The Value investment and Growth investment are investment strategies of choosing stocks. These two methods are adopted by international financial investment institutions and mutual fund managers. The study is aim to learn when we classify value stock and growth stock by market-to-book ratio and price-earning ratio, if the investment return would be higher than TSEC weighted index. In addition, we seek for a better investment strategy to improve investment performance further. The study also looks into market abnormal effects , such as January effect, size effect¡Ketc, and also discuss about the variables of stocks holding period and debt ratio. The monthly and yearly investment return rates are used to calculate 1, 2, 3, 4, and 5 year accumulated abnormal stock return ratio and evaluate if these variables affect investment performance of value stock and growth stock. The results are as following: 1. When classification of market-to-book ratio are adopted, the investment return of value stock is higher than growth stock. 2. When classification of market-to-book ratio are adopted, the investment return of low debt ratio stock is higher than high debt ratio stock. However, when classification of price-earning ratio are adopted, it is not obvious. 3. When bull market is formed in Taiwan stock market whose index is still low, invest in value stock could get a good long term investment performance. 4. Regarding the evaluation of risk, the vibration of growth stock is more than value stock. The vibration of TSEC weighted index is the least. 5. The January effect exists in Taiwan stock market. However, the size effect is not obvious. 6. TSEC weighted index and the Dow Jones Industrial index affect the investment return of value stock and growth stock; the TSEC weighted index, value stock and growth stock are positively correlated. The Dow Jones Industrial Index, value stock and growth stock are negatively correlated.

In this thesis, we propose a model which applies the genetic programming (GP) to train the profitable and stable trading strategy in the training period, and then the strategy is applied to trade stocks in the testing period. The variables for GP in our models include 6 basic information and 25 technical indicators. We perform our models on Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) from 2000/9/14 to 2010/5/21, approximately ten years. We conduct five experiments. In these experiments, we find that the trading strategies generated by GP with two arithmetic trees have more stable returns. In addition, if we obtain the trading strategies in three historical periods which are the most similar to the current training period, we earn higher return in the testing periods. In each experiment, 24 cases are considered, with training periods of 90, 180, 270, 365, 455, 545, 635 and 730 days, and testing periods of 90, 180 and 365 days, respectively. The testing period is rolling updated until the end of the experiment period. The best cumulative return 165.30\% occurs when 730-day training period pairs with 365-day testing period, which is much higher than the return of the buy-and-hold strategy 1.19\%.

The papers of stock-based employee compensation have increased dramatically in recent years, focusing attention on whether stock-based employee compensation can enhance employees¡¦ motivation or impact firm value. A number of recent papers have addressed conflicting evidence as to whether stock-based employee compensation enhance the performance of the firm. Some relatively new studies used use the Ohlson (1995,1999) and Feltham ¡® Ohlson (1999) models to investigate the market¡¦s perception of the economic effect of employee stock options on firm value(Aboody et al.2001; Bell et al., 2002). However, critics have questioned the validity of such studies (For a review of related studies, see Beaver 2002). In fact, stock-based employee compensation can influence firm value through improving performance of firm, and at the same time by diluting the shares of outstanding stocks, thus harms shareholder equity. This study was primarily designed to examine how stock-based employee compensation affects shareholder equity through Incentive and dilute effects. Stock-based employee compensation in this study comprises employee stock bonuses and employee stock options. First, the Incentive and dilute effects are combined in Ohlson model. The hypothesized relationships of constructs, observed variables and operational definitions are defined. The empirical work will be conducted by LISREL method to estimate the coefficients in the model. The estimated results will be dressed the following points. 1.Whether the stock-based employee compensation affects equity valuation. 2.Whether the stock-based employee compensation affects that the intrinsic value through improving abnormal earning? 3.Whether the stock-based employee compensation harms shareholder equity by diluting the shares of outstanding stocks? 4.Discuss employee stock bonuses and employee stock options respectively. In this study, we find the stock-based employee compensation is relevant to the equity value. Employee stock bonuses are relevant to shareholder equity and abnormal earning. In other words, employee stock bonuses have directly incentive effects. Otherwise, employee stock bonuses also have dilute effects. However, the dilute effects are smaller than the incentive effects. On the other hand, employee stock options aren¡¦t relevant to shareholder equity and abnormal earning. Otherwise, employee stock options don¡¦t have direct dilute effects in grant year.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 192-198). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 192-198). Also available on the Internet.

Thesis (M.B.A.)--University of Hong Kong, 1987.

