Nikkei Stock Market Price Index Prediction Using Machine Learning

Harahap, Lukman Adlin; Lipikorn, Rajalida; Kitamoto, Asanobu

doi:10.1088/1742-6596/1566/1/012043

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…Further, Singh [11] forecast the Nifty 50 (Indian Stock Market Index) using eight machine learning models, including Adaptive Boost (AdaBoost), k-Nearest Neighbors (KNN) and Artificial Neural Networks (ANNs), among others. As a final example, Harahap et al [12] present the usage of Deep Neural Networks (DNNs), Back Propagation Neural Networks (BPNNs) and SVR techniques for the forecast of the N225. A summary and a brief discussion of the results presented in this section are given in Section 5.3.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition to the evaluation made in Section 5.2, a further cross-reference [6][7][8][9][10][11][12] comparison against the results obtained in Section 5.1 is presented here. In this respect, Table 14 summarizes some of the results found in the literature and compares them to the results of the forecast using our AHC algorithm.…”

Section: Cross-reference Comparisonmentioning

confidence: 99%

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A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model

González-Núñez,

Trejo,

Kampouridis

2024

BDCC

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This research aims at applying the Artificial Organic Network (AON), a nature-inspired, supervised, metaheuristic machine learning framework, to develop a new algorithm based on this machine learning class. The focus of the new algorithm is to model and predict stock markets based on the Index Tracking Problem (ITP). In this work, we present a new algorithm, based on the AON framework, that we call Artificial Halocarbon Compounds, or the AHC algorithm for short. In this study, we compare the AHC algorithm against genetic algorithms (GAs), by forecasting eight stock market indices. Additionally, we performed a cross-reference comparison against results regarding the forecast of other stock market indices based on state-of-the-art machine learning methods. The efficacy of the AHC model is evaluated by modeling each index, producing highly promising results. For instance, in the case of the IPC Mexico index, the R-square is 0.9806, with a mean relative error of 7×10−4. Several new features characterize our new model, mainly adaptability, dynamism and topology reconfiguration. This model can be applied to systems requiring simulation analysis using time series data, providing a versatile solution to complex problems like financial forecasting.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Cross-reference Comparisonmentioning

confidence: 99%

A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model

González-Núñez,

Trejo,

Kampouridis

2024

BDCC

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“…A primary goal of this field (TS-forecasting) is to furnish a reliable forecast by mining the inherent structure and hidden information of the TS and applying it to the model appropriately. Recent works suggest that in TS-forecasting, there exist many approaches, e.g., exponential smoothing (ES) [2,3], auto-regressive integrated moving average (ARIMA) [4,5] artificial neural network (ANN) [6,7], extreme learning machine (ELM) [8,9], facebook-prophet (FB-Prophet) [10,11], support vector regression (SVR) [12,13]. Researchers have also applied ensemble approaches for TS-forecasting and realized effective forecasting, as apparent from the pieces of literature [14−16].…”

Section: *Author For Correspondencementioning

confidence: 99%

Forecasting non-linear macroeconomic indexes of India: an ensemble of MLP and Holt’s linear methods

2022

IJATEE

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Electricity and electric-equipment play a critical role in modern living, and precise price forecasting for these items aids decision-makers in anticipating changes, planning, and budgeting ahead of time. This present research focused on the wholesale price indexes of items from the "manufacture of electrical equipment (MEEQ)" group of India's existing wholesale price index (WPI) series. This work proposed a novel, state-of-art ensemble forecast approach that used multilayer perceptron (MLP) and Holt's linear (HL) approaches for some specified WPIs from this group. The researchers selected the WPIs that manifest non-linearity as determined by the curve-fit technique. The paper applied a variance-based weighted average scheme to generate the ensemble forecast. The statistical rigor-based curve-fit aided in identifying that seventeen out of forty-eight indexes manifest non-linear fits. The proposed MLP-HL ensemble approach exhibited excellent forecast results (relative root mean squared error, i.e., RRMSE < 10%) for all seventeen WPIs. For each of these seventeen WPI's, the current work compared the proposed MLP-HL with nineteen models (eight statistical, four machine learning, and seven contemporary ensemble strategies). In the majority of the cases, it outran the nineteen models in terms of mean absolute error (MAE) and mean absolute percentage error (MAPE). As an alternative pathway for forecasting these seventeen non-linear WPIs, the present work suggests using this proposed MLP-HL approach.

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“…Harga saham dipengaruhi oleh berbagai faktor, termasuk aktivitas jual beli oleh para investor (Bourezk et al, 2019;Gao et al, 2021). Saat ini, investor menggunakan dua pendekatan umum untuk pengambilan keputusan investasi atau perdagangan saham, yaitu analisis fundamental dan analisis teknikal (Ghosh et al, 2019;Harahap et al, 2020;Nti et al, 2020).…”

Section: Introductionunclassified

Comparison Random Forest Regression and Linear Regression For Forecasting BBCA Stock Price

Priyatno,

Tanjung,

Ramadhan

et al. 2023

JUTIN

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Stock trading is a popular financial instrument worldwide. In Indonesia, the stock market is known as the Indonesia Stock Exchange (BEI), and one actively traded stock is PT Bank Central Asia (BBCA). However, predicting stock price movements is challenging due to various influencing factors. Investors use fundamental and technical analyses for decision-making, but results often vary. Machine learning, particularly random forest regression and linear regression algorithms, can be used for stock price forecasting. In this paper, we compares these two machine learning methods to forecast BBCA stock prices, aiming to provide more accurate and effective solutions for investor's investment and trading decisions. The evaluation results of cross-validation mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for linear regression were 0.12848, 0.35807, 0.29570, and 0.0036%, respectively, while for random forest regression were 27473.76, 158.04, 142.70, and 1.7153%. These findings indicate that linear regression outperforms in forecasting performance.

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Nikkei Stock Market Price Index Prediction Using Machine Learning

Cited by 9 publications

References 9 publications

A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model

A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model

Forecasting non-linear macroeconomic indexes of India: an ensemble of MLP and Holt’s linear methods

Comparison Random Forest Regression and Linear Regression For Forecasting BBCA Stock Price

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