Neural Networks in the Capital Markets: An Application to Index Forecasting

In this article we construct an Index of Austrian Initial Public Offerings (IPOX) which is isomorph to the Austrian Traded Index (ATX). Conjecturing that the ATX qualifies as an explaining variable for the IPOX, we investigate the time trend properties of and the comovement between the two indices. We use the relationship to construct a TJ.eural network and a linear error-correction forecasting model for the IPOX and base a tracling scheme on either forecast. The results suggest that trading based on the forecasts significantly increases an investor's return as compared to Buy and Hold or simple Moving Average trading strategies.Series: Department of Economics Working Paper Serie

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

Modelling, forecasting and trading of commodity spreads

Historically, econometric models have been developed to model financial instruments and markets however the vast majority of these ‘traditional’ models have one thing in common, linearity. While this is convenient and sometimes intuitive many linear models fail to fully capture the dynamic and complex nature of financial instruments and markets. More recently, ‘sophisticated’ methodologies have been evolved to accurately capture ‘non-linear’ relationships that exist between financial time series. This rapidly advancing field in quantitative finance is known as Artifical Intelligence. The earliest forms of artificial intelligence are Neural Networks however these have since been developed using more accurate learning algoirthms. Neural networks are also of particular use because of their capability of being able to continually learn as new information is fed into the network. In this research new data is introduced using both fixed and sliding window approaches for training each of the networks. Futhermore, Genetic Programming Algorithms are also highly regarded in the financial industry and have been increasingly applied as an optimisation technique. Therefore, each of the non-linear models are supported by existing research and as a result these methodologies have become practical tools for optimising existing models and predicting future movements in financial assets. In the absence of computational algorithms to rationalise large amounts of data, investors are confronted with a difficult and seemingly impossible task of trying to comprehend large datasets of information. Nevertheless, advancements in computing technology have enabled market participants to benefit from the use of neural networks (NN) and genetic programming (GP) algorithms in order to optimise and identify patterns and trends between explanatory variables and target outputs. This is of particular importance in the agricultural market such as grains, precious metals and other commodities are informationally rich with large amounts of data being readily available to evaluate. Among the first to use neural networks for financial analysis were Rumelhart and McClelland (1986), Lippman (1987), and Medsker et al. (1993). More recently, neural networks and genetic programming algorithms have been extensively applied to the foreign exchange market (Hornik et al., 1989; Lawrenz and Westerhoff, 2003), for credit analysis (Tam and Kiang, 1992), volatility forecasting (Ormoneit and Neuneier, 1996; Donaldson and Kamstra, 1997), option pricing, (Hutchinson et al.,1994), portfolio optimisation (Chang et al., 2000; Lin et al., 2001), to both developed (Swales and Yoon, 1992) and emerging (Kimoto et al., 1990) stock markets, and for optimisation of technical trading rules (Tsai et al.,1999; Neely et al., 2003). The application of non-linear methodologies to futures contracts and inparticular, commodity spread trading, is limited. Trippi and DeSieno (1992) and Kaastra and Boyd (1995), however were among the first to explore and apply neural networks to forecast futures markets. Financial markets and assets are influenced by an array of factors including but not limited to; human behaviour, economic variables, and many other systematic and non-systematic factors . As a result, many academics and practioners have devised numerous approaches and models to explain financial time series such as fundamental analysis, technical analysis and behavioural finance. The purpose of this research however is to identify, forecast and trade daily changes in commodity spreads using a combination of novel nonlinear modeling techniques and performance enhancing trading filters. During the research process, non-linear models such as neural networks and genetic algorithms are used to identify trends in complex and expansive commodity datasets. Each of the methodologies are used to produce predictions for future time periods. In this research forecasts for t+1 horizons are examined. Progressively, each chapter presents an evolution of research in the area of non-linear forecasting to address inefficiencies associated with more traditional neural architectures. In total a collection of five non-linear methodologies are proposed and analysed to trade commodity ‘spreads’. These non-linear methodologies are benchmarked against linear models which include Naïve strategies, Moving Average Convergence Divergence (MACD) strategies, buy and hold strategies, Autoregressive Moving Average (ARMA) models, and Cointegration models. In the final chapter of the research a mixed model approach is employed to include linear outputs from benchmark models as inputs during the training of each neural network. The research includes various adaptations of existing non-linear methodologies such as neural networks and genetic programming. Through historical data input, each non-linear methodology is trained to construct ‘optimal’ trading models. Models are selected to trade commodity spreads using data from Exchange Traded Funds (ETFs) and Futures contracts. In all cases the reader is presented with results from both unfiltered and filtered trading simulations. The aim of this thesis is to benefit both hedgers and speculators who are interested in applying non-linear methodologies to the task of forecasting changes in commodity spreads. By allowing market participants to input numerous explanatory variables, non-linear methodologies such as neural networks and genetic programming algorithms can become a valuable tool for predicting changes in commodity spreads. Empirical evidence reveals that non-linear methodologies are statistically superior compared to existing linear models and they also produce higher risk adjusted returns. Moreover, by including output from linear models in the input dataset to train non-linear models, market participants are also able benefit from a ‘synergy’ of information using a ‘mixed model’ approach. In order to improve trading results the research also offers examples of numerous trading filters which can also be of use to hedgers and speculators. On the whole the research contributes a wealth of knowledge to academic studies as it offers conclusive evidence to support the widespread integration and use of non-linear modelling in the form of artificial intelligence. Empirical results are evaluated by statistical measures as well as financial performance measures which are widely used by financial institutions

