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

Adaptive Investment Strategies For Periodic Environments

In this paper, we present an adaptive investment strategy for environments with periodic returns on investment. In our approach, we consider an investment model where the agent decides at every time step the proportion of wealth to invest in a risky asset, keeping the rest of the budget in a risk-free asset. Every investment is evaluated in the market via a stylized return on investment function (RoI), which is modeled by a stochastic process with unknown periodicities and levels of noise. For comparison reasons, we present two reference strategies which represent the case of agents with zero-knowledge and complete-knowledge of the dynamics of the returns. We consider also an investment strategy based on technical analysis to forecast the next return by fitting a trend line to previous received returns. To account for the performance of the different strategies, we perform some computer experiments to calculate the average budget that can be obtained with them over a certain number of time steps. To assure for fair comparisons, we first tune the parameters of each strategy. Afterwards, we compare the performance of these strategies for RoIs with different periodicities and levels of noise.Comment: Paper submitted to Advances in Complex Systems (November, 2007) 22 pages, 9 figure

Forecasting sovereign bonds markets using machine learning: forecasting the portuguese government bond using machine learning approach

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementFinancial markets, due to their non-linear, volatile and complex nature turn any type of forecasting into a difficult task, as the classical statistical methods are no longer adequate. Many factors exist that can influence the government bonds yields and how these bonds behave. The consequence of the behaviour of these bonds are extended over geographies and individuals. As the financial markets grow bigger, more investors are trying to develop systematic approaches that are intended to predict prices and movements. Machine Learning algorithms already proven their value in predicting and finding patterns in many subjects. When it comes to financial markets, Machine Learning is not a new tool. It is already widely used to predict behaviours and trends with some degree of success. This dissertation aims to study the application of two Machine Learning algorithms - Genetic Programming (GP) and Long Short-Term Memory (LSTM) - to the Portuguese Government 10Y Bond and try to forecast the yield with accuracy. The construction of the predictive models is based on historical information of the bond and on other important factors that influence its behaviour, extracted through the Bloomberg Portal. In order to analyse the quality of the two models, the results of each algorithm will be compared. An analysis will be presented regarding the quality of the results from both algorithms and the respective time cost. In the end, each model will be discussed and conclusions will be taken about which one can be the answer to the main question of this study, which is “What will the Yield of the Portuguese Government 10Y Bond be on T+1?”. The results obtained showed that Genetic Programming can create a model with higher accuracy. However, Long Short-Term Memory should not be ignored because it can also point to good results. Regarding execution time, velocity is a problem when it comes to Genetic Programming. This algorithm takes more time to execute compared to LSTM. Long Short-Term Memory is considerably quicker to get results. In order to take the right decision about which model to choose one must keep in mind the priorities. In case accuracy is the priority, Genetic Programming will be the answer. Nevertheless, when velocity is the priority Long Short-Term Memory should be the choice

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

A study on variations of Genetic Programming applied to time series forecasting: Machine Learning for Energy Consumption Forecasting

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceEvolutionary Computation is a sub-field of Machine Learning algorithms based on Darwin’s theory of Evolution. Individuals are evolved using the principles of mutation, crossover and natural selection. One of the most known Evolutionary Algorithms is Genetic Programming (GP), that evolves as individuals computer programs in order to solve regression problems. In this thesis two variations of GP, namely Geometric Semantic Genetic Programming(GSGP) and Tree-based Pipeline Optimization Tool(TPOT), are applied to two energy consumption time series regression problems. Their performance are then compared to state-of-the-art models, LSTM and SVR optimized with DE, and to standard GP. It is showed that the variations of GP outperform standard GP and SVR optimized with DE, while also having comparable performance to LSTM. Additionally a study on the feature selection ability of GSGP is proposed, showing that the algorithm is not actually able to perform feature selection

Predicting exchange rate volatility: genetic programming vs. GARCH and RiskMetrics

This article investigates the use of genetic programming to forecast out-of-sample daily volatility in the foreign exchange market. Forecasting performance is evaluated relative to GARCH(1,1) and RiskMetrics models for two currencies, DEM and JPY. Although the GARCH/RiskMetrics models appear to have a inconsistent marginal edge over the genetic program using the mean-squared-error (MSE) and R2 criteria, the genetic program consistently produces lower mean absolute forecast errors (MAE) at all horizons and for both currencies.Foreign exchange rates ; Forecasting ; Programming (Mathematics)

Financial contagion: Evolutionary optimisation of a multinational agent-based model

Over the past two decades, financial market crises with similar features have occurred in different regions of the world. Unstable cross-market linkages during a crisis are referred to as financial contagion. We simulate crisis transmission in the context of a model of market participants adopting various strategies; this allows testing for financial contagion under alternative scenarios. Using a minority game approach, we develop an agent-based multinational model and investigate the reasons for contagion. Although the phenomenon has been extensively investigated in the financial literature, it has not been studied through computational intelligence techniques. Our simulations shed light on parameter values and characteristics which can be exploited to detect contagion at an earlier stage, hence recognising financial crises with the potential to destabilise cross-market linkages. In the real world, such information would be extremely valuable in developing appropriate risk management strategies