Extreme value theory and neural network for catastrophic fall prediction: a study of year 2008-2009

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

“…Weights and biases are adjusted to fulfil the required network input in unsupervised learning (Spining et al 1994;Hinton 2012). Based on this, the paper adopts the supervised learning rule as proposed by (Shrivastava et al 2014).…”

“…and so on, generated by previous experience, are employed in the process of training the network. Pairs would look alike and so on (Shrivastava et al 2014).…”

“…The goal of the learning algorithm in a neural network is to minimise errors, where α represents the output acquired from the network for a particular value of the specific parameter. The output of neuron α and the constant output T are equivalent, while the weight vector ω[ω 1 , ω 2 , ω 3 , ω 4 , ω 5 ] is not impacted (Shrivastava et al 2014). If the neuron's output is 1 but is expected to be 0 or T = 0, then the error e equals −1.…”

“…Where the output of neuron α is 0 but the Target T = 1, the input vector V and the weight vector V summed in order to enlarge the value of n, which means that the anticipated output is accomplished. The bias modified according to the value of the error term ∆b = e (Shrivastava et al 2014).…”

“…Various scholars have conducted extensive investigations into financial markets using extreme value analysis in combination with artificial neural networks for developed stock markets, including international markets like the NASDAQ index (Shrivastava et al 2014). In certain stock markets, the market indexes exposure is higher for extreme decreases in comparison to the likelihood of extreme gains (e.g., Hang Seng, DJ Euro Stoxx 50, Nikkei, Swiss Market Index, and FTSE100) (Gilli and Këllezi 2006).…”