Expected stock return and mixed frequency variance risk premium data

Liu, Ruobing; Yang, Jian; Ruan, Chuan-Yang

doi:10.1007/s12652-019-01528-3

Cited by 4 publications

(4 citation statements)

References 52 publications

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“…ere are many kinds of data processing methods for multiple attributes. Many scholars have done a lot of innovative research in this area, which has brought enlightenment to the research [44][45][46]. e factor analysis method is employed to study the internal dependence relationship between variables.…”

Section: Data Sources and Research Methodsmentioning

confidence: 99%

The Method of Measuring the Agglomeration Degree of High-Tech Industries and Its Influence Mechanism: Taking Guangdong Province as an Example

Han

Song

2021

Mathematical Problems in Engineering

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This paper selects the Herfindahl–Hirschman index and the Moran index to study the overall agglomeration and spatial distribution of high-tech industries in Guangdong, a province of China. The location entropy, dynamic agglomeration index, and output value share are also used in this study to measure the agglomeration and changes that occur in high-tech industries in various cities of Guangdong Province. The relevant data of high-tech industries for the province is collected between 2013 and 2018. Our results show that Guangdong Province’s overall agglomeration of high-tech industries strengthened between 2013 and 2018, but industrial transfer occurred within the cities in the Pearl River Delta region to a certain extent. Regional international trade, R&D investment and patent output of the whole society, number of local scientific research personnel, economies of scale, communication level, and local taxation policies are the main factors contributing to the changes in the agglomeration of high-tech industries. Guangdong should create an open environment to increase R&D investment and expand the scale of R&D personnel and economy as well as strengthening intellectual property protection and communication level. Preferential taxation policies should be also implemented to accelerate the agglomeration of high-tech industries.

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Section: Data Sources and Research Methodsmentioning

confidence: 99%

The Method of Measuring the Agglomeration Degree of High-Tech Industries and Its Influence Mechanism: Taking Guangdong Province as an Example

Han

Song

2021

Mathematical Problems in Engineering

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“…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]. Di sisi lain, Hu, et.al [6] menyatakan bahwa serangkaian imbal hasil saham dapat mencerminkan volatilitas pasar saham lebih baik dibandingkan dengan serangkaian harga saham, sehingga pendugaan dan pemodelan imbal hasil saham memiliki nilai penelitian yang lebih penting dibandingkan pendugaan harga saham.…”

Section: Pendahuluanunclassified

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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“…R&D investment forecasting is usually used to forecast single or a few closely related investment indicators. erefore, the methods used for R&D investment forecasting are mainly time series methods [12,13], semiparametric model methods (e.g., Rounaghi et al [14] and Dordonnat et al [15]), uncertain forecasting methods [16][17][18][19], and so on. R&D investment forecasting is very important to high-tech enterprises, but the uncontrollability in the forecasting process will increase the difficulty of forecasting and management [2].…”

Section: Introductionmentioning

confidence: 99%

Research and Development Investment Combination Forecasting Model of High-Tech Enterprises Based on Uncertain Information

Wei

Chen

Ruan

2021

Mathematical Problems in Engineering

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The high demand of the competitive market for innovation has brought the increase of research and development (R&D) investment. High-tech enterprises can reasonably control R&D cost and effectively manage R&D activities by accurately predicting R&D investment. Given the characteristics that high-tech enterprises have high uncertainty and frequently changing information in R&D investment, this paper uses the grey metabolic GM (1, 1) model and the exponential smoothing method in time series to establish a single prediction model of R&D investment in high-tech enterprises. With the analysis of the advantages and disadvantages of each single model, a combined forecast model of R&D investment in high-tech enterprises is thus established. The model was applied to the forecast of R&D investment of a high-tech enterprise in China from 2019 to 2023, and the results verified the higher accuracy and practicability of this model. The establishment of this model can provide effective support for high-tech enterprises in R&D cost management.

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Expected stock return and mixed frequency variance risk premium data

Cited by 4 publications

References 52 publications

The Method of Measuring the Agglomeration Degree of High-Tech Industries and Its Influence Mechanism: Taking Guangdong Province as an Example

The Method of Measuring the Agglomeration Degree of High-Tech Industries and Its Influence Mechanism: Taking Guangdong Province as an Example

Pendugaan Imbal Hasil Saham dengan Model Autoregressive Moving Average

Research and Development Investment Combination Forecasting Model of High-Tech Enterprises Based on Uncertain Information

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