Monte Carlo simulations are used to demonstrate that a very attractive tax-based trading strategy is to realize all capital losses, using excess losses to offset realized gains to rebalance the portfolio. This strategy increases the mean and median return by taking advantage of the tax-deductibility of losses, and mitigates risk by allowing low-cost portfolio rebalancing. This portfolio rebalan...

information asymmetry in stock market can increase the risk of investment which in turn increases the capital cost of firms. bhattacharya (1979) proposed a hypothesis that states dividend can act as a powerful signal in order to solve information asymmetry problem. we measured information asymmetry by lack of earnings transparency. therefore we examine the effect of earnings transparency on cap...

this paper analyses efficiency of short horizon event study methodology in general and efficiency of various test statistics based on price and trading volume in the period (iranian calendar) 1380:1389-q1 (2240 days) applying simulation method. we evaluate efficiency of 8 test statistics including parametric, non-parametric and induced variance statistics. we find various test statistics have e...

In this paper we investigate trading with optimal mean reverting portfolios subject to cardinality constraints. First, we identify the parameters of the underlying VAR(1) model of asset prices and then the quantities of the corresponding OrnsteinUhlenbeck (OU) process are estimated by pattern matching techniques. Portfolio optimization is performed according to two approaches: (i) maximizing th...

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...

In this paper, we first find out some good trading strategies from the historical series and apply them in the future. The profitable strategies are trained out by the gene expression programming (GEP), which involves some well-known stock technical indicators as features. Our data set collects the 100 stocks with the top capital from the listed companies in the Taiwan stock market. Accordingly...

The analysis of risk-return tradeoffs and their practical applications to portfolio analysis paved the way for Modern Portfolio Theory (MPT), which won Harry Markowitz a 1992 Nobel Prize in Economics. A typical approach in measuring a portfolio's expected return is based on the historical returns of the assets included in a portfolio. On the other hand, portfolio risk is usually measured using ...

T recent financial crisis highlights the importance of market crashes and the subsequent market illiquidity for optimal portfolio selection. We propose a tractable and flexible portfolio choice model where market crashes can trigger switching into another regime with a different investment opportunity set. We characterize the optimal trading strategy in terms of coupled integro-differential equ...

In this paper we apply evolutionary optimization techniques to compute optimal rule-based trading strategies based on financial sentiment data. The sentiment data was extracted from the social media service StockTwits to accommodate the level of bullishness or bearishness of the online trading community towards certain stocks. Numerical results for all stocks from the Dow Jones Industrial Avera...