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

Author: Grafiati

Published: 4 June 2021

Last updated: 26 January 2023

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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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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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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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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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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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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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11

WANG, HONG-YONG, HONG LI, and JIN-YE SHEN. "A NOVEL HYBRID FRACTAL INTERPOLATION-SVM MODEL FOR FORECASTING STOCK PRICE INDEXES." Fractals 27, no. 04 (2019): 1950055. http://dx.doi.org/10.1142/s0218348x19500555.

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Forecasting stock price indexes has been regarded as a challenging task in financial time series analysis. In order to improve the prediction accuracy, a novel hybrid model that integrates fractal interpolation with support vector machine (SVM) models has been developed in this paper to forecast the time series of stock price indexes. For this, a new method to calculate the vertical scaling factors of the fractal interpolation iterated function system is first proposed and an improved fractal interpolation model is then established. The improved fractal interpolation model and the SVM model ar

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Kushnir, M., and K. Tokarieva. "HYBRID MODEL OF SELF-ORGANIZING MAP AND ADAPTIVE NEURO FUZZY INFERENCE SYSTEM IN STOCK INDEXES FORECASTING." Bukovinian Mathematical Journal 9, no. 2 (2021): 70–80. http://dx.doi.org/10.31861/bmj2021.02.05.

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The paper investigates methods of artiﬁcial intelligence in the prognostication and analysis of ﬁnancial data time series. It is uncovered that scholars and practitioners face some diﬃculties in modelling complex system such as the stock market because it is nonlinear, chaotic, multi- dimensional, and spatial in nature, making forecasting a complex process. Models estimating nonstationary ﬁnancial time series may include noise and errors. The relationship between the input and output parameters of the models is essentially non-linear, where stock prices include higher-level variables, which co

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Hossain, Mohammad Raquibul, and Mohd Tahir Ismail. "EMPIRICAL MODE DECOMPOSITION BASED ON THETA METHOD FOR FORECASTING DAILY STOCK PRICE." Journal of Information and Communication Technology 19, Number 4 (2020): 533–58. http://dx.doi.org/10.32890/jict2020.19.4.4.

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Forecasting is a challenging task as time series data exhibit many features that cannot be captured by a single model. Therefore, many researchers have proposed various hybrid models in order to accommodate these features to improve forecasting results. This work proposed a hybrid method between Empirical Mode Decomposition (EMD) and Theta methods by considering better forecasting potentiality. Both EMD and Theta are efficient methods in their own ground of tasks for decomposition and forecasting, respectively. Combining them to obtain a better synergic outcome deserves consideration. EMD deco

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Panella, Massimo, Francesco Barcellona, and Rita L. D'Ecclesia. "Forecasting Energy Commodity Prices Using Neural Networks." Advances in Decision Sciences 2012 (December 31, 2012): 1–26. http://dx.doi.org/10.1155/2012/289810.

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A new machine learning approach for price modeling is proposed. The use of neural networks as an advanced signal processing tool may be successfully used to model and forecast energy commodity prices, such as crude oil, coal, natural gas, and electricity prices. Energy commodities have shown explosive growth in the last decade. They have become a new asset class used also for investment purposes. This creates a huge demand for better modeling as what occurred in the stock markets in the 1970s. Their price behavior presents unique features causing complex dynamics whose prediction is regarded a

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15

Karthik, C. R., Raghunandan, B. Ashwath Rao, and N. V. Subba Reddy. "Forecasting variance of NiftyIT index with RNN and DNN." Journal of Physics: Conference Series 2161, no. 1 (2022): 012005. http://dx.doi.org/10.1088/1742-6596/2161/1/012005.

