A Hybrid Method of Least Square Support Vector Machine and Bacterial Foraging Optimization Algorithm for Medium Term Electricity Price Forecasting

Predicting electricity price has now become an important task for planning and maintenance of power system. In medium term forecast, electricity price can be predicted for several weeks ahead up to a year or few months ahead. It is useful for resources reallocation where the market players have to manage the price risk on the expected market scenario. However, researches on medium term price forecast have also exhibit low forecast accuracy. This is due to the limited historical data for training and testing purposes. Therefore, an optimization technique of Bacterial Foraging Optimization Algorithm (BFOA) for Least Square Support Vector Machine (LSSVM) was developed in this study to provide an accurate electricity price forecast with optimized LSSVM parameters and input features. So far, no literature has been found on feature and parameter selections using the LSSVM-BFOA method for medium term price prediction. The model was examined on the Ontario power market; which is reported as among the most volatile market worldwide. Monthly average of Hourly Ontario Electricity Price (HOEP) for the past 12 months and month index are selected as the input features. The developed LSSVM-BFOA shows higher forecast accuracy with lower complexity than the existing models

Meta-heurísticas aplicadas ao problema de projeção do preço de ações na bolsa de valores

The stock prices prediction in the stock exchange is an attractive field for research due to its commercial applications and financial benefits offered. The objective of this work is to analyze the performance of two meta-heuristic algorithms, Bat Algorithm and Genetic Algorithm to the problem of stock prices prediction. The individuals in the population of the algorithms were modeled using 7 technical indicators. The profit at the end of a period is maximized by choosing the right time to buy and sell stocks. To evaluate the proposed methodology, experiments were performed using real historical data (2006-2012) of 92 stocks listed on the stock exchange in Brazil. Cross-validation was applied in the experiments to avoid the overfiting using 3 periods for training and 4 for testing. The results of the algorithms were compared among them and also the performance indicator BuyandHold (B&H).For 91.30% of the stocks, the algorithms obtained profit higher than the B&H, and in 79.35% of them Bat Algorithm had the best performance, while for 11.95% of the stocks Genetic Algorithm was better. The results indicate that it is promising to apply meta-heuristics with the proposed model to the problem of stock prices prediction in the stock exchange.A projeção do preço de ações na bolsa de valores é um campo atraente para a investigação devido às suas aplicações comerciais e os benefícios financeiros oferecidos. O objetivo deste trabalho é analisar o desempenho de dois algoritmos meta-heurísticos, o Algoritmo do Morcego e o Algoritmo Genético, para o problema de projeção do preço de ações. Os indivíduos da população dos algoritmos foram modelados utilizando os parâmetros de 7 indicadores técnicos. O lucro final ao fim de um período é maximizado através da escolha do momento adequado para compra e venda de ações. Para avaliar a metodologia proposta foram realizados experimentos utilizando dados históricos reais (2006-2012) de 92 ações listadas na bolsa de valores do Brasil. A validação cruzada foi aplicada nos experimentos para evitar o overfiting, utilizando 3 períodos para treinamento e 4 para teste. Os resultados dos algoritmos foram comparados entre si e com o indicador de desempenho Buy and Hold (B&H). Para 91,30% das ações os algoritmos obtiveram lucro superior ao B&H, sendo que em 79,35% delas o Algoritmo do Morcego teve o melhor desempenho, enquanto que para 11,95% das ações o Algoritmo Genético foi melhor. Os resultados alcançados indicam que é promissora a aplicação de meta-heurísticas com a modelagem proposta para o problema de projeção do preço de ações na bolsa de valores

Machine learning in stock indices trading and pairs trading

This thesis focuses on two fields of machine learning in quantitative trading. The first field uses machine learning to forecast financial time series (Chapters 2 and 3), and then builds a simple trading strategy based on the forecast results. The second (Chapter 4) applies machine learning to optimize decision-making for pairs trading. In Chapter 2, a hybrid Support Vector Machine (SVM) model is proposed and applied to the task of forecasting the daily returns of five popular stock indices in the world, including the S&P500, NKY, CAC, FTSE100 and DAX. The trading application covers the 1997 Asian financial crisis and 2007-2008 global financial crisis. The originality of this work is that the Binary Gravity Search Algorithm (BGSA) is utilized, in order to optimize the parameters and inputs of SVM. The results show that the forecasts made by this model are significantly better than the Random Walk (RW), SVM, best predictors and Buy-and-Hold. The average accuracy of BGSA-SVM for five stock indices is 52.6%-53.1%. The performance of the BGSA-SVM model is not affected by the market crisis, which shows the robustness of this model. In general, this study proves that a profitable trading strategy based on BGSA-SVM prediction can be realized in a real stock market. Chapter 3 focuses on the application of Artificial Neural Networks (ANNs) in forecasting stock indices. It applies the Multi-layer Perceptron (MLP), Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) neural network to the task of forecasting and trading FTSE100 and INDU indices. The forecasting accuracy and trading performances of MLP, CNN and LSTM are compared under the binary classifications architecture and eight classifications architecture. Then, Chapter 3 combines the forecasts of three ANNs (MLP, CNN and LSTM) by Simple Average, Granger-Ramanathan’s Regression Approach (GRR) and the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, this chapter uses different leverage ratios in trading according to the different daily forecasting probability to improve the trading performance. In Chapter 3, the statistical and trading performances are estimated throughout the period 2000-2018. LSTM slightly outperforms MLP and CNN in terms of average accuracy and average annualized returns. The combination methods do not present improved empirical evidence. Trading using different leverage ratios improves the annualized average return, while the volatility increases. Chapter 4 uses five pairs trading strategies to conduct in-sample training and backtesting on 35 commodities in the major commodity markets from 1980 to 2018. The Distance Method (DIM) and the Co-integration Approach (CA) are used for pairs formation. The Simple Thresholds (ST) strategy, Genetic Algorithm (GA) and Deep Reinforcement Learning (DRL) are employed to determine trading actions. Traditional DIM-ST, CA-ST and CA-DIM-ST are used as benchmark models. The GA is used to optimize the trading thresholds in ST strategy, which is called the CA-GA-ST strategy. Chapter 4 proposes a novel DRL structure for determining trading actions, which replaces the ST decision method. This novel DRL structure is then combined with CA and called the CA-DRL trading strategy. The average annualized returns of the traditional DIM-ST, CA-ST and CA-DIM-ST methods are close to zero. CA-GA-ST uses GA to optimize searches for thresholds. GA selects a smaller range of thresholds, which improves the in-sample performance. However, the average out-of-sample performance only improves slightly, with an average annual return of 1.84% but an increased risk. CA-DRL strategy uses CA to select pairs and then employs DRL to trade the pairs, providing a satisfactory trading performance: the average annualized return reaches 12.49%; the Sharpe Ratio reaches 1.853. Thus, the CA-DRL trading strategy is significantly superior to traditional methods and to CA-GA-ST