A Model for Stock Price Prediction Using the Soft Computing Approach

A number of research efforts had been devoted to forecasting stock price based on technical indicators which rely purely on historical stock price data. However, the performances of such technical indicators have not always satisfactory. The fact is, there are other influential factors that can affect the direction of stock market which form the basis of market experts’ opinion such as interest rate, inflation rate, foreign exchange rate, business sector, management caliber, investors’ confidence, government policy and political effects, among others. In this study, the effect of using hybrid market indicators such as technical and fundamental parameters as well as experts’ opinions for stock price prediction was examined. Values of variables representing these market hybrid indicators were fed into the artificial neural network (ANN) model for stock price prediction. The empirical results obtained with published stock data show that the proposed model is effective in improving the accuracy of stock price prediction. Also, the performance of the neural network predictive model developed in this study was compared with the conventional Box-Jenkins autoregressive integrated moving average (ARIMA) model which has been widely used for time series forecasting. Our findings revealed that ARIMA models cannot be effectively engaged profitably for stock price prediction. It was also observed that the pattern of ARIMA forecasting models were not satisfactory. The developed stock price predictive model with the ANN-based soft computing approach demonstrated superior performance over the ARIMA models; indeed, the actual and predicted value of the developed stock price predictive model were quite close

A contribution to exchange rate forecasting based on machine learning techniques

El propòsit d'aquesta tesi és examinar les aportacions a l'estudi de la predicció de la taxa de canvi basada en l'ús de tècniques d'aprenentatge automàtic. Aquestes aportacions es veuen facilitades i millorades per l'ús de variables econòmiques, indicadors tècnics i variables de tipus ‘business and consumer survey’. Aquesta investigació s’organitza entorn d’una recopilació de quatre articles. L'objectiu de cadascun dels quatre treballs de recerca d'aquesta tesi és el de contribuir a l'avanç del coneixement sobre els efectes i mecanismes mitjançant els quals l'ús de variables econòmiques, indicadors tècnics, variables de tipus ‘business and consumer survey’, i la selecció dels paràmetres de models predictius són capaços de millorar les prediccions de la taxa de canvi. Fent ús d'una tècnica de predicció no lineal, el primer article d'aquesta tesi es centra majoritàriament en l'impacte que tenen l'ús de variables econòmiques i la selecció dels paràmetres dels models en les prediccions de la taxa de canvi per a dos països. L'últim experiment d'aquest primer article fa ús de la taxa de canvi del període anterior i d'indicadors econòmics com a variables d'entrada en els models predictius. El segon article d'aquesta tesi analitza com la combinació de mitjanes mòbils, variables de tipus ‘business and consumer survey’ i la selecció dels paràmetres dels models milloren les prediccions del canvi per a dos països. A diferència del primer article, aquest segon treball de recerca afegeix mitjanes mòbils i variables de tipus ‘business and consumer survey’ com a variables d'entrada en els models predictius, i descarta l'ús de variables econòmiques. Un dels objectius d'aquest segon article és determinar el possible impacte de les variables de tipus ‘business and consumer survey’ en les taxes de canvi. El tercer article d'aquesta tesi té els mateixos objectius que el segon, però amb l'excepció que l'anàlisi abasta les taxes de canvi de set països. El quart article de la tesi compta amb els mateixos objectius que l'article anterior, però amb la diferència que fa ús d'un sol indicador tècnic. En general, l'enfocament d'aquesta tesi pretén examinar diferents alternatives per a millorar les prediccions del tipus de canvi a través de l'ús de màquines de suport vectorial. Una combinació de variables i la selecció dels paràmetres dels models predictius ajudaran a aconseguir aquest propòsit.El propósito de esta tesis es examinar las aportaciones al estudio de la predicción de la tasa de cambio basada en el uso de técnicas de aprendizaje automático. Dichas aportaciones se ven facilitadas y mejoradas por el uso de variables económicas, indicadores técnicos y variables de tipo ‘business and consumer survey’. Esta