Evolving Trading Strategies Using Directional Changes

The majority of forecasting methods use a physical time scale for studying price fluctuations of financial markets, making the flow of physical time discontinuous. Therefore, using a physical time scale may expose companies to risks, due to ignorance of some significant activities. In this paper, an alternative and original approach is explored to capture important activities in the market. The main idea is to use an event-based time scale based on a new way of summarising data, called Directional Changes. Combined with a genetic algorithm, the proposed approach aims to find a trading strategy that maximises profitability in foreign exchange markets. In order to evaluate its efficiency and robustness, we run rigorous experiments on 255 datasets from six different currency pairs, consisting of intra-day data from the foreign exchange spot market. The results from these experiments indicate that our proposed approach is able to generate new and profitable trading strategies, significantly outperforming other traditional types of trading strategies, such as technical analysis and buy and hold

Smart Money: The Forecasting Ability of CFTC Large Traders in Agricultural Futures Markets

The forecasting content of the Commodity Futures Trading Commission’s Commitments of Traders (COT) report is investigated. Bivariate Granger causality tests show very little evidence that traders’ positions are useful in forecasting (leading) returns in 10 agricultural futures markets. However, there is substantial evidence that traders respond to price changes. In particular, noncommercial traders display a tendency for trend following. The other trader classifications display mixed styles, perhaps indicating those trader categories capture a variety of traders. The results generally do not support use of the COT data in predicting price movements in agricultural futures markets.agricultural futures markets, commitments of traders, forecasting, prices, Agribusiness, Agricultural Finance,

The price dynamics of common trading strategies

A deterministic trading strategy can be regarded as a signal processing element that uses external information and past prices as inputs and incorporates them into future prices. This paper uses a market maker based method of price formation to study the price dynamics induced by several commonly used financial trading strategies, showing how they amplify noise, induce structure in prices, and cause phenomena such as excess and clustered volatility.Comment: 29 pages, 12 figure

Smart Money? The Forecasting Ability of CFTC Large Traders

The forecasting ability of the Commodity Futures Trading Commission’s Commitment’s of Traders data set is investigated. Bivariate Granger causality tests show very little evidence that traders’ positions are useful in forecasting (leading) market returns. However, there is substantial evidence that traders respond to price changes. In particular, non-commercial traders display a tendency for trend-following. The other trader classifications display mixed styles, perhaps indicating that those trader categories capture a variety of traders. The results generally do not support the use of the Commitment’s of Traders data in predicting market movements.Commitment’s of Traders, futures markets, forecasting, Agricultural Finance, Financial Economics,

The Strategic Exploitation of Limited Information and Opportunity in Networked Markets

This paper studies the effect of constraining interactions within a market. A model is analysed in which boundedly rational agents trade with and gather information from their neighbours within a trade network. It is demonstrated that a trader’s ability to profit and to identify the equilibrium price is positively correlated with its degree of connectivity within the market. Where traders differ in their number of potential trading partners, well-connected traders are found to benefit from aggressive trading behaviour.Where information propagation is constrained by the topology of the trade network, connectedness affects the nature of the strategies employed

Challenges arising from alternative investment management.

Alternative investment management differs from traditional asset management in a number of respects. First, it is distinct in terms of both its targets – aiming to achieve an absolute performance, regardless of trends in underlying markets – and its strategies, in particular exploiting inefficiencies in the valuation of financial assets via opportunistic and discretionary positions. It also differs in terms of the financial techniques implemented, e.g. the extensive use made of leverage, derivatives and short selling, and the specific investment vehicles used (ad hoc structures such as hedge funds that are not bound by ordinary law in the way traditional investment vehicles are). These particularities, alongside the fact that the alternative investment universe is somewhat opaque, make it difficult to measure a fund’s risks or a fund manager’s performance. Specific measurement tools are therefore required, which differ from those commonly used in traditional asset management. Over the past few years, the alternative investment management, a diverse and rapidly-evolving universe, has enjoyed a spectacular development, which is illustrated by the sharp rise in the amounts under management and the proliferation of investment vehicles offered to an increasingly broad investor base. In view of the specific nature of alternative fund managers’ modus operandi, the flourishing of the alternative investment industry raises questions as to its implications in terms of financial stability. It also raises new issues regarding the division of roles between market participants and supervisory authorities in the organisation and monitoring of this asset management sector.

Heterogeneous Agent Models in Economics and Finance, In: Handbook of Computational Economics II: Agent-Based Computational Economics, edited by Leigh Tesfatsion and Ken Judd , Elsevier, Amsterdam 2006, pp.1109-1186.

This chapter surveys work on dynamic heterogeneous agent models (HAMs) in economics and finance. Emphasis is given to simple models that, at least to some extent, are tractable by analytic methods in combination with computational tools. Most of these models are behavioral models with boundedly rational agents using different heuristics or rule of thumb strategies that may not be perfect, but perform reasonably well. Typically these models are highly nonlinear, e.g. due to evolutionary switching between strategies, and exhibit a wide range of dynamical behavior ranging from a unique stable steady state to complex, chaotic dynamics. Aggregation of simple interactions at the micro level may generate sophisticated structure at the macro level. Simple HAMs can explain important observed stylized facts in financial time series, such as excess volatility, high trading volume, temporary bubbles and trend following, sudden crashes and mean reversion, clustered volatility and fat tails in the returns distribution.