---
_id: '37312'
abstract:
- lang: eng
text: Optimal decision making requires appropriate evaluation of advice. Recent
literature reports that algorithm aversion reduces the effectiveness of predictive
algorithms. However, it remains unclear how people recover from bad advice given
by an otherwise good advisor. Previous work has focused on algorithm aversion
at a single time point. We extend this work by examining successive decisions
in a time series forecasting task using an online between-subjects experiment
(N = 87). Our empirical results do not confirm algorithm aversion immediately
after bad advice. The estimated effect suggests an increasing algorithm appreciation
over time. Our work extends the current knowledge on algorithm aversion with insights
into how weight on advice is adjusted over consecutive tasks. Since most forecasting
tasks are not one-off decisions, this also has implications for practitioners.
author:
- first_name: Dirk
full_name: Leffrang, Dirk
id: '51271'
last_name: Leffrang
orcid: 0000-0001-9004-2391
- first_name: Kevin
full_name: Bösch, Kevin
last_name: Bösch
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Leffrang D, Bösch K, Müller O. Do People Recover from Algorithm Aversion?
An Experimental Study of Algorithm Aversion over Time. In: Hawaii International
Conference on System Sciences. ; 2023.'
apa: Leffrang, D., Bösch, K., & Müller, O. (2023). Do People Recover from Algorithm
Aversion? An Experimental Study of Algorithm Aversion over Time. Hawaii International
Conference on System Sciences. Hawaii International Conference on System Sciences.
bibtex: '@inproceedings{Leffrang_Bösch_Müller_2023, title={Do People Recover from
Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time}, booktitle={Hawaii
International Conference on System Sciences}, author={Leffrang, Dirk and Bösch,
Kevin and Müller, Oliver}, year={2023} }'
chicago: Leffrang, Dirk, Kevin Bösch, and Oliver Müller. “Do People Recover from
Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time.” In
Hawaii International Conference on System Sciences, 2023.
ieee: D. Leffrang, K. Bösch, and O. Müller, “Do People Recover from Algorithm Aversion?
An Experimental Study of Algorithm Aversion over Time,” presented at the Hawaii
International Conference on System Sciences, 2023.
mla: Leffrang, Dirk, et al. “Do People Recover from Algorithm Aversion? An Experimental
Study of Algorithm Aversion over Time.” Hawaii International Conference on
System Sciences, 2023.
short: 'D. Leffrang, K. Bösch, O. Müller, in: Hawaii International Conference on
System Sciences, 2023.'
conference:
name: Hawaii International Conference on System Sciences
date_created: 2023-01-18T10:53:51Z
date_updated: 2024-01-10T09:52:59Z
department:
- _id: '196'
keyword:
- Algorithm aversion
- Time series
- Decision making
- Advice taking
- Forecasting
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://scholarspace.manoa.hawaii.edu/items/62b58ddc-895c-48c3-8194-522a1758a26f
oa: '1'
publication: Hawaii International Conference on System Sciences
status: public
title: Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm
Aversion over Time
type: conference
user_id: '51271'
year: '2023'
...
---
_id: '29317'
abstract:
- lang: eng
text: In this paper new semiparametric GARCH models with long memory are in- troduced.
The estimation of the nonparametric scale function is carried out by an adapted
version of the SEMIFAR algorithm (Beran et al., 2002). Recurring on the revised
recommendations by the Basel Committee to measure market risk in the banks' trading
books (Basel Committee on Banking Supervision, 2013), the semi- parametric GARCH
models are applied to obtain rolling one-step ahead forecasts for the Value at
Risk (VaR) and Expected Shortfall (ES) for market risk assets. In addition, standard
regulatory traffic light tests (Basel Committee on Banking Supervision, 1996)
and a newly introduced traffic light test for the ES are carried out for all models.
The practical relevance of our proposal is demonstrated by a comparative study.
