Virtual reality and a fjrms idiosyncratic risk: e-commerce case 1 - - PowerPoint PPT Presentation

Virtual reality and a fjrm’s idiosyncratic risk: e-commerce case1

Anna Loukianova, PhD (in Mathematics) Saint-Petersburg State University Ekaterina Smirnova Institute for Regional Economic Studies RAS

1This research was conducted with the use of library and information resources of the Federal

State Budgetary Educational Institution of Higher Education «Saint-Petersburg State University»

• A. Loukianova, E. Smirnova

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1

Background

2

The Data

3

Model

4

Results

5

Discussion

6

Software applied & References

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Background

Background

Photo by Lucrezia Carnelos on Unsplash

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Background

Virtual business…

Figure 1: width=2cm

”Even today, not all retailers have embraced data fully to the point where they think

• f themselves as data

companies, and it might be why many companies are sufgering” . (S.M. Datar, C.N. Bowler (as cited in (D. Gerdeman, 2018)a)

a https://digital.hbs.edu/data-and-

analysis/on-target-rethinking-the- retail-web#site/

Photo by Zane Lee on Unsplash

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Background

E-commerce market challenges

Study Topic Model/ method (Akter, Wamba, 2016) big data analytics use in e-commerce literature review (Singh et al., 2017) consumer reviews ’helpfulness’ assessment for the other consumers machine learning-based models with the use of textual features (Wang et al., 2016) last-mile delivery a network min-cost fmow model (Steinker et al., 2017) the impact of weather

• n fashion e-commerce

retailer’s operations correlation and regression analyses

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Background

E-commerce sales growth foresight

“Volume growth in our US e-commerce channel in Q4 2019 was lower than our initial forecast.” “… we now have a fully operational e-commerce fulfjllment center for Rugs in the US and are expecting solid growth in 2020-21” — Thomson Reuters Eikon. (2020). [Balta Industries n.v. (2020, March 6).Balta FY 2019 Results [Press release]]. Retrieved March 6, 2020 from https://eikon.thomsonreuters.com/index.html

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Background

What is the study’s aim?

An algorithm development for a company’s sales changes nowcast adjustment for the company’s idiosyncratic risk Is approached through the objectives: defjne the idiosyncratic risk choose an approach to the idiosyncratic risk modelling train the approach on the real data

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Background

What is the idiosyncratic risk The risk of the company’s cash fmows being afgected by the industry factors

The idiosyncratic risk has the following attributes: the concept has emerged from the studies on the boarder of the market risk and the fjrm-specifjc risk management the idiosyncratic risk is traditionally assessed with the use of accounting data

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The Data

The data source

Data used in the study is from Amadeus Bureau van Dijk database. The companies from the industries “Computer programming, consultancy and related activities” and “Computer programming activities” were selected according to NACE Rev. 2 classifjcation (Source: Amadeus, Bureau van Dijk).

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The Data

Companies’ country of headquarters

Belgium Sweden Romania United Kingdom Russian Federation Hungary Netherlands Germany Czech Republic 5 10 15 20

Percentage Country

Countries of Companies in the Sample

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The Data

Defjning the optimal number of clusters

According to the silhouette method, the optimal number of clusters inside the sample equals 2 clusters.

0.00 0.25 0.50 0.75 1 2 3 4 5 6 7 8 9 10 Number of clusters k Average silhouette width Silhouette method

Optimal number of clusters

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The Data

Cluster analysis

The two clusters in the sample evidence that software development market is low competitive: the ‘leaders’ have higher market share and tend to obtain higher book value: cluster 1 ‘all the others’ have similar book value, but tend to have minor market share: cluster 2

0.0 2.5 5.0 7.5 10.0 2 4 6 8

Sales growth in 2017 Total Assets in 2017

cluster 1 2 Cluster plot

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The Data

The sales growth values’ distribution

Sales growth-1st cluster Sales growth-2nd cluster Kolmogorov-Smirnov logistic logistic Cramer-von Mises logistic logistic Anderson-Darling logistic gamma AIC logistic logistic BIC logistic logistic The distribution analysis summarising provided the following conslusion: sales growth in bowth clusters follow primarily logistic distribution software development clusters are not homogeneous This conclusion may indicate, that the real situation is rather more complicated than only a two-cluster model.

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The Data

Imitation modelling

The data was simulated using the logistic distribution for both clusters with the parameters: Sales growth, cluster 1 Sales growth, cluster 2 location 0.60 0.46 scale 0.07 0.00 There were simulated 998 data sets of both clusters’ artifjcial sales growth

• values. Each artifjcial dataset contained 10 thousand observations for each

cluster.

