Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the implemented techniques and the type of source on which...

In recent years, support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a...

A growing literature analyzes the cross-section of single stock option returns, virtually always under the (implicit or explicit) assumption of a monotonically decreasing pricing kernel. Using option returns, we non-parametrically provide significant and robust evidence that the pricing kernel...

This paper investigates the lead-lag relationship in daily returns and volatilities between price movements of FTSE/ASE-20 futures and the underlying FTSE/ASE-20 cash index of the Athens Stock Exchange. The results suggest that there is a bidirectional causality between spot and futures returns,...

I show that the inventory risk faced by market-makers has a first-order effect on option prices. I introduce a simple approach that decomposes the price impact of trades into inventory risk and asymmetric information components. While both components are large for option trades, the inventory...

In this work we use Recurrent Neural Networks and Multilayer Perceptrons, to predict NYSE, NASDAQ and AMEX stock prices from historical data. We experiment with different architectures and compare data normalization techniques. Then, we leverage those findings to question the efficient-market...

We document that leverage-adjusted returns on S&P 500 index call and put portfolios are decreasing in their strike-to-price ratio over 1986-2010, contrary to the prediction of the Black-Scholes-Merton model. We test a large number of plausible unconditional factor models and find that only...

Equity option markets exhibit intense trading activity. We use the variability of option implied volatility spread as a proxy for the impounding of new information, and changes in the interpretation of existing information, into option prices. Over the 2006 – 2016 period, we find that the...

We propose a novel factor model for option returns. Option exposures are estimated nonparametrically and factor risk premia can vary nonlinearly with states. The model is estimated using regressions, with minimal assumptions on factor and option return dynamics. Using index options, we...

For the DAX index market, this paper analyses the development of return differences between exchange traded funds (ETFs) and the DAX index from the perspective of long-term investors. The newly introduced methodology provides the opportunity to continuously identify long-term costs of passively...