Our results indicate that semiparametric long memory GARCH models are an attractive
alternative to their conventional, parametric counterparts.
author:
- first_name: Sebastian
full_name: Letmathe, Sebastian
id: '23991'
last_name: Letmathe
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: André
full_name: Uhde, André
id: '36049'
last_name: Uhde
orcid: https://orcid.org/0000-0002-8058-8857
citation:
ama: Letmathe S, Feng Y, Uhde A. Semiparametric GARCH models with long memory applied
to Value at Risk and Expected Shortfall. Journal of Risk. doi:10.21314/JOR.2022.044
apa: Letmathe, S., Feng, Y., & Uhde, A. (n.d.). Semiparametric GARCH models
with long memory applied to Value at Risk and Expected Shortfall. Journal of
Risk. https://doi.org/10.21314/JOR.2022.044
bibtex: '@article{Letmathe_Feng_Uhde, title={Semiparametric GARCH models with long
memory applied to Value at Risk and Expected Shortfall}, DOI={10.21314/JOR.2022.044},
journal={Journal of Risk}, author={Letmathe, Sebastian and Feng, Yuanhua and Uhde,
André} }'
chicago: Letmathe, Sebastian, Yuanhua Feng, and André Uhde. “Semiparametric GARCH
Models with Long Memory Applied to Value at Risk and Expected Shortfall.” Journal
of Risk, n.d. https://doi.org/10.21314/JOR.2022.044.
ieee: 'S. Letmathe, Y. Feng, and A. Uhde, “Semiparametric GARCH models with long
memory applied to Value at Risk and Expected Shortfall,” Journal of Risk,
doi: 10.21314/JOR.2022.044.'
mla: Letmathe, Sebastian, et al. “Semiparametric GARCH Models with Long Memory Applied
to Value at Risk and Expected Shortfall.” Journal of Risk, doi:10.21314/JOR.2022.044.
short: S. Letmathe, Y. Feng, A. Uhde, Journal of Risk (n.d.).
date_created: 2022-01-13T11:23:02Z
date_updated: 2024-04-17T13:34:54Z
department:
- _id: '186'
- _id: '19'
doi: 10.21314/JOR.2022.044
jel:
- C14
- C51
- C52
- G17
- G32
keyword:
- Semiparametric
- long memory
- GARCH models
- forecasting
- Value at Risk
- Expected Shortfall
- traffic light test
- Basel Committee on Banking Supervision
language:
- iso: eng
publication: Journal of Risk
publication_status: inpress
status: public
title: Semiparametric GARCH models with long memory applied to Value at Risk and Expected
Shortfall
type: journal_article
user_id: '36049'
year: '2022'
...
---
_id: '17810'
abstract:
- lang: eng
text: In all fields, the significance of a reliable and accurate predictive model
is almost unquantifiable. With deep domain knowledge, models derived from first
principles typically outperforms other models in terms of reliability and accuracy.
When it may become a cumbersome or an unachievable task to build or validate such
models of complex (non-linear) systems, machine learning techniques are employed
to build predictive models. However, the accuracy of such techniques is not only
dependent on the hyper-parameters of the chosen algorithm, but also on the amount
and quality of data. This paper investigates the application of classical time
series forecasting approaches for the reliable prognostics of technical systems,
where black box machine learning techniques might not successfully be employed
given insufficient amount of data and where first principles models are infeasible
due to lack of domain specific data. Forecasting by analogy, forecasting by analytical
function fitting, an exponential smoothing forecasting method and the long short-term
memory (LSTM) are evaluated and compared against the ground truth data. As a case
study, the methods are applied to predict future crack lengths of riveted aluminium
plates under cyclic loading. The performance of the predictive models is evaluated
based on error metrics leading to a proposal of when to apply which forecasting
approach.
author:
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Aimiyekagbon OK, Bender A, Sextro W. Evaluation of time series forecasting
approaches for the reliable crack length prediction of riveted aluminium plates
given insufficient data. In: PHM Society European Conference. Vol 5. ;
2020.'
apa: Aimiyekagbon, O. K., Bender, A., & Sextro, W. (2020). Evaluation of time
series forecasting approaches for the reliable crack length prediction of riveted
aluminium plates given insufficient data. PHM Society European Conference,
5(1).
