Enhancement of stock market forecasting using an improved fundamental analysis-based approach

Chen, Yuh Jen; Chen, Yuh Min; Chang, Lu

doi:10.1007/s00500-016-2028-y

Cited by 40 publications

(13 citation statements)

References 26 publications

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“…Efendi et al 20 designed the Improved Fuzzy Random Auto-Regression (FR-AR) model, as the forecasting accuracy of the FR-AR model was enhanced by using this method and was capable of solving the biasness among the machine and human error, but it cannot be applied to handle the nonstationary time series data of the various area. Chen et al 23 modeled the improved fundamental analysis-based approach, which was useful for the investors to choose the optimal stocks and predict the future price trend. In addition, this system improved forecasting accuracy and helped in the enhancement of decision quality.…”

Section: Literature Reviewmentioning

confidence: 99%

Stock Market Prediction Using Optimized Deep-ConvLSTM Model

Kelotra

Pandey

2020

Big Data

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Stock market prediction acts as a challenging area for the investors for obtaining the profits in the financial markets. A greater number of models used in stock market forecasting is not capable of providing an accurate prediction. This article proposes a stock market prediction system that effectively predicts the state of the stock market. The deep convolutional long short-term memory (Deep-ConvLSTM) model acts as the prediction module, which is trained by using the proposed Rider-based monarch butterfly optimization (Rider-MBO) algorithm. The proposed Rider-MBO algorithm is the integration of rider optimization algorithm (ROA) and MBO. Initially, the data from the live stock market are subjected to the computation of the technical indicators, representing the features from which the necessary features are obtained through clustering by using the Sparse-Fuzzy C-Means (Sparse-FCM) followed with feature selection. The robust features are given to the Deep-ConvLSTM model to perform an accurate prediction. The evaluation is based on the evaluation metrics, such as mean squared error (MSE) and root mean squared error (RMSE), by using six forms of live stock market data. The proposed stock market prediction model acquired a minimal MSE and RMSE of 7.2487 and 2.6923 that shows the effectiveness of the proposed method in stock market prediction.

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Section: Literature Reviewmentioning

confidence: 99%

Stock Market Prediction Using Optimized Deep-ConvLSTM Model

Kelotra

Pandey

2020

Big Data

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“…Pada era saat ini investasi sudah mulai diminati dan dipraktikkan di kalangan masyarakat, salah satu investasi yang diminati adalah investasi saham (Cahya & W., 2019;Chen, Chen, & Lu, 2017;Nurlela, Sulastri, AJ, & Hanafi, 2019). Saham perusahaan bila dilihat dari sektornya terdiri atas sepuluh sektor, salah satunya sektor pertambangan (Ardelia & Dewi, 2016;Wijaya, et al, 2018).…”

Section: Pendahuluanunclassified

Analisa Harga Komoditas Dunia pada Indeks Harga Saham Sektor Pertambangan di Indonesia

Nugraha

Putera

2021

FCS

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Penelitian ini bertujuan untuk menganalisis efek harga komoditas dunia terhadap indeks harga saham sektor pertambangan di Indonesia. Harga komoditas meliputi harga batubara dunia, timah, tembaga, minyak mentah, nikel, dan emas. Data dalam penelitian ini adalah data bulanan yang diambil selama periode Januari 2010–Oktober 2020. Analisis data yang digunakan adalah VECM. Hasil penelitian menunjukkan bahwa harga batubara dan harga tembaga memiliki hubungan kausalitas satu arah dengan indeks harga saham sektor pertambangan. Harga komoditas dunia tidak berpengaruh jangka pendek terhadap indeks harga saham sektor pertambangan. Harga komoditas dunia berpengaruh jangka panjang terhadap indeks harga saham sektor pertambangan, dengan harga minyak mentah dan tembaga berpengaruh positif terhadap indeks harga saham sektor pertambangan, dan harga emas, batubara, nikel, dan timah berdampak negatif pada indeks harga saham sektor pertambangan di Indonesia.

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“…Industrial indicators are provided from Taiwan Stock Exchange Corporation. Moreover, 16 financial ratios were used in this study to achieve this objective (Chen, et. al., 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

“…It was concluded that inventory turnover ratio is the key indicator for sugar sector of India (Shukla and Nerlekar, 2015). (Chen, et. al., 2016) Taiwan They created a model related to investment decision.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Study of Outlier Detection Algorithms via Fundamental Analysis Variables

Emir¹,

Dınçer²,

Hacıoğlu

et al. 2015

IJRBS

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In a data set, an outlier refers to a data point that is considerably different from the others. Detecting outliers provides useful application-specific insights and leads to choosing right prediction models. Outlier detection (also known as anomaly detection or novelty detection) has been studied in statistics and machine learning for a long time. It is an essential preprocessing step of data mining process. In this study, outlier detection step in the data mining process is applied for identifying the top 20 outlier firms. Three outlier detection algorithms are utilized using fundamental analysis variables of firms listed in Borsa Istanbul for the 2011-2014 period. The results of each algorithm are presented and compared. Findings show that 15 different firms are identified by three different outlier detection methods. KCHOL and SAHOL have the greatest number of appearances with 12 observations among these firms. By investigating the results, it is concluded that each of three algorithms makes different outlier firm lists due to differences in their approaches for outlier detection.

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Enhancement of stock market forecasting using an improved fundamental analysis-based approach

Cited by 40 publications

References 26 publications

Stock Market Prediction Using Optimized Deep-ConvLSTM Model

Stock Market Prediction Using Optimized Deep-ConvLSTM Model

Analisa Harga Komoditas Dunia pada Indeks Harga Saham Sektor Pertambangan di Indonesia

Comparative Study of Outlier Detection Algorithms via Fundamental Analysis Variables

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