”En aktiemarknad för alla” : Integritet och marknadsomsorg i (själv)regleringen av den svenska värdepappersmarknaden

”A stock market for all”. Integrity and concern for the market in the (self-)regulation of the Swedish securities market This article deals with the transformation process that led to the substantial growth of the securities markets, and also led to a situation where Sweden became one of the leading countries when it comes to ordinary people investing in shares and mutual funds. The article discusses how social control and regulation of the market changed as a result of this process. A sudden and strong unanimity for knowledge tests in order for a stockbroker to be allowed to conduct brokerage, advisory services and asset management was the significant change in this transformation process. Knowledge tests were first introduced on a voluntary basis by the industry itself, but is now a mandatory requirement by the State. This article argues that the unanimity for knowledge tests best can be understood by studying the broadening of the financial markets. The broadening meant that more groups in society – with very varying capabilities – had started to place their assets in the security markets. They were encouraged to do so since this was considered to be the solution to the growing number of socioeconomic problems. This article is mainly based on market statistics and document analysis supplemented by interviews.

Financial time series analysis : Chaos and neurodynamics approach

This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.