tao et al [20] found that their proposed approach can effectively improve prediction accuracy for stock price direction and reduce forecast error.…”

Section: Introductionmentioning

confidence: 99%

Predicting Stock Price Trend Using MACD Optimized by Historical Volatility

Wang

Kim

2018

Mathematical Problems in Engineering

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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.

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K-Line Patterns’ Predictive Power Analysis Using the Methods of Similarity Match and Clustering

Cited by 6 publications

References 16 publications

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Stock Trend Prediction Using Candlestick Charting and Ensemble Machine Learning Techniques With a Novelty Feature Engineering Scheme

Predicting Stock Price Trend Using MACD Optimized by Historical Volatility

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