This chapter illustrates a data-mining approach to single-position day trading which uses an evolutionary algorithm to construct a fuzzy predictive model of a financial instrument. The model is expressed as a set of fuzzy IF-THEN rules. The model takes as inputs the open, high, low, and close prices, as well as the values of a number of popular technical indicators on day t and produces a go sh...

Portfolio selection is a resource allocation problem in a finance market. The investor’s asset optimization requires the distribution of a set of capital (resources) among a set of entities (assets) with the trade-off between risk and return. The ANN with nonlinear capability is proven to solve a large-scale complex problem effectively. It is suitable to solve NP-hard resource allocation proble...

We explore the possibility of using the genetic algorithm to optimize trading models based on the Hierarchical Temporal Memory (HTM) machine learning technology. Technical indicators, derived from intraday tick data for the E-mini S&P 500 futures market (ES), were used as feature vectors to the HTM models. All models were configured as binary classifiers, using a simple buy-and-hold trading str...

We have recently proposed a promising trading system for the S&P 500 index, which consists of a feature selection component and a simple filter for data preprocessing, two specialized neural networks for return prediction, and a rule base for prediction integration. The objective of this study is to explore if including additional knowledge for more sophisticated data filtering and return integ...

Extending the controversial findings from the relevant literature, the results from the quarterly transaction-based Nationwide indices from 1974 to 2009 provide further empirical evidence on the rejection of the weak-form version of efficiency in the U.K. housing market. In addition to conducting parametric and non-parametric tests, we apply technical trading strategies to test whether or not t...

Despite the voluminous empirical research on the potential predictability of stock returns, much less attention has been paid to the predictability of bear and bull stock markets. In this study, the aim is to predict U.S. bear and bull stock markets with dynamic binary time series models. Based on the analysis of the monthly U.S. data set, bear and bull markets are predictable in and out of sam...

Direct quotations from business leaders can communicate to the wider public the latent state of their organization as well as the beliefs of the organization’s leaders. Candid quotes from business leaders can have dramatic effects upon the share price of their organization. For example, Gerald Ratner in 1991 stated that his company’s products were crap and consequently his company (Ratners) los...

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 consider a multivariate nancial market with transaction costs as in Kabanov (1999). We study the problem of nding the minimal initial capital needed to hedge, without risk, European-type contingent claims. We prove that the value of this sto-chastic control problem is given by the cost of the cheapest buy-and-hold strategy. This is an extension of the already known result in the one-dimensio...

I derive the optimal timing strategy for an investor taking tax gains and losses on tax-exempt bonds (up to a 7% gain over buy-and-hold). I then derive the issuer’s optimal coupon rate, which maximizes the tax benefit for a tax-timing investor (up to a 3.5% gain over issuing at par, potentially more than the cost of issuance itself). All these gains are transfers from the U.S. Treasury to local...