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Model

Model

Photo by Jared Murray on Unsplash

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Model

The econometric models applied

The baseline model The “naive” nowcast, which implies that the average imaginary company’s sales growth would be distributed as in the past (base) period with the same parameters. The idiosyncratic risk-based model The simulation from pair copula model (Aas, Czado, Frigessi, & Bakken, 2009). Copula model developed by A. Sklar (as cited in (Genest, Ghoudi, & Rivest, 1995)) has the following defjnition: (𝑣1, 𝑣2, …, 𝑣𝑞) = 𝑄𝑠(𝐺1(𝑌1) ≤ 𝑣1, …, 𝐺𝑞(𝑌𝑞) ≤ 𝑣𝑞)), where (𝑌1, …, 𝑌𝑞) – a random vector with continuous marginals 𝐺𝑗(𝑦𝑗) = 𝑄𝑠(𝑌𝑗 ≤ 𝑦𝑗); C – its associated copula or dependence function, defjned for all (𝑣1, …, 𝑣𝑞) ∈ [0, 1]𝑞.

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Model

Idiosyncratic risk’s indicator

Kendall’s tau (Kendall, 1938) was applied as the idiosyncratic risk’s indicator: 𝜐 = 2𝑇 𝑜(𝑜 − 1), where 𝑇-the sum of the ranks; 𝑜 - the number of observations.

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Model

Copula model selection algorithm

Akaike Information Criterion (H. Akaike, as cited in (Nagler et al. 2019)) was used as the base for copula model selection: 𝐵𝐽𝐷 ∶= −2

𝑂

∑

𝑗=1

ln [𝑑(𝑣𝑗,1, 𝑣𝑗,2|𝜄)] + 2𝑙, where 𝑣1, 𝑣2− the two cluster companies’ sales growth; 𝑗 - an observation index; 𝑂 - the number of observations; 𝜄 - the copula’s parameter (parameters); 𝑙- the quantity of the copula’s parameters.

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Model

Copula model selection

Rotated BB6 90 degrees Rotated BB1 270 degrees Frank 10 25 40 Percentage Model

Simulation Modelling for a Model Selection

Copula model selection results indicate Frank copula as the most preferrable by the algorithm on the base of AIC criterion.

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Model

Sales growth simulation

The sales growth simulated from Frank copula for two notional companies, representing two clusters. Frank copula was proposed by the AIC-based algorithm in most cases.

0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 1 2 3 4 5 6

Company 1 Company 2 Pr

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Model

The Models’ Backtesting

Three copula models proposed by AIC-based algorithm were backtested with the use of 2017-2018 sales growth data.

0.00 0.25 0.50 0.75 1.00 factadj naive BB1(270) BB6(90) Frank Weighted

Model Sales changes adjusted

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Model

The Tukey test

−0.6 −0.4 −0.2 0.0 0.2 0.4 factadj−Weighted factadj−Frank Weighted−Frank factadj−BB6(90) Weighted−BB6(90) Frank−BB6(90) factadj−BB1(270) Weighted−BB1(270) Frank−BB1(270) BB6(90)−BB1(270) factadj−naive Weighted−naive Frank−naive BB6(90)−naive BB1(270)−naive

95% family−wise confidence level

Differences in mean levels of variable

The Tukey test indicated statistically signifjcant difgerences for the most of the models with the adjusted fact values of the sales growth and with each other. The

• nly exception is

Joe-Gumbel copula rotated by 90 degrees, which difgerence with the adjusted fact value is statistically insignifjcant.

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Results

Results

Photo by Shahadat Rahman on Unsplash

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Results

The sales growth’s idiosyncratic factors

copula model parameter 1 parameter 2 Kendall’s tau Frank

• 12.58
• - -
• 0.72

Rotated BB1 270 degrees

• 4.64
• 1.36
• 0.78

Rotated BB6 90 degrees

• 4.22
• 1.64
• 0.77

Kendall’s tau indicates strong degree of the rank negative association between the two clusters’ sales growth simulated. As Kendall’s tau in the study is used as the idiosyncratic risk indicator, the conclusion could be made, that on the e-commerce software development market the companies experience the ‘positive’ infmuence of the idiosyncratic risk.

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Results

A software companies’ sales growth nowcasting algorithm

Sales changes adjustment Sales changes distribution analysis Sales changes simulation Copula model selection Sales changes simulation from the copula model

The algorithm of the sales growth nowcast proposed difgers from the naive forecast model in two last steps. The idiosyncratic risk adjusted algorithm produced more accurate result during the process of backtesting.

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Results

E-commerce business model’s connecting points

Advertising Logistics Packaging

The analysis indicated the following e-commerce business critical points: marketing and advertising packaging logistics and supply chain management

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Discussion

Discussion

Current research proposes the idiosyncratic risk concept’s amplifjcation: the idiosyncratic risk’s indicator measurement with Kendall’s tau to add the idiosyncratic risk’s attribute “positive interconnection with an industry competitiveness level” To summarise, the idiosyncratic risk could be itself a proxy for an industry’s level of competition.