investigación está organizada en un compendio de cuatro artículos. El objetivo de cada uno de los cuatro trabajos de investigación de esta tesis es el de contribuir al avance del conocimiento sobre los efectos y mecanismos mediante los cuales el uso de variables económicas, indicadores técnicos, variables de tipo ‘business and consumer survey’, y la selección de los parámetros de modelos predictivos son capaces de mejorar las predicciones de la tasa de cambio. Haciendo uso de una técnica de predicción no lineal, el primer artículo de esta tesis se centra mayoritariamente en el impacto que tienen el uso de variables económicas y la selección de los parámetros de los modelos en las predicciones de la tasa de cambio para dos países. El último experimento de este primer artículo hace uso de la tasa de cambio del periodo anterior y de indicadores económicos como variables de entrada en los modelos predictivos. El segundo artículo de esta tesis analiza cómo la combinación de medias móviles, variables de tipo ‘business and consumer survey’ y la selección de los parámetros de los modelos mejoran las predicciones del cambio para dos países. A diferencia del primer artículo, este segundo trabajo de investigación añade medias móviles y variables de tipo ‘business and consumer survey’ como variables de entrada en los modelos predictivos, y descarta el uso de variables económicas. Uno de los objetivos de este segundo artículo es determinar el posible impacto de las variables de tipo ‘business and consumer survey’ en las tasas de cambio. El tercer artículo de esta tesis tiene los mismos objetivos que el segundo, pero con la salvedad de que el análisis abarca las tasas de cambio de siete países. El cuarto artículo de esta tesis cuenta con los mismos objetivos que el artículo anterior, pero con la diferencia de que hace uso de un solo indicador técnico. En general, el enfoque de esta tesis pretende examinar diferentes alternativas para mejorar las predicciones del tipo de cambio a través del uso de máquinas de soporte vectorial. Una combinación de variables y la selección de los parámetros de los modelos predictivos ayudarán a conseguir este propósito.The purpose of this thesis is to examine the contribution made by machine learning techniques on exchange rate forecasting. Such contributions are facilitated and enhanced by the use of fundamental economic variables, technical indicators and business and consumer survey variables as inputs in the forecasting models selected. This research has been organized in a compendium of four articles. The aim of each of these four articles is to contribute to advance our knowledge on the effects and means by which the use of fundamental economic variables, technical indicators, business and consumer surveys, and a model’s free-parameters selection is capable of improving exchange rate predictions. Through the use of a non-linear forecasting technique, one research paper examines the effect of fundamental economic variables and a model’s parameters selection on exchange rate forecasts, whereas the other three articles concentrate on the effect of technical indicators, a model’s parameters selection and business and consumer surveys variables on exchange rate forecasting. The first paper of this thesis has the objective of examining fundamental economic variables and a forecasting model’s parameters in an effort to understand the possible advantages or disadvantages these variables may bring to the exchange rate predictions in terms of forecasting performance and accuracy. The second paper of this thesis analyses how the combination of moving averages, business and consumer surveys and a forecasting model’s parameters improves exchange rate predictions. Compared to the first paper, this second paper adds moving averages and business and consumer surveys variables as inputs to the forecasting model, and disregards the use of fundamental economic variables. One of the goals of this paper is to determine the possible effects of business and consumer surveys on exchange rates. The third paper of this thesis has the same objectives as the second paper, but its analysis is expanded by taking into account the exchange rates of 7 countries. The fourth paper in this thesis takes a similar approach as the second and third papers, but makes use of a single technical indicator. In general, this thesis focuses on the improvement of exchange rate predictions through the use of support vector machines. A combination of variables and a model’s parameters selection enhances the way to achieve this purpose

