Application Of Fuzzy Models And Neural
Models In Financial Time Series
Author(s)
M. A. F. Allemão, A. G. Evsukoff & N. F. F. Ebecken
Abstract
Brazilian bank institutions daily perform thousands of trips for money collection
and delivery at their branches. The amount spent in these operations is very high
and has grown in recent years (U$ 35 million in 1999 and U$ 75 million in
2002). The reduction of money supplying and collecting trips at agencies is a
predominant factor in reducing the operational costs of the system. There is a
need to make reliable forecasts based on the behavior observed in the agency’s
past. According to the Bank’s analysts, the incoming resources are very strong
on Mondays and decrease on Tuesdays. Wednesdays are considered balanced
days. Thursdays have paying characteristics and Fridays too; the last days of the
week have more resources coming out than any other day. Regarding the
monthly cycle, the behavior of an agency has strong payments in the beginning
of each month, growing slightly to balance the remaining days and money
coming out at the end of the month. Concerning the months of the year, agencies
have larger withdrawals in the summer months, especially from December to
January. The balance is observed from the third or fourth month until
approximately the ninth and tenth month of the year. Considering the use of the
Vague Logic concepts to translate into mathematic terms the inaccurate
information contained in sentences expressed in natural language and in creating
models based on previous premises to map the agency’s behaviour, the aim is to
help analysts in the task of defining the optimal amount of money to be remitted.
Neural models have been developed to compare, they have reached lower
performance rates than those verified with the use of vague algorithms.
Keywords: fuzzy logic, neural networks, time series, forecast, money prediction,
agency’s behavior.
Keywords
fuzzy logic, neural networks, time series, forecast, money prediction,
agency’s behavior.
Related Book
Other papers in this volume
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