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Software applied & References

Software applied: The following R packages were used in the study:

ggpubr factoextra bookdown knitr reshape forcats webshot kableExtra RColorBrewer stringr png stats VineCopula purrr rsvg graphics CDVine readr svglite grDevices fjtdistrplus tidyr magrittr utils npsurv tibble DiagrammeRsvg datasets lsei tidyverse DiagrammeR methods survival zoo magick base MASS ggplot2 dplyr ggpubr

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Software applied & References

References I

Aas, Kjersti, Claudia Czado, Arnoldo Frigessi, and Henrik Bakken. 2009. “Pair-copula constructions of multiple dependence.” Insurance: Mathematics and Economics 44 (2): 182–98. https://doi.org/10.1016/j.insmatheco.2007.02.001. Akter, Shahriar, and Samuel Fosso Wamba. 2016. “Big data analytics in E-commerce: a systematic review and agenda for future research.” Electronic Markets 26 (2): 173–94. https://doi.org/10.1007/s12525-016-0219-0. Bache, Stefan Milton, and Hadley Wickham. 2014. Magrittr: A Forward-Pipe Operator for R. https://CRAN.R-project.org/package=magrittr. Balta Industries n.v. 2020. “Balta FY 2019 Results.” Thomson Reuters

• Eikon. https://eikon.thomsonreuters.com/index.html.
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Software applied & References

References II

Brechmann, Eike Christian, and Ulf Schepsmeier. 2013. “Modeling Dependence with c- and d-Vine Copulas: The R Package CDVine.” Journal of Statistical Software 52 (3): 1–27. http://www.jstatsoft.org/v52/i03/. Chang, Winston. 2019. Webshot: Take Screenshots of Web Pages. https://CRAN.R-project.org/package=webshot. Delignette-Muller, Marie Laure, and Christophe Dutang. 2015. “fjtdistrplus: An R Package for Fitting Distributions.” Journal of Statistical Software 64 (4): 1–34. http://www.jstatsoft.org/v64/i04/. Delignette-Muller, Marie-Laure, Christophe Dutang, and Aurelie

• Siberchicot. 2019. Fitdistrplus: Help to Fit of a Parametric

Distribution to Non-Censored or Censored Data. https://CRAN.R-project.org/package=fitdistrplus.

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Software applied & References

References III

Genest, C, K Ghoudi, and L.-P. Rivest. 1995. “A semiparametric estimation procedure of dependence parameters in multivariate families

• f distributions.” Biometrica 82 (3): 543–52.

Gerdeman, D. 2018. “On Target: rethinking the retail website | Harvard Business School Digital Initiative.” https://digital.hbs.edu/data-and- analysis/on-target-rethinking-the-retail-website/%7B/#%7Dsite/. Henry, Lionel, and Hadley Wickham. 2019. Purrr: Functional Programming Tools. https://CRAN.R-project.org/package=purrr. Iannone, Richard. 2016. DiagrammeRsvg: Export Diagrammer Graphviz Graphs as Svg. https://CRAN.R-project.org/package=DiagrammeRsvg. ———. 2020. DiagrammeR: Graph/Network Visualization. https://CRAN.R-project.org/package=DiagrammeR.

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Software applied & References

References IV

Kassambara, Alboukadel. 2020. Ggpubr: ’Ggplot2’ Based Publication Ready Plots. https://CRAN.R-project.org/package=ggpubr. Kassambara, Alboukadel, and Fabian Mundt. 2019. Factoextra: Extract and Visualize the Results of Multivariate Data Analyses. https://CRAN.R-project.org/package=factoextra. Kendall, M. G. 1938. “A NEW MEASURE OF RANK CORRELATION.” Biometrika 30 (1-2): 81–93. https://doi.org/10.1093/biomet/30.1-2.81. Muller, Kirill, and Hadley Wickham. 2019. Tibble: Simple Data Frames. https://CRAN.R-project.org/package=tibble.

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Software applied & References

References V

Nagler, Thomas, Ulf Schepsmeier, Jakob Stoeber, Eike Christian Brechmann, Benedikt Graeler, and Tobias Erhardt. 2019. VineCopula: Statistical Inference of Vine Copulas. https://CRAN.R-project.org/package=VineCopula. Neuwirth, Erich. 2014. RColorBrewer: ColorBrewer Palettes. https://CRAN.R-project.org/package=RColorBrewer. Ooms, Jeroen. 2018. Rsvg: Render Svg Images into Pdf, Png, Postscript,

• r Bitmap Arrays. https://CRAN.R-project.org/package=rsvg.