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Abstract A time series is an order of observations engaged serially in time. The prime objective of time series analysis is to build mathematical models that provide reasonable descriptions from training data. The goal of time series analysis is to forecast the forthcoming values of a series based on the history of the same series. Forecasting of stock markets is a thought-provoking problem because of the number of possible variables as well as volatile noise that may contribute to the prices of the stock. However, the capability to analyze stock market leanings could be vital to investors, tr

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Padhi, Dushmanta Kumar, Neelamadhab Padhy, Akash Kumar Bhoi, Jana Shafi, and Seid Hassen Yesuf. "An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction." Computational Intelligence and Neuroscience 2022 (June 23, 2022): 1–18. http://dx.doi.org/10.1155/2022/7588303.

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Developing reliable equity market models allows investors to make more informed decisions. A trading model can reduce the risks associated with investment and allow traders to choose the best-paying stocks. However, stock market analysis is complicated with batch processing techniques since stock prices are highly correlated. In recent years, advances in machine learning have given us a lot of chances to use forecasting theory and risk optimization together. The study postulates a unique two-stage framework. First, the mean-variance approach is utilized to select probable stocks (portfolio con

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17

Todorov, Ivan Borisov, and Fernando Sánchez Sánchez Lasheras. "Forecasting Applied to the Electricity, Energy, Gas and Oil Industries: A Systematic Review." Mathematics 10, no. 21 (2022): 3930. http://dx.doi.org/10.3390/math10213930.

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This paper presents a literature review in which methodologies employed for the forecast of the price of stock companies and raw materials in the fields of electricity, oil, gas and energy are studied. This research also makes an analysis of which data variables are employed for training the forecasting models. Three scientific databases were consulted to perform the present research: The Directory of Open Access Journals, the Multidisciplinary Digital Publishing Institute and the Springer Link. After running the same query in the three databases and considering the period from January 2017 to

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18

Zheng, Jianguo, Yilin Wang, Shihan Li, and Hancong Chen. "The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm." Algorithms 14, no. 10 (2021): 299. http://dx.doi.org/10.3390/a14100299.

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Accurate stock market prediction models can provide investors with convenient tools to make better data-based decisions and judgments. Moreover, retail investors and institutional investors could reduce their investment risk by selecting the optimal stock index with the help of these models. Predicting stock index price is one of the most effective tools for risk management and portfolio diversification. The continuous improvement of the accuracy of stock index price forecasts can promote the improvement and maturity of China’s capital market supervision and investment. It is also an important

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19

Zhao, Aiwu, Junhong Gao, and Hongjun Guan. "Forecasting Model for Stock Market Based on Probabilistic Linguistic Logical Relationship and Distance Measurement." Symmetry 12, no. 6 (2020): 954. http://dx.doi.org/10.3390/sym12060954.

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The fluctuation of the stock market has a symmetrical characteristic. To improve the performance of self-forecasting, it is crucial to summarize and accurately express internal fluctuation rules from the historical time series dataset. However, due to the influence of external interference factors, these internal rules are difficult to express by traditional mathematical models. In this paper, a novel forecasting model is proposed based on probabilistic linguistic logical relationships generated from historical time series dataset. The proposed model introduces linguistic variables with positi

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20

Zhang, Xinchen, Linghao Zhang, Qincheng Zhou, and Xu Jin. "A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model." Computational Intelligence and Neuroscience 2022 (May 5, 2022): 1–12. http://dx.doi.org/10.1155/2022/1643413.

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As a result of the fast growth of financial technology and artificial intelligence around the world, quantitative algorithms are now being employed in many classic futures and stock trading, as well as hot digital currency trades, among other applications today. Using the historical price series of Bitcoin and gold from 9/11/2016 to 9/10/2021, we investigate an LSTM-P neural network model for predicting the values of Bitcoin and gold in this research. We first employ a noise reduction approach based on the wavelet transform to smooth the fluctuations of the price data, which has been shown to

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21

Ebong, D. J., P. O. Ogunniyi, and S. O. Edeki. "Geometric progression and relative strength index applied to FX hedging." Journal of Physics: Conference Series 2199, no. 1 (2022): 012018. http://dx.doi.org/10.1088/1742-6596/2199/1/012018.

