Improving Stock Closing Price Prediction Using Recurrent Neural Network and Technical Indicators

This study focuses on predicting stock closing prices by using recurrent neural networks (RNNs). A long short-term memory (LSTM) model, a type of RNN coupled with stock basic trading data and technical indicators, is introduced as a novel method to predict the closing price of the stock market. We realize dimension reduction for the technical indicators by conducting principal component analysis (PCA). To train the model, some optimization strategies are followed, including adaptive moment estimation (Adam) and Glorot uniform initialization. Case studies are conducted on Standard & Poor's 500, NASDAQ, and Apple (AAPL). Plenty of comparison experiments are performed using a series of evaluation criteria to evaluate this model. Accurate prediction of stock market is considered an extremely challenging task because of the noisy environment and high volatility associated with the external factors. We hope the methodology we propose advances the research for analyzing and predicting stock time series. As the results of experiments suggest, the proposed model achieves a good level of fitness.

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Stock Price Prediction using Technical Indicators: A Predictive Model using Optimal Deep Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b3048.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 2297-2305

Keyword(s):

Deep Learning ◽

Stock Market ◽

Stock Price ◽

Short Term Memory ◽

Technical Analysis ◽

Learning Task ◽

Correlation Tensor ◽

Technical Indicators ◽

Stock Price Prediction ◽

Proposed Model

The stock market is highly volatile and complex in nature. Technical analysts often apply Technical Analysis (TA) on historical price data, which is an exhaustive task and might produce incorrect predictions. The machine learning coupled with fundamental and / or Technical Analysis also yields satisfactory results for stock market prediction. In this work an effort is made to predict the price and price trend of stocks by applying optimal Long Short Term Memory (O-LSTM) deep learning and adaptive Stock Technical Indicators (STIs). We also evaluated the model for taking buy-sell decision at the end of day. To optimize the deep learning task we utilized the concept of Correlation-Tensor built with appropriate STIs. The tensor with adaptive indicators is passed to the model for better and accurate prediction. The results are analyzed using popular metrics and compared with two benchmark ML classifiers and a recent classifier based on deep learning. The mean prediction accuracy achieved using proposed model is 59.25%, over number of stocks, which is much higher than benchmark approaches.

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A RNN-LSTM based Predictive Modelling Framework for Stock Market Prediction using Technical Indicators

International Journal of Rough Sets and Data Analysis ◽

10.4018/ijrsda.288521 ◽

2021 ◽

Vol 7 (1) ◽

pp. 0-0

Keyword(s):

Stock Market ◽

Short Term Memory ◽

Predictive Modelling ◽

Future Price ◽

Data Set ◽

Technical Indicators ◽

Modelling Framework ◽

Successful Prediction ◽

Input Dataset ◽

Two Phases

The successful prediction of the stocks’ future price would produce substantial profit to the investor. In this paper, we propose a framework with the help of various technical indicators of the stock market to predict the future prices of the stock using Recurrent Neural Network based Long Short-Term Memory (LSTM) algorithm. The historical transactional data set is amalgamated with the technical indicators to create a more effective input dataset. The historical data is taken from 2010-2019 ten years in total. The dataset is divided into 80% training set and 20% test set. The experiment is carried out in two phases first without the technical indicators and after adding technical indicators. In the experimental setup, it has been observed the LSTM with technical indicators have significantly reduced the error value by 2.42% and improved the overall performance of the system as compared to other machine learning frameworks that are not accounting the effect of technical indicators.

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

Science & Technology Development Journal - Economics - Law and Management ◽

10.32508/stdjelm.v4i1.593 ◽

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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Spatial Mixture Copula Model For Multiple Correlated Responses With An Environmental Application

10.21203/rs.3.rs-1169352/v1 ◽

2021 ◽

Author(s):

Mohomed Abraj ◽

M. Helen Thompson ◽

You-Gan Wang

Keyword(s):

Random Field ◽

Convex Combination ◽

Principal Component ◽

Nonlinear Dependence ◽

Copula Model ◽

Environmental Application ◽

Proposed Model ◽

Multiple Measurements ◽

Novel Method ◽

Bivariate Dependence

Abstract In environmental monitoring, multiple measurements are often collected at many locations and these measurements depend on each other in complex ways, such as nonlinear dependence. In this research, a novel copula-based geostatistical modelling approach was developed to model multivariate continuous spatial random fields using mixture copulas that captures both spatial and joint dependence of multiple responses over two-dimensional locations. In a bivariate context, the mixture copulas were used to capture the joint spatial dependence of a bivariate random field and the spatial copula of the bivariate random field was constructed as the convex combination of mixture copulas. The proposed model was applied to real forest data and simulated nonlinear data. The performance of the novel method was compared with existing spatial methods, which included a univariate spatial pair-copula model, a multivariate spatial pair-copula model that utilises nonlinear principal component analysis (NLPCA), and conventional kriging. The results show that the proposed model outperforms the existing methods in the interpolation of individual responses and reproduction of their bivariate dependence.

