Modelling and trading the Greek stock market with gene expression and genetic programing algorithms

This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter

Measuring Effectiveness of Quantitative Equity Portfolio Management Methods

In this paper, I use quantitative computer models to measure the effectiveness of Quantitative Equity Portfolio Management in predicting future stock returns using commonly accepted industry valuation factors. Industry knowledge and practices are first examined in order to determine strengths and weaknesses, as well as to build a foundation for the modeling. In order to assess the accuracy of the model and its inherent concepts, I employ up to ten years of historical data for a sample of stocks. The analysis examines the historical data to determine if there is any correlation between returns and the valuation factors. Results suggest that the price to cash flow and price to EBITDA exhibited significant predictors of future returns, while the price to earnings ratio is an insignificant predictor

Do Moving Average Rules Make Profits? A Study Using The Madrid Stock Market

(WP 03/04 Clave pdf) Previous studies have reported mixed results with regard to the success of technical trading rules.Studies that provide positive evidence are [Brock et al (1992), Karjalainen (1994), Bessembinder et al (1995),Mills (1997), and Fernandez et al (1999)]. Studies rejecting the utility of technical trading rules are [Hudson et al (1996) or Allen et al (1999)]. A recent body of work has applied evolutionary algorithms to the design of trading rules [see Karjalainen (1994), Allen et al (1999), Fernandez et al (2001) and Nuñez (2002)].This paper uses genetic algorithms to tests the forecastability of the moving average in the MSE.We report the lack of utility of this indicator.Genetic algorithms, Madrid Stock Exchange, Moving average, Trading rules

Is technical analysis in the foreign exchange market profitable? a genetic programming approach

Using genetic programming techniques to find technical trading rules, we find strong evidence of economically significant out-of-sample excess returns to those rules for each of six exchange rates, over the period 1981-1995. Further, when the dollar/deutschemark rules are allowed to determine trades in the other markets, there is a significant improvement in performance in all cases, except for the deutschemark/yen. Betas calculated for the returns according to various benchmark portfolios provide no evidence that the returns to these rules are compensation for bearing systematic risk. Bootstrapping results on the dollar/deutschemark indicate that the trading rules are detecting patterns in the data that are not captured by standard statistical models.Programming (Mathematics) ; Foreign exchange

A Comparative Study on the Use of Classification Algorithms in Financial Forecasting

Financial forecasting is a vital area in computational finance, where several studies have taken place over the years. One way of viewing financial forecasting is as a classification problem, where the goal is to find a model that represents the predictive relationships between predictor attribute values and class attribute values. In this paper we present a comparative study between two bio-inspired classification algorithms, a genetic programming algorithm especially designed for financial forecasting, and an ant colony optimization one, which is designed for classification problems. In addition, we compare the above algorithms with two other state-of-the-art classification algorithms, namely C4.5 and RIPPER. Results show that the ant colony optimization classification algorithm is very successful, significantly outperforming all other algorithms in the given classification problems, which provides insights for improving the design of specific financial forecasting algorithms

Nonlinear Combination of Financial Forecast with Genetic Algorithm

Complexity in the financial markets requires intelligent forecasting models for return volatility. In this paper, historical simulation, GARCH, GARCH with skewed student-t distribution and asymmetric normal mixture GRJ-GARCH models are combined with Extreme Value Theory Hill by using artificial neural networks with genetic algorithm as the combination platform. By employing daily closing values of the Istanbul Stock Exchange from 01/10/1996 to 11/07/2006, Kupiec and Christoffersen tests as the back-testing mechanisms are performed for forecast comparison of the models. Empirical findings show that the fat-tails are more properly captured by the combination of GARCH with skewed student-t distribution and Extreme Value Theory Hill. Modeling return volatility in the emerging markets needs “intelligent” combinations of Value-at-Risk models to capture the extreme movements in the markets rather than individual model forecast.Forecast combination; Artificial neural networks; GARCH models; Extreme value theory; Christoffersen test

Using CAViaR models with implied volatility for value-at-risk estimation

This paper proposes VaR estimation methods that are a synthesis of conditional autoregressive value at risk (CAViaR) time series models and implied volatility. The appeal of this proposal is that it merges information from the historical time series and the different information supplied by the market’s expectation of risk. Forecast combining methods, with weights estimated using quantile regression, are considered. We also investigate plugging implied volatility into the CAViaR models, a procedure that has not been considered in the VaR area so far. Results for daily index returns indicate that the newly proposed methods are comparable or superior to individual methods, such as the standard CAViaR models and quantiles constructed from implied volatility and the empirical distribution of standardised residual. We find that the implied volatility has more explanatory power as the focus moves further out into the left tail of the conditional distribution of S&P500 daily returns

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

TECHNICAL ANALYSIS IN COMMODITY MARKETS: RISK, RETURNS, AND VALUE

Although there is little academic research that supports the usefulness of technical analysis, its use remains widespread in commodity markets. Much prior research into technical analysis suffered from data-snooping biases. Using genetic programming, ex ante optimal technical trading strategies are identified. Because they are mechanically generated from simple arithmetic operators, they are free of the data-snooping bias common in technical analysis research. These rules are clearly capable of forecasting periods of high and low volatility, but rules generated for corn and soybeans cannot consistently generate profits in the presence of transactions costs. Rules generated for wheat futures produce profits that are weakly significant, both statistically and economically.Technical Analysis, Genetic Algorithms, Commodity Markets, Futures Markets, Marketing,