1,754 research outputs found
Introduction to the special issue on neural networks in financial engineering
There are several phases that an emerging field goes through before it reaches maturity, and computational finance is no exception. There is usually a trigger for the birth of the field. In our case, new techniques such as neural networks, significant progress in computing technology, and the need for results that rely on more realistic assumptions inspired new researchers to revisit the traditional problems of finance, problems that have often been tackled by introducing simplifying assumptions in the past. The result has been a wealth of new approaches to these time-honored problems, with significant improvements in many cases
mt5se: An Open Source Framework for Building Autonomous Traders
Autonomous trading robots have been studied in artificial intelligence area
for quite some time. Many AI techniques have been tested for building
autonomous agents able to trade financial assets. These initiatives include
traditional neural networks, fuzzy logic, reinforcement learning but also more
recent approaches like deep neural networks and deep reinforcement learning.
Many developers claim to be successful in creating robots with great
performance when simulating execution with historical price series, so called
backtesting. However, when these robots are used in real markets frequently
they present poor performance in terms of risks and return. In this paper, we
propose an open source framework, called mt5se, that helps the development,
backtesting, live testing and real operation of autonomous traders. We built
and tested several traders using mt5se. The results indicate that it may help
the development of better traders. Furthermore, we discuss the simple
architecture that is used in many studies and propose an alternative multiagent
architecture. Such architecture separates two main concerns for portfolio
manager (PM) : price prediction and capital allocation. More than achieve a
high accuracy, a PM should increase profits when it is right and reduce loss
when it is wrong. Furthermore, price prediction is highly dependent of asset's
nature and history, while capital allocation is dependent only on analyst's
prediction performance and assets' correlation. Finally, we discuss some
promising technologies in the area.Comment: This paper replaces an old version of the framework, called mt5b3,
which is now deprecate
The Evolution of Neural Network-Based Chart Patterns: A Preliminary Study
A neural network-based chart pattern represents adaptive parametric features,
including non-linear transformations, and a template that can be applied in the
feature space. The search of neural network-based chart patterns has been
unexplored despite its potential expressiveness. In this paper, we formulate a
general chart pattern search problem to enable cross-representational
quantitative comparison of various search schemes. We suggest a HyperNEAT
framework applying state-of-the-art deep neural network techniques to find
attractive neural network-based chart patterns; These techniques enable a fast
evaluation and search of robust patterns, as well as bringing a performance
gain. The proposed framework successfully found attractive patterns on the
Korean stock market. We compared newly found patterns with those found by
different search schemes, showing the proposed approach has potential.Comment: 8 pages, In proceedings of Genetic and Evolutionary Computation
Conference (GECCO 2017), Berlin, German
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
What can we do with the Research Institute for Social Complexity Sciences in Indonesia?
The article discussed about the research opportunities in social complexity studies, especially in Indonesia. This issue is connected to the establishment a social research institute in Indonesia, how to establish and maintain it regarding the interdisciplinary research field. However a lot of localities are taken into the consideration to maintain the social complexity research institute, there would always things that can be learnt by any other similar research institute
A simple decision market model
Economic modeling of decision markets has mainly considered the market scoring rule setup. Literature has made reference to the alternative, joint elicitation type decision market, but no in depth analysis of it appears to have been published. This paper develops a simple decision market model of the joint elicitation type, that provides a specific decision market nomenclature on which to base future analysis.A generally accepted prediction market model is modified, by introducing two additional concepts: “proper information market” and “relevant information”. Our work then provides original contributions to the theoretical discourse on information markets, including finding the sufficient and necessary condition for convergence to the best possible prediction. It is shown in our new prediction market model that “all agents express relevant information” is a sufficient and necessary condition for convergence to the direct communication equilibrium in a proper information (prediction) market.Our new prediction market model is used to formulate a simple decision market model of the joint elicitation market type. It is shown that our decision market will select the best decision if a specific selection and payout rule is defined. Importantly, our decision market model does not need to delay payment of any contracts to the observation of the desired outcome. Therefore, when dealing with long-term outcome projects, our decision market does not need to be a long running market. Future work will test for the statistical significance of relevant information (identified as important in our idealized decision market model) in laboratory and real world settings
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