Stock trend forecasting is a one of the main factors in choosing the best investment, hence prediction and comparison of different firms’ stock trend is one method for improving investment process. Stockholders need information for forecasting firm’s stock trend in order to make decision about firms’ stock trading. In this study stock trend, forecasting performs by data mining algorithm. It sho...

We examine the effects of trading and information flows on the short-run behavior of stock prices by comparing the behavior of stock return volatility during trading and nontrading periods. We define nontrading periods as periods when exchanges and businesses are open but traders endogenously choose not to trade. After correcting for the bid/ask bounce and stickiness in quotes, we find that a l...

• Amihud, Mendelson and Murgia (1990) studied the impact of the stock market microstructure on return volatility and on the value discovery process in the Milan Stock Exchange; the primary trading mechanism employed by this exchange is a call market, which is usually preceded and followed by trading in a continuous market. They found that the opening transaction in the continuous market had the...

Transaction costs can make it unprofitable to rebalance all the way to the ideal portfolio. A single-period analysis using mean-variance theory provides many interesting insights. With fixed or variable costs, there is a non-trading region within which trading does not pay. With only variable costs, any trading is to the boundary of the non-trading region, while fixed costs induce trading to th...

Since the May 6, 2010 flash crash in the U.S., appropriate measures ensuring safe, fair and reliable markets become more relevant from the perspective of investors and regulators. Circuit breakers in various forms are already implemented for individual markets to ensure price continuity and prevent potential market failure and crash scenarios. However, coordinated inter-market safeguards have h...

in this study, trading patterns and constituents of trading performances of individual and institutional investors have been investigated in a weekly manner, using an auto regressive model and a grinblatt & titman portfolio performance measure spanning 2008-2012. the results show that while individual investors have a herding behavior, institutional investors take contrarian trading strategies....

Evolutionary learning is one of the most popular techniques for designing quantitative investment (QI) products. Trend following (TF) strategies, owing to their briefness and efficiency, are widely accepted by investors. Surprisingly, to the best of our knowledge, no related research has investigated TF investment strategies within an evolutionary learning model. This paper proposes a hybrid lo...

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...