scholarly journals Simulation Research on Artificial Financial Market Based on Multiple Competitive Market-Makers

2012 ◽  
Vol 6-7 ◽  
pp. 722-729
Author(s):  
Xiao Feng Lin ◽  
Hong Tao Zhou ◽  
Wei Zeng ◽  
Hao Wang
Keyword(s):  
Financial Market ◽  
Simulation Experiment ◽  
Learning Rule ◽  
Competitive Market ◽  
Learning Ability ◽  
Market Maker ◽  
Simulation Research ◽  
Market Makers ◽  
The Past ◽  
Fundamental Value

Most of the past studies about dealership market involved only one monoplistic market-maker. The paper aims to build an artificial financial market based on multiple competitive market makers on ANYLOGIC platform, in which one market-maker adopts BAYES learning rule to estimate the fundamental value and the other employs a rough method. In order to validate the effectiveness of dealers’ quotes, we carried out two group simulation experiment. The results show that the quote of each dealer can converge to the fundamental value with certain deviation. What’s more, the deviation of the market-maker with learning ability is smaller while the converging speed slower.

scholarly journals Welfare Effects of Market Making in Continuous Double Auctions

2017 ◽  
Vol 59 ◽  
pp. 613-650 ◽  
Author(s):  
Elaine Wah ◽  
Mason Wright ◽  
Michael P. Wellman
Keyword(s):  
Allocative Efficiency ◽  
Market Model ◽  
Model Parameters ◽  
Market Maker ◽  
Market Making ◽  
Market Makers ◽  
Continuous Double Auctions ◽  
Thin Markets ◽  
Heuristic Strategies ◽  
Fundamental Value

We investigate the effects of market making on market performance, focusing on allocative efficiency as well as gains from trade accrued by background traders. We employ empirical simulation-based methods to evaluate heuristic strategies for market makers as well as background investors in a variety of complex trading environments. Our market model incorporates private and common valuation elements, with dynamic fundamental value and asymmetric information. In this context, we compare the surplus achieved by background traders in strategic equilibrium, with and without a market maker. Our findings indicate that the presence of the market maker strongly tends to increase total welfare across various environments. Market-maker profit may or may not exceed the welfare gain, thus the effect on background-investor surplus is ambiguous. We find that market making tends to benefit investors in relatively thin markets, and situations where background traders are impatient, due to limited trading opportunities. The presence of additional market makers increases these benefits, as competition drives the market makers to provide liquidity at lower price spreads. A thorough sensitivity analysis indicates that these results are robust to reasonable changes in model parameters.

scholarly journals Herd Behavior in a Laboratory Financial Market

2005 ◽  
Vol 95 (5) ◽  
pp. 1427-1443 ◽  
Author(s):  
Marco Cipriani ◽  
Antonio Guarino
Keyword(s):  
Financial Market ◽  
Private Information ◽  
Asset Price ◽  
Experimental Results ◽  
Herd Behavior ◽  
Market Maker ◽  
History Of ◽  
Fundamental Value

We study herd behavior in a laboratory financial market. Subjects receive private information on the fundamental value of an asset and trade it in sequence with a market maker. The market maker updates the asset price according to the history of trades. Theory predicts that agents should never herd. Our experimental results are in line with this prediction. Nevertheless, we observe a phenomenon not accounted for by the theory. In some cases, subjects decide not to use their private information and choose not to trade. In other cases, they ignore their private information to trade against the market (contrarian behavior).

Page Limits on Economics Articles: Evidence from Two Journals

2014 ◽  
Vol 28 (3) ◽  
pp. 149-168 ◽  
Author(s):  
David Card ◽  
Stefano DellaVigna
Keyword(s):  
Average Length ◽  
American Economic Review ◽  
Competitive Market ◽  
Monopoly Power ◽  
Median Length ◽  
European Economic ◽  
The Past ◽  
Economic Association ◽  
Economic Journals ◽  
Page Limit

Over the past four decades the median length of the papers published in the “top five” economic journals has grown by nearly 300 percent. We study the effects of a page limit policy introduced by the American Economic Review (AER) in mid-2008 and subsequently adopted by the Journal of the European Economic Association (JEEA) in 2009. We find that the imposition of a 40-page limit on submissions led to no change in the flow of new papers to the AER. Instead, authors responded by shortening and reformatting their papers. For JEEA, in contrast, we conclude that the page-limit policy led authors of longer papers to submit to other journals. These results imply that the AER has substantial monopoly power over submissions, while JEEA faces a very competitive market. Evidence from both journals, and from citations to published papers in the top journals, suggests that longer papers are of higher quality than shorter papers, so the loss of longer submissions at JEEA may have led to a drop in quality. Despite a modest impact of the AER's policy on the average length of submissions, the policy had little or no effect on the length of final accepted manuscripts.

