We investigate informed trading activity in equity options prior to the announcement of corporate mergers and acquisitions (M&A). For the target companies, we document pervasive directional options activity, consistent with strategies that would yield abnormal returns to investors with private information. This is demonstrated by positive abnormal trading volumes, excess implied volatility and ...

Innovative automated execution strategies like Algorithmic Trading gain significant market share on electronic market venues worldwide, although their impact on market outcome has not been investigated in depth yet. In order to assess the impact of such concepts, e.g. effects on the price formation or the volatility of prices, a simulation environment is presented that provides stylized impleme...

With the growing number of marketplaces and trading partners in the e–commerce environment, software tools designed to act on behalf of human traders are increasingly used to automate trading activities. This paper describes a model for constructing trading engines which are capable of concurrently participating in multiple interrelated negotiations with heterogeneous protocols. These tree–stru...

Financial markets such as stock exchanges and electronic prediction markets frequently use the services of an entity called the market-maker to ensure that the market’s traders can make their transactions. Recently, several strategies that can be used by market-makers to control market trading prices have been proposed by various researchers. A detailed comparison of these market maker strategi...

We study model-driven statistical arbitrage in U.S. equities. The trading signals are generated in two ways: using Principal Component Analysis and using sector ETFs. In both cases, we consider the residuals, or idiosyncratic components of stock returns, and model them as mean-reverting processes. This leads naturally to “contrarian” trading signals. The main contribution of the paper is the co...

The paper investigates downtrend algorithm and trading strategy based on chart pattern recognition and technical analysis in futures market. The proposed chart formation is a pattern with the lowest low in the middle and one higher low on each side. The contribution of this paper lies in the reinforcement of statements about the profitability of momentum trend trading strategies. Practical bene...

The goal of this paper was to attempt to characterize the traders in the bitcoin market. Specifically, this paper attempted to analyze whether any technical-analysis based trading strategies were being used traders in the bitcoin market, and if so, which strategies were being employed and to what degree. The paper focused on simple trading strategies based on well-understood indicators such as ...

Managing tracking error on an ex ante basis requires an ability to assess the possible effects of trades on a fund’s performance relative to its benchmark. Given a trading strategy, its potential for reducing tracking error must be balanced against trading costs and return expectations. This chapter presents several simple diagnostic tools to help fund managers evaluate alternative trading stra...

Abstract: Financial investment has become an important issue, there are many trading strategies and parameters based on quantitative models, this paper use neural network algorithm to optimization strategy parameters, various combinations of optimization strategies, as well as the evolution of new strategies to generate better returns. The empirical results show that this method has a stable an...

This paper uses a neural network methodology developed by Vanstone & Finnie[1] to develop a successful stockmarket trading system. The approach is based on these same 4 fundamental variables used within the Aby et al. fundamental trading strategies [2, 3], and demonstrates the important role neural networks have to play within complex and noisy environments, such as that provided by the stockma...