Doctor of Philosophy

dissertationHeterotrimeric G-protein signaling pathways modulate synaptic transmission in response to secreted factors. For example, Go, the most abundant G protein in the nervous system, negatively regulates neuron activity by acting on potassium channels and calcium channels in mammalian systems, although it is possible that other pathways may exist. In my thesis, I used a genetic approach in the nematode C. elegans to study the neuronal function of Go. I identified an activated Go mutant, which was then used to screen for downstream components of Go signaling. Several of the proteins I identified in my screen regulate neuronal excitability, including activation of the calcium- and voltage-activated potassium channel SLO-1, and the gap junction component innexin UNC-9, and inactivation of the novel ion channel NCA-1. Further genetic screens demonstrated that the NCA-1 channel requires accessory subunits UNC-79 and UNC-80. A functional channel complex can be reconstituted by expressing NCA-1, UNC-79 and UNC-80 in Xenopus oocytes. The channel forms a sodium-selective, voltage-dependent current. I propose a model in which Go and Gq converge on the RhoGEF UNC-73B and Go downregulates NCA-1 activity by antagonizing Gq activation of this pathway

Reinforced Recurrent Neural Networks for Multi-Step-Ahead Flood Forecasts

[[abstract]]Considering true values cannot be available at every time step in an online learning algorithm for multi-step-ahead (MSA) forecasts, a MSA reinforced real-time recurrent learning algorithm for recurrent neural networks (R-RTRL NN) is proposed. The main merit of the proposed method is to repeatedly adjust model parameters with the current information including the latest observed values and model’s outputs to enhance the reliability and the forecast accuracy of the proposed method. The sequential formulation of the R-RTRL NN is derived. To demonstrate its reliability and effectiveness, the proposed R-RTRL NN is implemented to make 2-, 4- and 6-step-ahead forecasts in a famous benchmark chaotic time series and a reservoir flood inflow series in North Taiwan. For comparison purpose, three comparative neural networks (two dynamic and one static neural networks) were performed. Numerical and experimental results indicate that the R-RTRL NN not only achieves superior performance to comparative networks but significantly improves the precision of MSA forecasts for both chaotic time series and reservoir inflow case during typhoon events with effective mitigation in the time-lag problem.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙

Using Hidden Markov Model for Stock Day Trade Forecasting

Around the world, the Hidden Markov Models (HMM) are the most popular methods in the machine learning and statistics for modeling sequences, especially in speech recognition domain. According to the number of patent applications for speech recognition technology form 1988 to 1998, the trend shows that this method has become very mature. In this thesis, we will make a new use of the HMM and apply it on day trading stock forecast. However, the HMM is based on probability and statistics theory. In a statistics framework, the HMM is a composition of two stochastic processes, a Hidden Markov chain, which accounts for temporal variability, and an observable process, which accounts for spectral variability. The combination contains uncertainly status just likes the stock walk trace. Therefore, the HMM and the stock walk trace have the same idea by coincidence. In this thesis, we will try to learn the stock syntax; just like how the HMM model was used in speech recognition in different languages, and the take the next step ahead in price prediction. Additionally, the stock market is the reflection of the economy. The stock trace is impacted by many factors such as policy, psychology, microeconomics, economics, and capital, etc. There, in this thesis, the TAIFEX Taiwan index futures (TX) and day trade are used to avoid all the uncertainty factors. After the all experiments, it is proven that the HMM is better than the benchmark methodRandom Walk method and the Investment Trust & Consulting Association method- Modified Trading method. Moreover, the result is very conspicuous by the statistics testing of significance

Using Pattern Recognition for Investment Decision Support in Taiwan Stock Market

In Taiwan stock market, it has been accumulated large amounts of time series stock data and successful investment strategies. The stock price, which is impacted by various factors, is the result of buyer-seller investment strategies. Since the stock price reflects numerous factors, its pattern can be described as the strategies of investors. In this paper, pattern recognition concept is adapted to match the current stock price trend with the repeatedly appearing past price data. Accordingly, a new method is introduced in this research that extracting features quickly from stock time series chart to find out the most critical feature points. The matching can be processed via the corresponding information of the feature points. In other words, the goal is to seek for the historical repeatedly appearing patterns, namely the similar trend, offering the investors to make investment strategies