scholarly journals Understanding Changes in the Topology and Geometry of Financial Market Correlations during a Market Crash

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1211
Author(s):  
Peter Tsung-Wen Yen ◽  
Kelin Xia ◽  
Siew Ann Cheong
Keyword(s):  
Financial Market ◽  
Information Filtering ◽  
Neck Region ◽  
Topological Data Analysis ◽  
Financial Networks ◽  
Minimal Spanning Tree ◽  
Geometric Information ◽  
Stock Market Crash ◽  
The Past

In econophysics, the achievements of information filtering methods over the past 20 years, such as the minimal spanning tree (MST) by Mantegna and the planar maximally filtered graph (PMFG) by Tumminello et al., should be celebrated. Here, we show how one can systematically improve upon this paradigm along two separate directions. First, we used topological data analysis (TDA) to extend the notions of nodes and links in networks to faces, tetrahedrons, or k-simplices in simplicial complexes. Second, we used the Ollivier-Ricci curvature (ORC) to acquire geometric information that cannot be provided by simple information filtering. In this sense, MSTs and PMFGs are but first steps to revealing the topological backbones of financial networks. This is something that TDA can elucidate more fully, following which the ORC can help us flesh out the geometry of financial networks. We applied these two approaches to a recent stock market crash in Taiwan and found that, beyond fusions and fissions, other non-fusion/fission processes such as cavitation, annihilation, rupture, healing, and puncture might also be important. We also successfully identified neck regions that emerged during the crash, based on their negative ORCs, and performed a case study on one such neck region.

scholarly journals Machine Learning and Artificial Intelligence in Online Fake Transaction Alerting

2015 ◽  
Vol 3 (2) ◽  
pp. 115-126 ◽  
Author(s):  
Naresh Babu Bynagari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fraud Detection ◽  
Learning Ability ◽  
The Core ◽  
The Past ◽  
Music Selection ◽  
Intelligent Analysis ◽  
Set Up ◽  
Detection Mechanisms

Artificial Intelligence (AI) is one of the most promising and intriguing innovations of modernity. Its potential is virtually unlimited, from smart music selection in personal gadgets to intelligent analysis of big data and real-time fraud detection and aversion. At the core of the AI philosophy lies an assumption that once a computer system is provided with enough data, it can learn based on that input. The more data is provided, the more sophisticated its learning ability becomes. This feature has acquired the name "machine learning" (ML). The opportunities explored with ML are plentiful today, and one of them is an ability to set up an evolving security system learning from the past cyber-fraud experiences and developing more rigorous fraud detection mechanisms. Read on to learn more about ML, the types and magnitude of fraud evidenced in modern banking, e-commerce, and healthcare, and how ML has become an innovative, timely, and efficient fraud prevention technology.

Budget Consolidation Options for the UK

2009 ◽  
Vol 210 ◽  
pp. 58-60
Author(s):  
Ray Barrell
Keyword(s):  
Public Sector ◽  
Financial Market ◽  
Economic Activity ◽  
The Past ◽  
Budget Consolidation ◽  
The Uk

The increase in UK public sector net borrowing in the past year, plotted in figure 1, has been in part a result of the decline in economic activity, and also a consequence of the change in housing and financial market transactions. The former is predictable with every 1 per cent decline in output below trend producing a decline in net revenues of of between one third and three fifths of a per cent of GDP depending upon the reason for the decline in output. The loss from the decline in asset-related revenues is harder to judge, but the April 2009 budget suggested that revenue losses might be more than 1 per cent of GDP.

On Bayesian model selection for INGARCH models viatrans-dimensional Markov chain Monte Carlo methods

2021 ◽  
pp. 1471082X2110347
Author(s):  
Panagiota Tsamtsakiri ◽  
Dimitris Karlis
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Bayesian Model ◽  
Linear Models ◽  
Simulation Experiment ◽  
Mean Values ◽  
The Past ◽  
Competing Models ◽  
Selection For ◽  
Log Linear

There is an increasing interest in models for discrete valued time series. Among them, the integer autoregressive conditional heteroscedastic (INGARCH) is a model that has found several applications. In the present article, we study the problem of model selection for this family of models. Namely we consider that an observation conditional on the past follows a Poisson distribution where its mean depends on its past mean values and on past observations. We consider both linear and log-linear models. Our purpose is to select the most appropriate order of such models, using a trans-dimensional Bayesian approach that allows jumps between competing models. A small simulation experiment supports the usage of the method. We apply the methodology to real datasets to illustrate the potential of the approach.

scholarly journals Effectiveness of Assessment, Diagnostic and Intervention ICT Tools for Children and Adolescents with ADHD

