Does Machine Learning Forecast Investor’s Risk Appetite?

Authors : Nesrin Özkan, Nurgül Ö Yalıncaklı

Pages : 143-154

Doi:10.54821/uiecd.1460617

View : 104 | Download : 128

Publication Date : 2024-09-30

Article Type : Research Paper

Abstract :Risk appetite is an important indicator that is monitored with interest by financial market participants. One of the risk appetite indices is nominated “RISE risk appetite index” calculated to measure the riskiness of the Turkey market in general. There are very limited studies in the literature on RISE risk appetite, and most of them use simple econometric methods to predict the risk appetite. To the best of our knowledge, there is no study using machine learning algorithms. Therefore, it creates curiosity on how the success will be in estimating the risk appetite using machine learning algorithms. Thus, the aim of this paper is to measure the estimation success of the RISE index using Long Short-term Memory (LSTM) and Multi-Layer Perceptron (MLP). The analysis is based on a weekly frequency dataset covering the years 2008 to 2023. The results are compared as per RMSE values, and LSTM presents rather high prediction success compared to MLP. Due to the forecasting ability of BIST 100 index on RISE, the current and lagged values of BIST 100 are compared, and lagged values of BIST 100 are found to have a higher ability to estimate RISE, approximately twice as much as current values. It is expected that this valuable finding will be a guide for market participants and financial analysts to shape their investment preferences by using deep learning algorithms in predicting market expectations and to make well-directed investments.
Keywords : Risk İştahı, RISE Endeksi, Tahminleme, LSTM, MLP