Theory Modeling and Empirical Evidence for Value-at-Risk based Assets Allocation Insurance Strategies

Constant Proportion Portfolio Insurance (CPPI) is the most popular portfolio insurance strategy using hedging strategy to protect principal while a wave upward or downward trend in the market is noted. Nevertheless, since the original CPPI was proposed, its performance has been limited to relevant parameters of strategy. And since there is no clear, definite and systematic rule of decision has get been proposed, it also has unstable performance and worse upside capture, especially for the multiplier (Mv) in model parameters, it has far great influence to end-of-period return. If Mv can be decided with its initial value setting and dynamic tuning via certain appropriate approach, under a decent mechanism of market timing selection, the strategy can therefore acquire excess return of min-max operation due to sharp improvement of upside capture, and also can provide hedging function within the insured volume when the market declines. This paper presents a systematic method using the value-at-risk control method to dynamically adjust the CPPI strategy parameter Mv, called asset allocation insurance strategy value-at-risk based asset allocation insurance strategy model (VALIS). We proof that the proposed model is a dynamic asset allocation insurance strategy, which is conservative but also aggressive; and shows that it is in compliance with the characteristics of idea portfolio insurance strategy, and is feasible and effective. From an empirical study of the Pan-Pacific market, we found that in any type of market or trend it is clearly better than the major benchmark indices, and it outperform other traditional portfolio insurance strategy

Why Do People Share Music Files in the P2P Environment: An Ethical Decision Perspective

Digitalized information and the Internet have brought great impacts on the music and movies industries. This study tested the ethical decision model of Hunt-Vitell to understand why and how people share unauthorized music files with others in the P2P network. Four scenarios of using P2P system and four norms related to them were proposed in the study. The results indicate that the deontological norm of anti-piracy, whether is theft of intellectual property or not, is not the main factors affecting P2P users’ ethical consideration regarding sharing music with others. The results also suggest the music companies should care more about how to realize the benefits of the digital and network technology to increase the consumers’ welfare instead of just declare the intellectual property they owned and resist the innovations caused by the new technologies

An Behavioral Finance Analysis Using Learning Vector Quantization in the Taiwan Stock Market Index Future

There are various types of trading behavior in the stock market. And the buying or selling activities in many investment strategies are influenced by numerous factors respectively, such as fundamental analysis, macroeconomic analysis, and news analysis. Consequently, various factors will reflect on market price. Random Walk in financial engineering is not the focus in this paper. Otherwise, the importance of the technique analysis about Taiwan Stock Index Futures will be emphasized in this research. It is the intention of this paper to investigate the information content of Open, High, Low, Close prices in the previous trading day and relative higher and lower points in the prior period of the current trading day, as well as their prices in analyzing Taiwan Stock Index Future. The predictability of Learning Vector Quantizationl Network can clearly be seen from the empirical result

Improving R&D Project Collaboration: A Concurrent Knowledge Learning Model

The collaboration issues on research and development (R&D) projects are getting critical. Shorten time-to-market (TTM) of new product is crucial competency of high-tech industries. However, lacks of post-project review, real-time communication and information sharing make barriers to reduce TTM. This would lead to downcast competition in the globalization. In this research, a concurrent knowledge learning (CKL) model is proposed to enhance R&D project collaboration. With the CKL model, R&D collaboration could be improved via the concurrent knowledge reusability and the collaboration mechanism among partners

A Novel Index Portefolio Model by Minimizing the Absolute Tracking Error -- Empirical Stusies in the Taiwan Stock Market

The index portfolio model attempts to form a portfolio whose time series in the market can trace the selected index as much as possible. The traditional index portfolio model, estimated coefficients models proposed by Salkin, established the portfolio by minimizing the square tracking error. In this paper, a novel index portfolio model formed by minimizing the absolute tracking error is proposed. In addition to preserving the characteristics of Salkin’s model, the proposed model can guarantee obtaining the global optimum solution and, in contrast to Salkin’s model, it can avoid the effect of the extreme value, which Salkin’s model may not. Also in contrast to the traditional model, the proposed one is a linear programming model and can then include practical constraints in the models, including the transaction cost constraints and limited stock catalog constraints. How the improved models address these constraints would be discussed as well. Moreover, different empirical studies in the Taiwan Stock Market are provided to demonstrate the proposed model’s effectivenes