隨著社會、經濟環境的變遷，使用者對資訊的需求日切，鑑於資訊的時效性，證管會在民國八十年度規定，公開發行公司應建立書面預算制度，按月編製現金、生產、銷售及資本預算，以為編製財務預測之參考。應證管會的要求，凡上市（櫃）公司募集與發行轉換公司債、現金增資，將申請上市（櫃）或公司自願，此四種情況，皆應附送財務預測報告（經會計師核閱）及有關文件。 　　本研究旨在分析目前國內上市公司，發佈經會計師核閱的財務預測，其對實際營業淨利及稅前淨利之準確性如何及何種因素影響其準確性。並分別以郵寄問卷調查方式蒐集資料，再以統計方法進行分析，了解財務預測資訊對我國機構投資者與企業財務主管的決策影響程度；會計師、證管會稽核人員及證券交易所上市部審查人員對財務預測實用性的看法。 　　分析結果顯示，目前所採行的預測制度，經會計師核閱，對決策有重要幫助，但由於許多外在環境影響及現行揭露預測規定，造成核閱者、編製者與閱表者皆不滿意現行之制度，且不確定（或不知道）企業公告的財務預測資訊有無其價值！

Since Engle and Granger formulated the concept of cointegration in 1987, the literature has extensively examined the unbiasedness of the commodity futures prices using the cointegration-based technique. Despite intense attention, many of the previous studies suffer from the contradicting empirical results. That is, the cointegration test and the stationarity test on the differential contradict each other. In marked contrast, my dissertation develops the no-arbitrage cost-of-carry model in the NYMEX light sweet crude oil futures market and tests stationarity of the spot-futures differential. It is demonstrated that the primary cause of the "cointegration paradox" is the model misspecifications resulting in omitted variable bias.

Despite the extent of a theoretical framework in financial market studies, a vast majority of the traders, investors and computer scientists have relied only on technical and timeseries data for predicting future prices. So far, the forecasting models have rarely incorporated macro-economic and market fundamentals successfully, especially with short-term predictions ranging less than a month. In this investigation on the predictability of certain financial markets, an attempt has been made to incorporate a un-exampled and encompassing set of parameters into an Artificial Neural Network prediction system. Experiments were carried out on three market instruments ??? namely currency exchange rates, share prices and oil prices. The choice of parameters for inclusion or exclusion, and the time frame adopted for the experimental sets were derived from the market literature. Good directional prediction accuracies were achieved for currency exchange rates and share prices with certain parameters as inputs, which consisted of predicting short-term movements based on past movements. These predictions were better than the results produced by a traditional least square prediction method. The trading strategy developed based on the predictions also achieved a higher percentage of winning trades. No significant predictions were observed for oil prices. These results open up questions in the microstructure of the markets and provide an insight into the inputs required for market forecasting in the corresponding time frame, for future investigation. The study concludes by advocating the use of trend based input parameters and suggests ways to improve neural network forecasting models.

Despite the extent of a theoretical framework in financial market studies, a vast majority of the traders, investors and computer scientists have relied only on technical and timeseries data for predicting future prices. So far, the forecasting models have rarely incorporated macro-economic and market fundamentals successfully, especially with short-term predictions ranging less than a month. In this investigation on the predictability of certain financial markets, an attempt has been made to incorporate a un-exampled and encompassing set of parameters into an Artificial Neural Network prediction system. Experiments were carried out on three market instruments ??? namely currency exchange rates, share prices and oil prices. The choice of parameters for inclusion or exclusion, and the time frame adopted for the experimental sets were derived from the market literature. Good directional prediction accuracies were achieved for currency exchange rates and share prices with certain parameters as inputs, which consisted of predicting short-term movements based on past movements. These predictions were better than the results produced by a traditional least square prediction method. The trading strategy developed based on the predictions also achieved a higher percentage of winning trades. No significant predictions were observed for oil prices. These results open up questions in the microstructure of the markets and provide an insight into the inputs required for market forecasting in the corresponding time frame, for future investigation. The study concludes by advocating the use of trend based input parameters and suggests ways to improve neural network forecasting models.