There is an old Wall Street adage goes, ‘‘It takes volume to make price move”. The contemporaneous relation between trading volume and stock returns has been studied since stock markets were first opened. Recent researchers such as Wang and Chin [Wang, C. Y., & Chin S. T. (2004). Profitability of return and volume-based investment strategies in China’s stock market. Pacific-Basin Finace Journal...

This paper presents new trading models for the stock market and test whether they are able to consistently generate excess returns from the Singapore Exchange (SGX). Instead of conventional ways of modeling stock prices, we construct models which relate the market indicators to a trading decision directly. Furthermore, unlike a reversal trading system or a binary system of buy and sell, we allo...

We study a stock trading method based on dynamic bayesian networks to model the dynamics of the trend of stock prices. We design a three level hierarchical hidden Markov model (HHMM). There are five states describing the trend in first level. Second and third levels are abstract and concrete hidden Markov models to produce the observed patterns. To train the HHMM, we adapt a semi-supervised lea...

In technical analysis, each trading strategy can trigger a buy or sell action whenever the specified conditions are satisfied. When a set of strategies are applied to a particular stock, a trader often receives conflicting recommendations from each strategy. In this paper, we propose a unified data mining approach in which the outcomes of each strategy are taken into consideration for decision ...

In this paper we present PRACTIONIST Studio, which is an integrated design and development environment for BDI agent-based systems, providing facilities and tools to represent the concepts and intentional elements underlying such a model as well as several common features offered by UMLbased tools. PRACTIONIST Studio aims at bridging the gap between the increasing trend of developing BDI-based ...

Technical analysis is aimed at devising trading rules capable of exploiting short-term 2uctuations on the 3nancial markets. Recent results indicate that this market timing approach may be a viable alternative to the buy-and-hold approach, where the assets are kept over a relatively long time period. In this paper, we propose genetic programming as a means to automatically generate such short-te...

While many studies have focused on trading volume in the stock market, little is known about why derivatives volume varies in the cross-section or over time. We study time-series properties as well as the determinants of the options/stock trading volume ratio (O/S) using a comprehensive cross-section and time-series of data on equities and their listed options. O/S is related to many intuitive ...

Confluence occurs when different trading filters generate signals that point to the same directional move. Using regression analysis, this paper investigates confluence trading signals associated with number preference and price exhaustion, for a sample of Australian stocks. The results show that certain price levels tend to act as psychological barriers, and that price exhaustion signals are a...

This report documents the development of an autonomous stock trading agent within the framework of the Penn-Lehman Automated Trading (PLAT) simulator. The three approaches presented take inspiration from reinforcement learning, myopic trading using regression-based price prediction, and market making. The performance of these approaches is assessed separately using a fixed opponent strategy, SO...