Nonparametric mean-lower partial moment model and enhanced index investment

Huang, Jinbo; Li, Yong; Yao, Haixiang

doi:10.1016/j.cor.2022.105814

Cited by 1 publication

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Passive strategies use rules-based investing and track the index by holding the constituent assets or automatically selecting representative samples (Anadu et al, 2020). Huang et al (2022) constructed an enhanced index investment model based on a new smooth nonparametric kernel (NPK) and lower partial moment (LPM).…”

Section: Quantitative Investment Strategiesmentioning

confidence: 99%

Quantitative Investment Decisions Based on Machine Learning and Investor Attention Analysis

Gao,

Mao,

et al. 2023

Technological and Economic Development of Economy

View full text Add to dashboard Cite

According to the trading rules and financial data structure of the stock index futures market, and considering the impact of major emergencies, we intend to build a quantitative investment decision-making model based on machine learning. We first adopt the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) signal decomposition technology to separate the short-term noise, cycle transformation and long-term trend from the original series, and use the CSI 500 Baidu index series to reflect the investors’ attention, which provides data support for establishing a more effective forecasting model. Then, the CEEMDANBP neural network model is designed based on the obtained effective information of low-frequency trend series, investor attention index and CSI 500 stock index futures market transaction data. Finally, an Attention-based Dual Thrust quantitative trading strategy is proposed and optimized. The optimized Attention-based Dual Thrust strategy solves the core problem of breakout interval determination, effectively avoids the risk of subjective selection, and can meet investors’ different risk preferences. The quantitative investment decision-making model based on CEEMDAN-BP neural network utilizes the advantages of different algorithms, avoids some defects of a single algorithm, and can make corresponding adjustments according to changes in investors’ attention and the occurrence of emergencies. The results show that considering investor attention can not only improve the predictive ability of the model, but also reduce the cognitive bias of the market, effectively control risks and obtain higher returns.

show abstract

Section: Quantitative Investment Strategiesmentioning

confidence: 99%