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

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>

Examination of Long Memory in Indian Stock Market: A Sectoral Juxtaposition

2021 ◽  
pp. 231971452110402
Author(s):  
Ramashanti Naik ◽  
Y. V. Reddy
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Long Memory ◽  
Stock Exchange ◽  
Moving Average ◽  
Long Range Dependence ◽  
Dynamic Nature ◽  
Portfolio Managers ◽  
Indian Stock Market ◽  
Data Frequency

One of the situations encountered in time series analysis is long-range dependence, also known as Long memory. We investigated the presence of long memory in the Indian sectoral indices returns and investigated whether the long memory behaviour is affected by the data frequency. We applied the autoregressive fractionally integrated moving average (ARFIMA) models to 13 sectoral indices of the National Stock Exchange of India and examined the long memory in daily, monthly and quarterly return series. The results indicate the persistence in daily return series and anti-persistence in monthly and quarterly return series. Thus, we conclude that the frequency of data does have a significant effect on the behaviour of long memory patterns. The results will be helpful for present and potential investors, institutional investors, portfolio managers and policymakers to understand the dynamic nature of long memory in the Indian stock market.

Multifractal detrended fluctuation analysis of SENSEX fluctuation in the Indian stock market

2010 ◽  
Vol 88 (8) ◽  
pp. 545-551 ◽  
Author(s):  
Srimonti Dutta
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Detrended Fluctuation Analysis ◽  
Nonstationary Time Series ◽  
Anomalous Behaviour ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Indian Stock Market ◽  
Detrended Fluctuation

The fluctuation of SENSEX in the Indian stock market for the period Jan 2003–Dec 2009 is studied using the multifractal detrended fluctuation analysis (MFDFA) approach. The effect of the fall in the stock market in 2008 is also investigated. The data exhibits that the nonstationary time series of SENSEX fluctuations are multifractal in nature. An increase in the degree of multifractality prior to the anomalous behaviour in the SENSEX values is also observed. The increase in the degree of correlation for the period 2007–2009 is also responsible for the meteoric rise and the catastrophic fall in the values of SENSEX.

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.

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