Forecasting the Artificial Intelligence Index Returns: A Hybrid Approach

Yue-Jun Zhang (), Han Zhang Author-hanalms@163.com and Rangan Gupta
Additional contact information
Han Zhang Author-hanalms@163.com: Business School, Hunan University, Changsha 410082, China; Center for Resource and Environmental Management, Hunan University, Changsha 410082, China

No 202182, Working Papers from University of Pretoria, Department of Economics

Abstract: Forecasting of the artificial intelligence index returns is of great significance for financial market stability and the development of artificial intelligence industry. To provide investors more reliable reference in terms of artificial intelligence index investment, this paper selects the Nasdaq CTA Artificial Intelligence and Robotics (AI) Index as the research target, and proposes novel hybrid methods to forecast the AI index returns by considering its nonlinear and time-varying characteristics. Specifically, this paper uses the ensemble empirical mode decomposition (EEMD) method to decompose the AI index returns, and combines the least square support vector machine approach together with the particle swarm optimization (PSO-LSSVM) method and the generalized autoregressive conditional heteroskedasticity (GARCH) model to construct novel hybrid forecasting methods. The empirical results indicate that: first, the decomposition and integration models usually produce superior forecasting accuracy than the single forecasting models, due to the complicated feature of the non-decomposed data. Second, the newly proposed hybrid forecasting method (i.e., the EEMD-PSO-LSSVM-GARCH model) which combines the advantage of traditional econometric models and machine learning techniques can yield the optimal forecasting performance for the AI index returns.

Keywords: AI index return forecasting; PSO-LSSVM model; GARCH model; Decomposition and integration model; Combination model (search for similar items in EconPapers)
JEL-codes: E37 G15 Q43 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2021-11
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:202182

Access Statistics for this paper

More papers in Working Papers from University of Pretoria, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Rangan Gupta ().