Multiresolution analysis of Bursa Malaysia KLCI time series

Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed

doi:10.1063/1.4983875

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Combining multiresolution wavelet methodology showed a significant improvement in data analysis and decomposition in many scientific areas including time series. Ismail and Dghais [41], for example, attempted to characterize financial time series using MRA. The study used Linear ARIMA with wavelets to address forecasting results using a multi-resolution fitting approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Combining Deep Learning and Multiresolution Analysis for Stock Market Forecasting

2021

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Due to its complexity, financial time-series forecasting is regarded as one of the most challenging problems. During the past two decades, nonlinear modeling techniques, such as artificial neural networks, were commonly employed to solve a variety of time-series problems. Recently, however, deep neural network has been found to be more efficient than those in many application domains. In this article, we propose a model based on deep neural networks that improves the forecasting of stock prices. We investigate the impact of combining deep learning techniques with multiresolution analysis to improve the forecasting accuracy. Our proposed model is based on an empirical wavelet transform which is shown to outperform traditional stationary wavelet transform in capturing price fluctuations at different time scales. The proposed model is demonstrated to be substantially more effective than other models when evaluated on the S&P500 stock index and Mackey-Glass time series.

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Section: Literature Reviewmentioning

confidence: 99%

Combining Deep Learning and Multiresolution Analysis for Stock Market Forecasting

2021

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“…MRA was included in the proposed solution because many researchers reported that they got better performance when they combined MRA with their predictive model. For example, MRA resulted in better model performance when combined with each of ARIMA [6], descriptive statistical modeling [7], ANN [8], [9], RNN [10], CNN [11], GRU [12], LSTM [13], and stacked autoencoders [14].…”

Section: Introductionmentioning

confidence: 99%

Day Trading Strategy Based on Transformer Model, Technical Indicators and Multiresolution Analysis

Mohammed

2024

IJACSA

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Stock prices are very volatile because they are affected by infinite number of factors, such as economical, social, political, and human behavior. This makes finding consistently profitable day trading strategy extremely challenging and that is why an overwhelming majority of stock traders loose money over time. Professional day traders, who are very few in number, have a trading strategy that can exploit this price volatility to consistently earn profit from the market. This study proposes a consistently profitable day trading strategy based on price volatility, transformer model, time2vec, technical indicators, and multiresolution analysis. The proposed trading strategy has eight trading systems, each with a different profit-target based on the risk taken per trade. This study shows that the proposed trading strategy results in consistent profits when the profit-target is 1.5 to 3.5 times the risk taken per trade. If the profit-target is not in that range, then it may result in a loss. The proposed trading strategy was compared with the buy-and-hold strategy and it showed consistent profits with all the stocks whereas the buy-andhold strategy was inconsistent and resulted in losses in half the stocks. Also three of the consistently profitable trading systems showed significantly higher average profits and expectancy than the buy-and-hold trading strategy.

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“…During past decades, the Malaysian stock market was growing in line with rapid economic expansion. As one of the largest bourses in Asia, Bursa Malaysia provides various integrated products and services, including 936 companies in the main market, 15 derivatives products, 742 Islamic stocks, 10 bond listing and 22 Sukuk listing, total market capitalization (MC) of companies are RM 6.46bn, market price-earnings ratio (P/E) is 27.61 and return on equity is 53.1% (Bursa Malaysia, 2017).…”

Section: Introductionmentioning

confidence: 99%

Are stock market returns affected by financial market variables? Evidence from Bursa Malaysia by panel generalized method of moments

Ruhani

Junoh

2022

IJOES

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Purpose This study aims to find the relationship of stock market returns and selected financial market variables (market capitalization, earnings per share, price-earnings multiples, dividend yield and trading volume) of Malaysia grounded by the arbitrage pricing theories. Design/methodology/approach This study empirically examines the effects of selected financial market variables on stock market returns using 64 companies listed in Malaysia's stock market with data spanning from 2005 to 2018. A systematic empirical study based on the Generalized Method of Moments following Arellano and Bond (1991) has been taken to estimate the effect. Findings The regression result of the financial market variables and stock market return shows that, except for trading volume, all selected financial market variables play significant roles in the stock market returns. Furthermore, market capitalization, earnings per share, price-earnings ratio, dividend yield and trading volume have a positive impact on stock market returns. Research limitations/implications The outcome of this study can contribute by helping domestic and global investors devise strategies to minimize their risks. Also, policy administrators can use the outcomes of this study to inform the micro- and macro-level policy formulation. Originality/value This study will contribute to filling the gap in knowledge concerning the new release of factors affecting the stock market returns of Malaysia.

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Multiresolution analysis of Bursa Malaysia KLCI time series

Cited by 3 publications

References 12 publications

Combining Deep Learning and Multiresolution Analysis for Stock Market Forecasting

Combining Deep Learning and Multiresolution Analysis for Stock Market Forecasting

Day Trading Strategy Based on Transformer Model, Technical Indicators and Multiresolution Analysis

Are stock market returns affected by financial market variables? Evidence from Bursa Malaysia by panel generalized method of moments

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