INFORMATION DYNAMICS IN FINANCIAL MARKETS

2000 ◽  
Vol 4 (2) ◽  
pp. 139-169 ◽  
Author(s):  
Patrick de Fontnouvelle
Keyword(s):  
Information Acquisition ◽  
Trading Volume ◽  
Rational Expectations ◽  
Information Costs ◽  
Information Dynamics ◽  
Expectation Formation ◽  
Asset Trading ◽  
Future Value ◽  
Simulated Time ◽  
Simulated Time Series

A noisy rational expectations model of asset trading is extended to incorporate costs of information acquisition and expectation formation. Because of the information costs, how much information to acquire becomes an important decision. Agents make this decision by choosing an expectations strategy about the future value of information. Because expectation formation is costly, agents often choose strategies that are simpler (and thus cheaper) than rational expectations. The model's dynamics can be expressed in terms of the market precision, which represents the amount of information acquired by the average agent. Under certain conditions, market precision follows an unstable and highly irregular time path. This irregularity directly affects observable market quantities. In particular, simulated time series for return volatility and trading volume display a copersistence similar to that found in actual financial data.

scholarly journals Scale-dependent intrinsic entropies of complex time series

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150204 ◽  
Author(s):  
Jia-Rong Yeh ◽  
Chung-Kang Peng ◽  
Norden E. Huang
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Spatial Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Complex Time ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Simulated Time ◽  
Simulated Time Series

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.

Model-based comparison of two ways to enhance WWTP capacity under stormwater conditions

2009 ◽  
Vol 60 (7) ◽  
pp. 1875-1883 ◽  
Author(s):  
M. Ahnert ◽  
J. Tränckner ◽  
N. Günther ◽  
S. Hoeft ◽  
P. Krebs
Keyword(s):  
Treatment Plant ◽  
Combined Sewer Overflows ◽  
Worst Case ◽  
Wwtp Effluent ◽  
Combined Sewer ◽  
Total Ammonia ◽  
Rain Events ◽  
Simulated Time ◽  
Simulated Time Series ◽  
Receiving Water

Two different approaches to increase the fraction of combined water treated in the wastewater treatment plant (WWTP) which would otherwise contribute to combined sewer overflows (CSO) are presented and compared based on modelling results with regard to their efficiencies during various rain events. The first option is to generally increase the WWTP inflow according to its actual capacity rather than pre-setting a maximum that applies to worst case loading. In the second option the WWTP inflow is also increased, however, the extra inflow of combined water is bypassing the activated sludge tank and directly discharged to the secondary clarifier. Both approaches have their advantages. For the simulated time series with various rain events, the reduction of total COD load from CSOs and WWTP effluent discharged to the receiving water was up to 20% for both approaches. The total ammonia load reduction was between 6% for the bypass and 11% for inflow increase. A combination of both approaches minimises the adverse effects and the overall emission to the receiving water.

scholarly journals Erfahrung und Erwartung – eine vernachlässigte wirtschaftshistorische Perspektive?

2018 ◽  
Vol 59 (2) ◽  
pp. 329-341
Author(s):  
Mark Jakob ◽  
Alexander Nützenadel ◽  
Jochen Streb
Keyword(s):  
Social Sciences ◽  
Rational Expectations ◽  
State Of The Art ◽  
Special Issue ◽  
Historical Events ◽  
Main Hypothesis ◽  
Expectation Formation ◽  
Economic Behaviour ◽  
Complex Process ◽  
Economic Expectations

Abstract The DFG Priority Programme “Experience and Expectation – Historical Foundations of Economic Behaviour” explores how economic actors form their expectations under certain historical conditions. This project’s main hypothesis is that the formation of economic expectations is a complex process that cannot be explained solely by simple concepts such as adaptive or rational expectations, and is shaped by historical events and experience. In this introduction, we review the state of the art of modelling expectation formation in social sciences and history and preview the main findings of the articles published in this special issue.

scholarly journals Asset trading volume in a production economy

2007 ◽  
Vol 39 (2) ◽  
pp. 231-258 ◽  
Author(s):  
Emilio Espino ◽  
Thomas Hintermaier
Keyword(s):  
Trading Volume ◽  
Production Economy ◽  
Asset Trading

scholarly journals LEARNING IN COBWEB EXPERIMENTS

2007 ◽  
Vol 11 (S1) ◽  
pp. 8-33 ◽  
Author(s):  
CARS HOMMES ◽  
JOEP SONNEMANS ◽  
JAN TUINSTRA ◽  
HENK VAN DE VELDEN
Keyword(s):  
Steady State ◽  
Rational Expectations ◽  
Dynamic Models ◽  
Good Description ◽  
Market Model ◽  
Equilibrium Equations ◽  
Market Prices ◽  
Sample Mean ◽  
Expectation Formation ◽  
Price Fluctuations

Different theories of expectation formation and learning usually yield different outcomes for realized market prices in dynamic models. The purpose of this paper is to investigate expectation formation and learning in a controlled experimental environment. Subjects are asked to predict the next period's aggregate price in a dynamic commodity market model with feedback from individual expectations. Subjects have no information about underlying market equilibrium equations, but can learn by observing past price realizations and predictions. We conduct a stable, an unstable, and a strongly unstable treatment. In the stable treatment, rational expectations (RE) yield a good description of observed aggregate price fluctuations: prices remain close to the RE steady state. In the unstable treatments, prices exhibit large fluctuations around the RE steady state. Although the sample mean of realized prices is close to the RE steady state, the amplitude of the price fluctuations as measured by the variance is significantly larger than the amplitude under RE, implying persistent excess volatility. However, agents' forecasts are boundedly rational in the sense that fluctuations in aggregate prices are unpredictable and exhibit no forecastable structure that could easily be exploited.

Singular Spectrum Analysis Based on L1-Norm

2016 ◽  
Vol 15 (01) ◽  
pp. 1650009 ◽  
Author(s):  
Mahdi Kalantari ◽  
Masoud Yarmohammadi ◽  
Hossein Hassani
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Frobenius Norm ◽  
L1 Norm ◽  
Empirical Results ◽  
Singular Spectrum ◽  
Simulated Time ◽  
Simulated Time Series ◽  
Basic Version

In recent years, the singular spectrum analysis (SSA) technique has been further developed and increasingly applied to solve many practical problems. The aim of this research is to introduce a new version of SSA based on [Formula: see text]-norm. The performance of the proposed approach is assessed by applying it to various real and simulated time series, especially with outliers. The results are compared with those obtained using the basic version of SSA which is based on the Frobenius norm or [Formula: see text]-norm. Different criteria are also examined including reconstruction errors and forecasting performances. The theoretical and empirical results confirm that SSA based on [Formula: see text]-norm can provide better reconstruction and forecasts in comparison to basic SSA when faced with time series which are polluted by outliers.

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