Time-Series Forecasting Techniques for Banking Variables
A large proportion of economic data is in the form of time series. For forecasting financial variables, risk analysts need to understand trend, cyclicality, seasonality, and randomness of the series.
It explains how banks in general can perform time series modeling of balance sheet to establish bank’s position during simulated stress.
Moody’s Analytics forecasts of bank financial statements uses data from as early as 1980, spanning several expansions and recessions. Banks’ internal deposit data often extend back 10 years or less, making realistic modeling dificult.
Forecasting Time Series in the Banking Sector Using a Machine …
This article is devoted to the applications of machine learning methods for solving optimization problems in the banking sector. The literature analysis on the application of methods for forecasting time series in economics and finance is presented.
The purpose of this paper is to present a multivariate time series analysis of deposits, loans, interest rates, and investments and to discuss the implications of the results for macroanalysis of the banking sector and for microanalysis of an individual bank's financial management. II. MULTIVARIATE TIME SERIES ANALYSIS.
Forecasting for Banking Using Time Series Methods
This course provides an introduction to time series methods for modeling and forecasting economic and financial variables within a banking context. Forecasting plays a crucial role in decision-making for both the public and private sectors and is especially significant for banking institutions, whether in management or supervision.
Recently, Cramer and Miller [9] have successfully applied time series analysis in the multivariate case using banking data. It is the purpose of this paper to uncover dynamic relationships in bank financial behavior through multivariate time series analysis. In essence, time series methods are in-
A Machine Learning Pipeline for Forecasting Time Series in the Banking …
Sep 27, 2021 · The application of a time series methodology adequately reflects the construction of the model when solving the problems of analysis and forecasting in the ATM network. This article proposes a method and shows its application to several tasks.
Testing whether financial markets are weak-form informationally efficient. Testing whether the CAPM or APT represent superior models for the determination of returns on risky assets. Measuring and forecasting the volatility of bond returns. Explaining the determinants of bond credit ratings used by the ratings agencies.
A Machine Learning Pipeline for Forecasting Time Series in the Banking …
Dec 20, 2021 · In recent years, machine learning algorithms have been very prolific in this area. This paper describes a systematic approach to building a machine learning predictive model for solving...