This paper devotes a first and direct attention on the feedback trading behaviors of one of the largest investor populations in the world. Using a unique data set of historical stock transaction, we examine cross-sectional and time-varying buy-sell activities of Chinese investor as a function of a series of stock returns in past. The time-horizons over which investors trade in response to retur...

Machine learning is increasingly prevalent in stock market trading. Though neural networks have seen success in computer vision and natural language processing, they have not been as useful in stock market trading. To demonstrate the applicability of a neural network in stock trading, we made a single-layer neural network that recommends buying or selling shares of a stock by comparing the high...

Competition among stock exchanges has increased dramatically over the last decade. To attract trading volume, most exchanges introduced makertaker fees, an incentive scheme that rewards liquidity suppliers and charges liquidity demanders. Using a change in fees on the Toronto Stock Exchange, we analyze how the breakdown of trading fees between liquidity demanders and suppliers affects market ou...

A model of competitive stock trading is developed in which investors are heterogeneous in their information and private investment opportunities and rationally trade for both informational and noninformational motives. I examine the link between the nature of heterogeneity among investors and the behavior of trading volume and its relation to price dynamics. It is found that volume is positivel...

It has been long recognized that trading volume provides valuable information for understanding stock price movement. As such, equivolume charting was developed to consider how stocks appear to move in a volume frame of reference as opposed to a time frame of reference. Two technical indicators, namely the volume adjusted moving average (VAMA) and the ease of movement (EMV) indicator, are devel...

In this paper, we are examining the relationship between stock trading costs and commitment components and find both abnormal and normal commitments associated with these costs are associated. Moreover, stock trading costs have a greater influence by both abnormal and normal negative commitments more than positive commitments. Further analysis has shown that in general, investors are unable to ...

Stock trading system to assist decision-making is an emerging research area and has great commercial potentials. Successful trading operations should occur near the reversal points of price trends. Traditional technical analysis, which usually appears as various trading rules, does aim to look for peaks and bottoms of trends and is widely used in stock market. Unfortunately, it is not convenien...

We examine the use and pro tability of technical trading rules in nancial markets by studying the Santa Fe Stock Market, an agent-based model of a stock market, in which traders make investment decisions by forecasting stock prices using technical and fundamental rules. We show that individual traders earn more by using technical rules, no matter what other traders do, so the use of technical t...

Prior research has shown that informed trading activity decreases the stock return volatility because trading causes stock prices to converge to fundamentals. On the contrary to existing studies, this paper documents the empirical asymmetric relation between informed trading activity and volatility. Stocks with relatively less private information are associated with lower participation of infor...

Genetic network programming (GNP) as an evolutionary computation method has been used for stock trading recently. Former researches confirm the efficiency of trading rules which are created by GNP. In this paper, GNP has been applied for stock portfolio optimization by generating risk-adjusted trading rules. There are two main novelties in this paper: 1) we use conditional Sharp ratio as a risk...