bibtex: '@inproceedings{Aimiyekagbon_Bender_Sextro_2020, title={Evaluation of time
series forecasting approaches for the reliable crack length prediction of riveted
aluminium plates given insufficient data}, volume={5}, number={1}, booktitle={PHM
Society European Conference}, author={Aimiyekagbon, Osarenren Kennedy and Bender,
Amelie and Sextro, Walter}, year={2020} }'
chicago: Aimiyekagbon, Osarenren Kennedy, Amelie Bender, and Walter Sextro. “Evaluation
of Time Series Forecasting Approaches for the Reliable Crack Length Prediction
of Riveted Aluminium Plates given Insufficient Data.” In PHM Society European
Conference, Vol. 5, 2020.
ieee: O. K. Aimiyekagbon, A. Bender, and W. Sextro, “Evaluation of time series forecasting
approaches for the reliable crack length prediction of riveted aluminium plates
given insufficient data,” in PHM Society European Conference, 2020, vol.
5, no. 1.
mla: Aimiyekagbon, Osarenren Kennedy, et al. “Evaluation of Time Series Forecasting
Approaches for the Reliable Crack Length Prediction of Riveted Aluminium Plates
given Insufficient Data.” PHM Society European Conference, vol. 5, no.
1, 2020.
short: 'O.K. Aimiyekagbon, A. Bender, W. Sextro, in: PHM Society European Conference,
2020.'
date_created: 2020-08-11T13:32:40Z
date_updated: 2023-09-22T09:13:16Z
department:
- _id: '151'
intvolume: ' 5'
issue: '1'
keyword:
- PHM 2019
- crack propagation
- forecasting
- unevenly spaced time series
- step ahead prediction
- short time series
language:
- iso: eng
publication: PHM Society European Conference
quality_controlled: '1'
status: public
title: Evaluation of time series forecasting approaches for the reliable crack length
prediction of riveted aluminium plates given insufficient data
type: conference
user_id: '9557'
volume: 5
year: '2020'
...
---
_id: '20869'
abstract:
- lang: eng
text: This study provides evidence of significant biases in multi-year management
forecasts by analyzing a proprietary dataset on venture-backed start-ups in Germany.
We find that revenues and expenses are highly overestimated in each of the investigated
one- to five-year-ahead planning periods. Furthermore, entrepreneurs underestimate
one-year-ahead profit forecasts but clearly overestimate their profit forecasts
for all longer-term forecast horizons. Additional analyses reveal that teams with
prior management experience issue even more overestimated forecasts and misrepresent
their forward-looking information. In contrast, greater asset verifiability and
corporate lead investors are associated with lower levels of forecast errors.
All key results hold if bias is either measured by traditionally comparing forecasts
to ex-post realizations or by using a cross-sectional projection approach based
on historical accounting data developed by prior research.
author:
- first_name: Sönke
full_name: Sievers, Sönke
id: '46447'
last_name: Sievers
- first_name: Christopher Frederik
full_name: Mokwa, Christopher Frederik
last_name: Mokwa
citation:
ama: 'Sievers S, Mokwa CF. Biases in Management Forecasts of Venture-Backed Start-Ups:
Evidence from Internal Due Diligence Documents of VC Investors.; 2012. doi:10.2139/ssrn.1714399'
apa: 'Sievers, S., & Mokwa, C. F. (2012). Biases in Management Forecasts
of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of
VC Investors. https://doi.org/10.2139/ssrn.1714399'
bibtex: '@book{Sievers_Mokwa_2012, title={Biases in Management Forecasts of Venture-Backed
Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors}, DOI={10.2139/ssrn.1714399}, author={Sievers,
Sönke and Mokwa, Christopher Frederik}, year={2012} }'
chicago: 'Sievers, Sönke, and Christopher Frederik Mokwa. Biases in Management
Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents
of VC Investors, 2012. https://doi.org/10.2139/ssrn.1714399.'
ieee: 'S. Sievers and C. F. Mokwa, Biases in Management Forecasts of Venture-Backed
Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors.
