Applying long short term momory neural networks for predicting stock closing price

Gao, Tingwei; Chai, Yi; Liu, Yi

doi:10.1109/icsess.2017.8342981

Cited by 61 publications

(22 citation statements)

References 5 publications

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“…Chen et al [2] predicted the movement of the Chinese stock market using a long short-term memory-(LSTM-) based model. Gao et al [3] also used LSTM to predict stock prices. However, few studies have been conducted on forecasting the CDS term structure.…”

Section: Introductionmentioning

confidence: 99%

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

2020

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In this study, we analyze the term structure of credit default swaps (CDSs) and predict future term structures using the Nelson–Siegel model, recurrent neural network (RNN), support vector regression (SVR), long short-term memory (LSTM), and group method of data handling (GMDH) using CDS term structure data from 2008 to 2019. Furthermore, we evaluate the change in the forecasting performance of the models through a subperiod analysis. According to the empirical results, we confirm that the Nelson–Siegel model can be used to predict not only the interest rate term structure but also the CDS term structure. Additionally, we demonstrate that machine-learning models, namely, SVR, RNN, LSTM, and GMDH, outperform the model-driven methods (in this case, the Nelson–Siegel model). Among the machine learning approaches, GMDH demonstrates the best performance in forecasting the CDS term structure. According to the subperiod analysis, the performance of all models was inconsistent with the data period. All the models were less predictable in highly volatile data periods than in less volatile periods. This study will enable traders and policymakers to invest efficiently and make policy decisions based on the current and future risk factors of a company or country.

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Section: Introductionmentioning

confidence: 99%

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

2020

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“…Based on this mechanism that can retain important information, it provides a good reference and application when constructing a predictive model for this study. The LSTM neural network has a new structure called memory cell (Gao, Chai & Liu, 2017). The memory cell contains four main components: input gate, Forget gate, Output gate and Neurons, through these three gates, decide what information to store and when to allow reading, writing and forgetting.…”

Section: Lstm Neural Networkmentioning

confidence: 99%

LSTM-based sentiment analysis for stock price forecast

Chang

2021

PeerJ Computer Science

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Investing in stocks is an important tool for modern people’s financial management, and how to forecast stock prices has become an important issue. In recent years, deep learning methods have successfully solved many forecast problems. In this paper, we utilized multiple factors for the stock price forecast. The news articles and PTT forum discussions are taken as the fundamental analysis, and the stock historical transaction information is treated as technical analysis. The state-of-the-art natural language processing tool BERT are used to recognize the sentiments of text, and the long short term memory neural network (LSTM), which is good at analyzing time series data, is applied to forecast the stock price with stock historical transaction information and text sentiments. According to experimental results using our proposed models, the average root mean square error (RMSE ) has 12.05 accuracy improvement.

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“…Xie et al [14] and that RNN is effective to forecast stock price and useful for stock market investors to make an investing decision. A robust and novel hybrid model was proposed for prediction of stock returns [15], which is constituted of auto regressive moving average model, exponential smoothing model and RNN [16].…”

Section: Related Workmentioning

confidence: 99%

Stock Market Predictor using Long short-term Memory (LSTM) Technique

Chandak*

2020

IJITEE

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In the stock market, it is important to have accurate prediction of future behavior of stock price..Because of the great chance of financial loss as well as scoring profits at the same time, it is mandatory to have a secure prediction of the values of the stocks. But when it comes to predicting the value of a stock in future we tend to follow stock market experts but as technology is progressing we may use these technologies rather than following human experts who may be biased many times. Stock price prediction has been interesting area for investors and researchers. This article proposes an approach towards prediction of stock price using machine learning model Long Short Term Memory. This is an ensemble learning method that has been an exceedingly successful model for predicting sequence of numbers and words. Long Short Term Memory is a machine learning model for prediction. This technique is used to forecast the future stock price of a specific stock by using historical data of the stock gathered from Yahoo! Finance.

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Applying long short term momory neural networks for predicting stock closing price

Cited by 61 publications

References 5 publications

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

LSTM-based sentiment analysis for stock price forecast

Stock Market Predictor using Long short-term Memory (LSTM) Technique

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