Automated Trading is the activity of buying and selling financial instruments for the purpose of gaining a profit, through the use of automated trading rules. This work presents an evolutionary approach for the design and optimization of artificial neural networks to the discovery of profitable automated trading rules. Experimental results indicate that, despite its simplicity, both in terms of...

Pairs trading is a comparative-value form of statistical arbitrage designed to exploit temporary random departures from equilibrium pricing between two shares. However, the strategy is not riskless. Market events as well as poor statistical modeling and parameter estimation may all erode potential profits. Since conventional loss limiting trading strategies are costly, a preferable situation is...

While there is an extensive literature, both theoretical and empirical, on determining optimal asset ratios for funds with alternative objectives, little has been written about an equally-important aspect of money management: how to implement these strategies optimally in the presence of transactions costs and capital gains taxes. Yet trading to implement investment objectives is regularly done...

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....

Accurate stock trend prediction is a difficult job because various intricate and complex factors affect changes in price, trading volume and trends of a stock market. On a macro scale, the factors could be the overall global economic environment, industry trends, individual economic environment (business operation and competitors’ development), the amount of floating capital in the market, etc....

We show that vector majorization and its related preference sets can be used to establish useful option pricing bounds for a robust option replacement investment strategy. This robust trading strategy can help to overcome some of the difficulties in implementing arbitrage option trading strategies when there exists model inaccuracy.

Finding Bertram’s optimal trading strategy for a pair of cointegrated assets following the Ornstein–Uhlenbeck price difference process can be formulated as an unconstrained convex optimization problem maximization expected profit per unit time. This model is generalized to form where riskiness profit, measured by its per-time-unit volatility, controlled (e.g. in case existence limits on strateg...

In this paper, a new trading decision strategy is proposed. This strategy combines the following concepts: KD-based rules, fuzzy logic inference mechanism and neural network learning ability. We used 115 stocks from different industries in Taiwan and their trading data during the period of Jan. 4, 1989 to June 30, 2000 as our experimental data. The data from Jan. 4, 1989 to Dec. 31, 1998 were u...

Uncertainty inherent in the financial market was usually consid- ered to be random. However, randomness is only one special type of uncer- tainty and appropriate when describing objective information. For describing subjective information it is preferred to assume that uncertainty is fuzzy. This paper defines the expected payoof trading strategies in a fuzzy financial market within the framewor...