Algorithmic trading

About: Algorithmic trading is a research topic. Over the lifetime, 6718 publications have been published within this topic receiving 162209 citations. The topic is also known as: algotrading & Algorithmic trading.

Adaptive conceding strategies for automated trading agents in dynamic, open markets

Abstract: One of the crucial issues of automated negotiation in multi-agent systems is how to reach an agreement when a negotiation environment becomes open and dynamic. Even though some strategies have been proposed by researchers, most of them can only work within a static negotiation environment. In this paper, we present a model for designing a strategy for agents that makes adjustable rates of concession by negotiating according to the changes of environments with uncertain and dynamic outside options. This proposal is based on the market-driven agents (MDAs) model, and is guided by four factors in order to determine the degree of concession. These factors are trading opportunity, trading competition, trading time and strategy, and eagerness. The contribution of this paper is extending the MDAs model to an open and dynamic negotiation environment by considering both the current and potential changes of the environment.

Spreads, Information Flows and Transparency Across Trading Systems Federal Reserve Bank of Chicago, Issues in Financial Regulation

Abstract: This paper analyzes the dynamics of price formation for a strictly identical derivatives contract which is traded simultaneously at two competing exchanges. The domestic exchange is situated in the country that issues the underlying instrument. The foreign exchange offers a large international capital centre with many diversificationpossibilities. In addition, the exchanges are characterized by different trading systems. The domestic exchange operates by automated trading, the foreign exchange uses open outcry with an automated late afternoon session. We will investigate whether these differences support the trading system segmentation hypothesis. Our working hypothesis is two-fold. First, we investigate whether the transparency of each trading system affects quote setting. Second, we analyze whether the relative transparency of each market influences the lead/lag relationship between the two markets. Both hypotheses are empirically tested for the Bund futures contract as it is traded in London (LIFFE) and Frankfurt (DTB).

Exchange Trading Rules, Surveillance, and Suspected Insider Trading

Abstract: We examine the impact of stock exchange trading rules and surveillance on the frequency and severity of suspected insider trading cases in 22 stock exchanges around the world over the period January 2003 through June 2011. Using new indices for market manipulation, insider trading, and broker-agency conflict based on the specific provisions of the trading rules of each stock exchange, along with surveillance to detect non-compliance with such rules, we show that more detailed exchange trading rules and surveillance over time and across markets significantly reduce the number of suspected cases, but increase the profits per suspected case.

An agent-based computational model for China's stock market and stock index futures market

Abstract: This study presents an agent-based computational cross-market model for Chinese equity market structure, which includes both stocks and CSI 300 index futures. In this model, we design several stocks and one index futures to simulate this structure. This model allows heterogeneous investors to make investment decisions with restrictions including wealth, market trading mechanism, and risk management. Investors' demands and order submissions are endogenously determined. Our model successfully reproduces several key features of the Chinese financial markets including spot-futures basis distribution, bid-ask spread distribution, volatility clustering and long memory in absolute returns. Our model can be applied in cross-market risk control, market mechanism design and arbitrage strategies analysis.

Media-aware quantitative trading based on public Web information

Abstract: Recent studies in behavioral finance discover that emotional impulses of stock investors affect stock prices. The challenge lies in how to quantify such sentiment to predict stock market movements. In this article, we propose a media-aware quantitative trading strategy utilizing sentiment information of Web media. This is achieved by capturing public mood from interactive behaviors of investors in social media and studying the impact of firm-specific news sentiment on stocks along with such public mood. Our experiments on the CSI 100 stocks during a three-month period show that a predictive performance in closeness to the actual future stock price is 0.612 in terms of root mean squared error, the same direction of price movement as the future price is 55.08%, and a simulation trading return is up to 166.11%.