Trading Strategies

A new approach is proposed to identify trading opportunities in the equity market by using the information contained in the bivariate dependence structure of two equities. The relationships between the equity pairs are modelled with bivariate copulas and the fitted copula structures are utilised to identify the trading opportunities. Two trading strategies are considered that take advantage of the relative mispricing between a pair of correlated stocks and involve taking a position on the stocks when they diverge from their historical relationship. The position is then reversed when the two stocks revert to their historical relationship. Only stock-pairs with relatively high correlations are considered. The dependence structures of the chosen stock-pairs very often exhibited both upper-and lower-tail dependence, which implies that copulas with the correct characteristics should be more effective than the more traditional approaches typically applied. To identify trading opportunities, the conditional copula functions are used to derive confidence intervals for the two stocks. It is shown that the number of trading opportunities is highly dependent on the confidence level and it is argued that the chosen confidence level should take the strength of the dependence between the two stocks into account. The backtest results of the pairs-trading strategy are disappointing in that even though the strategy leads to profits in most cases, the profits are largely consumed by the trading costs. The second trading strategy entails using single stock futures and it is shown to have more potential as a statistical arbitrage approach to construct a portfolio.

Investment objective of yielding higher return at lower risk is one of the challenges faced by participants (mainly investors) in share market.  With an aim of overcoming this common challenge, past scholars have tested various trading strategies and even proposed new strategies but the outcomes are still puzzling. These high evolutions pertaining to trading strategies that occurred in investment world are covering several aspects such as fundamental features (ratios of the companies) and the economic variables yet the results were discourage due to mix results reported. Ample of causes could attribute to these situations where one of the most identifiable reasons was unpredictability in global economic condition. Thus, this paper attempts to focus on the high yielding strategy of Dogs of the Dow Theory as one of the trading strategy in constructing portfolio in which this strategy are distinctive from the common high yielding approaches. This paper consists of several parts, namely the evolution of the trading strategies and the empirical evidences supporting Dogs of the Dow Theory as a trading strategy.

Investors who want to execute large orders always have to trade-off between price effect and opportunity cost. The purpose of this research is to investigate an optimal way to execute such orders. In this research we consider the possibility of order types and the optimal trading strategy based on the volume weighted average price (VWAP), using historical data of a share in Tehran Stock Exchange. In the simulated trading market, we also consider the price effect for large orders. The results show that for a large order on buy-side, execution strategy adopting multiple order types can perform better than those using single order type. The optimal strategy has managed to reduce the volume weighted average price (transaction costs) by 0.137 percent compared to the market.