Financial and capital markets (especially stock markets) are considered high return investment fields, which in the same time are dominated by uncertainty and volatility. Stock market prediction tries to reduce this uncertainty and consequently the risk. As stock markets are influenced by many economical, political and even psychological factors, it is very difficult to forecast the movement of...

We find that past stock market variance forecasts excess stock market returns and that its predictive ability is greatly enhanced if the consumption-wealth ratio is also included in the forecasting equation. While the risk-return tradeoff is found negative if we use the latter as the instrumental variable for the conditional moments, the former suggests a positive one. We argue that the consump...

non-linear time series models have become fashionable tools to describe and forecast stock market returns in recent years. a significant amount of evidence supports a negative relationship between volume and future returns. this suggests that volume could act as a suitable threshold variable in lstar and tar models. in this research, we compared the forecasting ability of lsatr and tar models w...

In this research, we proposed a new metaheuristic technique for stock portfolio multi-objective optimization employing the combination of Strength Pareto Evolutionary Algorithm (SPEA), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Arbitrage Pricing Theory (APT). To generate the more precise model, ANFIS has implemented to envisage long-term movement values of the Tehran Stock Exchange (TSE)...

This study investigates the algorithm of effective option trading strategy based on the superior volatility forecasts using actual option price data in Taiwan stock market. Forecast evaluation supports the significant incremental explanatory power of investor sentiments on the fitting and forecasting of future volatility to its adversarial multiple-factor model, especially the market turnover a...

We use daily price indices obtained from the Morgan Stanley Capital International to construct realized volatility for 18 individual stock markets, including the US, and the world stock market. In contrast with the CAPM, we find that volatility by itself does not forecast excess returns in most countries; however, it becomes a significant predictor when combined with the US consumptionwealth ra...

One of the challenging problems in forecasting the conditional volatility of stock market returns is that general kernel functions in support vector machine (SVM) cannot capture the cluster feature of volatility accurately. While wavelet function yields features that describe of the volatility time series both at various locations and at varying time granularities, so this paper construct a mul...