The use of intelligent systems for stock market predictions has been widely established. This paper deals with the application of hybridized soft computing techniques for automated stock market forecasting and trend analysis. We make use of a neural network for one day ahead stock forecasting and a neuro-fuzzy system for analyzing the trend of the predicted stock values. To demonstrate the prop...

Stock market prediction is important and of great interest because successful prediction of stock prices may promise attractive benefits. These tasks are highly complicated and very difficult. In this paper, we investigate the predictability of stock market return with Adaptive Network-Based Fuzzy Inference System (ANFIS). The objective of this study is to determine whether an ANFIS algorithm i...

This paper studies a discrete-time financial model with or without transaction costs, in which only partial information can be observed. Partial information model means that the investors in the market can observe no more information except the stock prices. This model has been investigated in Karatzas and Xue (1991), Lakner (1995, 1998), and Cheng (2004), etc. Applying stochastic filtering the...

The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. In this paper, we propose an empirical study on the Korean and Hong Kong stock market with an integrated machine learning framework that employs Principal Component Analysis (PCA) and Support Vector Machine ...

The current international integration of financial markets provides a channel for currency depreciation to affect stock prices. Moreover, the recent financial crisis in Asia with its accompanying exchange rate volatility affords a case study to examine that channel. This paper applies a bivariate GARCH-M model of the reduced form of stock market returns to investigate empirically the effects of...

This paper investigates optimal portfolio strategies in a market where the drift is driven by an unobserved Markov chain. Information on the state of this chain is obtained from stock prices and expert opinions in the form of signals at random discrete time points. As in Frey et al. (2012), Int. J. Theor. Appl. Finance, 15, No. 1, we use stochastic filtering to transform the original problem in...

Stock market is seen as a sum total of different happenings, especially if we speak about share price. However, price, any other economic phenomenon combination chance and law. Occasional nature price evident in its everyday trading. In this case law hidden under the opportunity to forecast prices. For longterm periods law-governed can be trend growth, which accompanied by stock crises. The cri...

This paper investigates the voluntary disclosure made by 297 Chinese listed firms in their 1995-2006 annual reports. It aims to determine how firms in the Chinese stock market have responded to the coercive pressure exerted upon them by the market regulatory body, the Chinese Security Regulatory Commission (CSRC) in terms of providing transparent information to the stock market. The findings sh...

We develop a simple behavioral macromodel to study interactions between the real economy and the stock market. The real economy is represented by a Keynesian-type goods market approach while the setup for the stock market includes heterogeneous speculators. Using a mixture of analytical and numerical tools we find, for instance, that speculators may create endogenous boom-bust dynamics in the s...