Experimental evidence for the interplay between individual wealth and transaction network

We conduct a market experiment with human agents in order to explore the structure of transaction networks and to study the dynamics of wealth accumulation. The experiment is carried out on our platform for 97 days with 2,095 effective participants and 16,936 times of transactions. From these data, the hybrid distribution (log-normal bulk and power-law tail) in the wealth is observed and we demonstrate that the transaction networks in our market are always scale-free and disassortative even for those with the size of the order of few hundred. We further discover that the individual wealth is correlated with its degree by a power-law function which allows us to relate the exponent of the transaction network degree distribution to the Pareto index in wealth distribution.Comment: 6 pages, 7 figure

A Spatial and Temporal Autoregressive Local Estimation for the Paris Housing Market

This original study examines the potential of a spatiotemporal autoregressive Local (LSTAR) approach in modelling transaction prices for the housing market in inner Paris. We use a data set from the Paris Region notary office (“Chambre des notaires d’Île-de-France”) which consists of approximately 250,000 transactions units between the first quarter of 1990 and the end of 2005. We use the exact X -- Y coordinates and transaction date to spatially and temporally sort each transaction. We first choose to use the spatiotemporal autoregressive (STAR) approach proposed by Pace, Barry, Clapp and Rodriguez (1998). This method incorporates a spatiotemporal filtering process into the conventional hedonic function and attempts to correct for spatial and temporal correlative effects. We find significant estimates of spatial dependence effects. Moreover, using an original methodology, we find evidence of a strong presence of both spatial and temporal heterogeneity in the model. It suggests that spatial and temporal drifts in households socio-economic profiles and local housing market structure effects are certainly major determinants of the price level for the Paris Housing Market.Hedonic Prices; Heterogeneity; Paris Housing Market; STAR Model

Asymmetric Stochastic Conditional Duration Model --A Mixture of Normals Approach"

This paper extends the stochastic conditional duration model by imposing mixtures of bivariate normal distributions on the innovations of the observation and latent equations of the duration process. This extension allows the model not only to capture the asymmetric behavior of the expected duration but also to easily accommodate a richer dependence structure between the two innovations. In addition, it proposes a novel estimation methodology based on the empirical characteristic function. A set of Monte Carlo experiments as well as empirical applications based on the IBM and Boeing transaction data are provided to assess and illustrate the performance of the proposed model and the estimation method. One main empirical finding in this paper is that there is a signicantly positive "leverage effect" under both the contemporaneous and lagged inter-temporal de pendence structures for the IBM and Boeing duration data.Stochastic Conditional Duration model; Leverage Effect; Discrete Mixtures of Normal; Empirical Characteristic Function

Survey performance Improvement FP-Tree Based Algorithms Analysis

Construction of a compact FP-tree ensures that subsequent mining can be performed with a rather compact data structure. For large databases, the research on improving the mining performance and precision is necessary; so many focuses of today on association rule mining are about new mining theories, algorithms and improvement to old methods. Association rules mining is a function of data mining research domain and arise many researchers interest to design a high efficient algorithm to mine association rules from transaction database. Generally the entire frequent item sets discovery from the database in the process of association rule mining shares of larger, these algorithms considered as efficient because of their compact structure and also for less generation of candidates item sets compare to Apriori .the price is also spending more. This paper introduces an improved aprior algorithm so called FP-growth algorithm

Determinants of Inter-Trade Durations and Hazard Rates Using Proportional Hazard ARMA Model

This paper puts focus on the hazard function of inter-trade durations to characterize the intraday trading process. It sheds light on the time varying trade intensity and, thus, on the liquidity of an asset and the informations channels which propagate price signals among asymmetrically informed market participants. We show, based on an exogenous information process, that the way traders aggregate information has implications for the shape of the hazard function. We use semiparametric proportional hazard model which is augmented by an ARMA structure very similar to the wide spread ACD model to obtain cinsistent estimates of the baseline survivor function and to capture well known serial dependencies in the trade intensity process. From an inspection of conditional transaction probabilities based on Bund future transaction data of the DTB we find a decreasing hazard shape providing evidence for the use of non-trading intervals as an indication for the absence of price information among market participants. However, this information content seems to be diluted by a high liquidity bade level, particularly with respect to a large inflow of potential traders from the U.S. Furthermore, we provide evidence that past sequences of prices and volumes have significant impact on the trading intensity in accordance with theoretical models.

Structure Learning and Break Detection in High-Frequency Data

The accurate learning of the underlying structure in high-frequency data has become critical in the analysis of time series for capturing valuable information that facilitates decision-making. The time series data in finance often is large, dynamic, heterogeneous and even structural unstable. Each aspect of these characteristics will add a degree of difficulty in efficient analysis. The goal of this dissertation is to discover the latent structure of dynamic high-frequency data that may have structural breaks, from both univariate and network perspective. We focus our analysis on durations between user-defined events in transaction-by-transaction stock prices from the Trade and Quotes (TAQ) data base at Wharton Research Data Services (WRDS). Our proposed approach can be easily adapted to other models. The dissertation has three main contributions. First, we propose a fast and accurate distribution-free approach using penalized martingale estimating functions on logarithmic autoregressive conditional duration (Log ACD) models. We discuss three approaches for parameter estimation. Our approach employs effective starting values from an approximating time series model and provides investigators accurate fits and predictions that can assist in trading decisions. Second, we propose a sequential monitoring scheme to detect structural breaks in the estimated parameters of a univariate piecewise Log ACD model. Based on martingale estimating function, this scheme does not require any distributional assumption. This monitoring scheme can detect structural breaks and choose model orders at the same time. Assuming data is given, we compare the performance of our scheme with that of a state-of-the-art offline scheme via simulation studies. Third, we propose a framework for detecting structural breaks in dynamic networks of a large number of stocks. In particular, we discover unobserved dynamic network structure from nodal observations governed by both the latent network and time. Our empirical analysis on the 30 most liquid stocks in S&P100 is an exploratory study. Such an analysis would be useful to economists studying the structural breaks in financial networks

TheChain: a fast, secure and parallel treatment of transactions

The Smart Distributed Ledger (aka blockchain) has attracted much attention in recent years. According to the European Parliament, this technology has the potential to change the lives of many people. The blockchain is a data structure built upon a hashed function in a distributed network, enabled by an incentive mechanism to discourage malicious nodes from participation. The consensus is at the core of the blockchain technology, and is driven by information embedded into a data structure that takes many forms such as linear, tree, and graph chains. The found related information will be subject to various validation incentives among the miners, such as proof of stake and proof of work. However, all the existing solutions suffer from a heavy state transition before dealing with the problem of a validation mechanism which suffers from resource consumption, monopoly or attacks. This work raises the following question: "Why is there a need for consensus where all participants can make a quick and correct decision?", and underlines the fact that sometimes ledger is subject to maintenance from regional parties in the data that leads to partial territories and eliminates monopoly, which is the hurdle to eliminating the trusted party. The validity of the blockchain transaction comes from the related information scattered above the data structure, and the authenticity lies in the digital signature. The aim is to switch from a validator based on incentives to a broadcaster governed by an unsupervised clustering algorithm, and the integrity does lie in the intersection among regions. However, the data structure takes advantage of the Petri network regarding its suitability. Building the entire ledger in the Petri network model will allow parallel processing of the transactions and securing of the total order between the participants on the memory reference layer. Moreover, it takes account of validation criteria quickly and safely before adding the new transaction list using the graph reachability