We develop a simple agent-based financial market model in which heterogeneous speculators apply technical and fundamental analysis to trade in two different stock markets. Speculators’ strategy/market selections are repeated at each time step and depend on predisposition effects, herding behavior and market circumstances. Simulations reveal that our model is able to explain a number of nontrivi...

Noise traders as one of the key elements of the market play a significant role in determining the market volatilities, returns, and stock market mispricing. Hence, this study attempts to scrutinize the role of noise trading in capital asset pricing. Therefore, by using daily data, samples including 14105 data of 200 companies listed on stock exchange were selected and noise trading index was es...

Finding useful patterns in stock market data requires tremendous analytical skills and effort. To help investors manage their portfolios, we developed a tool for clustering and visualizing stock market data using an unsupervised learning algorithm called Self-Organizing Map. Our tool is intended to assist users in identifying groups of stocks that have similar price movement patterns over a per...

Structural shifts characterize the volatility of the Korean stock and foreign exchange markets during the 1997 Asian financial crisis. This paper employs an unrestricted bivariate GARCH-M model of stock market returns to investigate empirically the effects of daily currency depreciation on Korean stock market returns. The evidence shows that currency depreciation significantly affects stock mar...

This study investigated the relationship between financial development and economic growth for Ireland for the period 1965-2007 using a vector error correction model (VECM). Questions were raised whether financial development causes economic growth or reversely taking into account the positive effect of industrial production index. Financial market development is estimated by the effect of cred...

In this paper we investigate the effect of oil price shocks on stock market index in Iran, by using of a structural VAR (SVAR) approach. We used four variables in the model namely Kilian index, global oil supply, real oil price and real stock market index. The data are monthly and spanning the period 1997M10-2014M12. We identify the effect of four different shocks on stock market including oil ...

In this paper, we propose a Hybrid Kansei-SOM model, using Kansei Evaluation integrated with Self-Organizing Map (SOM) for stock market investment strategies. The proposed approach, using a group Decision Support System (DSS), aims to aggregate experts’ preferences with the selection of the most suitable stocks, matching with investing strategies to achieve investment returns by dealing with co...

This study provides new insights into the relationships between social media sentiments and the stock market in China. Based on machine learning, we classify microblogs posted on Sina Weibo, a Twitter's variant in China into five detailed sentiments of anger, disgust, fear, joy, and sadness. Using wavelet analysis, we find close positive linkages between sentiments and the stock return, which h...