———. 2020. Magick: Advanced Graphics and Image-Processing in R. https://CRAN.R-project.org/package=magick. R Core Team. 2020. R: A Language and Environment for Statistical

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References VI

Ripley, Brian. 2019. MASS: Support Functions and Datasets for Venables and Ripley’s Mass. https://CRAN.R-project.org/package=MASS. Schepsmeier, Ulf, and Eike Christian Brechmann. 2015. CDVine: Statistical Inference of c- and d-Vine Copulas. https://CRAN.R-project.org/package=CDVine. Singh, Jyoti Prakash, Seda Irani, Nripendra P. Rana, Yogesh K. Dwivedi, Sunil Saumya, and Pradeep Kumar Roy. 2017. “Predicting the ‘helpfulness’ of online consumer reviews.” Journal of Business Research 70 (January): 346–55. https://doi.org/10.1016/j.jbusres.2016.08.008. Steinker, Sebastian, Kai Hoberg, and Ulrich W. Thonemann. 2017. “The Value of Weather Information for E-Commerce Operations.” Production and Operations Management 26 (10): 1854–74. https://doi.org/10.1111/poms.12721.

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References VII

Terry M. Therneau, and Patricia M. Grambsch. 2000. Modeling Survival Data: Extending the Cox Model. New York: Springer. Therneau, Terry M. 2020. Survival: Survival Analysis. https://CRAN.R-project.org/package=survival. Urbanek, Simon. 2013. Png: Read and Write Png Images. https://CRAN.R-project.org/package=png. Venables, W. N., and B. D. Ripley. 2002. Modern Applied Statistics with

• S. Fourth. New York: Springer.

http://www.stats.ox.ac.uk/pub/MASS4. Wang, Yong. 2017. Npsurv: Nonparametric Survival Analysis. https://CRAN.R-project.org/package=npsurv.

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References VIII

Wang, Yong, Charles L. Lawson, and Richard J. Hanson. 2017. Lsei: Solving Least Squares or Quadratic Programming Problems Under Equality/Inequality Constraints. https://CRAN.R-project.org/package=lsei. Wang, Yuan, Dongxiang Zhang, Qing Liu, Fumin Shen, and Loo Hay Lee.

• 2016. “Towards enhancing the last-mile delivery: An efgective

crowd-tasking model with scalable solutions.” Transportation Research Part E: Logistics and Transportation Review 93 (September): 279–93. https://doi.org/10.1016/j.tre.2016.06.002. Wickham, Hadley. 2007. “Reshaping Data with the Reshape Package.” Journal of Statistical Software 21 (12). http://www.jstatsoft.org/v21/i12/paper. ———. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.

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References IX

———. 2018. Reshape: Flexibly Reshape Data. https://CRAN.R-project.org/package=reshape. ———. 2019a. Stringr: Simple, Consistent Wrappers for Common String

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———. 2019b. Tidyverse: Easily Install and Load the ’Tidyverse’. https://CRAN.R-project.org/package=tidyverse. ———. 2020. Forcats: Tools for Working with Categorical Variables (Factors). https://CRAN.R-project.org/package=forcats. Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al.

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References X

Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, and Dewey

• Dunnington. 2020. Ggplot2: Create Elegant Data Visualisations Using

the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2. Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2020. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr. Wickham, Hadley, and Lionel Henry. 2020. Tidyr: Tidy Messy Data. https://CRAN.R-project.org/package=tidyr. Wickham, Hadley, Lionel Henry, Thomas Lin Pedersen, T Jake Luciani, Matthieu Decorde, and Vaudor Lise. 2020. Svglite: An ’Svg’ Graphics

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References XI

Wickham, Hadley, Jim Hester, and Romain Francois. 2018. Readr: Read Rectangular Text Data. https://CRAN.R-project.org/package=readr. Xie, Yihui. 2014. “Knitr: A Comprehensive Tool for Reproducible Research in R.” In Implementing Reproducible Computational Research, edited by Victoria Stodden, Friedrich Leisch, and Roger D. Peng. Chapman; Hall/CRC. http://www.crcpress.com/product/isbn/9781466561595. ———. 2015. Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC. https://yihui.org/knitr/. ———. 2016. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown. ———. 2020a. Bookdown: Authoring Books and Technical Documents with R Markdown. https://CRAN.R-project.org/package=bookdown.

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References XII

———. 2020b. Knitr: A General-Purpose Package for Dynamic Report Generation in R. https://CRAN.R-project.org/package=knitr. Zeileis, Achim, and Gabor Grothendieck. 2005. “Zoo: S3 Infrastructure for Regular and Irregular Time Series.” Journal of Statistical Software 14 (6): 1–27. https://doi.org/10.18637/jss.v014.i06. Zeileis, Achim, Gabor Grothendieck, and Jefgrey A. Ryan. 2020. Zoo: S3 Infrastructure for Regular and Irregular Time Series (Z’s Ordered Observations). https://CRAN.R-project.org/package=zoo. Zhu, Hao. 2019. KableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.

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