Stock portfolio structure of individual investors infers future trading behavior

Although the understanding of and motivation behind individual trading behavior is an important puzzle in finance, little is known about the connection between an investor's portfolio structure and her trading behavior in practice. In this paper, we investigate the relation between what stocks investors hold, and what stocks they buy, and show that investors with similar portfolio structures to a great extent trade in a similar way. With data from the central register of shareholdings in Sweden, we model the market in a similarity network, by considering investors as nodes, connected with links representing portfolio similarity. From the network, we find groups of investors that not only identify different investment strategies, but also represent groups of individual investors trading in a similar way. These findings suggest that the stock portfolios of investors hold meaningful information, which could be used to earn a better understanding of stock market dynamics.Comment: 9 pages, 4 figures, 1 tabl

Robustness of journal rankings by network flows with different amounts of memory

As the number of scientific journals has multiplied, journal rankings have become increasingly important for scientific decisions. From submissions and subscriptions to grants and hirings, researchers, policy makers, and funding agencies make important decisions with influence from journal rankings such as the ISI journal impact factor. Typically, the rankings are derived from the citation network between a selection of journals and unavoidably depend on this selection. However, little is known about how robust rankings are to the selection of included journals. Here we compare the robustness of three journal rankings based on network flows induced on citation networks. They model pathways of researchers navigating scholarly literature, stepping between journals and remembering their previous steps to different degree: zero-step memory as impact factor, one-step memory as Eigenfactor, and two-step memory, corresponding to zero-, first-, and second-order Markov models of citation flow between journals. We conclude that higher-order Markov models perform better and are more robust to the selection of journals. Whereas our analysis indicates that higher-order models perform better, the performance gain for the second-order Markov model comes at the cost of requiring more citation data over a longer time period.Comment: 9 pages, 5 figure

Mapping bilateral information interests using the activity of Wikipedia editors

We live in a global village where electronic communication has eliminated the geographical barriers of information exchange. The road is now open to worldwide convergence of information interests, shared values, and understanding. Nevertheless, interests still vary between countries around the world. This raises important questions about what today's world map of in- formation interests actually looks like and what factors cause the barriers of information exchange between countries. To quantitatively construct a world map of information interests, we devise a scalable statistical model that identifies countries with similar information interests and measures the countries' bilateral similarities. From the similarities we connect countries in a global network and find that countries can be mapped into 18 clusters with similar information interests. Through regression we find that language and religion best explain the strength of the bilateral ties and formation of clusters. Our findings provide a quantitative basis for further studies to better understand the complex interplay between shared interests and conflict on a global scale. The methodology can also be extended to track changes over time and capture important trends in global information exchange.Comment: 11 pages, 3 figures in Palgrave Communications 1 (2015

Network analysis of the share ownership structure on the Swedish stock market

The stock market is an example of a complex system, i.e. it consists of a number of traders, interacting in such a way that their collective behaviour, the behaviour of the market, is not a simple combination of their individual behaviour. One of the most important tasks in modern finance is finding efficient ways of summarizing and visualizing the stock market data to obtain useful information about the behavior of the market. In this thesis we investigate the possibility of finding a way to summarize and cluster share ownership data from the Swedish stock market. This is done by using a network approach to analyze the structure of the share ownership in order to find significant patterns in the data. The analysis of the network is performed with the community detection algorithm InfoMap, which turns the problem of finding clusters into the problem of optimally compressing the flow of information on the structure of the network. The results of the analysis indicate that it is possible to find significant patterns in the ownership data when looking at the holdings of individuals using a binary approach. By using the clusters with the largest information flow, a majority of the analyzed individuals are categorized into clusters that accommodates for different properties regarding the ownership of the included individuals. The clustering results are visualized using alluvial diagrams which also are used to display changes that occur in the ownership structure between two dates

