Finansal piyasalara odaklanan ve farklı amaçlarla yürütülen bir çok araştırmada fiyat veya getiri tah-minine yönelik yoğun bir ilgi görülmektedir. Bu ilginin temel nedeni finansal piyasaların dinamiğini tespit etmektir. Piyasa davranışının belirlenmesi; makro yatırım politikalarından bireysel tasarruflara kadar birikimlerin yönlendirilmesinde, portföy optimizasyonundan piyasa ekonomisinin işleyişine kadar bir çok konuda yol gösterici olacaktır. Başka bir deyişle hisse senedi fiyat hareketlerinin tahmin edilebi-lirliğinin araştırılması; akademisyenlerin etkin pazar kuramını sınamasına yardımcı olurken; rasyonel ilişkiler tespit edilirse, yatırım uzmanları güvenilir getiri sağlayan bir işlem sistemi oluşturulabilecek-lerdir. Bu çalışmanın amacı, İMKB-100 endeks hareketlerini tahmin etmede; gayrısafi milli hasıla, sa-nayi üretim endeksi gibi makro ekonomik değişkenlerin ve reel gösterge faiz oranı, döviz kurları gibi finansal verilerin kullanılabilirliğini araştırmaktır. Finansal piyasalarda fiyat tahminine yönelik bir çok modelleme çalışması yürütülmüş, ancak her dönem ve her piyasada kullanılabilir bir sistem oluşturu-lamamıştır. Zira hisse senedi fiyatları üzerinde, tümü tahmin modellemesinde kullanılamayacak kadar çok sayıda lokal ve global değişkenin (meteorolojik ve astrolojik değişkenlere kadar) etkisi söz konusu-dur. Ülkemizde de farklı istatistiksel çalışmalarda finansal göstergelerin, İMKB Hisse Senedi Piyasa-sı’ndaki fiyat hareketleri ile ilişkisi araştırılmıştır. Ayrıca son yıllarda sözkonusu ilişkilerin analizi yeri-ne, değişkenlerin doğrudan endeks tahmininde kullanılabilirliği incelenmektedir. Çalışmada girdiler, mevsimsel ve enflasyonist etkilerden arındırıldıktan sonra İMKB-100 Endeksi ile ilişkileri irdelenmiş ve son olarak İleri Beslemeli Geri Yayılımlı (İBGYYSA) bir ağ tipi için uygun normalizasyon yöntemi araştırılmıştır.

In the last decade, with the growth of computational facilities, there are many academic attempts about financial market prediction. Because of the huge sort of local and global data that may affect market trends, forecasting models developed until today have been only effective in few markets and for a limited period of time. Studies about Istanbul Stock Exchange (ISE) are limited. Determining the com-plex relationship between financial markets and economic parameters needs a chaotic approach. Chaos is a nonlinear deterministic process which only appears random because it cannot be easily expressed. Multivariate analytical techniques using both quantitative and qualitative variables have re-peatedly been used to help form the basis of investor stock price expectations and hence, influence in-vestment decision making. However, the perfor-mance of multivariate analytical techniques is often less than conclusive and needs to be improved to more accurately forecast stock price performance. A neural network (NN) method has demonstrated its capability of addressing complex problems. A neural network method may be able to enhance an in-vestor's forecasting ability. There are several motivations for trying to predict stock market prices. But initial background needs of almost everyone is to investigate the market behav-ior, because each study about financial markets starts on that base. The most principal motivation is naturally financial gain. Finding any system that can consistently give greater revenue then the aver-age return in the dynamic market place, is very im-portant for the owner of the system, to give market players the impression of being trustworthy. Moreo-ver, many investors are continually looking for this superior system which will yield them higher re-turns. Another motivation for researchers is the Effi-cient Market Hypothesis (EMH). It has been pro-posed in the EMH that markets are so efficient and profit opportunities are discovered so quickly that no one can catch superior performance. Hypothesis states that no system can continually beat the market because if this system becomes public, everyone will use it, thus negating its potential gain. There has been an ongoing debate on the validity of the EMH, some researchers use forecast results to validate their claims, other observers trying to demonstrate that the market is weak in efficiency. Validity of EMH is very important for the board managers point of view. If it can be proved that the market be-havior is rational, while researchers evaluate the concept of a new efficient market, it would be possi-ble to attract new resources to the market and create a new transaction system in which investors believe. No matter what the prediction purpose and method-ology are the first step is to choose the parameters that really represent the market direction of move-ment and the level of variation. On the other hand, typical investors trading behav-iors have been guided by two main perspectives; namely; Fundamental and technical analysis. While technical analysis uses only past market prices and volume, fundamental analysis is derived from exter-nal information which comes from the economic sys-tem encircling the market. This information consists of interest rates, prices and returns of other assets, and many other macro or micro economic variables. The aim of this study was to evaluate the effective-ness of using macroeconomic indicators such as; gross national product, industrial production level and financial data such as; real benchmark, curren-cy exchange rates, in predicting movements in the ISE 100 Index (ISE-100). Availability of midterm economic and financial indicators to estimate the ISE-100 trends was investigated in this research. If the investigation findings show that these variables are efficient to explain the price movements in the ISE, then two basic judgments will be reached. Firstly, the EMH will be canceled or at least in a most optimistic view, a weak form efficiency in the market will be proven. Secondly, an investment de-cision support system based on NN estimates will be developed for investment advisors and investors. Also, the EMH is presented and contrasted with chaos theory and neural networks. This paper re-futes the EMH based on previous neural network work. Finally, future directions for applying neural networks to the financial markets are discussed. Us-ing statistical investigation we find causal relations between some indicators including macroeconomic, inflation, alternative financial instruments, and the Turkish Security Market. After adjusting seasonal and influationist impacts; input variable has been investigated their midterm relationship with ISE-100 Index. In the last section, appropriate normalization method for a backpropagation feedforward net-works (BPNN) was researched.