2012.'
mla: 'Sievers, Sönke, and Christopher Frederik Mokwa. Biases in Management Forecasts
of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of
VC Investors. 2012, doi:10.2139/ssrn.1714399.'
short: 'S. Sievers, C.F. Mokwa, Biases in Management Forecasts of Venture-Backed
Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors, 2012.'
date_created: 2021-01-05T11:52:51Z
date_updated: 2022-01-06T06:54:41Z
doi: 10.2139/ssrn.1714399
extern: '1'
jel:
- G24
- G32
- M13
- M41
keyword:
- Management forecasts
- Forecasting biases
- Venture-backed start-ups
- Projection methods
language:
- iso: eng
main_file_link:
- url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1714399
page: '42'
publication_status: published
status: public
title: 'Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from
Internal Due Diligence Documents of VC Investors'
type: working_paper
user_id: '46447'
year: '2012'
...
---
_id: '5198'
abstract:
- lang: eng
text: 'This study provides evidence of significant biases in multi-year management
forecasts by analyzing a proprietary dataset on venture-backed start-ups in Germany.
We find that revenues and expenses are highly overestimated in each of the investigated
one- to five-year-ahead planning periods. Furthermore, entrepreneurs underestimate
one-year-ahead profit forecasts but clearly overestimate their profit forecasts
for all longer-term forecast horizons. Additional analyses reveal that teams with
prior management experience issue even more overestimated forecasts and misrepresent
their forward-looking information. In contrast, greater asset verifiability and
corporate lead investors are associated with lower levels of forecast errors.
All key results hold if bias is either measured by traditionally comparing forecasts
to ex-post realizations or by using a cross-sectional projection approach based
on historical accounting data developed by prior research. '
author:
- first_name: Christopher
full_name: Mokwa, Christopher
last_name: Mokwa
- first_name: Sönke
full_name: Sievers, Sönke
last_name: Sievers
citation:
ama: 'Mokwa C, Sievers S. Biases in Management Forecasts of Venture-Backed Start-Ups:
Evidence from Internal Due Diligence Documents of VC Investors. SSRN Electronic
Journal. 2012. doi:10.2139/ssrn.1714399'
apa: 'Mokwa, C., & Sievers, S. (2012). Biases in Management Forecasts of Venture-Backed
Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors. SSRN
Electronic Journal. https://doi.org/10.2139/ssrn.1714399'
bibtex: '@article{Mokwa_Sievers_2012, title={Biases in Management Forecasts of Venture-Backed
Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors}, DOI={10.2139/ssrn.1714399}, journal={SSRN
Electronic Journal}, author={Mokwa, Christopher and Sievers, Sönke}, year={2012}
}'
chicago: 'Mokwa, Christopher, and Sönke Sievers. “Biases in Management Forecasts
of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of
VC Investors.” SSRN Electronic Journal, 2012. https://doi.org/10.2139/ssrn.1714399.'
ieee: 'C. Mokwa and S. Sievers, “Biases in Management Forecasts of Venture-Backed
Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors,” SSRN
Electronic Journal, 2012.'
mla: 'Mokwa, Christopher, and Sönke Sievers. “Biases in Management Forecasts of
Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC
Investors.” SSRN Electronic Journal, 2012, doi:10.2139/ssrn.1714399.'
short: C. Mokwa, S. Sievers, SSRN Electronic Journal (2012).
date_created: 2018-10-31T12:16:45Z
date_updated: 2022-01-06T07:01:43Z
department:
- _id: '275'
doi: 10.2139/ssrn.1714399
jel:
- G24
- G32
- M13
- M41
keyword:
- Management forecasts
- Forecasting biases
- Venture-backed start-ups
- Projection methods
language:
- iso: eng
publication: SSRN Electronic Journal
publication_status: published
status: public
title: 'Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from
Internal Due Diligence Documents of VC Investors'
type: journal_article
user_id: '64756'
year: '2012'
...