Stock markets strive to provide an efficient trading platform for investors. Trading rules and mechanisms issued to accomplish this differ among stock markets, and are subject to modification over time. Furthermore, market participants assume a broad range of roles and trading strategies. Such variation poses problems to those involved in the study of market dynamics, when developing an artific...

This paper examines the extent to which the daily movements of three large emerging markets stock indices are predictable. Lagged technical indicators are used as explanatory variables. In the analysis we employed seven classification techniques and assessed the discriminatory power of the classifiers through the area under the receiver operating characteristic (ROC) curve. The results show tha...

This paper examines the predictability of real estate asset returns using a number of time series techniques. A vector autoregressive model, which incorporates financial spreads, is able to improve upon the out of sample forecasting performance of univariate time series models at a short forecasting horizon. However, as the forecasting horizon increases, the explanatory power of such models is ...

This paper analyzes the stability of the exchange rate in an economy with noise traders. Noise trading is restricted to agents investing in the domestic stock market. The agents pricing foreign exchange hold rational expectations. Monetary policy is affected by the behavior of investors in the domestic stock market and in turn affects fundamental stock evaluations as well as noise trading. We s...

A number of published techniques have emerged in the trading community for stock prediction tasks. Among them is neural network (NN). In this paper, the theoretical background of NNs and the backpropagation algorithm is reviewed. Subsequently, an attempt to build a stock buying/selling alert system using a backpropagation NN, NN5, is presented. The system is tested with data from one Hong Kong ...

This paper derives in closed form the optimal dynamic portfolio policy when trading is costly and security returns are predictable by signals with different mean-reversion speeds. The optimal updated portfolio is a linear combination of the existing portfolio, the optimal portfolio absent trading costs, and the optimal portfolio based on future expected returns and transaction costs. Predictors...

We examined the effects of trading volume on the persistence of the time-varying conditional volatility of returns and the dynamic relations between trading volume and returns (and volatility) for both domestic and cross-country markets. We considered daily prices and trading volume in four Asian stock exchanges (Korea, Japan, China, and Hong Kong). For the analysis, we used the GARCH model, wh...

Stock trading is an important financial activity of human society. Machine learning techniques are adopted to provide trading decision support by predicting the stock price or trading signals of the next day. Decisions are made by analyzing technical indices and fundamental analysis of companies. There are two major machine learning research problems for stock trading decision support: classifi...

Recent studies in financial markets suggest that technical analysis can be a very useful tool in predicting the trend. Trading systems are widely used for market assessment. This paper employs a genetic algorithm to evolve an optimized stock market trading system. Our proposed system can decide a trading strategy for each day and produce a high profit for each stock. Our decision-making model i...

Traders in a stock market exchange stock shares and form a stock trading network. Trades at different positions of the stock trading network may contain different information. We construct stock trading networks based on the limit order book data and classify traders into k classes using the k-shell decomposition method. We investigate the influences of trading behaviors on the price impact by ...