We investigate the generalization properties of a data-mining approach to single-position day trading which uses an evolutionary algorithm to construct fuzzy predictive models of financial instruments. The models, expressed as fuzzy rule bases, take a number of popular technical indicators on day t as inputs and produce a trading signal for day t+ 1 based on a dataset of past observations of wh...

This paper tries to find a widely accessible measure of liquidity and studies its impact on asset pricing. Using trading turnover as a measure of liquidity and the 19761993 Tokyo Stock Exchange data, I find that, cross-sectionally, stocks with higher turnover tend to have a lower expected return. This evidence is consistent with predictions derived from an Amihud-Mendelson type of transaction c...

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...

We propose a prediction model based on the minority game in which traders continuously evaluate a complete set of trading strategies with different memory lengths using the strategies’ past performance. Based on the chosen trading strategy they determine their prediction of the movement for the following time period of a single asset. We find empirically using stocks from the S&P500 that our pr...

Using a novel proxy of investors’ speculative demand constructed from online search interest in “concept stocks”, we examine how speculative demand affects the returns and trading volume of Chinese stock indices. We find that returns and trading volume increase with the contemporaneous speculative demand. In addition, the high speculative demand causes lower near future returns, while recent hi...

We develop a new methodology to estimate the impact of financial transaction tax (FTT) on market outcomes. In our sequential trading model, there are price-elastic noise and informed traders. model through maximum likelihood for sample 60 NYSE stocks in 2017. quantify effect introducing an FTT given parameter estimates. An increases proportion trading, improves information aggregation, but lowe...

In this paper we examine changes in dollar and relative bid-ask spreads of stocks following large price movements. We investigate large increases and decreases separately and link our results to current market microstructure theories on trading activities and spreads. We also look at changes in volume and selling pressure to interpret the changes in trading activity. Our results show that the m...

In this paper, we use high-frequency data on five frequently traded stocks listed on the New York Stock Exchange (NYSE) in the year 1999 to examine the price impact of trades and its relation to the trading intensity. We show that the distribution of the absolute price change with fast trading firstorder stochastically dominates the distribution of the absolute price change with slow trading. M...

Asymmetric information models predict comovements among trade characteristics such as returns, bid-ask spread, and trade volume on one hand and the trading intensity on the other hand. In this paper we investigate empirically the two-sided causality between trade characteristics and trading intensity. We apply a VAR-model for returns, bid-ask spread, trade volume, and trading intensity to trans...

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...