Algorithmic determination of the maximum possible earnings for investment strategies

Authors:
Olivier Brandouy;Philippe Mathieu;Iryna Veryzhenko
Affiliations:
GREGOR, EA 2474, Sorbonne Graduate Business School (IAE de Paris), 21 rue Broca, F-75005, Paris, France;CNRS-LIFL (UMR 8122), Computer Science Department, University of Lille 1, Cité Scientifique, F-59655, Villeneuve d'Ascq Cedex, France;ESSCA School of Management, LUNAM University, 55 quai Alphonse Le Gallo, F-92513, Boulogne-Billancourt Cedex, France
Venue:
Decision Support Systems
Year:
2013

Citing 12
Cited 0

Predictable variation and profitable trading of US equities: a trading simulation using neural networks

Computers and Operations Research - Neural networks in business
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
Application of neural networks to an emerging financial market: forecasting and trading the Taiwan stock index

Computers and Operations Research - Special issue: Emerging economics
Generating trading rules on the stock markets with genetic programming

Computers and Operations Research
Regression neural network for error correction in foreign exchange forecasting and trading

Computers and Operations Research
A computational implementation of stock charting: abrupt volume increase as signal for movement in New York stock exchange composite index

Decision Support Systems - Special issue: Data mining for financial decision making
Neural network techniques for financial performance prediction: integrating fundamental and technical analysis

Decision Support Systems - Special issue: Data mining for financial decision making
Applying rough sets to market timing decisions

Decision Support Systems - Special issue: Data mining for financial decision making
Forecasting stock market movement direction with support vector machine

Computers and Operations Research
A decision support system for strategic asset allocation

Decision Support Systems
Algorithms

Algorithms
A new Chance-Variance optimization criterion for portfolio selection in uncertain decision systems

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a new method for determining the upper bound of any investment strategy's maximum profit, applied in a given time window [0,T]. This upper bound is defined once all the prices are known at time T and therefore represents the ex-post maximum efficiency of any investment strategy determined during the relevant time interval. This approach allows us to gauge in absolute terms those behaviors defined through atomic ''buy'' and ''sell'' actions, and can be extended to more complex strategies. We show that, even in the ex-post framework, establishing this upper bound when transaction costs are implemented is extremely complex. We first describe this problem using a linear programming framework. Thereafter, we propose to embed this question in a graph theory framework and to show that determining the best investment behavior is equivalent to identifying an optimal path in an oriented, weighted, bipartite network or a weighted, directed, acyclic graph. We illustrate this method using real world data and introduce a new theory about absolute optimal behavior in the financial world.