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Empirical Analysis of an Online Algorithm for Multiple Trading Problems
Esther Mohr1)
Günter Schmidt1+2)
1) Saarland University
2) University of Liechtenstein
em@itm.uni-sb.de
gs@itm.uni-sb.de
Abstract: If we trade in financial markets we are interested in buying at low and selling at
high prices. We suggest an active trading algorithm which tries to solve this type of problem.
The algorithm is based on reservation prices. The effectiveness of the algorithm is analyzed
from a worst case and an average case point of view. We want to give an answer to the
questions if the suggested active trading algorithm shows a superior behaviour to buy-and-
hold policies. We also calculate the average competitive performance of our algorithm using
simulation on historical data.
Keywords: online algorithms, average case analysis, stock trading, trading rules, performance
analysis, competitive analysis, trading problem, empirical analysis
1 Introduction
Many major stock markets are electronic market places where trading is be carried out
automatically. Trading policies which have the potential to operate without human interaction
are of great importance in electronic stock markets. Very often such policies are based on data
from technical analysis [She02, RL99, RS03]. Many researchers have also studied trading
policies from the perspective of artificial intelligence, software agents and neural networks
[CE08, FRY04, SR05].
In order to carry out trading policies automatically they have to be converted into
trading algorithms. Before a trading algorithm is applied one might be interested in its
performance. The performance analysis of trading algorithms can basically be carried by three
different approaches. One is Bayesian analysis where a given probability distribution for asset
prices is a basic assumption. Another one is assuming uncertainty about asset prices and
analysing the trading algorithm under worst case outcomes; this approach is called
competitive analysis. The third on