In this paper we focus on analyzing the predictive accuracy of three different types of forecasting techniques, Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and Singular Spectral Analysis (SSA), used for predicting chaotic time series data. These techniques...

The study reports empirical evidence that artificial neural network based models are applicable to forecasting of stock market returns. The Nigerian stock market logarithmic returns time series was tested for the presence of memory using the Hurst coefficient before the models were trained. The...

This study investigates the cross-country impact of U.S. equity market skewness risk. We find that a large decrease in the U.S. market skewness significantly predicts high future returns on international equity markets. The predictability remains significant after controlling for a set of U.S....

According to present-value models, a financial valuation ratio should predict future stock returns or cash flows but empirically shows little power. This paper develops insights about stock return predictability and reconciles the contradicting findings. We decompose a financial ratio into (1) a...

Movements in the stock market should reflect expectations regarding future economic conditions and lead the macroeconomy. However, evidence for stock returns providing such predictive power is mixed. We argue this arises as stock returns are noisy and consider the predictive ability of derived...

This paper examines the ability of bond and stock markets to predict subsequent GDP growth over a range of horizons for twelve international countries. The results, using linear, probit, time- and regime-varying in-sample regressions and out-of-sample forecasting, confirm the view that both...

Recent evidence on the relationship between investor sentiment and subsequent monthly market returns in China shows that investor sentiment is a reliable momentum predictor since an increase (decrease) in investor sentiment leads to higher (lower) future returns. However, we suggest that...

This study aims at comparing Google Search Volume Indices (GSVIs—including market crash and bear market) and VIX (Investor Fear Gauge Index) in terms of explaining the S&P 500 returns. The VIX is found a more robust predictor of stock market returns than Google indices, and it does granger...

While numerous studies have analyzed the asset allocation issue of US stock market from various angles, much less attention has been paid to the asset allocation issue of Chinese stock market. This article investigates the asset allocation in Chinese stock market from a perspective of...

This paper investigates the predictive ability of international volatility risk for the daily aggregate Chinese stock market returns. We employ the innovations in implied volatility indices of seven major international markets as our international volatility risk proxies. We find that...