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Abstract This research aims at profit maximization and loss minimization in any FX market trading. A geometric progression (geometric sequence), is said to be a non-zero number progression or sequence in which each term following the first is obtained via multiplying the prior by the common ratio, which is a predetermined non-zero value. This method seeks to open an opposite position to an existing initial position in order to hedge that initial position in the event that the market moves against our trade. There are a number of mathematical models to develop new hedging strategies for Forex t

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22

Qiu, Wangren, Xiaodong Liu, and Hailin Li. "High-Order Fuzzy Time Series Model Based on Generalized Fuzzy Logical Relationship." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/927394.

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In view of techniques for constructing high-order fuzzy time series models, there are three methods which are based on advanced algorithms, computational methods, and grouping the fuzzy logical relationships, respectively. The last kind model has been widely applied and researched for the reason that it is easy to be understood by the decision makers. To improve the fuzzy time series forecasting model, this paper presents a novel high-order fuzzy time series models denoted asGTS(M,N)on the basis of generalized fuzzy logical relationships. Firstly, the paper introduces some concepts of the gene

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Shcherbinina, A. V., and A. V. Alzheev. "Comparative analysis of the forecasting quality of the classical statistical model and the machine learning model on the data of the Russian stock market." Scientific notes of the Russian academy of entrepreneurship 20, no. 3 (2021): 52–63. http://dx.doi.org/10.24182/2073-6258-2021-20-3-52-63.

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The main objective of this work is to compare the predictive ability of the classical machine learning model — ARIMA, as the most common and well-studied baseline model, and the ML model based on a sequential neural network — in this case, LSTM. The goal is to maximize accuracy and minimize error — selecting the most appropriate model for predicting time series with the highest accuracy. A description is given for these mathematical models. An algorithm is also proposed for forecasting time series using these models, based on the «Rolling window» approach. Practical implementation is implement

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Zhang, Heng-Chang, Qing Wu, Fei-Yan Li, and Hong Li. "Multitask Learning Based on Least Squares Support Vector Regression for Stock Forecast." Axioms 11, no. 6 (2022): 292. http://dx.doi.org/10.3390/axioms11060292.

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Various factors make stock market forecasting difficult and arduous. Single-task learning models fail to achieve good results because they ignore the correlation between multiple related tasks. Multitask learning methods can capture the cross-correlation among subtasks and achieve a satisfactory learning effect by training all tasks simultaneously. With this motivation, we assume that the related tasks are close enough to share a common model whereas having their own independent models. Based on this hypothesis, we propose a multitask learning least squares support vector regression (MTL-LS-SV

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Plakandaras, Vasilios, Periklis Gogas, and Theophilos Papadimitriou. "The Effects of Geopolitical Uncertainty in Forecasting Financial Markets: A Machine Learning Approach." Algorithms 12, no. 1 (2018): 1. http://dx.doi.org/10.3390/a12010001.

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An important ingredient in economic policy planning both in the public or the private sector is risk management. In economics and finance, risk manifests through many forms and it is subject to the sector that it entails (financial, fiscal, international, etc.). An under-investigated form is the risk stemming from geopolitical events, such as wars, political tensions, and conflicts. In contrast, the effects of terrorist acts have been thoroughly examined in the relevant literature. In this paper, we examine the potential ability of geopolitical risk of 14 emerging countries to forecast several

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Tang, Zhenpeng, Tingting Zhang, Junchuan Wu, Xiaoxu Du, and Kaijie Chen. "Multistep-Ahead Stock Price Forecasting Based on Secondary Decomposition Technique and Extreme Learning Machine Optimized by the Differential Evolution Algorithm." Mathematical Problems in Engineering 2020 (July 28, 2020): 1–13. http://dx.doi.org/10.1155/2020/2604915.

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The prediction research of the stock market prices is of great significance. Based on the secondary decomposition techniques of variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD), this paper constructs a new hybrid prediction model by combining with extreme learning machine (ELM) optimized by the differential evolution (DE) algorithm. The hybrid model applies VMD technology to the original stock index price sequence to obtain different modal components and the residual item, then applies EEMD technology to the residual item, and then superimposes the predicti

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Zhou, Dehui. "Financial Market Prediction and Simulation Based on the FEPA Model." Journal of Mathematics 2021 (December 26, 2021): 1–11. http://dx.doi.org/10.1155/2021/5955375.

