Risk "Theorizing"

Will a stock price increase or decrease? Would the Fed increase interest rates, leave them unchanged or decrease them? Can the budget to be presented in Transylvania’s parliament affect the country’s current inflation rate? These and so many other questions are reflections of our lack of knowledge and its effects on financial markets performance. In this environment, uncertainty regarding future events and their consequences must be assessed, predictions made and decisions taken. Our ability to improve forecasts and reach consistently good decisions can
therefore be very profitable. To a large extent, this is one of the essential preoccupations of finance, financial data analysis and theory-building. Pricing financial assets, predicting the stock market, speculating to make money and hedging financial risks to avoid losses summarizes some of these activities. Predictions, for example, are reached in several ways such as:

‘Theorizing’, providing a structured approach to modelling, as is the case in financial theory and generally called fundamental theory. In this case, economic and financial theories are combined to generate a body of knowledge regarding trades and financial behaviour that make it possible to price financial assets.

• Financial data analysis using statistical methodologies has grown into a field called financial statistical data analysis for the purposes of modelling, testing theories and technical analysis.
• Modelling using metaphors (such as those borrowed from physics and other areas of related interest) or simply constructing model equations that are fitted one way or another to available data
• Data analysis, for the purpose of looking into data to determine patterns or relationships that were hitherto unseen. Computer techniques, such as neural networks, data mining and the like, are used for such purposes and thereby make more money. In these, as well as in the other cases, the ‘proof of the pudding is in the eating’. In other words, it is by making money, or at least making it possible for others to make money, that theories, models and techniques are
  validated.
• Prophecies we cannot explain but sometimes are true.

Throughout these ‘forecasting approaches and issues’ financial managers deal practically with uncertainty, defining it, structuring it and modelling its causes, explainable and unexplainable, for the purpose of assessing their effects on financial performance. This is far from trivial. First, many theories, both financial and statistical, depend largely on how we represent and model uncertainty. Dealing with uncertainty is also of the utmost importance, reflecting individual preferences and behaviours and attitudes towards risk. Decision Making Under Uncertainty
(DMUU) is in fact an extensive body of approaches and knowledge that attempts to provide systematically and rationally an approach to reaching decisions in such an environment. Issues such as ‘rationality’, ‘bounded rationality’ etc., as we will present subsequently, have an effect on both the approach we use and the techniques we apply to resolve the fundamental and practical problems that finance is assumed to address. In a simplistic manner, uncertainty is characterized by probabilities. Adverse consequences denote the risk for which decisions must be taken to properly balance the potential payoffs and the risks implied by decisions – trades, investments, the exercise of options etc. Of course, the more ambiguous, the less structured and the more uncertain the situations, the harder it is to take such decisions. Further, the information needed to make decisions is often not readily available and consequences cannot be predicted. Risks are then hard to determine. For example, for a corporate finance manager, the decision may be to issue or not to issue a new bond. An insurance firm may or may not confer a certain insurance contract. A Central Bank economist may recommend reducing the borrowing interest rate, leaving it unchanged or increasing it, depending on multiple economic indicators he may have at his disposal. These, and many other issues, involve uncertainty. Whatever the action taken, its consequences may be uncertain. Further, not all traders who are equally equipped with the same tools, education and background will reach the same decision (of course, when they
differ, the scope of decisions reached may be that much broader). Some are well informed, some are not, some believe they are well informed, but mostly, all traders may have various degrees of intuition, introspection and understanding, which is specific yet not quantifiable. A historical perspective of events may be useful to some and useless to others in predicting the future. Quantitative training may have the same effect, enriching some and confusing others. While in theory we seek to eliminate some of the uncertainty by better theorizing, in practice uncertainty wipes out those traders who reach the wrong conclusions and the wrong decisions. In this sense, no one method dominates another: all are important. A political and historical appreciation of events, an ability to compute, an understanding of economic laws and fundamental finance theory, use of statistics and computers to augment one’s ability in predicting and making decisions under uncertainty are only part of the tool-kit needed to venture into trading speculation and into financial risk management.