The application of AI techniques for stock price prediction leads to voluminous growth of wealth of investors with the advent of technology. Several prediction and estimations are coming up for almost all sectors of the market. Particularly any kind of stock price prediction is not at all possible without excessive data manipulation which can be done effectively only thru data mining. The syste...

An optimal selling strategy for stock trading is presented in this paper. An investor with a long position in one stock decides to close the position before a given time. The investor continuously observes the stock price performance and has to determine the point in time to close out the position (selling strategy) so that the stock price is as close as possible to the maximum price. The proba...

Recently, numerous investigations for stock price prediction and portfolio management using machine learning have been trying to develop efficient mechanical trading systems. But these systems have a limitation in that they are mainly based on the supervised leaming which is not so adequate for leaming problems with long-term goals and delayed rewards. This paper proposes a method of applying r...

The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock pr...

We investigated the inter-day effects of price limits policies that are employed in agent-based simulations. To isolate the impact of price limits from the impact of other factors, we built an artificial stock market with higher frequency price limits hitting. The trading mechanisms in this market are the same as the trading mechanisms in China's stock market. Then, we designed a series of simu...

there are two major methods in predincing the pattern of share value in the stock market: fundamental analysis and technical analysis. i this study we developed a novel model for fundamental analysis of share price and validated it by predicting the behavior of phrama share price in tehran stock market. the efficiency of the fundamental method was compared with that of indicator-based technical...

The optimal strategies for a long-term static investor are studied. Given a portfolio of a stock and a bond, we derive the optimal allocation of the capitols to maximize the expected long-term growth rate of a utility function of the wealth. When the bond has constant interest rate, three models for the underlying stock price processes are studied: Heston model, 3/2 model and jump diffusion mod...

â â â  â â â â â  the main purpose of present study is to analyze the relationship between stock and exchange markets in two asian countries, iran and south korea. a monthly time series of stock price and exchange rate are used over the period 2002: 05 - 2012: 03. the data is collected from the central bank of each country and wdi. the calculated stock return and real exchange rate change are u...

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...

We derive optimal portfolio weights for an investor who has specific beliefs regarding the distribution of a stock price at a future time. For example, a fundamental investor will want to take advantage of the information his analysis provides when constructing a portfolio. In this regard, we examine the optimal weights for models in which the investor believes that there is a range in which th...