The main goal of this paper is to propose a new approach for efficiency measurement and ranking of stocks. Data envelopment analysis (DEA) is one of the popular and applicable techniques that can be used to reach this goal. However, there are always concerns about negative data and uncertainty in financial markets. Since the classical DEA models cannot deal with negative and imprecise values, i...

Real-world financial time series often contain both linear and nonlinear patterns. However, traditional time series analysis models, such as ARIMA, hold the assumption that a linear correlation exists among time series values while leaving nonlinear relation into error terms. Based on financial theories, we argue that investor sentiment is the main contributor to nonlinear pattern of stock time...

The real time fuzzy personalized stock information agent is designed based on fuzzy logic, intelligent agent, personalization, Jakarta-Tomcat-3.2.1 (Web sever) and Microsoft SQL Server 7.0 (database). In addition, a variety of languages, including Java, JSP (Java Sever Page), HTML, Java Servlet, XML, XSL (Extensible Stylesheet Language) are also used. The smart agent enables the users to create...

Using novel data from a crowdsourcing platform for ranking stocks, we investigate how individuals form expectations about future stock returns in the cross-section. In each contest on this platform, participants rank 10 stocks based on their perceived future performance of these stocks over the course of the contest (usually one week). We find that, when forming expectations, investors extrapol...

This paper investigates the profitability of a trading strategy based on training a model to identify stocks with high or low predicted returns. A tail set is defined to be a group of stocks whose volatility-adjusted price change is in the highest or lowest quantile, for example the highest or lowest 5%. Each stock is represented by a set of technical and fundamental features computed using CRS...

Due to the fast delivery of news articles by news providers on the Internet and/or via news datafeeds, it becomes an important research issue of predicting the risk of stocks by utilizing such textual information available in addition to the time series information. In the literature, the issue of predicting stock price up/down trend based on news articles has been studied. In this paper, we st...

The inability to predict the earnings of growth stocks, such as biotechnology and internet stocks, leads to the high volatility of share prices and di±culty in applying the traditional valuation methods. This paper attempts to demonstrate that the high volatility of share prices can nevertheless be used in building a model that leads to a particular size distribution, which can then be applied ...

The portfolio optimization problem has attracted researchers from many disciplines to resolve the issue of poor out-of-sample performance due to estimation errors in the expected returns. A popular method is to use assets' ordering information, expressed in the form of preferences over the stocks, instead of the exact expected returns for portfolio construction. Motivating from the fact that th...

Recent literature shows that momentum strategies exhibit significant downside risks over certain periods, called “momentum crashes”. We find high uncertainty of strategy returns is sourced from the cross-sectional volatility individual stocks. Stocks with realised formation period tend to lose effect. propose a new approach, generalised risk-adjusted (GRJMOM), mitigate negative impact momentum-...