29,133 research outputs found
Agent-Based Modeling of the Prediction Markets
We propose a simple agent-based model of the political election prediction market which reflects the intrinsic feature of the prediction market as an information aggregation mechanism. Each agent has a vote, and all agentsâ votes determine the election result. Some of the agents participate in the prediction market. Agents form their beliefs by observing their neighborsâ voting disposition, and trade with these beliefs by following some forms of the zero-intelligence strategy. In this model, the mean price of the market is used as a forecast of the election result. We study the effect of the radius of agentsâ neighborhood and the geographical distribution of information on the prediction accuracy. In addition, we also identify one of the mechanisms which can replicate the favorite-longshot bias, a stylized fact in the prediction market. This model can then provide a framework for further analysis on the prediction market when market participants have more sophisticated trading behavior.Prediction market, Agent-based simulation, Information aggregation mechanism, Prediction accuracy, Zero-intelligence agents, Favorite-longshot bias
Betting and Belief: Prediction Markets and Attribution of Climate Change
Despite much scientific evidence, a large fraction of the American public
doubts that greenhouse gases are causing global warming. We present a
simulation model as a computational test-bed for climate prediction markets.
Traders adapt their beliefs about future temperatures based on the profits of
other traders in their social network. We simulate two alternative climate
futures, in which global temperatures are primarily driven either by carbon
dioxide or by solar irradiance. These represent, respectively, the scientific
consensus and a hypothesis advanced by prominent skeptics. We conduct
sensitivity analyses to determine how a variety of factors describing both the
market and the physical climate may affect traders' beliefs about the cause of
global climate change. Market participation causes most traders to converge
quickly toward believing the "true" climate model, suggesting that a climate
market could be useful for building public consensus.Comment: All code and data for the model is available at
http://johnjnay.com/predMarket/. Forthcoming in Proceedings of the 2016
Winter Simulation Conference. IEEE Pres
The virtues and vices of equilibrium and the future of financial economics
The use of equilibrium models in economics springs from the desire for
parsimonious models of economic phenomena that take human reasoning into
account. This approach has been the cornerstone of modern economic theory. We
explain why this is so, extolling the virtues of equilibrium theory; then we
present a critique and describe why this approach is inherently limited, and
why economics needs to move in new directions if it is to continue to make
progress. We stress that this shouldn't be a question of dogma, but should be
resolved empirically. There are situations where equilibrium models provide
useful predictions and there are situations where they can never provide useful
predictions. There are also many situations where the jury is still out, i.e.,
where so far they fail to provide a good description of the world, but where
proper extensions might change this. Our goal is to convince the skeptics that
equilibrium models can be useful, but also to make traditional economists more
aware of the limitations of equilibrium models. We sketch some alternative
approaches and discuss why they should play an important role in future
research in economics.Comment: 68 pages, one figur
Price dynamics, informational efficiency and wealth distribution in continuous double auction markets
This paper studies the properties of the continuous double auction trading mechanishm using an artificial market populated by heterogeneous computational agents. In particular, we investigate how changes in the population of traders and in market microstructure characteristics affect price dynamics, information dissemination and distribution of wealth across agents. In our computer simulated market only a small fraction of the population observe the risky asset's fundamental value with noise, while the rest of agents try to forecast the asset's price from past transaction data. In contrast to other artificial markets, we assume that the risky asset pays no dividend, so agents cannot learn from past transaction prices and subsequent dividend payments. We find that private information can effectively disseminate in the market unless market regulation prevents informed investors from short selling or borrowing the asset, and these investors do not constitute a critical mass. In such case, not only are markets less efficient informationally, but may even experience crashes and bubbles. Finally, increased informational efficiency has a negative impact on informed agents' trading profits and a positive impact on artificial intelligent agents' profits
Strategies used as spectroscopy of financial markets reveal new stylized facts
We propose a new set of stylized facts quantifying the structure of financial
markets. The key idea is to study the combined structure of both investment
strategies and prices in order to open a qualitatively new level of
understanding of financial and economic markets. We study the detailed order
flow on the Shenzhen Stock Exchange of China for the whole year of 2003. This
enormous dataset allows us to compare (i) a closed national market (A-shares)
with an international market (B-shares), (ii) individuals and institutions and
(iii) real investors to random strategies with respect to timing that share
otherwise all other characteristics. We find that more trading results in
smaller net return due to trading frictions. We unveiled quantitative power
laws with non-trivial exponents, that quantify the deterioration of performance
with frequency and with holding period of the strategies used by investors.
Random strategies are found to perform much better than real ones, both for
winners and losers. Surprising large arbitrage opportunities exist, especially
when using zero-intelligence strategies. This is a diagnostic of possible
inefficiencies of these financial markets.Comment: 13 pages including 5 figures and 1 tabl
The Predictive Power of Zero Intelligence in Financial Markets
Standard models in economics stress the role of intelligent agents who
maximize utility. However, there may be situations where, for some purposes,
constraints imposed by market institutions dominate intelligent agent behavior.
We use data from the London Stock Exchange to test a simple model in which zero
intelligence agents place orders to trade at random. The model treats the
statistical mechanics of order placement, price formation, and the accumulation
of revealed supply and demand within the context of the continuous double
auction, and yields simple laws relating order arrival rates to statistical
properties of the market. We test the validity of these laws in explaining the
cross-sectional variation for eleven stocks. The model explains 96% of the
variance of the bid-ask spread, and 76% of the variance of the price diffusion
rate, with only one free parameter. We also study the market impact function,
describing the response of quoted prices to the arrival of new orders. The
non-dimensional coordinates dictated by the model approximately collapse data
from different stocks onto a single curve. This work is important from a
practical point of view because it demonstrates the existence of simple laws
relating prices to order flows, and in a broader context, because it suggests
that there are circumstances where institutions are more important than
strategic considerations
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