modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

Toni Rowe* Jungsun (Sunny) Kim The gaming industry, previous to 2007, had experienced a continued increase in revenues and stock prices, but in late 2007, the industry started to be affected by a recession. To have a better understanding of the relationship between this external economic factor (recession) and a gaming company's systematic risk (beta), this study analyzed which financial ratios...

Data mining using integration of clustering and decision tree algorithm has been proposed for predicting the stock market prices. This mechanism involves studying stock price patterns in time by attempting to predict future results of a time-series by simply studying patterns in the time-series of stock prices. The goal of this project is to implement data mining in order to predict the Time-Se...

This paper introduces endogenous preference evolution into a Lucas-type economy and explores its consequences for investors’ trading strategy and the dynamics of asset prices. In equilibrium, investors herd and hold the same portfolio of risky assets which is biased toward stocks of sectors that produce a socially preferred good. Price-dividend ratios, expected returns and return volatility are...

The theory of cost shifting posits that nonprofit firms “share the pain” of negative financial shocks with their stakeholders, for example, by raising prices. We examine how nonprofit hospitals responded to the sharp reductions in their assets caused by the 2008 stock market collapse. The average hospital did not raise prices, but hospitals with substantial market power did cost shift in this w...

 This paper characterizes the trailing-stop strategy for stock trading and provides a simulation model to evaluate its validity. Based on a discrete time computational model, we perform probabilistic analyses of the risks, rewards and trade-offs of such a trading strategy. Numerical examples using real data from the S&P 500 and the Dow Jones Industrial Average indicate that the trailing-stop s...

This paper develops a model of information acquisition and portfolio choice under short-sale constraints. We show that short-sale constraints reduce information acquisition and both the constraints on short-selling and the reduced information acquisition affect investment decisions. The effects of short-sale constraints on investment decisions and asset prices are driven largely by the effects ...

Stock trading is an important decision-making problem that involves both stock selection and asset management. Though many promising results have been reported for predicting prices, selecting stocks, and managing assets using machine-learning techniques, considering all of them is challenging because of their complexity. In this paper, we present a new stock trading method that incorporates dy...

This paper presents a novel encoderdecoder model for automatically generating market comments from stock prices. The model first encodes both shortand long-term series of stock prices so that it can mention shortand long-term changes in stock prices. In the decoding phase, our model can also generate a numerical value by selecting an appropriate arithmetic operation such as subtraction or round...

This study determined the value relevance of assets and liabilities after the adoption of IFRS among listed Nigerian firms. Ohlson Model (1995) model of stock price regressions tested the relationship between assets and liabilities with the stock price, which has been widely adopted by accounting researchers. A sample of 126 firms listed in Nigeria stock market is used for the study. Data is co...