The Asymptotic Decision Scenarios of an Emerging Stock Exchange Market: Extreme Value Theory and Artificial Neural Network

Abdul-Aziz Ibn Musah, Jianguo Du, Hira Salah Ud din Khan and Alhassan Alolo Abdul-Rasheed Akeji
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Abdul-Aziz Ibn Musah: School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China
Jianguo Du: School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China
Hira Salah Ud din Khan: School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China
Alhassan Alolo Abdul-Rasheed Akeji: Department of Marketing, Tamale Technical University, 3 E/R Northern Region, Tamale C4JM+Q9, Ghana

Risks, 2018, vol. 6, issue 4, 1-24

Abstract: In recent times, investing in volatile security increases the risk of losses and reduces gains. Many traders who depend on these risks indulge in multiple volatility procedures to inform their trading strategies. We explore two models to measure the tails behaviour and the period the stock will gain or fall within a five-month trading period. We obtained data from the Ghana stock exchange and applied generalized extreme value distribution validated by backtesting and an artificial neural network for forecasting. The network training produces and manages more than 90% accuracy respectively for gains and falls for given input-output pairs. Based on this, estimates of extreme value distribution proves that it is formidable. There is a significant development in market prediction in assessing the results of actual and forecast performance. The study reveals that once every five months, at a 5% confidence level, the market is expected to gain and fall 2.12% and 2.23%, respectively. The Ghana stock exchange market showed a maximum monthly stock gain above or below 2.12% in the fourth and fifth months, whiles maximum monthly stock fell above or below 2.23% in the third and fourth months. The study reveals that once every five months’ trading period, the stock market will gain and fall by almost an equal percentage, with a significant increase in value-at-risk and expected shortfall at the left tail as the quantiles increases compared to the right tail.

Keywords: extreme value theory (EVT); peak-over-threshold (POT); value-at-risk ( VaR ); expected shortfall ( ES ); return level; neural network (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2018
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