High-dimensional Cross-market Dependence Modeling and Portfolio Forecasting by Copula Variational LSTM

Xu, Jia; Cao, Longbing

doi:10.2139/ssrn.3881474

Cited by 1 publication

(1 citation statement)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hamilton (1989) combined the HMM structure with autoregressive models in order to capture the market trend, where parameters of an autoregressive model were considered to arise from an HMM. Elliott and van der Hoek (1997) and Elliott et al (2010) and Cao (2021) have incorporated a vine-coupla into an artificial neural network so that the inter-market correlations were considered while estimating the return of a portfolio.…”

Section: Introductionmentioning

confidence: 99%

Optimal Stock Portfolio Selection with a Multivariate Hidden Markov Model

Majumder

Neerchal

2022

Sankhya B

View full text Add to dashboard Cite

The underlying market trends that drive stock price fluctuations are often referred to in terms of bull and bear markets. Optimal stock portfolio selection methods need to take into account these market trends; however, the bull and bear market states tend to be unobserved and can only be assigned retrospectively. We fit a linked hidden Markov model (LHMM) to relative stock price changes for S&P 500 stocks from 2011-2016 based on weekly closing values. The LHMM consists of a multivariate state process whose individual components correspond to HMMs for each of the 12 sectors of the S&P 500 stocks. The state processes are linked using a Gaussian copula so that the states of the component chains are correlated at any given time point. The LHMM allows us to capture more heterogeneity in the underlying market dynamics for each sector. In this study, stock performances are evaluated in terms of capital gains using the LHMM by utilizing historical stock price data. Based on the fitted LHMM, optimal stock portfolios are constructed to maximize capital gain while balancing reward and risk. Under out-of-sample testing, the annual capital gain for the portfolios for 2016-2017 are calculated. Portfolios constructed using the LHMM are able to generate returns comparable to the S&P 500 index.

show abstract

Section: Introductionmentioning

confidence: 99%