Investors adopt varied investment strategies depending on the time scales (τ) of short-term and long-term investment time horizons (ITH). The nature of the market is very different in various investment τ. Empirical mode decomposition (EMD) based Hurst exponents (H) and normalized variance (NV) techniques have been applied to identify the τ and characteristics of the market in different time horizons. The values of H and NV have been estimated for the decomposed intrinsic mode functions (IMF) of the stock price. We obtained H1=0.5±0.04 and H1≥0.75 for the IMFs with τ ranging from a few days to 3 months and τ≥ 5 months, respectively. Based on the value of H1, two time series have been reconstructed from the IMFs: a) short-term time series [XST(t)] with H1=0.5±0.04 and τ from a few days to 3 months; b) long-term time series [XLT(t)] with H1≥0.75 and τ≥ 5 months. The XST(t) and XLT(t) show that market dynamics is random in short-term ITH and correlated in long-term ITH. We have also found that the NV is very small in the short-term ITH and gradually increases for long-term ITH. The results further show that the stock prices are correlated with the fundamental variables of the company in the long-term ITH. The finding may help the investors to design investment and trading strategies in both short-term and long-term investment horizons.

投资者根据时间范围采取不同的投资策略（τ）的短期和长期投资时间范围（ITH）。市场性质在各种投资中有很大不同τ。经验模式分解（EMD） 基于赫斯特指数（H）和归一化方差（NV）技术已用于识别 τ不同时间范围内的市场特点。的值H已针对股票价格的分解固有模式函数（IMF）估计了NV和NV。我们获得了H1个=0.5±0.04 和 H1个≥0.75 为国际货币基金组织 τ 从几天到三个月不等 τ≥5个月。根据价值H1个，从 一世中号Fs：a）短期时间序列[X小号Ť（Ť）]与 H1个=0.5±0.04 和 τ从几天到三个月；b）长期时间序列[X大号Ť（Ť）] 与 H1个≥0.75 和 τ≥5个月。的X小号Ť（Ť） 和 X大号Ť（Ť） 表明短期内市场动态是随机的 一世ŤH 并长期相关 一世ŤH。我们还发现ñV 短期ITH很小，长期逐渐增加 一世ŤH。结果进一步表明，股票价格长期与公司的基本变量相关。一世ŤH。这一发现可能有助于投资者在短期和长期投资范围内设计投资和交易策略。