We document a robust positive relationship between the belief dispersion about macroeconomic conditions among household investors and the stock market trading volume, using more than 30 years of household survey data and a novel approach to measuring belief dispersions. Notably, such a relationship prevails even after various series of professional analysts’ belief dispersions are controlled fo...

With an easy access to share information and data nowadays, many investors worldwide are interested in predicting stock prices. The prediction of stock prices using data mining techniques applied to technical variables has been widely researched but not much research to date has been done in applying data mining techniques to both technical and fundamental information. This paper is based on a ...

Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize ma...

This paper focuses on how market risk, economic activities, nancial leverage, in ation shocks and trading activities a ect REIT return volatility using U.S equity REITs data from 1995 to 2009. The ndings suggest that systematic risk positively a ects REIT return volatility, with a higher impact in up markets than in down markets. Dividend Yield (DY) and Return On Average Equity (ROAE) negativel...

A novel method of stock portfolio management by using technical indicators is proposed in this paper. The method hybridizes the consensus trading signals generated by the gene expression programming (GEP) proposed by Lee et al., and the portfolio redemption scheme proposed by Tsai et al. with our stock ranking functions. The indicators were used not only for trading, but also for selecting prom...

Using novel data from a crowdsourcing platform for ranking stocks, we investigate how investors form expectations about stock returns over the next week. We find that extrapolate stocks’ recent past returns, with more weight on especially when are negative, salient, or dispersed cross-section. Such extrapolative beliefs stronger among nonprofessionals and large stocks. Moreover, consensus ranki...

We document that real estate investment trust (REIT) stock prices deviate from net asset values (NAV), as measured by Green Street Advisors, a buy-side research firm. Using REIT data since 1990, we find large positive excess returns to a strategy of buying stocks that trade at a discount to NAV, and shorting stocks trading at a premium to NAV. Estimated alphas are between 1.2% and 1.8% per mont...

After negative shocks, investors with short trading horizons are inclined or forced to sell their holdings to a larger extent than investors with longer trading horizons. This may amplify the effects of market-wide shocks on stock prices. We test the relevance of this mechanism by exploiting the negative shock caused by Lehman Brothers’ bankruptcy in September 2008. Consistent with our conjectu...

We examine the ability of online ticker searches (e.g. XOM for Exxon Mobil) to forecast abnormal stock returns and trading volumes. Specifically, we argue that online ticker search serves as a valid proxy for investor sentiment – a set of beliefs about cash flows and investments risks that are not necessarily justified by the facts at hand – which is generally associated with less sophisticated...

The daily stock turning point detection problems are investigated in this study. The Support Vector Regression model has been applied in various forecasting applications and proved to be with stable performances. In this research, SVR has been used to predict the trading signal since it could handle overall information effectively even under the complex environment of stock price variations. Th...