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Relevant bibliographies by topics / Stock price forecasting – Mathematical models

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Journal articles

Academic literature on the topic 'Stock price forecasting – Mathematical models'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 January 2023

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Journal articles on the topic "Stock price forecasting – Mathematical models"

1

Lv, Jiehua, Chao Wang, Wei Gao, and Qiumin Zhao. "An Economic Forecasting Method Based on the LightGBM-Optimized LSTM and Time-Series Model." Computational Intelligence and Neuroscience 2021 (September 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/8128879.

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Stock price prediction is very important in financial decision-making, and it is also the most difficult part of economic forecasting. The factors affecting stock prices are complex and changeable, and stock price fluctuations have a certain degree of randomness. If we can accurately predict stock prices, regulatory authorities can conduct reasonable supervision of the stock market and provide investors with valuable investment decision-making information. As we know, the LSTM (Long Short-Term Memory) algorithm is mainly used in large-scale data mining competitions, but it has not yet been use

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2

Lascsáková, Marcela. "Improving Accuracy of the Numerical Model Forecasting Commodity Prices." Applied Mechanics and Materials 708 (December 2014): 251–56. http://dx.doi.org/10.4028/www.scientific.net/amm.708.251.

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In mathematical models, for forecasting prices on commodity exchanges different mathematical methods are used. In the paper the numerical model based on the exponential approximation of commodity stock exchanges was derived. The price prognoses of aluminium on the London Metal Exchange were determined as numerical solution of the Cauchy initial problem for the 1st order ordinary differential equation. To make the numerical model more accurate the idea of the modification of the initial condition value by the stock exchange was realized. The derived numerical model was observed to determine the

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3

Ma, Guifen, Ping Chen, Zhaoshan Liu, and Jia Liu. "The Prediction of Enterprise Stock Change Trend by Deep Neural Network Model." Computational Intelligence and Neuroscience 2022 (August 2, 2022): 1–9. http://dx.doi.org/10.1155/2022/9193055.

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This study aims to accurately predict the changing trend of stocks in stock trading so that company investors can obtain higher returns. In building a financial forecasting model, historical data and learned parameters are used to predict future stock prices. Firstly, the relevant theories of stock forecasting are discussed, and problems in stock forecasting are raised. Secondly, the inadequacies of deep neural network (DNN) models are discussed. A prediction trend model of enterprise stock is established based on long short-term memory (LSTM). The uniqueness and innovation lie in using the st

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4

Areerak, Tidarut. "Mathematical Model of Stock Prices via a Fractional Brownian Motion Model with Adaptive Parameters." ISRN Applied Mathematics 2014 (April 7, 2014): 1–6. http://dx.doi.org/10.1155/2014/791418.

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The paper presents a mathematical model of stock prices using a fractional Brownian motion model with adaptive parameters (FBMAP). The accuracy index of the proposed model is compared with the Brownian motion model with adaptive parameters (BMAP). The parameters in both models are adapted at any time. The ADVANC Info Service Public Company Limited (ADVANC) and Land and Houses Public Company Limited (LH) closed prices are concerned in the paper. The Brownian motion model with adaptive parameters (BMAP) and fractional Brownian motion model with adaptive parameters (FBMAP) are applied to identify

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5

Dan, Jingpei, Wenbo Guo, Weiren Shi, Bin Fang, and Tingping Zhang. "Deterministic Echo State Networks Based Stock Price Forecasting." Abstract and Applied Analysis 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/137148.

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Echo state networks (ESNs), as efficient and powerful computational models for approximating nonlinear dynamical systems, have been successfully applied in financial time series forecasting. Reservoir constructions in standard ESNs rely on trials and errors in real applications due to a series of randomized model building stages. A novel form of ESN with deterministically constructed reservoir is competitive with standard ESN by minimal complexity and possibility of optimizations for ESN specifications. In this paper, forecasting performances of deterministic ESNs are investigated in stock pri

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6

Alenezy, Abdullah H., Mohd Tahir Ismail, Jamil J. Jaber, S. AL Wadi, and Rami S. Alkhawaldeh. "Hybrid fuzzy inference rules of descent method and wavelet function for volatility forecasting." PLOS ONE 17, no. 12 (2022): e0278835. http://dx.doi.org/10.1371/journal.pone.0278835.

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This research employs the gradient descent learning (FIR.DM) approach as a learning process in a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) to improve volatility prediction of daily stock market prices using Saudi Arabia’s stock exchange (Tadawul) data. The MODWT comprises five mathematical functions and fuzzy inference rules. The inputs are the oil price (Loil) and repo rate (Repo) according to multiple regression correlation, and the Engle and Granger Causality test Engle RF, (1987). The logarithm of the stock market price (LSCS) in Tadawul reflects th

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7

Adebiyi, Ayodele Ariyo, Aderemi Oluyinka Adewumi, and Charles Korede Ayo. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction." Journal of Applied Mathematics 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/614342.

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This paper examines the forecasting performance of ARIMA and artificial neural networks model with published stock data obtained from New York Stock Exchange. The empirical results obtained reveal the superiority of neural networks model over ARIMA model. The findings further resolve and clarify contradictory opinions reported in literature over the superiority of neural networks and ARIMA model and vice versa.

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8

Kumar Jaiswal, Jitendra, and Raja Das. "Artificial Neural Network Algorithms based Nonlinear Data Analysis for Forecasting in the Finance Sector." International Journal of Engineering & Technology 7, no. 4.10 (2018): 169. http://dx.doi.org/10.14419/ijet.v7i4.10.20829.

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The involvement of big populace in the quantitative trading has been increased remarkably since the wired and wireless systems have become quite ubiquitous in the fields of finance and economics. Statistical, mathematical and technical analysis in parallel with machine learning and artificial intelligence are frequently being applied to perceive prices moving pattern and forecasting. However stock price do not follow any deterministic regulatory function, factor or circumstances rather than many considerations such as economy and finance, political environments, demand and supply, buying and s

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9

Wang, Pengyue, Xuesheng Li, Zhiliang Qin, Yuanyuan Qu, and Zhongkai Zhang. "Stock Price Forecasting Based on Wavelet Filtering and Ensembled Machine Learning Model." Mathematical Problems in Engineering 2022 (June 24, 2022): 1–12. http://dx.doi.org/10.1155/2022/4024953.

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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 decompos

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10

Alenezy, Abdullah H., Mohd Tahir Ismail, S. Al Wadi, et al. "Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions." Journal of Mathematics 2021 (August 27, 2021): 1–10. http://dx.doi.org/10.1155/2021/9954341.

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This study aims to model and enhance the forecasting accuracy of Saudi Arabia stock exchange (Tadawul) data patterns using the daily stock price indices data with 2026 observations from October 2011 to December 2019. This study employs a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions, namely, Haar, Daubechies (Db), Least Square (LA-8), Best localization (BL14), and Coiflet (C6) in conjunction with adaptive network-based fuzzy inference system (ANFIS). We have selected oil price (Loil) and repo rate (Repo) as input values acco

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