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Rede de unidades recorrentes chaveadas e transformadas discretas de ondaletas à previsão e operação no mercado financeiro

10.31414/ee.2021.d.131312 ◽

2021 ◽

Author(s):

◽

V. Biazon

Keyword(s):

Wavelet Transform ◽

Stock Market ◽

Stock Price ◽

Time Windows ◽

Training Data ◽

Discrete Wavelet ◽

Time Step ◽

Financial Return ◽

Proposed Model ◽

Closing Price

Trading in the stock market always comes with the challenge of choosing the best decision to take on each time step. The problem is intensified by the theory that it is not possible to predict stock market time series as all information related to the stock price is already contained in it, which theory is known as Efficient Market Hypotesis. Although the market, in general, has no distinguishable tendencies, thus being consistent to the EMH, there are several time windows where there is some predictability, to some extent in the data, if we consider the use of technical indicators. In this work, a novel model is proposed to seek benefit from said periods operating to choose its actions and waiting for the best moment to execute them. This model, called Discrete Wavelet Transform Gated Recurrent Unit Network) (DWT-GRU), is divided in three modules, them being, the preprocessing of the data by the wavelet transform, the training and prediction of the closing price two days in the future and the decision making based on the evaluation of the gradient of the closing price. The proposed model was compared to other RNN architectures, with and without the use of wavelet preprocessing, and the "buy-and-hold" strategy. The results shown that the proposed model surpassed all the statistical metrics of accuracy, precision, recall, F1 and financial return of all the estabilshed comparisson models in the analysed stocks of the Brazilian Stock Market. The analysed stocks as the base for the study were the blue-chips of the IBOVESPA index, them being, PETR4, VALE3, ITUB4, ABEV3, and the ETF that mirrors the index itself, BOVA11. As training data the analysed period was since 2001 for the stocks and 2008 for the Fundo de Índice Negociado em Bolsa - ExchangeTraded Fund (ETF) BOVA11. At last, it is presented the financial results of the application of the algorithm in real time swing-trade operations validating its efficiency and winning over the buy-and-hold strategy

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Simulation of Technical Indicators for Better Profits in the Indian Stock Market

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4256.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 1612-1619

Keyword(s):

Stock Market ◽

Moving Average ◽

Relative Strength ◽

Strength Index ◽

Technical Indicators ◽

Long Run ◽

Closing Price ◽

One Step ◽

Indian Stock Market

This article designs models and uses simulation to examine optimization of technical indicators in stock market: the Moving Average Convergence Divergence (MACD) and the Relative Strength Index (RSI). Based on sector-wise Nifty 50 group of companies’ daily closing price of the stocks from the year January 2013 to September 2018. This study is to demonstrate how the simulation of technical indicators MACD and RSI helps investor in reducing the trading cycles of investment with better profits in the long run. Results concluded that the experimentation of optimization of technical indicators is one-step forward in making profitable trades as it is evident from the nifty50 stocks. Furthermore, it also proves that both the optimized MACD and RSI outperformed the standard MACD, standard RSI and Buy& Hold strategy.

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Stock Prediction Based on News Text Analysis

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2021.p0581 ◽

2021 ◽

Vol 25 (5) ◽

pp. 581-591

Author(s):

Wentao Gu ◽

◽

Linghong Zhang ◽

Houjiao Xi ◽

Suhao Zheng

Keyword(s):

Stock Market ◽

Stock Returns ◽

News Media ◽

Short Term Memory ◽

Investor Sentiment ◽

Principal Component ◽

Online News ◽

Stock Market Returns ◽

Financial News ◽

Sentiment Lexicon

With the vigorous development of information technology, the textual data of financial news have grown massively, and this ever-rich online news information can influence investors’ decision-making behavior, which affects the stock market. Thus, online news is an important factor affecting market volatility. Quantifying the sentiment of news media and applying it to stock-market prediction has become a popular research topic. In this study, a financial news sentiment lexicon and an auxiliary lexicon applicable to the financial field are constructed, and a sentiment index (SI) is constructed by defining the weight of semantic rules. Then, a comprehensive sentiment index (CSI) is constructed via principal component analysis of the sentiment index and structured stock-market trading data. Finally, these two sentiment indices are added to the generalized autoregressive conditional heteroscedastic (GARCH) and the Long short-term memory (LSTM) models to predict stock returns. The results indicate that the prediction results of LSTM models are better than those of GARCH models. Compared with general-purpose lexicons, the financial lexicons constructed in this study are more stable, and the inclusion of a comprehensive investor sentiment index improves the accuracy of measuring sentiment information. Thus, the proposed lexicons allow more comprehensive measurement of the effects of external sentiment factors on stock-market returns and can improve the prediction effect of stock-return models.

