Share price prediction of aerospace relevant companies with recurrent neural networks based on PCA

Zheng, Linyu; He, Hongmei

doi:10.1016/j.eswa.2021.115384

Cited by 23 publications

(16 citation statements)

References 44 publications

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“…The idea of PCA is to project n -dimensional data onto k -dimensional (

) hyperplane, thus minimising the projection distance from each sample point to the hyperplane and maximising the variance. As described in [ 26 ], the implementation of PCA mainly includes five steps: Standardise the data samples by mean normalisation. Calculate the covariance matrix of the data.…”

Section: Second Approach: Pca and Q Statisticsmentioning

confidence: 99%

Comparative Study between Physics-Informed CNN and PCA in Induction Motor Broken Bars MCSA Detection

Boushaba

Cauët

Chamroo

et al. 2022

Sensors

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In this article, two methods for broken bar detection in induction motors are considered and tested using data collected from the LIAS laboratory at the University of Poitiers. The first approach is Motor Current Signature Analysis (MCSA) with Convolutional Neural Networks (CNN), in which measurements have to be processed in the frequency domain before training the CNN to ensure that the resulting model is physically informed. A double input CNN has been introduced to perform a 100% detection regardless of the speed and load torque value. A second approach is the Principal Components Analysis (PCA), in which the processing is undertaken in the time domain. The PCA is applied on the induction motor currents to eventually calculate the Q statistic that serves as a threshold for detecting anomalies/faults. Even if obtained results show that both approaches work very well, there are major differences that need to be pointed out, and this is the aim of the current paper.

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“…The idea of PCA is to project n -dimensional data onto k -dimensional (

Section: Second Approach: Pca and Q Statisticsmentioning

confidence: 99%

Comparative Study between Physics-Informed CNN and PCA in Induction Motor Broken Bars MCSA Detection

Boushaba

Cauët

Chamroo

et al. 2022

Sensors

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show abstract

“…Furthermore, since PCA is a dimension reduction technique, the first eigenvalues may capture technical indicators such as directionality and market momentum. Recent examples in the literature can be found in Liang et al (2020) extracting common factors in commodity futures, and Zheng and He (2021) for dimension reduction and forecasting.…”

Section: Gaussian Distributionsmentioning

confidence: 99%

Straightening skewed markets with an index tracking optimizationless portfolio

Bufalo¹,

Bufalo²,

Cesarone³

et al. 2022

Preprint

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Among professionals and academics alike, it is well known that active portfolio management is unable to provide additional risk-adjusted returns relative to their benchmarks. For this reason, passive wealth management has emerged in recent decades to offer returns close to benchmarks at a lower cost. In this article, we first refine the existing results on the theoretical properties of oblique Brownian motion. Then, assuming that the returns follow skew geometric Brownian motions and that they are correlated, we describe some statistical properties for the ex-post, the ex-ante tracking errors, and the forecasted tracking portfolio. To this end, we develop an innovative statistical methodology, based on a benchmark-asset principal component factorization, to determine a tracking portfolio that replicates the performance of a benchmark by investing in a subset of the investable universe. This strategy, named hybrid Principal Component Analysis (hPCA), is applied both on normal and skew distributions. In the case of skew-normal returns, we propose a framework for calibrating the model parameters, based on the maximum likelihood estimation method. For testing and validation, we compare four alternative models for index tracking. The first two are based on the hPCA when returns are assumed to be normal or skew-normal. The third model adopts a standard optimizationbased approach and the last one is used in the financial sector by some practitioners. For validation and testing, we present a thorough comparison of these strategies on real-world data, both in terms of performance and computational efficiency. A noticeable result is that, not only, the suggested lean PCA-based portfolio selection approach compares well versus cumbersome algorithms for optimization-based portfolios, but, also, it could provide a better service to the asset management industry.

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“…In recent years, with the development of artificial intelligence, many researchers have applied machine learning methods to the financial field [ 10 – 20 ], including genetic algorithm, convolutional neural network, and support vector machine. Hammad [ 21 ] found the artificial neural network has a strong predictive ability and high precision compared with econometric analysis methods.…”

Section: Introductionmentioning

confidence: 99%

The prediction of fluctuation in the order-driven financial market

et al. 2021

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Risk prediction is one of the important issues that draws much attention from academia and industry. And the fluctuation—absolute value of the change of price, is one of the indexes of risk. In this paper, we focus on the relationship between fluctuation and order volume. Based on the observation that the price would move when the volume of order changes, the prediction of price fluctuation can be converted into the prediction of order volume. Modelling the trader’s behaviours—order placement and order cancellation, we propose an order-based fluctuation prediction model. And our model outperforms better than baseline in OKCoin and BTC-e datasets.

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Share price prediction of aerospace relevant companies with recurrent neural networks based on PCA

Cited by 23 publications

References 44 publications

Comparative Study between Physics-Informed CNN and PCA in Induction Motor Broken Bars MCSA Detection

Comparative Study between Physics-Informed CNN and PCA in Induction Motor Broken Bars MCSA Detection

Straightening skewed markets with an index tracking optimizationless portfolio

The prediction of fluctuation in the order-driven financial market

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