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Since the birth of the financial market, the industry and academia want to find a method to accurately predict the future trend of the financial market. The ultimate goal of this paper is to build a mathematical model that can effectively predict the short-term trend of the financial time series. This paper presents a new combined forecasting model: its name is Financial Time Series-Empirical Mode Decomposition-Principal Component Analysis-Artificial Neural Network (FEPA) model. This model is mainly composed of three components, which are based on financial time series special empirical mode d

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Dvoryatkina, S., and D. Golovin. "Neural network technologies for analysis and risk assessment in forecasting the market of industrial financial instruments." Journal of Physics: Conference Series 2176, no. 1 (2022): 012091. http://dx.doi.org/10.1088/1742-6596/2176/1/012091.

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Abstract The issues of risk analysis and assessment in forecasting the dynamics of prices on the stock exchange and the OTC market have always been a difficult task for many researchers and analysts. Successful investment is largely determined by knowledge of the future situation in the financial market. The article deals with the issues of search and development of technical tools for risk analysis and assessment in forecasting the market of derivative financial instruments under conditions of uncertainty. A recurrent network with a long-term short-term memory cell LSTM, which is one of the m

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Zolotova, T. V., and D. A. Volkova. "Intelligent Data Processing Methods for the Atypical Values Correction of Stock Quotes." Statistics and Economics 19, no. 2 (2022): 4–13. http://dx.doi.org/10.21686/2500-3925-2022-2-.

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Purpose of the study. The purpose of the study is to carry out a comparative analysis of various methods for correcting atypical values of statistical data on the stock market and to develop recommendations for their use.Materials and methods. The article analyzes Russian and foreign bibliography on the research problem. Consideration of machine learning methods for detecting and correcting outliers in time series is proposed. The mathematical basis of machine learning methods is the Z-score method, the isolation forest method, support vector method for outlier detection, and winsorization and

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Zolotova, T. V., and D. A. Volkova. "Intelligent Data Processing Methods for the Atypical Values Correction of Stock Quotes." Statistics and Economics 19, no. 2 (2022): 4–13. http://dx.doi.org/10.21686/2500-3925-2022-2-4-13.

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Purpose of the study. The purpose of the study is to carry out a comparative analysis of various methods for correcting atypical values of statistical data on the stock market and to develop recommendations for their use.Materials and methods. The article analyzes Russian and foreign bibliography on the research problem. Consideration of machine learning methods for detecting and correcting outliers in time series is proposed. The mathematical basis of machine learning methods is the Z-score method, the isolation forest method, support vector method for outlier detection, and winsorization and

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Venediktov, G. L., and V. M. Kochetkov. "Comprehensive optimization of passenger trains operation based on an automated system for managing the profitability of passenger traffic." VNIIZHT Scientific Journal 79, no. 6 (2021): 343–50. http://dx.doi.org/10.21780/2223-9731-2020-79-6-343-350.

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The article is devoted to solving the problem of optimizing the tariff policy, which is relevant for the passenger complex, with the most rational use of the rolling stock. Principles of economic and mathematical modeling are presented in order to determine the optimal number of cars in passenger trains and prices for travel in a single calculation process called complex optimization. Developed models make it possible to form optimal train schemes in accordance with the predicted demand, balancing supply and demand for transportation, which, in turn, radically increases its economic efficiency

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Chen, Yijun, Chongshi Gu, Chenfei Shao, et al. "An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction." Mathematical Problems in Engineering 2020 (April 13, 2020): 1–23. http://dx.doi.org/10.1155/2020/9434065.