Toward higher-order network models

Complex systems play an essential role in our daily lives. These systems consist of many connected components that interact with each other. Consider, for example, society with billions of collaborating individuals, the stock market with numerous buyers and sellers that trade equities, or communication infrastructures with billions of phones, computers and satellites. The key to understanding complex systems is to understand the interaction patterns between their components - their networks. To create the network, we need data from the system and a model that organizes the given data in a network representation. Today's increasing availability of data and improved computational capacity for analyzing networks have created great opportunities for the network approach to further prosper. However, increasingly rich data also gives rise to new challenges that question the effectiveness of the conventional approach to modeling data as a network. In this thesis, we explore those challenges and provide methods for simplifying and highlighting important interaction patterns in network models that make use of richer data. Using data from real-world complex systems, we first show that conventional network modeling can provide valuable insights about the function of the underlying system. To explore the impact of using richer data in the network representation, we then expand the analysis for higher-order models of networks and show why we need to go beyond conventional models when there is data that allows us to do so. In addition, we also present a new framework for higher-order network modeling and analysis. We find that network models that capture richer data can provide more accurate representations of many real-world complex systems

Toward higher-order network models

Complex systems play an essential role in our daily lives. These systems consist of many connected components that interact with each other. Consider, for example, society with billions of collaborating individuals, the stock market with numerous buyers and sellers that trade equities, or communication infrastructures with billions of phones, computers and satellites. The key to understanding complex systems is to understand the interaction patterns between their components - their networks. To create the network, we need data from the system and a model that organizes the given data in a network representation. Today's increasing availability of data and improved computational capacity for analyzing networks have created great opportunities for the network approach to further prosper. However, increasingly rich data also gives rise to new challenges that question the effectiveness of the conventional approach to modeling data as a network. In this thesis, we explore those challenges and provide methods for simplifying and highlighting important interaction patterns in network models that make use of richer data. Using data from real-world complex systems, we first show that conventional network modeling can provide valuable insights about the function of the underlying system. To explore the impact of using richer data in the network representation, we then expand the analysis for higher-order models of networks and show why we need to go beyond conventional models when there is data that allows us to do so. In addition, we also present a new framework for higher-order network modeling and analysis. We find that network models that capture richer data can provide more accurate representations of many real-world complex systems

Toward higher-order network models

Complex systems play an essential role in our daily lives. These systems consist of many connected components that interact with each other. Consider, for example, society with billions of collaborating individuals, the stock market with numerous buyers and sellers that trade equities, or communication infrastructures with billions of phones, computers and satellites. The key to understanding complex systems is to understand the interaction patterns between their components - their networks. To create the network, we need data from the system and a model that organizes the given data in a network representation. Today's increasing availability of data and improved computational capacity for analyzing networks have created great opportunities for the network approach to further prosper. However, increasingly rich data also gives rise to new challenges that question the effectiveness of the conventional approach to modeling data as a network. In this thesis, we explore those challenges and provide methods for simplifying and highlighting important interaction patterns in network models that make use of richer data. Using data from real-world complex systems, we first show that conventional network modeling can provide valuable insights about the function of the underlying system. To explore the impact of using richer data in the network representation, we then expand the analysis for higher-order models of networks and show why we need to go beyond conventional models when there is data that allows us to do so. In addition, we also present a new framework for higher-order network modeling and analysis. We find that network models that capture richer data can provide more accurate representations of many real-world complex systems