Applications of artificial neural networks in financial market forecasting

This thesis evaluates the utility of Artificial Neural Networks (ANNs) applied to financial market and macroeconomic forecasting. In application, ANNs are evaluated in comparison to traditional forecasting models to evaluate if their nonlinear and adaptive properties yield superior forecasting performance in terms of robustness and accuracy. Furthermore, as ANNs are data-driven models, an emphasis is placed on the data collection stage by compiling extensive candidate input variable pools, a task frequently underperformed by prior research. In evaluating their performance, ANNs are applied to the domains of: exchange rate forecasting, volatility forecasting, and macroeconomic forecasting. Regarding exchange rate forecasting, ANNs are applied to forecast the daily logarithmic returns of the EUR/USD over a short-term forecast horizon of one period. Initially, the analytic method of Technical Analysis (TA) and its sub-section of technical indicators are utilized to compile an extensive candidate input variable pool featuring standard and advanced technical indicators measuring all technical aspects of the EUR/USD time series. The candidate input variable pool is then subjected to a two-stage Input Variable Selection (IVS) process, producing an informative subset of technical indicators to serve as inputs to the ANNs. A collection of ANNs is then trained and tested on the EUR/USD time series data with their performance evaluated over a 5-year sample period (2012 to 2016), reserving the last two years for out of sample testing. A Moving Average Convergence Divergence (MACD) model serves as a benchmark with the in-sample and out-of-sample empirical results demonstrating the MACD is a superior forecasting model across most forecast evaluation metrics. For volatility forecasting, ANNs are applied to forecast the volatility of the Nikkei 225 Index over a short-term forecast horizon of one period. Initially, an extensive candidate input variable pool is compiled consisting of implied volatility models and historical volatility models. The candidate input variable pool is then subjected to a two-stage IVS process. A collection of ANNs is then trained and tested on the Nikkei 225 Index time series data with their performance evaluated over a 4-year sample period (2014 to 2017), reserving the last year for out-of-sample testing. A GARCH (1,1) model serves as a benchmark with the out-of-sample empirical results finding the GARCH (1,1) model to be the superior volatility forecasting model. The research concludes with ANNs applied to macroeconomic forecasting, where ANNs are applied to forecast the monthly per cent-change in U.S. civilian unemployment and the quarterly per cent-change in U.S. Gross Domestic Product (GDP). For both studies, an extensive candidate input variable pool is compiled using relevant macroeconomic indicator data sourced from the Federal Bank of St Louis. The candidate input variable pools are then subjected to a two-stage IVS process. A collection of ANNs is trained and tested on the U.S. unemployment time series data (UNEMPLOY) and U.S. GDP time series data. The sample periods are (1972 to 2017) and (1960 to 2016) respectively, reserving the last 20% of data for out of sample testing. In both studies, the performance of the ANNs is benchmarked against a Support Vector Regression (SVR) model and a Naïve forecast. In both studies, the ANNs outperform the SVR benchmark model. The empirical results demonstrate that ANNs are superior forecasting models in the domain of macroeconomic forecasting, with the Modular Neural Network performing notably well. However, the empirical results question the utility of ANNs in the domains of exchange rate forecasting and volatility forecasting. A MACD model outperforms ANNs in exchange rate forecasting both in-sample and out-of-sample, and a GARCH (1,1) model outperforms ANNs in volatility forecasting

The development of hybrid intelligent systems for technical analysis based equivolume charting

This dissertation proposes the development of a hybrid intelligent system applied to technical analysis based equivolume charting for stock trading. A Neuro-Fuzzy based Genetic Algorithms (NF-GA) system of the Volume Adjusted Moving Average (VAMA) membership functions is introduced to evaluate the effectiveness of using a hybrid intelligent system that integrates neural networks, fuzzy logic, and genetic algorithms techniques for increasing the efficiency of technical analysis based equivolume charting for trading stocks --Introduction, page 1