Stock market trend prediction using a functional time series approach

DOI10.1080/14697688.2019.1651452zbMath1431.91380OpenAlexW2969723604MaRDI QIDQ5215439

May-Ru Chen, Shih-Feng Huang, Mei-Hui Guo

Publication date: 10 February 2020

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697688.2019.1651452

zbMATH Keywords

B-splinemulti-resolutionvector autoregressive modelarea under curvefunctional autoregressive model

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Financial markets (91G15)

Related Items (2)

Stock market prediction based on adaptive training algorithm in machine learning ⋮ Lagged covariance and cross-covariance operators of processes in Cartesian products of abstract Hilbert spaces

Uses Software

• fda (R)
• XGBoost

Cites Work

• Unnamed Item
• Convolutional autoregressive models for functional time series
• Linear processes in function spaces. Theory and applications
• Functional data analysis.
• Autoregressive Forecasting of Some Functional Climatic Variations
• A Stochastic Model for Order Book Dynamics
• Latency and liquidity provision in a limit order book
• Investors’ Interacting Demand and Supply Curves for Common Stocks*
• Forecasting limit order book liquidity supply–demand curves with functional autoregressive dynamics
• An algorithm for information structuring and retrieval
• A practical guide to splines.

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