Applications of physics to finance and economics: returns, trading activity and income

This dissertation reports work where physics methods are applied to financial and economical problems. The first part studies stock market data (chapter 1 to 5). The second part is devoted to personal income in the USA (chapter 6). We first study the probability distribution of stock returns at mesoscopic time lags (return horizons) ranging from about an hour to about a month. For mesoscopic times the bulk of the distribution (more than 99% of the probability) follows an exponential law. At longer times, the exponential law continuously evolves into Gaussian distribution. After characterizing the stock returns at mesoscopic time lags, we study the subordination hypothesis. The integrated volatility V_t constructed from the number of trades process can be used as a subordinator for a Brownian motion. This subordination is able to describe approximatly 85% of the stock returns for time lags that start at 1 hour but are shorter than one day. Finally, we show that the CIR process describes well enough the empirical V_t process, such that the corresponding Heston model is able to describe the log-returns x_t process, with approximately the maximum quality that the subordination allows. Finally, we study the time evolution of the personal income distribution. We find that the personal income distribution in the USA has a well-defined two-income-class structure. The majority of population (97-99%) belongs to the lower income class characterized by the exponential Boltzmann-Gibb(``thermal'') distribution, whereas the higher income class (1-3% of population) has a Pareto power-law (``superthermal'') distribution. We show that the ``thermal'' part is stationary in time.Comment: 24 pages and 45 figures. PhD thesis presented to the committee members on May 10th 2005. This thesis is based on 3 published papers with one chapter (chapter 5) with new unpublished result

Temporal evolution of the "thermal" and "superthermal" income classes in the USA during 1983-2001

Personal income distribution in the USA has a well-defined two-class structure. The majority of population (97-99%) belongs to the lower class characterized by the exponential Boltzmann-Gibbs ("thermal") distribution, whereas the upper class (1-3% of population) has a Pareto power-law ("superthermal") distribution. By analyzing income data for 1983-2001, we show that the "thermal" part is stationary in time, save for a gradual increase of the effective temperature, whereas the "superthermal" tail swells and shrinks following the stock market. We discuss the concept of equilibrium inequality in a society, based on the principle of maximal entropy, and quantitatively show that it applies to the majority of population.Comment: v.3: 7 pages, 5 figures, EPL style, more references adde

Evidence for regulated expression of Telomeric Repeat-containing RNAs (TERRA) in parasitic trypanosomatids

The Telomeric Repeat-containing RNAs (TERRA) participate in the homeostasis of telomeres in higher eukaryotes. Here, we investigated the expression of TERRA in Leishmania spp. and Trypanosoma brucei and found evidences for its expression as a specific RNA class. The trypanosomatid TERRA are heterogeneous in size and partially polyadenylated. The levels of TERRA transcripts appear to be modulated through the life cycle in both trypanosomatids investigated, suggesting that TERRA play a stage-specific role in the life cycle of these early-branching eukaryotes

Identification of "pathologs" (disease-related genes) from the RIKEN mouse cDNA dataset using human curation plus FACTS, a new biological information extraction system

BACKGROUND: A major goal in the post-genomic era is to identify and characterise disease susceptibility genes and to apply this knowledge to disease prevention and treatment. Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways. In this work we utilised the latest information on the mouse transcriptome as revealed by the RIKEN FANTOM2 project to identify novel human disease-related candidate genes. We define a new term "patholog" to mean a homolog of a human disease-related gene encoding a product (transcript, anti-sense or protein) potentially relevant to disease. Rather than just focus on Mendelian inheritance, we applied the analysis to all potential pathologs regardless of their inheritance pattern. RESULTS: Bioinformatic analysis and human curation of 60,770 RIKEN full-length mouse cDNA clones produced 2,578 sequences that showed similarity (70–85% identity) to known human-disease genes. Using a newly developed biological information extraction and annotation tool (FACTS) in parallel with human expert analysis of 17,051 MEDLINE scientific abstracts we identified 182 novel potential pathologs. Of these, 36 were identified by computational tools only, 49 by human expert analysis only and 97 by both methods. These pathologs were related to neoplastic (53%), hereditary (24%), immunological (5%), cardio-vascular (4%), or other (14%), disorders. CONCLUSIONS: Large scale genome projects continue to produce a vast amount of data with potential application to the study of human disease. For this potential to be realised we need intelligent strategies for data categorisation and the ability to link sequence data with relevant literature. This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets