A deep learning based hybrid framework for stock price prediction

Mundra, Ankit; Mundra, Shikha; Verma, Vivek Kumar; Srivastava, Jai Shankar

doi:10.3233/jifs-179681

Cited by 8 publications

(1 citation statement)

References 6 publications

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“…Many machine techniques have already been applied to forecast the stock market. For example, logistic regression (LR) and Neural Network (NNs) [21] [22], deep neural networks (DNN) [23], decision trees (DTs) [24][25] [26], support vector machines (SVM) [27], k-nearest neighbors (KNN) [28], random forests (RFs) [29] [30] and long shortterm memory networks (LSTMs) [31] [32] have been used to predict the stock market. Moreover, many authors try to improve the prediction ability by combining machine learning models with other methods.…”

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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