This paper examines the effect of introducing insider tratlin~; restrictions on the behaviour of the Amsterdam Stock Exchange. From 198'7 on, insiders are no longer allowed to trade two months before an annual earnings announcement. The results indicate that stocks became less liquid (when liquidity is measured by trading volume) when insiders were not allowed to trade. We also find some eviden...

This paper is to explore the possibility to use alternative data and artificial intelligence techniques to trade stocks. The efficacy of the daily Twitter sentiment on predicting the stock return is examined using machine learning methods. Reinforcement learning(Q-learning) is applied to generate the optimal trading policy based on the sentiment signal. The predicting power of the sentiment sig...

In this paper we provide empirical findings on the significance of positive feedback trading for the return behavior in the German stock market. Relying on the ShillerSentana-Wadhwani model, we use the link between index return auto-correlation and volatility to obtain a better understanding into the return characteristics generated by traders adhering to positive feedback trading strategies. O...

for their helpful comments and research support. The comments and point of views expressed in the paper, however, are the authors own, and do not necessarily reflect the opinions of the New York Stock Exchange and the Shanghai Stock Exchange. Therefore, the authors are responsible for all remaining errors. Abstract This paper studies the impact of the minimum price variation (tick size) on clos...

Two alternative trading mechanisms for securities markets are compared using laboratory experimentation and computer simulation. One mechanism is the floor-based specialist auction in place in most U.S. stock exchanges today, and the other is an electronic alternative employing automatic order matching. We conclude that transition from the established floor-based exchanges to potentially superi...

I n this paper, trading symbols of the 30 largest companies listed in the Tehran Stock Exchange (TSE) were ranked based on the asymmetry information risk. Using the Ersan and Alici (2016) modified clustering algorithm (EA), we estimated the probability of informed trading (PIN) to measure the asymmetry information among traders for each trading symbol and trading day through two-year...

We propose a multi-stock automated trading system that relies on a layered structure consisting of a machine learning algorithm, an online learning utility, and a risk management overlay. Alternating decision tree (ADT), which is implemented with Logitboost, was chosen as the underlying algorithm. One of the strengths of our approach is that the algorithm is able to select the best combination ...

We propose a model with heterogeneous interacting traders which can explain some of the stylized facts of stock market returns A generalized version of the Random Field Ising Model RFIM is introduced to describe trading behavior Imitation e ects which induce agents to trade can generate avalanches in trading volume and large gaps in demand and supply A trade friction is introduced which by resp...

Research on stock price prediction and trading model using evolutionary computation has been done in recent years. As we know, prediction in the stock market is quite difficult for a number of reasons. First, the ultimate goal of our research is not to minimize the prediction error, but to maximize the profits. Second, the weak relationships among variables tend to be nonlinear, and they may ho...