Examination of the profitability of technical analysis based on moving average strategies in BRICS

Souza, Matheus José Silva de; Ramos, Danilo Guimarães Franco; Pena, Marina Garcia; Sobreiro, Vinícius Amorim; Kimura, Herbert

doi:10.1186/s40854-018-0087-z

Cited by 54 publications

(43 citation statements)

References 48 publications

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“…Uninformed trading decisions can leave traders and investors prone to financial risk and experience monetary losses. On the contrary, careful investment choices can maximize profits (de Souza et al, 2018). Conventional research focused on time series and technical analysis of a stock, i.e., using patterns from historical price signals to forecast stock movements (B et al, * Equal contribution.…”

Section: Introductionmentioning

confidence: 99%

Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations

Sawhney

Agarwal²,

Wadhwa

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task. Stock forecasting is complex, given the stochastic dynamics and non-stationary behavior of the market. Stock movements are influenced by varied factors beyond the conventionally studied historical prices, such as social media and correlations among stocks. The rising ubiquity of online content and knowledge mandates an exploration of models that factor in such multimodal signals for accurate stock forecasting. We introduce an architecture that achieves a potent blend of chaotic temporal signals from financial data, social media, and inter-stock relationships via a graph neural network in a hierarchical temporal fashion. Through experiments on real-world S&P 500 index data and English tweets, we show the practical applicability of our model as a tool for investment decision making and trading.

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Section: Introductionmentioning

confidence: 99%

Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations

Sawhney

Agarwal²,

Wadhwa

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…Technical analysis based on trading pattern curves is an important method for quantitative analysis of stocks, and in recent years, there have been a number of scholars who have proved the effectiveness of technical analysis from different perspectives as an effective means of extracting information from market prices [1][2][3]. With the development of financial engineering, computer technology, and mathematical algorithms, technical analysts using traditional trading model analysis methods now regularly use statistical software packages and other data processing techniques.…”

Section: Introductionmentioning

confidence: 99%

A Study on Stock Graph Recognition Based on Wavelet Denoising and DTW Algorithm

Luo

2021

Mathematical Problems in Engineering

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The “classical pattern” of stock price formation has long been widely used in the determination of future price trends of stocks, and the identification and analysis of classical price patterns have an important guiding role in investors’ decision-making and trading. The wavelet transform is a useful tool to remove some of the noise of time series because it has the characteristic of multiresolution. In this study, we propose a method for stock price pattern recognition based on the wavelet transform and dynamic time warp (DTW). A pattern recognition method with similar quantified results is developed to obtain accurate pattern recognition results. That is, using the wavelet transform to smooth the original price graph, and then using the DTW algorithm improved in this study to find the graph with the smallest distance from the target graph under the sliding window method, the identification and analysis of the target graph can be realized. In order to improve the recognition rate of the target graph, we preprocessed the raw price sequence using the moving average convergence and divergence (MACD) algorithm based on the control experiments set up in this study. The pattern recognition method used in this study will identify the price patterns of a certain time window as a whole, thus avoiding the problem of how to objectively select the important points that constitute a price pattern and the mathematical definition of different price patterns in the previous traditional methods.

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“…Users of technical analysis are commonly referred to as chartists. Users believe that a stock's market trends and action signals can be obtained based on certain shapes and patterns from the stock price chart (Hu et al, 2017;de Souza et al, 2018). Another form of this analysis is the use of analytic lines applied to price charts according to individual users' opinions.…”

Section: Introductionmentioning

confidence: 99%

The Profitablity Graph Of Financing Companies In Indonesia

Lubis

Ismail²,

Tarigan³

et al. 2019

Proceedings of the Proceedings of the 1st International Conference on Finance Economics and Business, ICOFEB 2018, 12-13 Novemb

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The purpose of this research is to understand the profitability of finance companies in Indonesia. The method of research using a time series analysis. The data used are secondary data sourced from the Financial Sevices Authority. The method of analysis used in this research is the graph method using Smart PLS software. The results show that the profitability of finance companies in Indonesia has decreased in the graph of financing profitability in the previous period.

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Examination of the profitability of technical analysis based on moving average strategies in BRICS

Cited by 54 publications

References 48 publications

Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations

Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations

A Study on Stock Graph Recognition Based on Wavelet Denoising and DTW Algorithm

The Profitablity Graph Of Financing Companies In Indonesia

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