1 / 12

The Effect of Contaminated Customer Expectation on Investment Decisions

The Effect of Contaminated Customer Expectation on Investment Decisions. Vplyv kontaminovaných očakávaní zákazníkov na investičné rozhodnutie. EU PHF Košice, Katedra Hospodárskej Informatiky a Matematiky. Ing. Štefan Lyócsa, PhD. Goal of the Paper.

thaddeus-oneill
Download Presentation

The Effect of Contaminated Customer Expectation on Investment Decisions

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Effect of Contaminated Customer Expectation on Investment Decisions Vplyv kontaminovaných očakávaní zákazníkov na investičné rozhodnutie EU PHF Košice, Katedra Hospodárskej Informatiky a Matematiky Ing. Štefan Lyócsa, PhD.

  2. Goal of the Paper To define the robustness of an investment plan againstcustomer data contamination Ing. Štefan Lyócsa, PhD.

  3. Problem Introduction • Data contamination extremevalues unexpected distribution Ing. Štefan Lyócsa, PhD.

  4. Problem Introduction • Assumption 1 • Everyinvestmentproject`s payoff is • linked with customer expectation • Assumption 2 • For higher customer expectations it is • more difficult to overwhelm these • expectations Ing. Štefan Lyócsa, PhD.

  5. Problem Introduction Data about customer expectations: • financial decisions: • to make an investment (or not) • to abandon an investment • to wait with an investment Assessing the data contamination: • quantitatively & qualitatively Ing. Štefan Lyócsa, PhD.

  6. Problem Introduction High level of uncertainty Is this data contaminated? Ing. Štefan Lyócsa, PhD.

  7. Problem Introduction true customers contamination (differentcustomers) GOAL: To define the robustness of an investment plan againstcustomer data contamination Ing. Štefan Lyócsa, PhD.

  8. The Model time f (x) time f (x) performance parameter performance parameter Probably the worst case time f (x) performance parameter

  9. The Model Expectedvalueofaninvestmentplanconditioned to thedecisionmaker`s expected level of achieved performance parameter i.e. How much Am I expecting to surpass customer expectations?

  10. The Model Project`s value Decision maker`s expectations Treshold point Invest if PV>0 Decision to make Don`t invest if PV>0

  11. RCE Robustness to Customer Expectations Robust project Not Robust project Treshold point when customer expectations are not contaminated The worst case scenario, the highest Treshold point, when all the data are contaminated The best case scenario, the lowest Treshold point, when all the data are contaminated

  12. The Effect of Contaminated Customer Expectation on Investment Decisions Vplyv kontaminovaných očakávaní zákazníkov na investičné rozhodnutie EU PHF Košice, Katedra Hospodárskej Informatiky a Matematiky Ing. Štefan Lyócsa, PhD.

More Related