Chapter 3: Appraisal of Financial Time Series Forecasting
3.3 Technical Analysis
3.3.2 Artificial intelligence models
This review's evaluated classical statistical techniques assume a linear correlation structure and statistical stationarity to exist in the time series. Additionally, conventional statistical methods
require data to be normally distributed and the availability of historical data for ameliorating prediction. However, the time series of stock market prices usually exhibit strong nonlinear trends, seasonal, cyclical patterns, and random walks and are inherently sensitive to many interdependent factors. Such non-stationary attributes of stock market prices and indices make the data chaotic and predictions complex. These factors recently influenced stock market price analysts to utilise Artificial Intelligence (AI) techniques for more reliable predictions.
In 1990, Robert J. Schalkoff, in his famous textbook [142], defined AI as a field of study that seeks to explain and emulate intelligent behaviour with the aid of computational processes. AI combines mathematics with engineering to create machines to perform functions that require significant brainpower when human beings complete them. In AI, Evolutionary Algorithms (EAs) refer to a collection of algorithms stimulated by biological and natural evolutionary processes such as selection, mutation and reproduction. EAs work well with situations with no exact solution due to computational intensity or conditions where humans do not find answers. Most optimisation problems fall into this predicament, and EAs often identify whether reasonable solutions exist. EAs have been applied to stock market prediction where the solution space is complex and irregular. It is, therefore, unwise to employ conventional optimisation technique/s to explore the global optimum. The Genetic Algorithm (GA), a search heuristic function, is the most common EA and is often applied to optimisation riddles. Fuzzy logic (FL), introduced by Zadeh [143], is based on the Fuzzy Set Theory, which provides a foundation for the set communion intermediate values between 'in the set' and 'not in the set'. Thus, FL is established on the norm of the 'degree of truth', that is, the truth values of the statements to be any value between 1 (absolutely truth) and 0 (absolutely false) rather than the ‘precise (exact)’ information. FL enables computers to mimic human cognition effectively by making the most logical decisions based on incomplete sets of information.
Hence, FL can be applied effectively to find meaningful solutions to noisy, ambiguous, vague
and chaotic time series such as stock market prices. Artificial Neural Networks (ANNs) are information processing paradigms whose origin can be traced back to modelling biological neural systems. The origin of ANN started with McCulloch and Pitts's [144] modelling in 1943.
Since then, ANNs have been used in a wide range of models for pattern and character recognition [145], image compression [146], the Travelling Salesman's Problem [147], medical research [148], [149] and financial market prediction, among others.
3.3.2.1 Application of AI models for financial market prediction
Many attempts [150]–[176] have been carried out from a technical analysis perspective to formulate time series prediction models accommodating either AI models, CS models, or HBs combining AI/or CS models. These studies investigated the forecasting effectiveness of linear and nonlinear models.
For example,[150] used GA and Evolution Strategies (ES) to predict the stock prices of eight companies and discovered that GA is marginally superior in predicting five out of eight cases. [151] projected an integrated system of wavelet transforms and Recurrent Neural network (RNN) based on the Artificial Bee Colony (ABC) algorithm (ABC-RNN). The proposed model was compared with the back-propagation ANN, conventional ANN optimised by the ABC, Conventional Fuzzy Time Series (FTS) model of Chen [177] and Yu [178].
RMSE, MAE, MAPE and Theil U were used to evaluate the forecasting effectiveness of the tested models and found that ABC-RNN is a superior model. [152] tested the back-propagation Neural Network (BPNN) and Adaptive Evolution Strategy (AES) to predict the movement of the Stock Exchange of Thailand Index (SET Index) and observedthat BPNN forecasts the SET index with a significant lower prediction error than ES. [153] evaluated the effectiveness of Neural Network models, namely Multi-Layer Perceptron (MLP), Dynamic Artificial Neural Network (DANN) and the hybrid neural networks, which incorporate GARCH to extract new
input variables. They used Mean Square Error (MSE) and Mean Absolute Deviation (MAD) to compare the models and revealed that MLP is the best. [155] evaluated the prediction accuracy of three neural networks, namely the Time-Delay Neural Network (TDNN), the Recurrent Neural Network (RNN) and Probabilistic Neural Networks (PNN). Daily data on selected stocks found the RNN to be more potent than the other methods evaluated, although implementation complexities existed. [156], proposed an HB model combining ES, ARIMA and BPNN, and they tested the model on two indices of monthly time series. Their results found that the proposed HB model outperforms all traditional models, including ESM, ARIMA, BPNN, the Equal Weight Hybrid model (EWH), and the RW model. [157] developed an HB model analysed on a daily series of two Indices and nine currencies. The proposed model was a hybrid method combining the Autoregressive Fractional Integrated Moving Average (ARFIMA) model and Fuzzy Time Series (FTS) for the forecast of long memory (long-range) time series. They tested Chen [177], Yu [178]–average-based-length, Yu [178]–distribution- based-length, Huarng & Yu [179], Chen and Chen [180], ARFIMA, ARIMA, Exponential Smoothing State Pace (ETS) model and their proposed model. Using RMSE, MAPE, and Wilcoxon Rank-Sum test (WRST), [157] found that the proposed model outperformed the assessed models. [158] proposed an HB model based on Learning Automata Particle Swarm Optimisation (LAPSO) and Polynomial Fuzzy Time Series (PFTS) (PFTS–LAPSO). They compared the proposed model with Chen [177], Yu [178]–average-based-length, Yu [178]–
distribution-based-length, Huarng & Yu [179], Chen and Chen [180], multi-layer, ARIMA, GARCH, ETS models. Using absolute percentage error (APE), RMSE and Relative Mean Absolute Error (RelMAE), they discovered the proposed PFTS–LAPSO is superior in forecasting. [159] proposed an HB model combining Wavelet Transform, Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) (Wavelet- MARS-SVR). They compared the proposed model with five challenging models: Wavelet-
SVR, Wavelet-MARS, single ARIMA, single SVR, and single Adaptive Neuro-Fuzzy Inference System (ANFIS). RMSE, MAD, MAPE, and root mean square percentage error (RMSPE) were used as performance evaluation measures and discovered the predictive superiority of the Wavelet-MARS-SVR model. [162] appraised Chen [177], Yu [178], and a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS). Using RMSE as the performance evaluation measure, they discovered that the proposed ANFIS is a better predictive model than the models evaluated.
Although the above studies validate the superiority of AI models over CS models, none of the devised models was recognised as a generalised model for time series of stock market prediction.
3.3.2.2 AI models applied to the New Zealand stock market
Applying soft computing methodologies to predict New Zealand financial time series is rare.
[181] assessed the effectiveness of the rough set model for financial time series data analysis and forecasting. They applied the rough set model for the New Zealand Exchange Limited (NZX) from 31 July 1991 till 27 April 2000. They discovered that predicting future stock index values using rough sets achieves high accuracy and coverage rules. [182] applied Radial Basis Function Neural Network (RBF-NN) methodology to develop a prediction algorithm for a daily time series of New Zealand exchange rates [United States Dollar and New Zealand Dollar (USD/NZD)]. They found that the RBF-NN model has proved to be efficient for USD/NZD time series modelling and performed better than the conventional Linear Autoregressive (LAR) model. [183] investigated the predictive efficiency of ANN in comparison to the ARIMA model. They analysed the New Zealand and Australian exchange (AUD/NZD) rates and five other Australian exchange rates [US Dollar (AUD/USD), Japanese Yen (AUD/JPY), Great British Pound (AUD/GBP), Singapore Dollar (AUD/SGD) and Swiss Franc (AUD/CHF)].
Their research confirmed that ANN models outperformed the ARIMA models and that the
ANN models can closely forecast the FOREX rates. [184] evaluated the New Zealand exchange rates [NZ dollar and Australian dollar (NZD/AUD) and NZ Dollar versus American dollar (NZD/USD) movement during 1990 2003 using ANN. [184] discovered that ANN did not perform better than the RW model for exchange rate forecasting. However, [179]
investigated New Zealand exchange rates (USD/NZD) and a few other exchange rates and found that the proposed Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) outperformed the rest of the models (NN, NN with Wavelet Denoising, NN with Wavelet Packet Denoising, and so on) tested.