Abstract- Financial investment decision making is extremely difficult due to the complexity of the domain. Many factors could influence the change of share prices. FGP (Financial Genetic Programming) is a genetic programming based forecasting system, which is designed to help users evaluate impact of factors and explore their interactions in relation to future prices. Users channel into FGP factors that they believe are relevant to the prediction. Examples of such factors may include fundamental factors such as "price-earning ratio", "inflation rate " or/and technical factors such as "5-days moving average", "63-days trading range breakout", etc. FGP uses the power of genetic programming to generate decision trees through combination of technical rules with self-adjusted thresholds. In earlier papers, we have reported how FGP used well-known technical analysis rules to make investment decisions. This paper tests the versatility of FGP by testing it on shorter-term investment decisions. To evaluate FGP more thoroughly, we also compare it with C4.5, a well-known machine learning classifier system. We used six and a half years ’ daily closing price of the Dow Jones Industrial Average (DJIA) index for training and over three and half years ’ data for testing and obtained favourable results for FGP. 1

EDDIE is a genetic programming based decision support tool for financial forecasting. EDDIE itself does not replace forecasting experts. It serves to improve the productivity of experts in searching the space of decision trees, with the aim to improve the odds in its user's favour. The efficacy of EDDIE has been reported in the literature. However, discovering patterns in historical data is only the first step towards building a practical financial forecasting tool. Data preparation, rules organization and application are all important issues. This paper describes an architecture that embeds EDDIE for learning from and monitoring the stock market.

Stock markets are very important in modern societies and their behaviour have serious implications in a wide spectrum of the world’s population. Investors, governing bodies and the society as a whole could benefit from better understanding of the behavior of stock markets. The traditional approach to analyze such systems is the use of analytical models. However, the complexity of financial markets represents a big challenge to the analytical approach. Most analytical models make simplifying assumptions, such as perfect rationality and homogeneous investors, which threaten the validity of analytical results. This motivates alternative methods. In this work, we developed an artificial financial market and used it to study the behavior of stock markets. In this market, we model technical, fundamental and noise traders. The technical traders are sophisticated genetic programming based agents that co-evolve (by means of their fitness function) by predicting investment opportunities in the market using technical analysis as the main tool. With this endogenous artificial market, we identified conditions under which the statistical properties of price series in the artificial market resembles those of the real financial markets. Additionally, we modeled the pressure to beat the market by a behavioral constraint imposed on the agents reflecting the Red Queen principle in evolution. We have demonstrated how evolutionary computation could play a key role in studying stock markets.

Abstract. In recent years the computers have shown to be a powerful tool in financial forecasting. Many machine learning techniques have been utilized to predict movements in financial markets. Machine learning classifiers involve extending the past experiences into the future. However the rareness of some events makes difficult to create a model that detect them. For example bubbles burst and crashes are rare cases, however their detection is crucial since they have a significant impact on the investment. One of the main problems for any machine learning classifier is to deal with unbalanced classes. Specifically Genetic Programming has limitation to deal with unbalanced environments. In a previous work we described the Repository Method, it is a technique that analyses decision trees produced by Genetic Programming to discover classification rules. The aim of that work was to forecast future opportunities in financial stock markets on situations where positive instances are rare. The objective is to extract and collect different rules that classify the positive cases. It lets model the rare instances in different ways, increasing the possibility of identifying similar cases in the future. The objective of the present work is to find out the factors that work in favour of Repository Method, for that purpose a series of experiments was performed. 1

Ordinal data play an important part in financial forecasting. For example, advice from expert sources may take the form of “bullish”, “bearish ” or “sluggish”, or "buy " or "do not buy". This paper describes an application of using Genetic Programming (GP) to combine investment opinions. The aim is to combine ordinal forecast from different opinion sources in order to make better predictions. We tested our implementation, FGP (Financial Genetic Programming), on two data sets. In both cases, FGP generated more accurate rules than the individual input rules.