2019 ◽  
Vol 7 (3) ◽  
pp. 51
Author(s):  
Arhondoula Alexopoulou ◽  
Alexandra Batsou ◽  
Athanasios S. Drigas
Keyword(s):  
New Technologies ◽  
Memory Capacity ◽  
Learning Ability ◽  
Attention And Memory ◽  
Special Educational Needs ◽  
Modern Education ◽  
The Past ◽  
The People ◽  
Information And Communication ◽  
Ict Tools

<p class="0keywords">The major technological leaps that have taken place over the last years, one of which is the creation and increasing use of ICT (Technology, Information and Communication), require a reconsideration of the capability of the computers to meet the expectations of modern education, especially in the field of Special Education. Researches confirm that new technologies offer liberating and amazing opportunities to people with disabilities, as these are not just limited to simple information management but can also operate supportively, improving the learning ability, the academic performance and functionality of the people that have special needs and those with special, educational needs. In this review there is a brief reference on some of the ICT assessment, diagnostic and intervention tools of the past decade, for children with attention and hyperactivity disorders (ADHD). It also refers to the direct connection and interaction between attention and memory capacity as well as, how, with the help of technology, we evaluate, improve memory, and thus attention. The deficit of ADHD in its executive functions and how these can be improved with the help of technology is also brought up in this review.<strong></strong></p>

LIQUIDITY PROVISION IN CAPACITY-CONSTRAINED MARKETS

2011 ◽  
Vol 15 (S1) ◽  
pp. 119-144 ◽  
Author(s):  
Pierre-Olivier Weill
Keyword(s):  
Financial Market ◽  
Asset Allocation ◽  
Capacity Constraint ◽  
Market Makers ◽  
Liquidity Provision ◽  
Bid Ask Spread ◽  
Time Variations ◽  
Capacity Constrained ◽  
Constrained Markets ◽  
Over Time

We study a competitive dynamic financial market subject to a transient selling pressure when market makers face a capacity constraint on their number of trades per unit of time with outside investors. We show that profit-maximizing market makers provide liquidity in order to manage their trading capacity constraint optimally over time: they use slack trading capacity early to accumulate assets when the selling pressure is strong in order to relax their trading capacity constraint and sell to buyers more quickly when the selling pressure subsides. When the trading capacity constraint binds, the bid–ask spread is strictly positive, widening and narrowing as market makers build up and unwind their inventories. Because the equilibrium asset allocation is constrained Pareto-optimal, the time variations in bid–ask spread are not a symptom of inefficient liquidity provision.

scholarly journals Monitoring global tropospheric OH concentrations using satellite observations of atmospheric methane

2018 ◽  
Vol 18 (21) ◽  
pp. 15959-15973 ◽  
Author(s):  
Yuzhong Zhang ◽  
Daniel J. Jacob ◽  
Joannes D. Maasakkers ◽  
Melissa P. Sulprizio ◽  
Jian-Xiong Sheng ◽  
...  
Keyword(s):  
Simulation Experiment ◽  
Short Wave ◽  
Methane Emissions ◽  
Satellite Observations ◽  
Atmospheric Methane ◽  
The Past ◽  
Methyl Chloroform ◽  
Systematic Component ◽  
Observing System Simulation Experiment ◽  
Better Than

Abstract. The hydroxyl radical (OH) is the main tropospheric oxidant and the main sink for atmospheric methane. The global abundance of OH has been monitored for the past decades using atmospheric methyl chloroform (CH3CCl3) as a proxy. This method is becoming ineffective as atmospheric CH3CCl3 concentrations decline. Here we propose that satellite observations of atmospheric methane in the short-wave infrared (SWIR) and thermal infrared (TIR) can provide an alternative method for monitoring global OH concentrations. The premise is that the atmospheric signature of the methane sink from oxidation by OH is distinct from that of methane emissions. We evaluate this method in an observing system simulation experiment (OSSE) framework using synthetic SWIR and TIR satellite observations representative of the TROPOMI and CrIS instruments, respectively. The synthetic observations are interpreted with a Bayesian inverse analysis, optimizing both gridded methane emissions and global OH concentrations. The optimization is done analytically to provide complete error accounting, including error correlations between posterior emissions and OH concentrations. The potential bias caused by prior errors in the 3-D seasonal OH distribution is examined using OH fields from 12 different models in the ACCMIP archive. We find that the satellite observations of methane have the potential to constrain the global tropospheric OH concentration with a precision better than 1 % and an accuracy of about 3 % for SWIR and 7 % for TIR. The inversion can successfully separate the effects of perturbations to methane emissions and to OH concentrations. Interhemispheric differences in OH concentrations can also be successfully retrieved. Error estimates may be overoptimistic because we assume in this OSSE that errors are strictly random and have no systematic component. The availability of TROPOMI and CrIS data will soon provide an opportunity to test the method with actual observations.

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