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A Review of Artificially Intelligent Applications in the Financial Domain Read chapter Read first chapter

2016 | OriginalPaper | Chapter

9. Portfolio Selection as a Multi-period Choice Problem Under Uncertainty: An Interaction-Based Approach

Author : Matjaz Steinbacher

Published in: Artificial Intelligence in Financial Markets

Publisher: Palgrave Macmillan UK

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Abstract

The chapter is a simulation-based survey that applies an interaction-based approach to examine portfolio selection as a multi-period choice problem under uncertainty. The model includes cognitive agents, who are modelled through the suspiciousness parameter. The runs have demonstrated that risk is a decisive factor in agents’ portfolio selections. Although the selections are done upon realized returns, we find the lowest-risk mean-variance portfolios attainable and desired. Agents tend to synchronize their selections and are capable of distinguishing the winners from the losers. Selections of suspicious agents are slightly more dispersed than that of unsuspicious, even though both groups mostly identify the same winners. This conclusion is supported in both bull and bear markets, except that riskier portfolios are chosen in the former. The results also show that agents do not prefer highly diversified portfolios. Finally, consistency tests have demonstrated that agents are the most consistent on the most desired or the least desired choices, with the unsuspicious agents being much more consistent than the suspicious agents.

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previous chapter Artificial Intelligence for Islamic Sukuk Rating Predictions
next chapter Handling Model Risk in Portfolio Selection Using Multi-Objective Genetic Algorithm
Footnotes
1
Information diffuses over the network by the word-of-mouth. Communications-based models in economics often employ the word-of-mouth communication as a source for idea diffusion [23‐30].
 
2
By definition, a mutual consent is required to establish an undirected link. Therefore, if agent i is connected with agent j, then agent j is also connected with agent i. This is not the case in directed networks in which one of the two agents is not aware of the link. An extensive review of social networks and network models is given in Wasserman and Faust [55], Boccaletti et al. [56], Goyal [57] and Jackson [58].
 
3
The diameter of a network is the largest distance between any two nodes on the network.
 
4
By referring to games, we do not have the usual game theoretical framework in mind but computer-based experiments. Alternatively, the games on networks could also be referred to as the activities on networks.
 
5
Strictly speaking, reinforcement learning is not learning as we know, but imitation. It has been extensively used in the ‘cheap-talk’ communication games of social learning to describe the result of some types of ‘emulation’ by economic agents.
 
6
The rationale for including the suspiciousness parameter is manifold. Selten [61] argues that errors may arise between the decision to take a certain action and the action itself. The level of suspiciousness may also relate to a (dis)trust, suspiciousness of the data, temporary mood, or some other agent-specific factors, even luck, etc. In addition, the switch to the alternative with small potential benefits may not be preferred. The agents’ unwillingness to do a switch is even more likely if transaction costs are present. Agents may also fail to have persistent preferences and rather use heuristics before acting, which may again lead to ‘errors’ [62]. Thaler et al. [63] argue that short evaluation periods force investors to make poorer decisions. Contributions of these factors are very indeterminate, but cannot be neglected. The variable thus includes all the factors for which an investor might behave differently than expected. From a technical perspective, it may also be considered a noise.
 
7
In order to make the proofs more tractable, we use A i and A j to designate agents’ wealth; W t (A i ) and W t (A j ), respectively.
 
8
100 basis points = 1 percentage point.
 
9
The initial portfolio is randomly assigned to each liquidity agent in the same way as it is to all the rest.
 
10
This is a simplification. In reality, agents can put a different proportion of assets into their portfolios, thus the number of different portfolios approaches infinity.
 
11
Effective as of June 8, 2009, Citigroup (NYSE: C) was delisted from the index and was replaced by its sister insurance company Travelers (NYSE: TRV).
 
12
Illiquidity has been found to have substantial effects on stock markets [67‐69].
 
13
This assumption is limited by the effects such costs might have on the trading policy. Constantinides [70] and Lo et al. [71] argue that agents accommodate large transaction costs by reducing the frequency and volume of trade. Moreover, transaction costs also direct the choice towards riskless securities and determine the number of securities in a portfolio.
 
14
The choice for a market portfolio would designate a naïve 1/n allocation [72].
 
15
Fragmented pseudo-code is provided in the Appendix.
 
16
It might be the case that even though the series repeated itself, different agents might hold the same portfolios.
 
17
For this reason, we also refer to row variability in the sequel to be explicit that we measure the consistency within different time units, while not also between time units. This is measured with MC.
 
18
In a baseline case T = 264, in a bull market T = 209 and T = 120 in a bear market.
 
19
Median is used instead of the mean to reduce the influence of extreme values.
 
20
The correlation coefficient has typically been used as an informal measure of the goodness of fit of a distribution to a power law [76]. For mathematical derivations of a scale-free distribution see Mitzenmacher [77] and Newman [78].
 
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Metadata
Title
Portfolio Selection as a Multi-period Choice Problem Under Uncertainty: An Interaction-Based Approach
Author
Matjaz Steinbacher
Copyright Year
2016
Book
Artificial Intelligence in Financial Markets

Print ISBN: 978-1-137-48879-4

Electronic ISBN: 978-1-137-48880-0

Copyright Year: 2016

DOI
https://doi.org/10.1057/978-1-137-48880-0_9