Ralph Grothmann

Multi-agent modeling of multiple FX-markets by neural networks

We introduce an explanatory multi-agent approach of multiple FX-market modeling based on neural networks. We consider the explicit and implicit dynamics of the market price. This paper extends previous work of modeling a single FX-market to an integrated approach, which allows one to treat several FX-markets simultaneously. Our approach is based on feedforward neural networks. Neural networks allow the fitting of high-dimensional nonlinear models, which is often utilized in econometrics. Merging the economic theory of multi-agents with neural networks, our model concerns semantic specifications instead of being limited to ad hoc functional relationships. As an advantage, our multi-agent model allows one to fit the behavior of real-world financial data. We exemplify the USD/DEM and YEN/DEM FX-Market simultaneously. Fitting real-world data, our approach is superior to more conventional forecasting techniques.

Modeling Dynamical Systems by Error Correction Neural Networks

We introduce a new time delay recurrent neural network called ECNN, which includes the last model error as an additional input. Hence, the learning can interpret the models misspecification as an external shock which can be used to guide the model dynamics afterwards.

Forecasting with Recurrent Neural Networks: 12 Tricks

Recurrent neural networks (RNNs) are typically considered as relatively simple architectures, which come along with complicated learning algorithms. This paper has a different view: We start from the fact that RNNs can model any high dimensional, nonlinear dynamical system. Rather than focusing on learning algorithms, we concentrate on the design of network architectures. Unfolding in time is a well-known example of this modeling philosophy. Here a temporal algorithm is transferred into an architectural framework such that the learning can be performed by an extension of standard error backpropagation.

Semantic Traffic-Aware Routing Using the LarKC Platform

The popularity of location-based services and automotive navigation systems calls for a new generation of intelligent solutions to support users in mobility. This article presents a traffic-aware semantic routing service for mobile users based on the Large Knowledge Collider (LarKC) Semantic Web pluggable platform. It proposes a technique for integrating conceptual query answering with statistical learning and operations research algorithms. The presented prototype of a traffic-aware semantic routing service works efficiently with large, heterogeneous information sources and delivers value-added services to mobile users.

Multi-Agent Market Modeling Of Foreign Exchange Rates

A market mechanism is basically driven by a superposition of decisions of many agents optimizing their profit. The macroeconomic price dynamic is a consequence of the cumulated excess demand/supply created on this micro level. The behavior analysis of a small number of agents is well understood through the game theory. In case of a large number of agents one may use the limiting case that an individual agent does not have an influence on the market, which allows the aggregation of agents by statistic methods. In contrast to this restriction, we can omit the assumption of an atomic market structure, if we model the market through a multi-agent approach.The contribution of the mathematical theory of neural networks to the market price formation is mostly seen on the econometric side: neural networks allow the fitting of high dimensional nonlinear dynamic models. Furthermore, in our opinion, there is a close relationship between economics and the modeling ability of neural networks because a neuron can be interpreted as a simple model of decision making. With this in mind, a neural network models the interaction of many decisions and, hence, can be interpreted as the price formation mechanism of a market.

Dynamical consistent recurrent neural networks

Recurrent neural networks aretypically consid- eredasrelatively simple architectures, whichcomealong with complicated learning algorithms. Mostresearchers focus onthe improvement ofthesealgorithms. Ourapproach isdifferent: Rather thanfocusing onlearning andoptimization algorithms, weconcentrate onthedesign ofthenetwork architecture. Aswewill show, manydifficulties inthemodeling ofdynam- ical systems canbesolved witha pre-design ofthenetwork architecture. We willfocus onlarge networks withthetask ofmodeling complete highdimensional systems (e.g. financial markets) instead ofsmallsetsoftimeseries. Standard neural networks tendtooverfit like anyother statistical learning system. Wewill introduce anewrecurrent neural network architecture in whichoverfitting andtheassociated loss ofgeneralization abilities isnota majorproblem. We willenhance thesenetworks by dynamical consistency.

Market Modeling, Forecasting and Risk Analysis with Historical Consistent Neural Networks

Business management requires precise forecasts in order to enhance the quality of planning throughout the value chain. Furthermore, the uncertainty in forecasting has to be taken into account.

Optimal asset allocation for a large number of investment opportunities

This paper introduces a stock-picking algorithm that can be used to perform an optimal asset allocation for a large number of investment opportunities. The allocation scheme is based upon the idea of causal risk. Instead of referring to the volatility of the assets time series, the stock-picking algorithm determines the risk exposure of the portfolio by concerning the non-forecastability of the assets. The underlying expected return forecasts are based on time-delay recurrent error correction neural networks, which utilize the last model error as an auxiliary input to evaluate their own misspecification. We demonstrate the profitability of our stock-picking approach by constructing portfolios from 68 different assets of the German stock market. It turns out that our approach is superior to a preset benchmark portfolio. Copyright

Forecasting of clustered time series with recurrent neural networks and a fuzzy clustering scheme

Fuzzy c-neural network models (FCNNM) combine clustering techniques with advanced neural networks for time series modeling in order to make predictions for a possibly large set of time series using only a small number of models. Given a set of time series, FCNNM finds a partition matrix that quantifies to which degree each time series is associated with each prediction model, as well as the parameters of the neural network models for each cluster. FCNNM allows to automatically identify groups of time series with similar dynamics. This results in higher data efficiency, being of particular interest in cases of poor data availability. We illustrate the application of FCNNM to cash withdrawal series as part of an effective cash management.

Forecasting Market Prices with Causal-Retro-Causal Neural Networks

Forecasting of market prices is a basis of rational decision making [Zim94]. Especially recurrent neural networks (RNN) offer a framework for the computation of a complete temporal development. Our applications include short- (20 days) and long-term (52 weeks) forecast models. We describe neural networks (NN) along a correspondence principle, representing them in form of equations, architectures and embedded local algorithms.