We employ neural network models to forecast the direction and the level of change in Istanbul Stock Exchange (ISE) Composite Index and 10 sector indices. We use 7 domestic and 15 international economic variables and stock indices. Three types of forecast methods were employed for each sector...

A central consideration for the use of any pricing model is the ability to calibrate that model to market or historical prices. Whether the information needed by the model can be effectively implied from the data or not is one part of the calibration problem. However, in many applications, the...

Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community for their superior predictive properties including robustness to over...

This paper aims to forecast the Market Risk premium (MRP) in the US stock market by applying machine learning techniques, namely the Multilayer Perceptron Network (MLP), the Elman Network (EN) and the Higher Order Neural Network (HONN). Furthermore, Univariate ARMA and Exponential Smoothing...

Application of artificial neural networks for economic forecasting is described and empirically examined with Nestle financial reporting data. For the experiments, panel data of the exchange rates as well as trading profit, volume of sales, currency retranslations, and effects of exchange rate...

We review key aspects of forecasting using nonlinear models. Because economic models are typically misspecified, the resulting forecasts provide only an approximation to the best possible forecast. Although it is in principle possible to obtain superior approximations to the optimal forecast...

The topic of this chapter is forecasting with nonlinear models. First, a number of well-known nonlinear models are introduced and their properties discussed. These include the smooth transition regression model, the switching regression model whose univariate counterpart is called threshold...

Managing inflation is vital for a stable economy, but forecasting remains challenging. ML methods, like neural networks, have shown promise in forecasting inflation and other macroeconomic variables. In this paper, I propose DPCNet, a deep multi-task learning model, to jointly forecast inflation...

We present a hierarchical architecture based on recurrent neural networks for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused on predicting headline inflation, many economic and financial institutions are...

Current applications of Graph Neural Networks in citywide short-term crash risk prediction have been limited by a gridded representation of space, which restricts the network’s capability to effectively capture the spatial and temporal dependency of crash occurrences. In addition, a grided...