Abstract. The aim of this work is to forecast future events in financial data sets, in particular, we focus our attention on situations where positive instances are rare, which falls into the domain of Chance Discovery. Machine learning classifiers extend the past experiences into the future. However the imbalance between positive and negative cases poses a serious challenge to machine learning techniques. Because it favours negative classifications, which has a high chance of being correct due to the nature of the data. Genetic Algorithms have the ability to create multiple solutions for a single problem. To exploit this feature we propose to analyse the decision trees created by Genetic Programming. The objective is to extract and collect different rules that classify the positive cases. It lets model the rare cases in different ways, increasing the possibility of identifying similar cases in the future. The over-learning produced by this method attempts to compensate the lack of positive cases. To illustrate our approach, it was applied to a data set composed by closing prices from the London Financial Market for finding investment opportunities with very high returns. From experiment results we showed that the Repository Method can consistently improve both the recall and the precision. Keywords: Chance Discovery, Classification, Genetic Programming. 1

The objective in this paper is to develop and implement FGP-2 (Financial Genetic Programming) on intra daily tick data for stock index options and futures arbitrage in a manner that is suitable for online trading when windows of profitable arbitrage opportunities exist for short periods from one to ten minutes. Our benchmark for FGP-2 is the textbook rule for detecting arbitrage profits. This rule has the drawback that it awaits a contemporaneous profitable signal to implement an arbitrage in the same direction. A novel methodology of randomised sampling is used to train FGP-2 to pick up the fundamental arbitrage patterns. Care is taken to fine tune weights in the fitness function to enhance performance. As arbitrage opportunities are few, missed opportunities can be as costly as wrong recommendations to trade. Unlike conventional genetic programs, FGP-2 has a constraint satisfaction feature supplementing the fitness function that enables the user to train the FGP to specify a minimum and a maximum number of profitable arbitrage opportunities that are being sought. Historical sample data on arbitrage opportunities enables the user to set these minimum and maximum bounds. Good FGP rules for arbitrage are found to make a 3-fold improvement in profitability over the textbook rule. This application demonstrates the success of FGP-2 in its interactive capacity that allows experts to channel their knowledge into machine discovery.

presented to discover patterns in financial data sets to detect investment opportunities. ECR is designed to classify in extreme imbalanced environments. This is particularly useful in financial forecasting given that very often the number of profitable chances is scarce. The proposed approach offers a range of solutions to suit the investor’s risk guidelines and so, the user could choose the best trade-off between miss-classification and false alarm costs according to the investor’s requirements. Receiver Operating Characteristics (ROC) curve and the Area Under the ROC (AUC) have been used to measure the performance of ECR. Following from this analysis, the results obtained by our approach have been compared with those one found by standard Genetic Programming (GP), EDDIE-ARB and C.5, which show that our approach can be effectively used in data sets with rare positive instances. Key words: Evolving comprehensible rules, machine learning, evolutionary algorithms. 1

Recent studies in financial economics suggest that technical analysis may have merit to predictability of stock. When attempting to create an efficient portfolio of stocks, there are numerous factors to consider. The problem is that the evaluation involves many qualitative factors, which causes most approximations to go off track. This paper presents a genetic programming approach to portfolio evaluation. By using a set of fitness heuristics over a population of stock portfolios, the goal is to find a portfolio that has a high expected return over investment. 1.

Abstract — This work is motivated by financial forecasting using Genetic Programming. This paper presents a method to post-process decision trees. The processing procedure is based on the analysis and evaluation of the components of each tree, followed by pruning. The idea behind this approach is to identify and eliminate rules that cause misclassification. As a result we expect to keep and generate rules that enhance the classification. This method was tested on decision trees generated by a genetic program whose aim was to discover classification rules in financial stock markets. From experimental results we can conclude that our method is able to improve the accuracy and precision of the classification. I.