Many techniques like technical analysis, fundamental analysis, neural networks etc are used to forecast market behavior but none of these methods has been consistently acceptable forecasting tool. This thesis surveys more than 200 related published articles that study investor sentiment techniques as derived and applied to forecasting equity, debt and alternative markets. From the literatures, it shows that the application of investor sentiment for evaluating market behavior is gaining wide acceptance. Changes in investor sentiment can trigger changes in the valuation and pricing of assets, therefore offering the ability to forecasting market directions more accurately than other techniques. This study is the most comprehensive survey on investor sentiment techniques and its impact on forecasting a panel of assets in the equity, debt, derivative and other alternative investment markets. It examines forecasting as it affects sentiment, investor sentiment, it influence on market returns, news analytics and its use as profit and risk management tool.
Research on Forecasting Volatility Rate of Financial Market based on GARCH and Neural Networks
