Stock Price Movements Classification Using Machine and Deep Learning Techniques-The Case Study of Indian Stock Market

Naik, Nagaraj; Mohan, Biju R.

doi:10.1007/978-3-030-20257-6_38

Cited by 34 publications

(17 citation statements)

References 14 publications

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“…Naik, N. proposed a DNN model that used the Boruta feature selection technique to solve the problem of technical indicator feature selection and identification of the relevant technical indicators. And the results showed that his model performed much better than ANN and SVM model [47].…”

Section: Cnnmentioning

confidence: 96%

“…Table 19 shows the details of the papers' accuracy. In this table, papers [3,13,16,19,22,23,25,26] used a CNN-based model; papers [34,36,45,50,[52][53][54]56,58,[61][62][63][64] used an LSTM based model; papers [67,68,74] used a DNN-based model; papers [28,29] used an RNN-based model; and papers [87,89,92] used other deep learning methods.…”

Section: Analysis Based On the Accuracymentioning

confidence: 99%

“…Table 19 shows the details of papers' accuracy. In this table, paper [10], [13], [16], [19], [20], [76], [77], [78], [90] used CNN based model; paper [23], [25], [34], [39], [41], [42], [43], [45], [80], [83], [91], [92], [93] used LSTM based model;paper [46], [47], [87] used DNN based model; paper [54], [55] used RNN based model and paper [68], [70], [86] used other deep learning method. Reference no.…”

Section: Analysis Based On the Accuracymentioning

confidence: 99%

“…As for MAE, paper [71] had a lower MAE than paper [72], and the accuracy of paper [68] was higher than that of papers [67] and [74]. Furthermore, paper [71] had a higher return rate than paper [70].…”

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confidence: 95%

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A Survey of Forex and Stock Price Prediction Using Deep Learning

Zhao

Khushi

2021

ASI

198

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Predictions of stock and foreign exchange (Forex) have always been a hot and profitable area of study. Deep learning applications have been proven to yield better accuracy and return in the field of financial prediction and forecasting. In this survey, we selected papers from the Digital Bibliography & Library Project (DBLP) database for comparison and analysis. We classified papers according to different deep learning methods, which included Convolutional neural network (CNN); Long Short-Term Memory (LSTM); Deep neural network (DNN); Recurrent Neural Network (RNN); Reinforcement Learning; and other deep learning methods such as Hybrid Attention Networks (HAN), self-paced learning mechanism (NLP), and Wavenet. Furthermore, this paper reviews the dataset, variable, model, and results of each article. The survey used presents the results through the most used performance metrics: Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE), accuracy, Sharpe ratio, and return rate. We identified that recent models combining LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning methods yielded great returns and performances. We conclude that, in recent years, the trend of using deep-learning-based methods for financial modeling is rising exponentially.

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Section: Cnnmentioning

confidence: 96%

Section: Analysis Based On the Accuracymentioning

confidence: 99%

Section: Analysis Based On the Accuracymentioning

confidence: 99%

mentioning

confidence: 95%

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A Survey of Forex and Stock Price Prediction Using Deep Learning

Zhao

Khushi

2021

ASI

198

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“…Mojtaba Nabipour et al trained four groups of machine learning models by comparing the stock data at 1, 2, 5, 10, 15, 20, and 30 days in advance [ 54 ]. Other studies on market direction prediction basically used the same approach by comparing the price or yield rate at two different time points [ 55 , 56 , 57 , 58 , 59 ].…”

Section: Introductionmentioning

confidence: 99%

A Labeling Method for Financial Time Series Prediction Based on Trends

Wang

et al. 2020

Entropy

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Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic. Existing labeling methods of financial time series mainly label data by comparing the current data with those of a short time period in the future. However, financial time series data are typically non-linear with obvious short-term randomness. Therefore, these labeling methods have not captured the continuous trend features of financial time series data, leading to a difference between their labeling results and real market trends. In this paper, a new labeling method called “continuous trend labeling” is proposed to address the above problem. In the feature preprocessing stage, this paper proposed a new method that can avoid the problem of look-ahead bias in traditional data standardization or normalization processes. Then, a detailed logical explanation given, a definition of continuous trend labeling was proposed and also an automatic labeling algorithm was given to extract the continuous trend features of financial time series data. Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification accuracy and some other classification evaluation metrics. The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data.

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