Financial tools turn quantitative information (numbers such as prices and rates) and qualitative information (news, rumours, recommendations) into support for investment decisions. Conventional tools rely on statistics and heuristics; innovative tools borrow from artificial intelligence, such as neural networks and expert systems.
Over the last twenty years, finance has benefited enormously from powerful technology and the connections between systems.
Markets can now be accessed almost instantly, even by non-professional investors, and market data is continuously stored by computers powerful enough to analyse it in real time. Investors have access to a vast amount of information from news agencies, historical archives, government agencies and private organisations that collect, organise, process and distribute data. Connected to news providers in real time over the Internet, operators assimilate news within moments of it happening — and as the cost of this information has fallen, it has become accessible even to small, private investors.
This information varies widely in quality. Quantitative information can be expressed in numbers, so investors and computer programs can use it directly, without further processing. Qualitative information, by contrast, cannot be expressed in numeric terms.
Financial tools
Financial tools select qualitative and quantitative data and infer new data from it. They support investors' decisions by summarising, selecting or analysing the source data — or by suggesting what to do. In short, they are instruments for the economic analysis of the stock market. Many kinds have been developed; our research focuses on tools that support decisions to trade securities issued by companies and listed on stock exchanges, though they extend naturally to other financial markets.
Conventional tools
Conventional tools rely on mathematical, statistical and probabilistic algorithms: thoroughly studied, highly optimised, effective on specific data sets and proven over many years. Those we work on are based on statistics, analysing historical time series of share prices, and on heuristics, which explain financial events not through an underlying economic or mathematical relationship but through experimentation, trial and error, and knowledge shared within the financial community.
Innovative tools
The innovative tools we work on emerged over the last twenty years and borrow mainly from artificial intelligence: neural networks, a connectionist model of information processing, and expert systems, programs that hold specialist knowledge normally found only in human experts.
Common questions
What is the difference between quantitative and qualitative information in finance?
Quantitative information can be expressed in numbers — prices, rates, time series — and used directly by investors and programs. Qualitative information, such as news and rumours, cannot be expressed numerically.
What is a financial tool?
An instrument that selects qualitative and quantitative data and infers new data from it, supporting investors’ decisions by summarising, selecting or analysing the source data, or by suggesting what to do.
What are conventional and innovative financial tools?
Conventional tools rely on statistics and heuristics. Innovative tools borrow from artificial intelligence — neural networks and expert systems.
