Roughly speaking, statistical arbitrage is a long horizon trading strategy that generates a riskless profit. The concept of statistical arbitrage is motivated by numerous empirical studies that construct trading strategies to profit from persistent anomalies. However, we demonstrate by example that positive expected trading profits are not sufficient to reject market efficiency. Instead, this p...

From experimental evaluation, we reasonably infer that online trading algorithms can beat the market. We consider the scenario of trading in financial market and present an extensive experimental study to answer the question “Can online trading algorithms beat the market?”. We evaluate the selected set of online trading algorithms on DAX30 and measure the performance against buy-and-hold strate...

This paper studies four trading algorithms of a professional trader, in a realistic two-sided limit order book whose dynamics are driven by the order book events. The identity of the trader can be either privileged or regular, either a hedge fund or a brokery agency. The speed and cost of trading can be balanced by properly choosing active strategies on the displayed orders in the book and pass...

This paper introduces two new characterizations of the Top Trading Cycles algorithm. The key to our characterizations is a new condition, independence of irrelevant rankings (IIR). Intuitively, a mechanism satisfies IIR if whenever an agent’s ranking at an object is irrelevant to her assignment, then it is irrelevant to the assignment of all agents. We demonstrate that a mechanism is Pareto eff...

It is commonly believed that prices in secondary financial markets play an important allocational role because they contain information that facilitates the efficient allocation of resources. This paper identifies a limitation inherent in this role of prices. It shows that the presence of a feedback effect from the financial market to the real value of a firm creates an incentive for an uninfor...

An artificial stock market is established based on multi-agent modeling method. Each agent has a limit memory of the history of stock price, and will choose an action according to his memory and trading strategy. The trading strategy of each agent evolves ceaselessly as a result of self-teaching mechanism. The “market data-like” time series are generated by our model, and by defining a variable...

In his seminal work, Constantinides (1986) finds that transaction cost has only a second order effect on liquidity premia. In this paper, we show that incorporating the well-established time-varying return dynamics across trading and nontrading periods can produce a first order effect that is much greater than that found by the existing literature and comparable to empirical evidence. Surprisin...

Stock trading system to assist decision-making is an emerging research area and has great commercial potentials. Successful trading operations should occur near the reversal points of price trends. Traditional technical analysis, which usually appears as various trading rules, does aim to look for peaks and bottoms of trends and is widely used in stock market. Unfortunately, it is not convenien...

We examine the use and pro tability of technical trading rules in nancial markets by studying the Santa Fe Stock Market, an agent-based model of a stock market, in which traders make investment decisions by forecasting stock prices using technical and fundamental rules. We show that individual traders earn more by using technical rules, no matter what other traders do, so the use of technical t...

Computational finance is one of the fields where machine learning and data mining have found in recent years a large application. Neverthless, there are still many open issues regarding the predictability of the stock market, and the possibility to build an automatic intelligent trader able to make forecasts on stock prices, and to develop a profitable trading strategy. In this paper, we propos...