Method for Improving the Performance of Technical Analysis Indicators By Neural Network Models

Shi, Yong; Li, Bo; Long, Wen; Dai, Wei

doi:10.1007/s10614-021-10116-7

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…The possible range of RSI is 0–100 and several interoperations could be found for it. As a common approach, a large (small) value of RSI is the indicator of an overbought (oversold) condition and we also selected this idea for comparison [ 57 ].…”

Section: Preliminariesmentioning

confidence: 99%

A novel application of statistical process control charts in financial market surveillance with the idea of profile monitoring

Yeganeh

Shongwe

2023

PLoS ONE

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The implementation of statistical techniques in on-line surveillance of financial markets has been frequently studied more recently. As a novel approach, statistical control charts which are famous tools for monitoring industrial processes, have been applied in various financial applications in the last three decades. The aim of this study is to propose a novel application of control charts called profile monitoring in the surveillance of the cryptocurrency markets. In this way, a new control chart is proposed to monitor the price variation of a pair of two most famous cryptocurrencies i.e., Bitcoin (BTC) and Ethereum (ETH). Parameter estimation, tuning and sensitivity analysis are conducted assuming that the random explanatory variable follows a symmetric normal distribution. The triggered signals from the proposed method are interpreted to convert the BTC and ETH at proper times to increase their total value. Hence, the proposed method could be considered a financial indicator so that its signal can lead to a tangible increase of the pair of assets. The performance of the proposed method is investigated through different parameter adjustments and compared with some common technical indicators under a real data set. The results show the acceptable and superior performance of the proposed method.

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Section: Preliminariesmentioning

confidence: 99%

A novel application of statistical process control charts in financial market surveillance with the idea of profile monitoring

Yeganeh

Shongwe

2023

PLoS ONE

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“…It derived from the stochastic oscillator, which, however, differs from the latter by including an extra J line. Values of K and D lines show whether a stock is overbought or oversold; while the J line represents the divergence of the D line from the K line [22]. e indicator incorporates price levels accounting for the amplitude of fluctuations in the prices.…”

Section: Financial Technical Indicatorsmentioning

confidence: 99%

Stock Price Forecasting Based on Wavelet Filtering and Ensembled Machine Learning Model

Wang

Qin

et al. 2022

Mathematical Problems in Engineering

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Financial data are not only characterized by time-domain correlations but also heavily influenced by numerous market factors. In stock price analysis, the prediction of short-term movements is of much interest to investors and traders. In this paper, we consider forecasting price movements based on ensembled machine learning models, which is generally viewed as a challenging task due to noise components inherent in the data and uncertainties in various forms of financial information related to stock prices. To enhance the accuracy of trend predictions, we propose to use wavelet packet decomposition (WPD) and kernel-based smoothing techniques to remove high-frequency noise from the data, based on which we further perform feature engineering to obtain a comprehensive list of multidimensional technical features. Subsequently, we employ the light gradient boosting machine (lightGBM) algorithm to classify the change in the direction of the price trend that occurs in ten trading days. Numerical results on the Shanghai composite index show that the proposed approach has noticeable advantages over traditional statistical and machine learning methods when predicting near term price trends. Index terms—ensembled machine learning, feature correlation, financial data, LGBM, and wavelet denoising.

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Prediction of Precious Metal Index Based on Ensemble Learning and SHAP Interpretable Method

Zhang,

Liang,

2024

Comput Econ

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Method for Improving the Performance of Technical Analysis Indicators By Neural Network Models

Cited by 6 publications

References 24 publications

A novel application of statistical process control charts in financial market surveillance with the idea of profile monitoring

A novel application of statistical process control charts in financial market surveillance with the idea of profile monitoring

Stock Price Forecasting Based on Wavelet Filtering and Ensembled Machine Learning Model

Prediction of Precious Metal Index Based on Ensemble Learning and SHAP Interpretable Method

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