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A dam deformation prediction model based on adaptive weighted least squares support vector machines (AWLSSVM) coupled with modified Ant Lion Optimization (ALO) is proposed, which can be utilized to evaluate the operational states of concrete dams. First, the Ant Lion Optimizer, a novel metaheuristic algorithm, is used to determine the punishment factor and kernel width in the least squares support vector machine (LSSVM) model, which simulates the hunting process of antlions in nature. Second, aiming to solve the premature convergence phenomenon, Levy flight is introduced into the ALO to improv

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Yu, Menghan, Panji Wang, and Tong Wang. "Application of Hidden Markov Models in Stock Forecasting." Proceedings of Business and Economic Studies 5, no. 6 (2022): 14–21. http://dx.doi.org/10.26689/pbes.v5i6.4453.

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In this paper, we tested our methodology on the stocks of four representative companies: Apple, Comcast Corporation (CMCST), Google, and Qualcomm. We compared their performance to several stocks using the hidden Markov model (HMM) and forecasts using mean absolute percentage error (MAPE). For simplicity, we considered four main features in these stocks: open, close, high, and low prices. When using the HMM for forecasting, the HMM has the best prediction for the daily low stock price and daily high stock price of Apple and CMCST, respectively. By calculating the MAPE for the four data sets of

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Sunthornwat, Rapin, and Yupaporn Areepong. "Forecasting Cyclical and Non-cyclical Stock Prices on the Stock Exchange of Thailand." Malaysian Journal of Fundamental and Applied Sciences 17, no. 5 (2021): 550–65. http://dx.doi.org/10.11113/mjfas.v17n5.2175.

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Forecasting is an important role in organizations for decision making and planning. This research is to forecast the cyclical and non-cyclical weekly stock prices on the Stock Exchange of Thailand by using the models of Geometric Brownian motion, Fourier’s series, and Cauchy initial value problem. The accuracy and performance of the models are based on the minimum root mean squared percentage error which is the error between actual and forecasted stock prices. The results showed that Geometric Brownian motion is suitable for forecasting both cyclical and non-cyclical stock prices because of mi

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Shahi, Tej Bahadur, Ashish Shrestha, Arjun Neupane, and William Guo. "Stock Price Forecasting with Deep Learning: A Comparative Study." Mathematics 8, no. 9 (2020): 1441. http://dx.doi.org/10.3390/math8091441.

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The long short-term memory (LSTM) and gated recurrent unit (GRU) models are popular deep-learning architectures for stock market forecasting. Various studies have speculated that incorporating financial news sentiment in forecasting could produce a better performance than using stock features alone. This study carried a normalized comparison on the performances of LSTM and GRU for stock market forecasting under the same conditions and objectively assessed the significance of incorporating the financial news sentiments in stock market forecasting. This comparative study is conducted on the coop

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Rubio, Lihki, and Keyla Alba. "Forecasting Selected Colombian Shares Using a Hybrid ARIMA-SVR Model." Mathematics 10, no. 13 (2022): 2181. http://dx.doi.org/10.3390/math10132181.

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Forecasting future values of Colombian companies traded on the New York Stock Exchange is a daily challenge for investors, due to these stocks’ high volatility. There are several forecasting models for forecasting time series data, such as the autoregressive integrated moving average (ARIMA) model, which has been considered the most-used regression model in time series prediction for the last four decades, although the ARIMA model cannot estimate non-linear regression behavior caused by high volatility in the time series. In addition, the support vector regression (SVR) model is a pioneering m

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You, Zixuan. "Evaluation of two models for predicting Amazon stock based on machine learning." BCP Business & Management 34 (December 14, 2022): 39–47. http://dx.doi.org/10.54691/bcpbm.v34i.2862.

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With the fast growth of artificial intelligence and technology, the use of machine learning techniques in financial markets is gaining popularity. As a result, many opportunities arise, such as predicting future stock movements. Financial markets are complex and constantly evolving environments, so analyzing them can be challenging and interesting. There are no specific rules to predict or estimate the value of a stock in the stock market, so one can do stock price prediction by various methods. In this project, the stock price data of Amazon for the past five years, ‘Date’, ‘Starting price’,

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Lu, Wenjie, Jiazheng Li, Yifan Li, Aijun Sun, and Jingyang Wang. "A CNN-LSTM-Based Model to Forecast Stock Prices." Complexity 2020 (November 23, 2020): 1–10. http://dx.doi.org/10.1155/2020/6622927.

