ترغب بنشر مسار تعليمي؟ اضغط هنا

Inferring the Composition of a Trader Population in a Financial Market

106   0   0.0 ( 0 )
 نشر من قبل Nachi Gupta
 تاريخ النشر 2007
والبحث باللغة English




اسأل ChatGPT حول البحث

We discuss a method for predicting financial movements and finding pockets of predictability in the price-series, which is built around inferring the heterogeneity of trading strategies in a multi-agent trader population. This work explores extensions to our previous framework (arXiv:physics/0506134). Here we allow for more intelligent agents possessing a richer strategy set, and we no longer constrain the estimate for the heterogeneity of the agents to a probability space. We also introduce a scheme which allows the incorporation of models with a wide variety of agent types, and discuss a mechanism for the removal of bias from relevant parameters.



قيم البحث

اقرأ أيضاً

Financial options are contracts that specify the right to buy or sell an underlying asset at a strike price by an expiration date. Standard exchanges offer options of predetermined strike values and trade options of different strikes independently, e ven for those written on the same underlying asset. Such independent market design can introduce arbitrage opportunities and lead to the thin market problem. The paper first proposes a mechanism that consolidates and matches orders on standard options related to the same underlying asset, while providing agents the flexibility to specify any custom strike value. The mechanism generalizes the classic double auction, runs in time polynomial to the number of orders, and poses no risk to the exchange, regardless of the value of the underlying asset at expiration. Empirical analysis on real-market options data shows that the mechanism can find new matches for options of different strike prices and reduce bid-ask spreads. Extending standard options written on a single asset, we propose and define a new derivative instrument -- combinatorial financial options that offer contract holders the right to buy or sell any linear combination of multiple underlying assets. We generalize our single-asset mechanism to match options written on different combinations of assets, and prove that optimal clearing of combinatorial financial options is coNP-hard. To facilitate market operations, we propose an algorithm that finds the exact optimal match through iterative constraint generation, and evaluate its performance on synthetically generated combinatorial options markets of different scales. As option prices reveal the markets collective belief of an underlying assets future value, a combinatorial options market enables the expression of aggregate belief about future correlations among assets.
We perform a scaling analysis on NYSE daily returns. We show that volatility correlations are power-laws on a time range from one day to one year and, more important, that they exhibit a multiscale behaviour.
This paper summarises a successful application of functional programming within a commercial environment. We report on experience at Accentures Financial Services Solution Centre in London with simulating an object-oriented financial system in order to assist analysis and design. The work was part of a large IT project for an international investment bank and provides a pragmatic case study.
This paper presents a new financial market simulator that may be used as a tool in both industry and academia for research in market microstructure. It allows multiple automated traders and/or researchers to simultaneously connect to an exchange-like environment, where they are able to asynchronously trade several financial assets at the same time. In its current iteration, this order-driven market implements the basic rules of U.S. equity markets, supporting both market and limit orders, and executing them in a first-in-first-out fashion. We overview the system architecture and we present possible use cases. We demonstrate how a set of automated agents is capable of producing a price process with characteristics similar to the statistics of real price from financial markets. Finally, we detail a market stress scenario and we draw, what we believe to be, interesting conclusions about crash events.
The three-state agent-based 2D model of financial markets in the version proposed by Giulia Iori in 2002 has been herein extended. We have introduced the increase of herding behaviour by modelling the altering trust of an agent in his nearest neighbo urs. The trust increases if the neighbour has foreseen the price change correctly and the trust decreases in the opposite case. Our version only slightly increases the number of parameters present in the Iori model. This version well reproduces the main stylized facts observed on financial markets. That is, it reproduces log-returns clustering, fat-tail log-returns distribution and power-law decay in time of the volatility autocorrelation function.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا