Hybrid VAR, neural network, and evolutionary computation for predicting Asian Pacific market lead-lag dynamics.

by Ao, Sio Iong.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references.Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.2Chapter 1.2 --- Topics of this Study --- p.3Chapter 1.3 --- Econometric Analysis --- p.3Chapter 1.4 --- Computational Intelligence --- p.4Chapter 1.4.1 --- Overview --- p.4Chapter 1.4.2 --- Successful Cases of Applying CI in Time Series Analysis --- p.4Chapter 2 --- Background --- p.6Chapter 2.1 --- Market Descriptions --- p.6Chapter 2.1.1 --- Overview of the Markets --- p.6Chapter 2.2 --- VAR method --- p.10Chapter 2.2.1 --- Introduction --- p.11Chapter 2.2.2 --- Implementation of VAR by RATS --- p.12Chapter 2.2.3 --- Impulse Response Functions --- p.12Chapter 2.3 --- Neural Network --- p.14Chapter 2.3.1 --- Introduction --- p.14Chapter 2.3.2 --- Supervised vs Unsupervised learning --- p.15Chapter 2.3.3 --- Back-Propagation network --- p.15Chapter 2.4 --- Evolutionary Computation --- p.19Chapter 2.4.1 --- Motivation of Employing Evolutionary Computation --- p.19Chapter 2.4.2 --- Brief Description --- p.21Chapter 2.4.3 --- Genetic Algorithm --- p.21Chapter 3 --- Analysis of their Interdependence and SD --- p.23Chapter 3.1 --- Interdependence of the Asian Indices --- p.23Chapter 3.2 --- Forecasting Index Price with the Help of Neural Network --- p.26Chapter 3.3 --- Interdependence of the Standard Deviations of the Stock Indices --- p.28Chapter 3.4 --- Using the Neural Network to Make Forecasting of the Stan- dard Deviations --- p.29Chapter 3.5 --- Summary --- p.33Chapter 4 --- Forecasting Opening Prices --- p.34Chapter 4.1 --- Step 1: Identificating of the Interdependence of the Opening Price on Different Stock Indices by VAR --- p.36Chapter 4.2 --- Step 2: Using the Neural Network to Make Forecasting of the Opening Prices --- p.38Chapter 4.3 --- Summary --- p.39Chapter 5 --- Incorporating Correlated Markets --- p.41Chapter 5.1 --- Overview of the Markets from the Prespectives of VAR --- p.43Chapter 5.2 --- Investigation of the Correlations by VAR Method --- p.43Chapter 5.3 --- Prediction of the Market by Neural Network --- p.46Chapter 5.4 --- Hypothesis: the Correlations of the Markets Are Time-Dependent --- p.46Chapter 5.5 --- Testing this Hypothesis with Predictions by Neural Network . --- p.48Chapter 5.6 --- Summary --- p.51Chapter 5.7 --- F-tests Results on Different Periods of HK Markets --- p.51Chapter 6 --- Hybrid VAR-NN-EC System --- p.53Chapter 6.1 --- Introduction --- p.53Chapter 6.1.1 --- Overview of the Econometric Analysis of the Lead-Lag Relationship of Stock Markets --- p.54Chapter 6.1.2 --- Previous Results of Employing the Stand-alone Neural Network --- p.55Chapter 6.2 --- Working Mechanism of the Hybrid VAR-NN-EC --- p.56Chapter 6.3 --- Comparing Results from the VAR-NN-EC System --- p.58Chapter 6.4 --- Summary --- p.60Chapter 7 --- Hybrid System for Dual-Listing Indices --- p.61Chapter 7.1 --- Introduction --- p.61Chapter 7.2 --- HSI vs HSLRI --- p.62Chapter 7.2.1 --- HSI's Selection Criteria --- p.62Chapter 7.2.2 --- Hang Seng London Reference Index --- p.63Chapter 7.2.3 --- Motivation for the Study --- p.63Chapter 7.3 --- Data Descriptions --- p.64Chapter 7.4 --- Overviews of this Analysis System --- p.64Chapter 7.5 --- Results from the Simplified AR-NN System --- p.65Chapter 7.5.1 --- Regression Results --- p.66Chapter 7.5.2 --- NN Results --- p.67Chapter 7.6 --- Summary --- p.68Chapter 8 --- Using EC for Selecting Stock Experts --- p.70Chapter 8.1 --- Example of Evolutionary Computation --- p.71Chapter 8.2 --- Comparison of Results from the VAR-NN-EC System --- p.72Chapter 8.3 --- Summary --- p.73Chapter 9 --- Conclusion --- p.74Bibliography --- p.

An electronic financial system adviser for investors: the case of Saudi Arabia

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonFinancial markets, particularly capital and stock markets, play an important role in mobilizing and canalising the idle savings of individuals and institutions to the investment options where they are really required for productive purposes. The prediction of stock prices and returns is carried out in order to enhance the quality of investment decisions in stock markets, but it is considered to be tricky and complicates tasks as these prices behave in a random fashion and vary with time. Owing to the potential of returns and inherent risk factors in stock market returns. Various stock market prediction models and decision support systems such as Capital asset pricing model, the arbitrage pricing theory of Ross, the inter-temporal capital asset pricing model of Merton ,Fama and French five-factor model, and zero beta model to provide investors with an optimal forecast of stock prices and returns. In this research thesis, a stock market prediction model consisting of two parts is presented and discussed. The first is the three factors of the Fama and French model (FF) at the micro level to forecast the return of the portfolios on the Saudi Arabian Stock Exchange (SASE) and the second is a Value Based Management (VBM) model of decision-making. The latter is based on the expectations of shareholders and portfolio investors about taking investment decisions, and on the behaviour of stock prices using an accurate modern nonlinear technique in forecasting, known as Artificial Neural Networks (ANN). This study examined monthly data relating to common stocks from the listed companies of the Saudi Arabian Stock Exchange from January 2007 to December 2011. The stock returns were predicted using the linear form of asset pricing models (capital asset pricing model as well as Fama and French three factor model). In addition, non-linear models were also estimated by using various artificial neural network techniques, and adaptive neural fuzzy inference systems. Six portfolios of stock predictors are combined using: average, weighted average, and genetic algorithm optimized weighted average. Moreover, value-based management models were applied to the investment decision-making process in combination with stock prediction model results for both the shareholders’ perspective and the share prices’ perspective. The results from this study indicate that the ANN technique can be used to predict stock portfolio returns; the investment decisions and the behaviour of stock prices, optimized by the genetic algorithm weighted average, provided better results in terms of error and prediction accuracy compared to the simple linear form of stock price prediction models. The Fama and French model of stock prediction is better suited to Saudi Arabian Stock Exchange investment activities in comparison to the conventional capital assets pricing model. Moreover, the multi-stage type1 model, which is a combination of Fama and French predicted stock returns and a value-based management model, gives more accurate results for the stock market decision-making process for investment or divestment decisions, as well as for observing variation in and the behaviour of stock prices on the Saudi stock market. Furthermore, the study also designed a graphic user interface in order to simplify the decision-making process based upon Fama and French and value-based management, which might help Saudi investors to make investment decisions quickly and with greater precision. Finally, the study also gives some practical implications for investors and regulators, along with proposing future research in this area

Essays on mergers and acquisitions

“Essays on Mergers and Acquisitions" tackles some of the most prominent business challenges related to M&A activity. The Introduction examines the reasons why deals fail through well-known case studies; Chapter 1 presents a new index measuring countries M&A maturity worldwide; Chapter 2 focus on the importance of corporate governance when conducting deals in unknown territories; whilst Chapter 3 and 4 conduct research on companies’ decision to tap capital markets and their subsequent M&A activity

Thoughts on the Theory and Practice of Speculative Markets qua Event Predictors

This paper analyzes the proposed use of combinatorial derivatives markets for event prediction, especially for catastrophic events such as terrorism, war, or political assasination. Following a presentation of the philosophical principles underlying these politico-economic tools, I examine case studies (U.S. DoD proposals) that evaluate their advantages and disadvantages in terms of both efficacy and moral considerations. I conclude that these markets are both fatally flawed due to internal conceptual contradictions and morally problematic

Where do financial markets come from? Historical sociology of financial derivatives

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Application of Machine Learning to Financial Time Series Analysis

This multidisciplinary thesis investigates the application of machine learning to financial time series analysis. The research is motivated by the following thesis question: ‘Can one improve upon the state of the art in financial time series analysis through the application of machine learning?’ The work is split according to the following time series trichotomy: 1) characterization — determine the fundamental properties of the time series; 2) modelling — find a description that accurately captures features of the long-term behaviour of the system; and 3) forecasting — accurately predict the short-term evolution of the system

Trading in Structured Products: Investor Behavior and Pricing Policies

In my work I focus on structured products offering numerous risk-return variations on a broad landscape of underlying assets. I answer the question whether investors put structured products to good use and whether issuers provide a fair and transparent environment