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Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. The data utilized in this research concern the daily stock prices from July 1, 1991, to August 31,

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Dan, Jingpei, Wenbo Guo, Weiren Shi, Bin Fang, and Tingping Zhang. "PSO Based Deterministic ESN Models for Stock Price Forecasting." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 2 (2015): 312–18. http://dx.doi.org/10.20965/jaciii.2015.p0312.

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Deterministic echo state network (ESN) models integrated with particle swarm optimization (PSO) are proposed to improve the accuracy and efficiency of stock price forecasting. ESNs have been successfully applied to financial time series forecasting because of their efficient and powerful computational ability in approximating nonlinear dynamical systems. However, reservoir construction in standard ESNs is primarily driven by a series of randomized model-building stages, because of which both researchers and practitioners have to rely on a series of trials and errors to determine parameters. An

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Murthi, M. V. Narayana. "Comparing Two ARIMA Models for Daily Stock Price Data." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 1606–10. http://dx.doi.org/10.22214/ijraset.2021.38943.

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Abstract: Analyzing the past data and planning for future is very important for every public and private organizational decisions. Now a days individuals also using forecasting methods to invest in Stock market. Investments in mutual funds and in registered companies in companies in stock market is the order of the day. In this paper, advanced forecasting methods are fitted to the time related stock price data to study its effectiveness in forecasting future events. Auto correlation and standard models have been analyzed before fitting this model to the above data. The forecasting can be done

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Pai, Ping-Feng, Ping-Teng Chang, Kuo-Ping Lin, and Wei-Chiang Hongg. "Hybrid learning fuzzy neural models in stock price forecasting." Journal of Information and Optimization Sciences 26, no. 3 (2005): 495–508. http://dx.doi.org/10.1080/02522667.2005.10699661.

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Leangarun, Teema, Poj Tangamchit, and Suttipong Thajchayapong. "Stock Price Manipulation Detection Based on Mathematical Models." International Journal of Trade, Economics and Finance 7, no. 3 (2016): 81–88. http://dx.doi.org/10.18178/ijtef.2016.7.3.503.

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Wankhade, Sunil B., Divyesh Surana, Neel J. Mansatta, and Karan Shah. "Hybrid Model based on unification of Technical Analysis and Sentiment Analysis for Stock Price Prediction." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 9 (2013): 3025–33. http://dx.doi.org/10.24297/ijct.v11i9.3415.

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Stock price forecasting phenomenon has been majorly made on the basis of quantitative information. Over the time, with the advent of technology, stock forecasting used technical analysis to get more accurate predictions. Until recently, studies have demonstrated that sentiment information hidden in corporate reports can be effectively incorporated to predict short-run stock price returns. Soft computing methods, like neural networks, fuzzy models and support vector regression, have shown great results in the forecasting of stock price due to their ability to model complex non-linear systems.In

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Xaba, Diteboho, Ntebogang Dinah Moroke, Johnson Arkaah, and Charlemagne Pooe. "A Comparative Study Of Stock Price Forecasting Using Nonlinear Models." Risk Governance and Control: Financial Markets and Institutions 7, no. 2 (2017): 7–17. http://dx.doi.org/10.22495/rgcv7i2art1.

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This study compared the in-sample forecasting accuracy of three forecasting nonlinear models namely: the Smooth Transition Regression (STR) model, the Threshold Autoregressive (TAR) model and the Markov-switching Autoregressive (MS-AR) model. Nonlinearity tests were used to confirm the validity of the assumptions of the study. The study used model selection criteria, SBC to select the optimal lag order and for the selection of appropriate models. The Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) served as the error measures in evaluating the forecasting a

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Reddy, Dinesh, and Abhinav Karthik. "Forecasting Stock Price using LSTM-CNN Method." International Journal of Engineering and Advanced Technology 11, no. 1 (2021): 1–8. http://dx.doi.org/10.35940/ijeat.a3117.1011121.

