An Empirical Analysis on the Portfolio of Transnational Auto Market Based on ARIMA-LSTM

Yiping Hong ()
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Yiping Hong: Hainan University, School of Economics and Management

A chapter in Proceedings of the 2022 2nd International Conference on Financial Management and Economic Transition (FMET 2022), 2023, pp 130-142 from Springer

Abstract: Abstract Due to the rise of raw material prices, the reduction of automobile production capacity caused by COVID-19, the trend of global electrification and other factors, the investment in transnational automobile stocks has gradually become a new target for global investors. Because the general stock price data has both linear and non-linear characteristics, the integrated model is much more favourable than the single models in prediction, resulting in more and more widespread application in financial time series. According to the above, a total of 10 groups of auto stock closing prices listed in the US, Japan, China, Germany and the UK from January 1, 2016 to December 31, 2021 were selected as research samples to establish ARIMA-LSTM model to forecast the closing price in the next 10 working days. Subsequently, the mean variance model is constructed, and the optimal portfolio is obtained in terms of the maximum Sharpe ratio. The results illustrate that: (1) The accuracy of ARIMA-LSTM integrated model is validated to more excellent than that of single ARIMA and LSTM; (2) Based on the stock closing prices in the next 10 working days predicted by the combination model, the performance of the maximum Sharpe ratio portfolio is better than that of the market average.

Keywords: Transnational automobile stock price; ARIMA-LSTM; Mean-variance model; Sharpe ratio (search for similar items in EconPapers)
Date: 2023
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DOI: 10.2991/978-94-6463-054-1_16

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