Abstract: In this study, we introduce a new method of assessing the credit risk of corporate bonds;where in addition to the historical market data news sentiment data is used. Typically,a higher yield spread is usually associated with higher credit risk. By predicting the upward/downward movement of yield and yield spread accurately, the credit risk associated to the bonds can be detected precisely. The corporate bonds studied are issued after 1 January 2007 by seven chosen companies listed in Euro Stoxx 50 index.The time series of bond yields and news sentiment cover the period from 1 January 2007 to 15 May 2017. The modelling of the dynamics of corporate bond yields and credit spreads are based on ARIMA and ARIMAX models. In the ARIMAX model, macroeconomic and firm-specific news sentiment are used as the external explanatory variable. We examine the effect of several categories of macroeconomics news sentiment and firm-specific news sentiment on corporate bond yield spreads.Furthermore, we separate the positive and negative sentiment and investigate their impact on the forecast of corporate bond yields. It is found that negative country news sentiment and central bank news sentiment are effective during a recession period and positive country news sentiment is effective in the recovery period. Negative government and firm-specific news sentiment, in general, affect corporate bond yield spreads more than positive government and company news sentiment. [Read full paper...]

Abstract: Momentum strategy is one of the most popular strategies that market participants use to make investment decisions. In the past two decades, many researchers have shown that momentum strategy beats the market, and provides attractive portfolio returns. In this study we investigate Dow Jones Industry Average (DJIA) index and include news data and social media sentiment data to improve the performance of momentum strategy.Particularly, we select StockTwits as the social media source. Four weekly momentum strategies are built and compared over a five-year back-testing period. This research starts with using market data to calculate 5-day Relative Strength Indicator (RSI) that captures the momentum of price. A momentum strategy is constructed based on the overbought/oversold (70/30) signals of RSI proposed by Wilder (1978). Furthermore, the news and social media sentiment data are applied separately to enhance the RSI selections of momentum strategy. News impact scores are used to give more precise evaluations toward news sentiment. Finally, news and social media sentiment data are applied as a double filter to enhance the momentum strategy. The results show that news sentiment and social media data improves the performance of the momentum strategy. [Read full paper...]

Abstract: The aim of this project s to forecast futures spreads of WTI Crude Oil. The motivation for this project springs from the fact that trading with calendar futures spreads is much more advantageous than trading with many other financial instruments. We make use of the fact that futures prices follow the mean-reverting process (Ornstein-Uhlenbeck process, OU). We propose a new method which combines three linear Gaussian state space models, namely one factor model, one factor model with risk premium, and one factor model with seasonality. Thereafter, we directly model futures spreads. Kalman filter and Maximum Likelihood Estimate (MLE) are used to estimate the model parameters. It is shown that this new approach, using the ratio between the nearest prices over spot prices as a latent variable and calendar futures spreads vector as the observed variable, is more accurate and robust than the indirect forecasting method which inputs both spot prices and futures prices as the latent variable and the observed variable respectively. Results on calibration and comparison for three models and two methods, as well as out-of-sample forecasting results are then presented and discussed. [Read full paper...]

Abstract: We describe a method for generating daily trading signals to construct trade portfolios of exchange traded securities. Our model uses Second Order Stochastic Dominance (SSD) as the choice criterion for both long and short positions. We control dynamic risk of ‘draw down’ by applying money management. The asset choice for long and short positions are influenced by market sentiment; the market sentiments are in turn acquired from news wires and microblogs. The solution method is challenging as it requires processing stochastic integer programming (SIP) models as well as computing the impact of market sentiment. The computation of SSD portfolios are well known to be computationally hard as this involves processing of large discrete MIP problems. The solution approach is based on our well-established solver system FortSP which uses CPLEX as its embedded solver engine to process SIP models. [Read full paper...]