Over the last few years there has been a surge of activity within the physics community in the emerging field of Econophysics—the study of economic systems from a physicist's perspective. Physicists tend to take a different view than economists and other social scientists, being interested in such topics as phase transitions and fluctuations. In this dissertation two simple models of stock exchange are developed and simulated numerically. The first is characterized by centralized trading with a market maker. Fluctuations are driven by a stochastic component in the agents' forecasts. As the scale of the fluctuations is varied a critical phase transition is discovered. Unfortunately, this model is unable to generate realistic market dynamics. The second model discards the requirement of centralized trading. In this case the stochastic driving force is Gaussian-distributed "news events" which are public knowledge. Under variation of the control parameter the model exhibits two phase transitions: both a first- and a second-order (critical). The decentralized model is able to capture many of the interesting properties observed in empirical markets such as fat tails in the distribution of returns, a brief memory in the return series, and long-range correlations in volatility. Significantly, these properties only emerge when the parameters are tuned such that the model spans the critical point. This suggests that real markets may operate at or near a critical point, but is unable to explain why this should be. This remains an interesting open question worth further investigation. One of the main points of the thesis is that these empirical phenomena are not present in the stochastic driving force, but emerge endogenously from interactions between agents. Further, they emerge despite the simplicity of the modeled agents; suggesting complex market dynamics do not arise from the complexity of individual investors but simply from interactions between (even simple) investors. Although the emphasis of this thesis is on the extent to which multi-agent models can produce complex dynamics, some attempt is also made to relate this work with empirical data. Firstly, the trading strategy applied by the agents in the second model is demonstrated to be adequate, if not optimal, and to have some surprising consequences. Secondly, the claim put forth by Sornette et al. that large financial crashes may be heralded by accelerating precursory oscillations is also tested. It is shown that there is weak evidence for the existence of log-periodic precursors but the signal is probably too indistinct to allow for reliable predictions.

This research investigated the feasibility and capability of neural network-based approaches for predicting the direction of the Australian Stock market index (the target market). It includes several aspects: univariate feature selection from the historical time series of the target market, inter-market analysis for finding the most relevant influential markets, investigations of the effect of time cycles on the target market and the discovery of the optimal neural network architectures. Previous research on US stock markets and other international markets have shown that the neural network approach is one of most powerful techniques for predicting stock market behaviour. Neural networks are capable of capturing the non-linear stochastic and chaotic patterns in the stock market time series data. This study discovered that the relative return series of the Open, High, Low and Close prices of the target market, show 6-day cycles during the studied period of about 14 years. Multi-layer feedforward neural networks trained with a backpropagation algorithm were used for the experiments. Two major testing methods: testing with randomly selected test data and forward testing, were examined and compared. The best neural network developed in this study has achieved 87%, 81% 83% and 81% accuracy respectively in predicting the next-day direction of the relative return of the Open, High, Low and Close prices of the target market. The architecture of this network consists of 33 input features, one hidden layer with 3 neurons and 4 output neurons. The best input features set includes the relative returns from 1 to 6 days in the past of the Open, High, Low and Close prices of the target market, the day of the week, and the previous day’s relative return of the Close prices of the US S&P 500 Index, US Dow Jones Industrial Average Index, US Gold/Silver Index, and the US Oil Index. / Master of Information Technology by Research

This research investigated the feasibility and capability of neural network-based approaches for predicting the direction of the Australian Stock market index (the target market). It includes several aspects: univariate feature selection from the historical time series of the target market, inter-market analysis for finding the most relevant influential markets, investigations of the effect of time cycles on the target market and the discovery of the optimal neural network architectures. Previous research on US stock markets and other international markets have shown that the neural network approach is one of most powerful techniques for predicting stock market behaviour. Neural networks are capable of capturing the non-linear stochastic and chaotic patterns in the stock market time series data. This study discovered that the relative return series of the Open, High, Low and Close prices of the target market, show 6-day cycles during the studied period of about 14 years. Multi-layer feedforward neural networks trained with a backpropagation algorithm were used for the experiments. Two major testing methods: testing with randomly selected test data and forward testing, were examined and compared. The best neural network developed in this study has achieved 87%, 81% 83% and 81% accuracy respectively in predicting the next-day direction of the relative return of the Open, High, Low and Close prices of the target market. The architecture of this network consists of 33 input features, one hidden layer with 3 neurons and 4 output neurons. The best input features set includes the relative returns from 1 to 6 days in the past of the Open, High, Low and Close prices of the target market, the day of the week, and the previous day’s relative return of the Close prices of the US S&P 500 Index, US Dow Jones Industrial Average Index, US Gold/Silver Index, and the US Oil Index. / Doctor of Philosophy

This research investigated the feasibility and capability of neural network-based approaches for predicting the direction of the Australian Stock market index (the target market). It includes several aspects: univariate feature selection from the historical time series of the target market, inter-market analysis for finding the most relevant influential markets, investigations of the effect of time cycles on the target market and the discovery of the optimal neural network architectures. Previous research on US stock markets and other international markets have shown that the neural network approach is one of most powerful techniques for predicting stock market behaviour. Neural networks are capable of capturing the non-linear stochastic and chaotic patterns in the stock market time series data. This study discovered that the relative return series of the Open, High, Low and Close prices of the target market, show 6-day cycles during the studied period of about 14 years. Multi-layer feedforward neural networks trained with a backpropagation algorithm were used for the experiments. Two major testing methods: testing with randomly selected test data and forward testing, were examined and compared. The best neural network developed in this study has achieved 87%, 81% 83% and 81% accuracy respectively in predicting the next-day direction of the relative return of the Open, High, Low and Close prices of the target market. The architecture of this network consists of 33 input features, one hidden layer with 3 neurons and 4 output neurons. The best input features set includes the relative returns from 1 to 6 days in the past of the Open, High, Low and Close prices of the target market, the day of the week, and the previous day’s relative return of the Close prices of the US S&P 500 Index, US Dow Jones Industrial Average Index, US Gold/Silver Index, and the US Oil Index. / Master of Information Technology by Research