History of Modelling Languages

The field of natural language processing (NLP) has evolved significantly in recent years. In this chapter we consider two leading and well-established methodologies, namely, those due to Loughran McDonald, and FinBERT. We then contrast our approach to these two approaches and compare our performance against these methods which are considered to be benchmarks. We use S&P 500 market data for our investigations and describe the results obtained following our strategies. Our main consideration is the Earnings Calls for the S&P 500 stocks. We vindicate our findings and present the performance of our trading and fund management strategy which shows better results.

Keywords— Natural Language Processing, Sentiment Analysis, Earnings Calls

The introduction of new technologies and concepts has redefined the relative positioning of information systems (IS) and decision technologies in a corporate context. Corporate IS have been extended to include not only transaction processing databases but also analytical databases, often known as Data Warehouses. On-line analytical processing (OLAP) , as introduced by Codd et al. , is capable of capturing the structure of the real world data in the form of multidimensional tables which are known as ‘datacubes’ by management information systems (MIS) and statistical systems specialists. Manipulation and presentation of such information through multidimensional views and graphical displays provide invaluable support for the decision-maker. We illustrate the natural coupling, which exists between data modelling, symbolic modelling and ‘What if’ analysis phases of a decision support system (DSS) . In particular, we explore the power of roll-up and drill-down features of OLAP and show how these translate into aggregation and disagreggation of the underlying decision models. Our approach sets out a paradigm for analysing the data, applying DSS tools and progressing through the information value chain to create organisational knowledge.

LP models are usually constructed using index sets and data tables which are closely related to the attributes and relations of relational database (RDB) systems. We extend the syntax of MPL, an existing LP modelling language, in order to connect it to a given RDB system. This approach reuses existing modelling and database software, provides a rich modelling environment and achieves model and data independence. This integrated software enables Mathematical Programming to be widely used as a decision support tool by unlocking the data residing in corporate databases.

UIMP is a matrix-generator report-writer system designed to aid the realization (generation) of mathematical programming models and also the analysis-reporting of the solutions of such models. The data structure facility of the system allows the underlying structure of a user model to be captured and helps to define such models. This data-structure feature is not only a powerful modelling aid, it also finds use in the analysis of solutions and report generation. The experience of using the system, its shortcomings and possible extensions are also discussed.