A-Stock Price Fluctuation Forecast Model Based on LSTM

Zeng, Yawen; Liu, Xing

doi:10.1109/skg.2018.00044

Cited by 12 publications

(15 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…Zeng, Y. proposed an LSTM model which is based on the dataset SSE50, and the results showed that the accuracy is above 65% [39].…”

Section: Cnnmentioning

confidence: 99%

“…1.0077 0.119 [82] 0.91 [84] 1.37 [91] 1.65 [95] 0.6346 [27] 0.0242 Precision [42] 0.553 [28] 1. 1-10 0.0015 [22] 0.004845492 [94] 0.02295 [81] 0.000379 MAE [21] 0.21035 [24] 120731.4 [24] 262.42 [27] 19.7096 [26] 0.1895 [28] 0.019 [27] 0.0169 [29] 0.00098 [29] 0.023 [30] 7.56 [31] 0.01069 [31] 0.00149 [30] 1.975 [32] 1.012 Accuracy [23] 54.58% MCC [23] 0.0478 [25] 98.49% R2 [24] 0.83 [34] 60.60% HMAE [31] 0.42911 [39] 65% HMSE [31] 0.23492 [41] 83.91% IC [37] 0.1259 [42] 55.90% AR [37] 0.2015 [43] 63.34% IR [37] 3.0521 [45] 27.20% Score [44] 0.4271 [80] 53.2% [83] 87.86% [91] 70.56% [92] 75.89% [93] 75.58% F-measure [42] 0.209…”

Section: Lstmmentioning

confidence: 99%

“…Paper [32] and [30] were in the fourth lowest order of magnitude, paper [27] was in the fifth-lowest order of magnitude, and paper [24] had the highest order of magnitude of MAE. 9.As for accuracy, paper [25] had the highest accuracy followed by [83], [41], [39], [43], [34], [42], [23] and paper [45] had the lowest accuracy.…”

Section: Lstmmentioning

confidence: 99%

“…For accuracy, paper [36] had the highest accuracy, followed by [34,45,50,[52][53][54]61], and paper [56] had the lowest accuracy.…”

mentioning

confidence: 99%

See 4 more Smart Citations

A Survey of Forex and Stock Price Prediction Using Deep Learning

Zhao

Khushi

2021

ASI

211

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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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