A literature review of technical analysis on stock markets

Nazário, Rodolfo Toríbio Farias; Silva, Jéssica Lima e; Sobreiro, Vinícius Amorim; Kimura, Herbert

doi:10.1016/j.qref.2017.01.014

Cited by 137 publications

(43 citation statements)

References 118 publications

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“…In [6] the author has used convolution neural networks which help to improve upon the 4 out of 7 tasks. The research paper [3] codes and classifies previously published research papers to summarize and bridges the gaps in the analysis of security exchanges, multi commodity exchange etc. [8] The research paper used clustering algorithm and correlation coefficient of financial time series, where a map is assigned to each company and then using the mentioned techniques, the robustness between maps is determined.…”

Section: Related Work Donementioning

confidence: 99%

Predicting the uptrend and downtrend in stock markets for intraday trading using machine learning algorithms

Bhatt¹,

Kartik²,

Vij³

2020

IJETMS

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A country’s economy is dependent on several parameters among these parameters stock markets plays a very important role. There are typically two sorts of risks in regard with the security exchange which are systematic risk and unsystematic risks and this is the reason why stock market is stochastic in nature. From years, scholars are trying to find a definitive solution for better decision making in market to generate more returns and reduce risk. There are many ratios, formulas and theorems which attempts to predict the stock market but in reality these theorems are made on countless assumptions. With the new age technology and fast computing, we can now solve this problem by advanced algorithms and machine learning. We will take help of probability to solve problems generating because of stochastic nature of Stock market. Computing series of probability at different scenarios and parameters of stock market by using machine learning.

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Section: Related Work Donementioning

confidence: 99%

Predicting the uptrend and downtrend in stock markets for intraday trading using machine learning algorithms

Bhatt¹,

Kartik²,

Vij³

2020

IJETMS

View full text Add to dashboard Cite

show abstract

“…The set of tools available to the decision-makers is growing constantly almost every single month. Concepts related to technical analysis [4], fundamental analysis [5], social trading [6], and different data presentations are extended by new propositions. Despite the undeniable simplicity of the technical analysis and other simplifications of the financial data representations, decision-makers still struggle with the difficulty of the analysis for the single financial instruments.…”

Section: Introductionmentioning

confidence: 99%

Permutation Entropy as a Measure of Information Gain/Loss in the Different Symbolic Descriptions of Financial Data

Kozák

Kania

Juszczuk

2020

Entropy

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Financial markets give a large number of trading opportunities. However, over-complicated systems make it very difficult to be effectively used by decision-makers. Volatility and noise present in the markets evoke a need to simplify the market picture derived for the decision-makers. Symbolic representation fits in this concept and greatly reduces data complexity. However, at the same time, some information from the market is lost. Our motivation is to answer the question: What is the impact of introducing different data representation on the overall amount of information derived for the decision-maker? We concentrate on the possibility of using entropy as a measure of the information gain/loss for the financial data, and as a basic form, we assume permutation entropy with later modifications. We investigate different symbolic representations and compare them with classical data representation in terms of entropy. The real-world data covering the time span of 10 years are used in the experiments. The results and the statistical verification show that extending the symbolic description of the time series does not affect the permutation entropy values.

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“…Ensemble learning techniques have been studied only recently [29], [30]. Therefore, this can be considered as a significant room in the area, as ensemble methods have been confirmed to be outweighed compared to other algorithms [8], [15], [29], [31]. However, the main issue in providing a good voting algorithm to fuse the weight of different classifiers and provide a correct aggregated decision is not optimal as it faces the local optima problem that is tackled by heuristic techniques, making the approach very limited and not algorithmic for the general class of problems.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Stock Price Prediction Using Gradient Boosting Machine with Feature Engineering (GBM-wFE)

Nabi

Saeed

Harron

2020

KJAR

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The prediction of stock prices has become an exciting area for researchers as well as academicians due to its economic impact and potential business profits. This study proposes a novel multiclass classification ensemble learning approach for predicting stock prices based on historical data using feature engineering. The proposed approach comprises four main steps, which are pre-processing, feature selection, feature engineering, and ensemble methods. We use 11 datasets from Nasdaq and S&P 500 to ensure the accuracy of the proposed approach. Furthermore, eight feature selection algorithms are studied and implemented. More importantly, a feature engineering concept is applied to construct two new features, which are appears to be very auspicious in terms of improving classification accuracy, and this is considered the first study to use feature engineering for multiclass classification using ensemble methods. Finally, seven ensemble machine learning (ML) algorithms are used and compared to discover the ultimate collaboration prediction model. Besides, the best feature selection algorithm is proposed. This study proposes a novel multiclass classification approach called Gradient Boosting Machine with Feature Engineering (GBM-wFE) and Principal Component Analysis (PCA) as the feature selection. We find that GBM-wFE outperforms the previous studies and the overall prediction results are auspicious, as MAPE of 0.0406% is achieved, which is considered the best result compared to the available studies in the literature.

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A literature review of technical analysis on stock markets

Cited by 137 publications

References 118 publications

Predicting the uptrend and downtrend in stock markets for intraday trading using machine learning algorithms

Predicting the uptrend and downtrend in stock markets for intraday trading using machine learning algorithms

Permutation Entropy as a Measure of Information Gain/Loss in the Different Symbolic Descriptions of Financial Data

A Novel Approach for Stock Price Prediction Using Gradient Boosting Machine with Feature Engineering (GBM-wFE)

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