Applied Computational Intelligence for finance and economics

This article introduces some relevant research works on computational intelligence applied to finance and economics. The objective is to offer an appropriate context and a starting point for those who are new to computational intelligence in finance and economics and to give an overview of the most recent works. A classification with five different main areas is presented. Those areas are related with different applications of the most modern computational intelligence techniques showing a new perspective for approaching finance and economics problems. Each research area is described with several works and applications. Finally, a review of the research works selected for this special issue is given.Publicad

Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction

In this paper we develop a novel neural network model for predicting implied volatility surface. Prior financial domain knowledge is taken into account. A new activation function that incorporates volatility smile is proposed, which is used for the hidden nodes that process the underlying asset price. In addition, financial conditions, such as the absence of arbitrage, the boundaries and the asymptotic slope, are embedded into the loss function. This is one of the very first studies which discuss a methodological framework that incorporates prior financial domain knowledge into neural network architecture design and model training. The proposed model outperforms the benchmarked models with the option data on the S&P 500 index over 20 years. More importantly, the domain knowledge is satisfied empirically, showing the model is consistent with the existing financial theories and conditions related to implied volatility surface.Comment: 8 pages, SIGKDD 202

Improving the pricing of options: a neural network approach

In this paper we apply statistical inference techniques to build neural network models which are able to explain the prices of call options written on the German stock index DAX. By testing for the explanatory power of several input variables serving as network inputs, some insight into the pricing process of the option market is obtained. The results indicate that statistical specification strategies lead to parsimonious networks which have a superior out-of-sample performance when compared to the Black/Scholes model. We further validate our results by providing plausible hedge parameters. --Option Pricing,Neural Networks,Statistical Inference,Model Selection

Using Neural Networks to Forecast the Implied Volatility: the Case of S&P100XEO

Currently the most popular method of estimating volatility is the implied volatility. It is calculated using the Black-Scholes option price formula, and is considered by traders to be a significant factor in signaling price movements in the underlying market. A trader is able to establish the proper strategic position in anticipation of changes in market trends if she/he could   accurately forecast future volatility. There is an abundance of ways to compute the volatility. For two decades neural networks has been developed to forecast future volatility, using past volatilities and other options market factors. In this article a network is created for this purpose whose performance demonstrates the value of neural networks as a predictive tool in volatility analysis

An Option Pricing Model That Combines Neural Network Approach and Black Scholes Formula

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Machine Learning and Options Pricing: a Comparison of Black-Scholes and a Deep Neural Network in Pricing and Hedging DAX 30 Index Options

In this paper I study whether a deep feedforward network model performs better than the Black- Scholes model in pricing and delta hedging European-style call options. I apply the methodologies from selected original works on daily prices of call options written on DAX 30 between years 2013 and 2017. My results are mostly consistent with earlier literature and they indicate that the out-of- sample pricing performance of the neural network is superior to Black-Scholes with medium-term and long-term maturities and the out-of-sample delta-hedging performance of the neural network is superior with out-of-the-money options

Black-Scholes and Neural Networks

Neural networks have been proven to be universal approximators. We use neural networks to investigate the relationship between the quality of input data and the quality of outputted predictions from a neural network. We show that neural networks perform better on option pricing data with quality data and perform worse with lower quality data

Accelerated American option pricing with deep neural networks

Given the competitiveness of a market-making environment, the ability to speedily quote option prices consistent with an ever-changing market environment is essential. Thus, the smallest acceleration or improvement over traditional pricing methods is crucial to avoid arbitrage. We propose a method for accelerating the pricing of American options to near-instantaneous using a feed-forward neural network. This neural network is trained over the chosen (e.g., Heston) stochastic volatility specification. Such an approach facilitates parameter interpretability, as generally required by the regulators, and establishes our method in the area of eXplainable Artificial Intelligence (XAI) for finance. We show that the proposed deep explainable pricer induces a speed-accuracy trade-off compared to the typical Monte Carlo or Partial Differential Equation-based pricing methods. Moreover, the proposed approach allows for pricing derivatives with path-dependent and more complex payoffs and is, given the sufficient accuracy of computation and its tractable nature, applicable in a market-making environment