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Integrating Independent Component Analysis and Principal Component Analysis with Neural Network to Predict Chinese Stock Market

Mathematical Problems in Engineering ◽

10.1155/2011/382659 ◽

2011 ◽

Vol 2011 ◽

pp. 1-15 ◽

Cited By ~ 18

Author(s):

Haifan Liu ◽

Jun Wang

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Independent Component Analysis ◽

Stock Market ◽

Principal Component ◽

Component Analysis ◽

Independent Component ◽

Chinese Stock Market ◽

Proposed Model ◽

Bp Model

We investigate the statistical behaviors of Chinese stock market fluctuations by independent component analysis. The independent component analysis (ICA) method is integrated into the neural network model. The proposed approach uses ICA method to analyze the input data of neural network and can obtain the latent independent components (ICs). After analyzing and removing the IC that represents noise, the rest of ICs are used as the input of neural network. In order to forect the fluctuations of Chinese stock market, the data of Shanghai Composite Index is selected and analyzed, and we compare the forecasting performance of the proposed model with those of common BP model integrating principal component analysis (PCA) and single BP model. Experimental results show that the proposed model outperforms the other two models no matter in relatively small or relatively large sample, and the performance of BP model integrating PCA is closer to that of the proposed model in relatively large sample. Further, the prediction results on the points where the prices fluctuate violently by the above three models relatively deviate from the corresponding real market data.

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A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network

European Neurology ◽

10.1159/000511306 ◽

2020 ◽

Vol 83 (5) ◽

pp. 468-486

Author(s):

Foad Moradi ◽

Hiwa Mohammadi ◽

Mohammad Rezaei ◽

Payam Sariaslani ◽

Nazanin Razazian ◽

...

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Recurrent Neural Network ◽

Short Term Memory ◽

Sleep Stage ◽

Sleep Stages ◽

Discrete Wavelet ◽

Eeg Signals ◽

Proposed Model ◽

Novel Method

Introduction: Visual sleep-stage scoring is a time-consuming technique that cannot extract the nonlinear characteristics of electroencephalogram (EEG). This article presents a novel method for sleep-stage differentiation based on sonification of sleep-EEG signals using wavelet transform and recurrent neural network (RNN). Methods: Two RNNs were designed and trained separately based on a database of classical guitar pieces and Kurdish tanbur Makams using a long short-term memory model. Moreover, discrete wavelet transform and wavelet packet decomposition were used to determine the association between the EEG signals and musical pitches. Continuous wavelet transform was applied to extract musical beat-based features from the EEG. Then, the pretrained RNN was used to generate music. To test the proposed model, 11 sleep EEGs were mapped onto the guitar and tanbur frequency intervals and presented to the pretrained RNN. Next, the generated music was randomly presented to 2 neurologists. Results: The proposed model classified the sleep stages with an accuracy of >81% for tanbur and more than 93% for guitar musical pieces. The inter-rater reliability measured by Cohen’s kappa coefficient (κ) revealed good reliability for both tanbur (κ = 0.64, p < 0.001) and guitar musical pieces (κ = 0.85, p < 0.001). Conclusion: The present EEG sonification method leads to valid sleep staging by clinicians. The method could be used on various EEG databases for classification, differentiation, diagnosis, and treatment purposes. Real-time EEG sonification can be used as a feedback tool for replanning of neurophysiological functions for the management of many neurological and psychiatric disorders in the future.

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Exploring the effectiveness of deep neural networks with technical analysis applied to stock market prediction

Computer Science and Information Systems ◽

10.2298/csis200301002l ◽

2021 ◽

Vol 18 (2) ◽

pp. 401-418

Author(s):

Ming-Che Lee ◽

Jia-Wei Chang ◽

Jason Hung ◽

Bae-Ling Chen

Keyword(s):

Stock Market ◽

Capital Market ◽

Stock Prices ◽

Short Term Memory ◽

Technical Analysis ◽

Short Term ◽

Continuous Growth ◽

Technical Indicators ◽

Deep Network ◽

The Capital Market

The sustainable development of the national economy depends on the continuous growth and growth of the capital market, and the stock market is an important factor of the capital market. The growth of the stock market can generate a huge positive force for the country's economic strength, and the steady growth of the stock market also plays a pivotal role in the overall economic pulsation and is very helpful to the country's high economic development. There are different views on whether the technical analysis of the stock market is efficient. This study aims to explore the feasibility and efficiency of using deep network and technical analysis indicators to estimate short-term price movements of stocks. The subject of this study is TWSE 0050, which is the most traded ETF in Taiwan's stock exchange, and the experimental transaction range is 2017/01 ~ 2019 Q3. A four layer Long Short-Term Memory (LSTM) model was constructed. This research uses well-known technical indicators such as the KD, RSI, BIAS, Williams% R, and MACD, combined with the opening price, closing price, daily high and low prices, etc., to predict the trend of stock prices. The results show that the combination of technical indicators and the LSTM deep network model can achieve 83.6% accuracy in the three categories of rise, fall, and flatness.

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