Hybrid ARIMA-BPNN model for time series prediction of the Chinese stock market

2017 ◽  
Author(s):  
Li Xiong ◽  
Yue Lu
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Prediction ◽  
Chinese Stock Market ◽  
Bpnn Model

scholarly journals Comparative Research on Influencing Factors of LSTM Deep Neural Network in Stock Market Time Series Prediction

2019 ◽  
Vol 4 (1) ◽  
pp. 84
Author(s):  
TANG Yin ◽  
YANG Jin Yu ◽  
CHEN Jian
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Influencing Factors ◽  
Network Structure ◽  
Time Series Prediction ◽  
Structure Optimization ◽  
Optimization Techniques ◽  
Training Process ◽  
Bear Market ◽  
Network Structure Optimization

<p><em>During training process of LSTM, the prediction accuracy is affected by a variation of factors, including the selection of training samples, the network structure, the optimization algorithm, and the stock market status. This paper tries to conduct a systematic research on several influencing factors of LSTM training in context of time series prediction. The experiment uses Shanghai and Shenzhen 300 constituent stocks from 2006 to 2017 as samples. The influencing factors of the study include indicator sampling, sample length, network structure, optimization method, and data of the bull and bear market, and this experiment compared the effects of PCA, dropout, and L2 regularization on predict accuracy and efficiency. Indice sampling, number of samples, network structure, optimization techniques, and PCA are found to be have their scope of application. Further, dropout and L2 regularization are found positive to improve the accuracy. The experiments cover most of the factors, however have to be compared by data overseas. This paper is of significance for feature and parameter selection in LSTM training process.</em></p>

scholarly journals Optimizing LSTM for time series prediction in Indian stock market

2020 ◽  
Vol 167 ◽  
pp. 2091-2100 ◽  
Author(s):  
Anita Yadav ◽  
C K Jha ◽  
Aditi Sharan
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Prediction ◽  
Indian Stock Market

Empirical Study on the Non-Periodic Cycles of Fractal Time Series: Examples of Indices of the Chinese Stock Market

2015 ◽  
Vol 734 ◽  
pp. 637-641
Author(s):  
Yang Li ◽  
Wei Yu Zhang ◽  
Yong Wei ◽  
Jin Hui Sun
Keyword(s):  
Time Series ◽  
Random Walk ◽  
Stock Market ◽  
Composite Index ◽  
Chinese Stock Market ◽  
Biased Random Walk ◽  
Fractal Time ◽  
Study Support ◽  
Two Indices ◽  
Periodic Cycles

By R/S analysis, non-periodic cycles of the SSE Composite Index and SZSE Composite Index are studied in this paper. With a different determinant method from the previous works about fractal behaviors of the Chinese stock market, the empirical results obtained in this study support the non-periodic cycle results but with different values. With more data available, the analysis shows that the two indices follow a biased random walk with two non-periodic cycles, one about 4.5 years and another about 9 years, which may be tied to the economic and politic cycles.

Improved RBF neural network algorithm in financial time series prediction

2021 ◽  
pp. 1-9
Author(s):  
Jian Zhu ◽  
Haiming Long ◽  
Saihong Liu ◽  
Wenzhi Wu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Stock Market ◽  
Financial Time Series ◽  
Rbf Neural Network ◽  
Time Series Prediction ◽  
Market Price ◽  
Prediction Errors ◽  
Financial Time ◽  
Sample Data

The financial market is often unpredictable and extremely susceptible to political, economic and other factors. How to achieve accurate predictions of financial time series is very important for scientific research and financial enterprise management. Based on this, this article takes the application of the improved RBF neural network(NN) algorithm in financial time series forecasting as the research object, and explores how to use the improved RBF NN algorithm to predict the stock market price, with a view to reducing investment risks and increasing returns for the majority of stock investors to provide help. This article uses the stock market prices of three listed companies in May 2019 as the data samples for this survey, including 72 training sample data and 21 test sample data. These three stocks were predicted by using the improved RBF NN algorithm Experiments, the experimental results show that the prediction errors of the improved RBF NN algorithm for the three stocks are 2.14%, 0.69% and 1.47%, while the traditional RBF NN algorithm’s prediction errors for the stocks are 5.74%, 2.38% and 11.37%. This shows that the improved algorithm is significantly more accurate and more effective than traditional algorithms. Therefore, the application of the improved RBF NN algorithm in financial time series prediction will be more extensive in the future.

scholarly journals Fuzzy Transfer Learning in Time Series Forecasting for Stock Market Prices

2021 ◽  
Author(s):  
Shanoli Samui Pal ◽  
Samarjit Kar
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Prior Knowledge ◽  
Transfer Learning ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Series Data ◽  
Data Sets ◽  
Market Data

Abstract Transfer learning involves transferring prior knowledge of solving similar problems in order to achieve quick and efficient solution. The aim of fuzzy transfer learning is to transfer prior knowledge in an imprecise environment. Time series like stock market data are non-linear in nature and movement of stock is uncertain, so it is quite difficult following the stock market and in decision making. In this study, we propose a method to forecast stock market time series in the situation when we can use prior experience to make decisions. Fuzzy transfer learning (FuzzyTL) is based on knowledge transfer in that and adapting rules obtained domain. Three different stock market time series data sets are used for comparative study. It is observed that the effect of knowledge transferring works well together with smoothing of dependent attributes as the stock market data fluctuate with time. Finally, we give an empirical application in Shenzhen stock market with larger data sets to demonstrate the performance of the model. We have explored FuzzyTL in time series prediction to unerstand the essence of FuzzyTL. We were working on the question of the capability of FuzzyTL in improving prediction accuracy. From the comparisons, it can be said fuzzy transfer learning with smoothing improves prediction accuracy efficiently.

scholarly journals Time series prediction: A combination of Long Short-Term Memory and structural time series models

2020 ◽  
Vol 4 (1) ◽  
pp. 500-515
Author(s):  
Quoc Luu ◽  
Son Nguyen ◽  
Uyen Pham
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Time Series Prediction ◽  
Structural Models ◽  
Short Term ◽  
Proposed Model ◽  
Auto Regressive ◽  
Long Short Term Memory

Stock market is an important capital mobilization channel for economy. However, the market has potential loss due to fluctuations of stock prices to reflect uncertain events such as political news, supply and demand of daily trading volume. There are many approaches to reduce risk such as portfolio construction and optimization, hedging strategies. Hence, it is critical to leverage time series prediction techniques to achieve higher performance in stock market. Recently, Vietnam stock markets have gained more and more attention as their performance and capitalization improvement. In this work, we use market data from Vietnam’s two stock market to develop an incorporated model that combines Sequence to Sequence with Long-Short Term Memory model of deep learning and structural models time series. We choose 21 most traded stocks with over 500 trading days from VN-Index of Ho Chi Minh Stock Exchange and HNX-Index of Hanoi Stock Exchange (Vietnam) to perform the proposed model and compare their performance with pure structural models and Sequence to Sequence. For back testing, we use our model to decide long or short position to trade VN30F1M (VN30 Index Futures contract settle within one month) that are traded on HNX exchange. Results suggest that the Sequence to Sequence with LSTM model of deep learning and structural models time series achieve higher performance with lower prediction errors in terms of mean absolute error than existing models for stock price prediction and positive profit for derivative trading. This work significantly contribute to literature of time series prediction as our approach can relax heavy assumptions of existing methodologies such as Auto-regressive–moving-average model, Generalized Auto-regressive Conditional Heteroskedasticity. In practical, investors from Vietnam stock market can use the proposed model to develop trading strategies.

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