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Foreseeing assumes an indispensable part in setting an exchanging methodology or deciding the ideal opportunity to purchase or sell stock. We propose an element combination long transient memory-convolutional neural organization (LSTM-CNN) model, which joins highlights gained from various presentations of similar information, i.e., stock timetable and stock outline pictures, to anticipate stock costs. The proposed model is created by LSTM and CNN, which extricate impermanent and picture components. We assessed the proposed single model (CNN and LSTM) utilizing SPDR S&P 500 ETF information.

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Weng, Qiangwei, Ruohan Liu, and Zheng Tao. "Forecasting Tesla’s Stock Price Using the ARIMA Model." Proceedings of Business and Economic Studies 5, no. 5 (2022): 38–45. http://dx.doi.org/10.26689/pbes.v5i5.4331.

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   The stock market is an important economic information center. The economic benefits generated by stock price prediction have attracted much attention. Although the stock market cannot be predicted accurately, the stock market’s prediction of the trend of stock prices helps in grasping the operation law of the stock market and the influence mechanism on the economy. The autoregressive integrated moving average (ARIMA) model is one of the most widely accepted and used time series forecasting models. Therefore, this paper first compares the return on investment (ROI) of Apple

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Maya Citra. "Comparative Study of Stock Price Forecasting Models PT. Unilever Indonesia Tbk Using Arima and Garch." International Journal of Community Service (IJCS) 2, no. 1 (2021): 1–22. http://dx.doi.org/10.55299/ijcs.v2i1.220.

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The purpose of this study is to know the comparison of forecasting models in predicting the stock price of PT. Unilever Indonesia Tbk. In this study, there are 2 forecasting models, namely ARIMA and GARCH forecasting. The population in this study is data on the daily closing price of PT. Unilever Indonesia Tbk for the period January 2018 to June 2021, so the sample in this study is 1090 time series data. The results showed that the best forecasting model to predict the stock price of PT. Unilever Indonesia Tbk, namely ARIMA (1,1,1) and GARCH (1,1). In the ARIMA model (1,1,1) there are assumpti

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Najand, Mohammad. "Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models." Financial Review 37, no. 1 (2002): 93–104. http://dx.doi.org/10.1111/1540-6288.00006.

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Gao, Ya, Rong Wang, and Enmin Zhou. "Stock Prediction Based on Optimized LSTM and GRU Models." Scientific Programming 2021 (September 29, 2021): 1–8. http://dx.doi.org/10.1155/2021/4055281.

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Stock market prediction has always been an important research topic in the financial field. In the past, inventors used traditional analysis methods such as K-line diagrams to predict stock trends, but with the progress of science and technology and the development of market economy, the price trend of a stock is disturbed by various factors. The traditional analysis method is far from being able to resolve the stock price fluctuations in the hidden important information. So, the prediction accuracy is greatly reduced. In this paper, we design a new model for optimizing stock forecasting. We i

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Alwi, Wahidah, Aprilia Pratiwi S, and Ilham Syata. "Forcasting Stock Price PT. Indonesian Telecomunication with ARCH-GARCH Model." Jurnal Varian 5, no. 2 (2022): 125–36. http://dx.doi.org/10.30812/varian.v5i2.1543.

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This research discusses the modeling of time series using R software, focusing on forecasting the stock price of PT. Indonesian telecommunications with ARCH-GARCH model. The data used daily closing data on stock prices from January 6, 2020, to January 6, 2021 was obtained from the website www.finance.yahoo.com. The goal is to find out the best model arch-garch on PT. Indonesian telecommunications to find out the results of stock price forecasting the next day using the ARCH-GARCH model. The best model was ARIMA (2,1,3). The results of the ARCH-LM test showed the data contained heteroskedastici

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