Geofysiska mätmetoder för prospektering till ballastmaterial

The Swedish rock-mass industry is currently in a transition period because new rules and regulations strongly limit the possibility to use naturally sorted aggregates for construction and concrete purposes. Furthermore, research and development within the industry is more oriented towards crushed bedrock aggregates. Whilst the crystalline bedrock of Scandinavia mostly consists of Precambrian gneiss that has been de¬formed a great number of times for over one billion ye¬ars, the quality of the bedrock varies rigorously within small areas. This report aims at creating an instrument to visualize the subsurface, by using near surface geop¬hysics, and connect collected data with variations in the quality of what will be aggregates for construction purposes. This survey has been performed at three different quarries, all of which differ from a geologic perspec¬tive. The electric method used was continuous vertical electrical sounding (CVES) where resistivity and IP-data were collected. 11 parallel profiles were performed with an equal distance of 4 meters and a length of 160 meters for all three sites where the survey was performed. The data collected were inverted both as two-dimensional pro¬files and a three-dimensional grid. A seismic profile was also performed at each site. The profile was 166 meters and was placed along one CVES-profile of special interest. Along the profile there were 39 shoots and the source of energy was a shotgun. Two 24-channel seis¬mographs were used for maximum data coverage. As electromagnetic method, magnetometry was chosen. The measurements were only perfor¬med in two of the sites because no significant magnetic anomaly was expected at the third site. The results of this report show that a combination of the near surface geophysical methods used in this sur¬vey can generate a good picture of the subsurface. The report also shows how joints and fracture zo¬nes, dykes, aggregates of clay and weak structures in the bedrock surface can be detected. The fracture frequency has also been interpreted and attempts were made to correlate the geophysical data with the standard mechanical tests for rock aggre¬gates. One conclusion of this report is that quarries can gain a lot by performing more geophysical surveys.Bergmaterialindustrin i Sverige befinner sig just nu i en omstruktureringsfas då nya regler starkt begränsar möjligheten att använda sig av naturgrus för så väl bygg- och anläggningsändamål som för betongindustrin. Detta har även lett till att teknikutvecklingen stävar mot en övergång mot krossat berg, och inte sällan har det lett till bergmaterial med egenskaper som överträffar dem för naturgrus. Då Skandinaviens berggrund till stor del består av prekambrisk kristallin gnejs som utsatts för upprepad deformation under ett par tusen miljoner år varierar kvaliteten, med hänseende på de mekaniska egenskaperna, i krossat bergmaterial ofta stort inom små områden. Denna studie syftar till att med hjälp av marknära geofysiska undersökningsmetoder skapa ett instrument för att i större utsträckning kunna visualisera dels berggrundens överyta, dels att försöka koppla samman data som metoderna genererar till variationer i kvaliteten som berggrunden besitter. Undersökningarna genomfördes vid tre olika stenbrott, som alla skiljer sig från varandra ur ett geologiskt perspektiv. Som elektrisk undersökningsmetod användes multi-elektrodmätning, så kallad CVES (Countinous Vertical Electrical Sounding), där resistivitets- och IP-data samlades in. Elva parallella profiler utfördes med 4 meters inbördes avstånd och 160 meters längd för vart och ett av de tre stenbrotten. De data som erhölls inverterades både som tvådimensionella profiler och som en tredimensionell kropp. En seismisk profil utfördes per undersökningslokal. Profilen var 166 meter lång och drogs längs en utvald CVES-profil. Längs profilen gjordes 39 skottpunkter med ”shotgun” som energikälla. Två stycken seismografer användes med vardera 24 geofoner för att samla in data. Som elektromagnetisk metod användes magnetometri. Magnetometrimätningarna utfördes endast på två av undersökningslokaliteterna då den tredje inte förväntades uppvisa någon större magnetisk anomali. En lokalitet gav användbar data. Resultaten visar att en kombination av de använda geofysiska undersökningsmetoderna kan ge en god bild av markens underyta. Rapporten visar bland annat hur man kan detektera sprickzoner, gångbergarter, leransamlingar och svaghetszoner samt den kristallina bergrundens överytan. Även berggrundens sprickfrekvens har kunnat uppskattas. Försök har även gjorts att direkt korrelera geofysisk data med mekaniska kvalitetstester. En slutsats av vikt i rapporten är att särskilt vid större bergtäkter finns mycket att vinna på en ökad användning av geofysisk undersökningsmetodik