每股盈餘是公司的重要財務資訊之一，它可以反應公司的經營績效，因此一方面可以提供給投資者作為投資決策之參考，另一方面提供給管理者作為管理評量的參考指標之一。過去在每股盈餘等財務預測往往以統計方法進行，因此在自變數選擇上常受到限制，同時有些預測模式其輸出結果往往只能以常長或衰退等二元式的結果表示。而另一方面，以類神經網路預測方式的預測模式可能因變數增加，使得網路變的較複雜。本研究嘗試以人工智慧中的遺傳演算法來作為預測的工具，發展財務預測模型，來預測每股盈餘，解決過去預測方式的限制或缺點。同時也將對過去的遺傳演算法稍做修正，並嘗試以實際值的編碼方式進行編碼，以符合需求。最後進一步比較遺傳演算法和其他預測方式，瞭解以遺傳演算法做於預測每股盈餘工具的特性及優缺點。

Earnings per share (EPS) is one of the important financial

indicators to a corporation. It reflects the operating

performance of a corporation. On one hand, EPS provides

information available to investors for decision making; on the other hand, it is an indexfor measurement of management. In the past, financial forecasting was often done by using statistical models. However, the input variables were limited by using these statistical models. Besides, some stastical models only provide dichotomy output ,such as either "grwoth" or"decline". The neural network forecasting model will be more of complexity, when the input variable increases. This research attempts to develop a financial forecasting model to forecast the EPS by using the Genetic Algorithms, which is a new topic of artificial intelligence. This model excludes both the limitations and disadvantages of the models mentioned above. Here, the genetic algorithms will be modified and the real number will be used to code as a gene of achromosome to meet the requirements of the finacial model. Finally,we compare the genetic algorithms

financial forecasting model with the other ones in order to

understand the features, advantages and disadvantages of genetic algorithms as being a financial forecasting tool .

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