An intelligent system for risk classification of stock investment projects

The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange

Practical Deep Reinforcement Learning Approach for Stock Trading

Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns

Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization

The motivation for this paper is to introduce a hybrid Neural Network architecture of Particle Swarm Optimization and Adaptive Radial Basis Function (ARBF-PSO), a time varying leverage trading strategy based on Glosten, Jagannathan and Runkle (GJR) volatility forecasts and a Neural Network fitness function for financial forecasting purposes. This is done by benchmarking the ARBF-PSO results with those of three different Neural Networks architectures, a Nearest Neighbors algorithm (k-NN), an autoregressive moving average model (ARMA), a moving average convergence/divergence model (MACD) plus a naïve strategy. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the EUR/USD, EUR/GBP and EUR/JPY ECB exchange rate fixing time series over the period January 1999 to March 2011 using the last two years for out-of-sample testing

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

Adaptive Statistical Evaluation Tools for Equity Ranking Models

A major challenge in the investment management business is to identify which stocks are likely to outperform in the future, and which are likely to perform relatively poorly. To this end the strategy adopted by Genus is to identify factors (auxiliary information about the stock such as earnings-to-price ratio or dividend yield) that they believe are associated with future out-performance (i.e. factors that have predictive ability). The best of these factors are then combined (Genus use a weighted average) into a model which is used to rank the universe of stocks month-by-month. This ranking is then used to as the input to a trading strategy, resulting in a modified portfolio. Genus had provided us with sample data, consisting of just over 12 years worth of monthly returns on a universe of 60 stocks, along with time series of 34 factors for each of the stocks. Using these data, the approach was to build software (MATLAB) models for: 1. ranking the stocks based on factor information; 2. implementing a trading strategy based on a stock ranking and assessing the performance of a given trading strategy by looking at measures such as hit ratio, information ratio and spread. The IPSW team implemented a simplified trading strategy of selling the entire portfolio each month, and using the proceeds to invest equally in the top 20% of stocks as given by the computed ranking. They also implemented the following measures of portfolio performance: excess return, hit ratio and information ratio