Forecasting stock market return with nonlinearity: a genetic programming approach

Ding, Shusheng; Cui, Tianxiang; Xiong, Xihan; Bai, Ruibin

doi:10.1007/s12652-020-01762-0

Cited by 13 publications

(2 citation statements)

References 63 publications

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“…Evaluasi terhadap suatu aset dapat dilakukan dengan menggunakan imbal hasil saham tersebut, yang secara konvensional didefinisikan sebagai fungsi logaritma natural terhadap perubahan harga [1]. Namun, apakah imbal hasil saham di masa depan dapat diduga menjadi pertanyaan yang krusial dikemukakan oleh Ding, et.al [2]. Beberapa penelitian menunjukkan bahwa imbal hasil saham dapat diduga menggunakan variabel yang relevan seperti dividen [3]- [5].…”

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Pendugaan Imbal Hasil Saham dengan Model Autoregressive Moving Average

2021

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ABSTRAKSeorang investor pada umumnya berharap untuk membeli suatu saham dengan harga yang rendah dan menjual saham tersebut dengan harga yang lebih tinggi untuk memperoleh imbal hasil yang tinggi. Namun, kapan waktu yang tepat melakukannya menjadi tantangan tersendiri bagi para investor. Oleh sebab itu, dibutuhkan suatu model yang mampu menduga imbal hasil saham dengan baik, salah satunya adalah model autoregressive moving average (ARMA). Tujuan dari penelitian ini adalah untuk menerapkan model autoregressive (AR), model moving average (MA), atau model autoregressive moving average (ARMA) pada data observasi untuk menduga imbal hasil saham bank central asia (BCA). Terdapat empat prosedur dalam membangun sebuah model AR, MA atau ARMA. Pertama, data yang digunakan harus weakly stationary. Kedua, orde dari model harus diidentifikasi untuk memperoleh model yang terbaik. Ketiga, parameter setiap model harus ditentukan. Keempat, kelayakan model harus diperiksa dengan melakukan analisis residual untuk memperoleh model yang terbaik. Pada akhirnya, model ARMA (1,1) adalah model terbaik dan akurat dalam menduga imbal hasil saham BCA. ABSTRACTGenerally, investor always wish to be able to buy a stock at a low price and sell it at a higher price to obtain high returns. However, when is the best time to buy or sell it is a challenge for investor. Therefore, proper models are needed to predict a stock return, one of them is autoregressive moving average (ARMA) model. The first purpose of this paper is to apply the autoregressive (AR), moving average (MA) or ARMA models to the observations to predict stock returns. There are four procedures which is used to build an AR, MA, or ARMA model. First, the observations must be weakly stationary. Second, the order of the models must be identified to obtain the best model. Third, the unknown parameters of the models are estimated by maximum likelihood. Fourth, through residual analysis, diagnostic checks are performed to determine the adequacy of the model. In this paper, stock returns of BCA are used as data observation. Finally, the ARMA (1,1) model is the best model and appropriate to predict the stock returns BCA in the future.

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Pendugaan Imbal Hasil Saham dengan Model Autoregressive Moving Average

2021

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“…More recently, Michell and Kristjanpoller (2020) [ 19 ] employ GP to develop trading rules in the US stock market. Ding et al (2020) [ 20 ] apply GP to forecast future stock returns in different stock markets. Our paper extends the GP application to the oil market volatility forecasting, which is a crucial commodity market.…”

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Liquidity effects on oil volatility forecasting: From fintech perspective

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Fin-tech is an emerging field, inspiring revolutionary innovations in the financial field. It may initiate the evolutionary episode of the financial research, where volatility forecasting is a crucial topic in finance. For forecasting volatility, GARCH model is a prevailing model, however, further improvement of the GARCH model is still challenging. In this paper, we demonstrate how Fintech can play a part in volatility forecasting by employing a metaheuristic procedure called Genetic Programming. On the basis, we are able to develop a new volatility forecasting model, which can beat GARCH family models (including GARCH, IGARCH and TGARCH models) in a significant way. Since genetic programming is an evolutionary algorithm based on the principles of natural selection, this innovative work will be a breakthrough point in the financial area. The innovation of this paper demonstrates how GP technology can be applied in the financial field, attempting to explore the volatility forecasting area from the combination of new technology and finance, known as fintech. More importantly, when the formula of volatility forecasting is unknown as we introduce a new factor, namely, the liquidity factor, we unveil that how GP method can be helpful in determining the specific volatility forecasting model format. We thereby exhibit the liquidity effects on volatility forecasting filed from the fintech perspective.

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RETRACTED ARTICLE: A hybrid algorithm based on artificial bat and backpropagation algorithms for multiplicative neuron model artificial neural networks

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Eğrioğlu

Yolcu

2020

J Ambient Intell Human Comput

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In the literature, the multiplicative neuron model artificial neural networks are trained by gradient-based or some artificial intelligence optimization algorithms. It is well known that the hybrid algorithms give successful results than classical algorithms in the literature and the use of hybrid systems increase day by day. From this point of view, different from other studies contribute to multiplicative neuron model artificial neural networks, the properties of an artificial intelligence optimization technique, artificial bat algorithm, and a gradient-based algorithm, backpropagation learning algorithm, is used together firstly by using the proposed method in this study. Thus, both a derivative and a heuristic algorithm were used together firstly for multiplicative neuron model artificial neural networks. The proposed method is applied to three well-known different real-world time series data. The performance of the proposed method is both compared with gradient-based optimization algorithms, some artificial optimization algorithms used for the training of artificial neural networks and some popular analyze methods. The analysis results show that the proposed hybrid method has superior performance than other methods.

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Forecasting stock market return with nonlinearity: a genetic programming approach

Cited by 13 publications

References 63 publications

Pendugaan Imbal Hasil Saham dengan Model Autoregressive Moving Average

Pendugaan Imbal Hasil Saham dengan Model Autoregressive Moving Average

Liquidity effects on oil volatility forecasting: From fintech perspective

RETRACTED ARTICLE: A hybrid algorithm based on artificial bat and backpropagation algorithms for multiplicative neuron model artificial neural networks

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