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„Was ist eine Modellbank?“

Eine Modellbank ist laut Prof.  Lackes

„eine computergestützte Sammlung von betriebswirtschaftlichen Modellen, in der Strukturen realer Probleme abgebildet werden. Mithilfe von Verfahren aus der Methodenbank können die Modelle bearbeitet (z.B. Optimierungsrechnungen ausgeführt) werden. I.Allg. liegen eine gemeinsame Datenbasis (Datenbank) und eine einheitliche Benutzeroberfläche vor.“

Prof. Dr. Richard Lackes & Dr. Markus Siepermann (2019): Modellbank.
https://wirtschaftslexikon.gabler.de/definition/modellbank-37663#references

Das Geschäftsmodell der Nubank beruht auf wissenschaftlichen Erkenntnissen. Ziel ist es, die Funktionalität einer Bank in die zugrunde liegenden Kernalgorithmen und das Geschäftslogikmodell zu zerlegen.

Ask-a-Woman.com (AAW) stellt ein interdisziplinäres Team zur Verfügung, um auf der Grundlage wissenschaftlicher Forschung und Entwicklung ein nachhaltiges und zukunftsfähiges Geschäftsmodell für Banken zu formulieren.

Wesley Gray & Tobias Carlisle verfolgen einen interessanten Ansatz, indem sie die grundlegenden Modell-basiert die Algorithmen und Variablendefinitionen der Entscheidungsfindungsprozesse im Bankwesen formulieren. Ihre Einblicke in Value Investing & Quantitative Investing bestimmen die Aktienselektion unter Berücksichtigung bspw. von Verhaltensfehlern. Gray Carlisle (2013) bauen ihr Modell auf vier Schlüsselelementen auf (vgl. Gray / Carlisle 2013, Cover):

  • Vermeiden von Aktien, die dauerhaften Kapitalverlust verursachen
  • Finden von Aktien mit hoher Qualität
  • Finden von unterbewerteten Aktien
  • Vier Signale, die mit Smart Money gesendet werden:
    • Insider
    • Leerverkäufer
    • Aktionärsaktivisten
    • Institutionelle Investmentmanager

GREENBLATT’S MAGIC FORMULA is a quantitative translation of Warren Buffett’s investment strategy based on two main factors:

  • a „wonderful“ company and
  • a fair price.

Buffett’s vision is to prefer the strategy of buying a „wonderful“ company with a fair price rather than buying a fair company with a „wonderful“ price. GREENBALTT’S FORMULA introduced solid definitions for the “wonderful company” and “fair price” factors. (cf. Grey/Carlisle 2013, p.36).

A wonderful Business

As defined by Buffet, high return on equity capital is the accurate indication of a successful business. Greenblatt translated this definition into the following formula (cf. Grey/Carlisle 2013, p.36):


Return on Capital (ROC) =

Earnings before interest and taxes (EBIT) / Capital


Capital =

Net property plant and equipment + Net working capital

with

Net Working Capital =

current assets – current liabilities

The ROC measures how efficiently the business capital employed (excluding cash and interest bearing assets) is managed.

A higher ROC is an indication of more revenue generated against capital business employed. (cf. Grey/Carlisle 2013, p.37).

A Fair Price (Bargain Price)

Greenblatt used the following formula to address the price issue (cf. Grey/Carlisle 2013, p.37):

Earnings Yield= EBIT/ TEV

with

EBIT (Earnings before Interest and Taxes)

TEV (Total Enterprise Value) =

market capitalization + total debt – excess cash + preferred stock + minority interests

with

excess cash = cash + current asset – current liabilities

The introduced formula of Earnings Yield takes capital structure into account and enables an apple-to-apple comparison of stocks with different capital structures and avoids any misleading stock calculations resulting from focusing only on earnings from market capitalization. A stock’s market capitalization does not provide accurate information about stock debts or stock preference. (cf. Grey/Carlisle 2013, p.38).

Reference

Grey, Wesley ; Tobias Carlisle (2013): Quantitative Value. A Practioners guide to automating intelligent investment and eliminating behavioral errors. John Wily & Sons. Hoboken, New Jersey.


Greenblatt tested his algorithm through starting with the largest 3,500 stocks trading on the major US stock exchange. Then a rank from 1 to 3,500 was assigned to each stock based on its ROC, ranking stock with highest ROC as 1. The process is then repeated according to calculated earnings yield factor, ranking the stock with highest EBIT / TEV as rank 1 and stock with lowest EBIT / TEV as rank 3,500. A combined rank for each stock is then generated by summing ROC rank with earnings yield factor rank for each stock. As example: stock of “ROC rank” equals 12 and “earnings yield factor rank” equals 587 will have a combined rank of 599. Stock with lower combined rank considered more attractive than those with higher ranks.  Greenblatt then studied the performance of theoretical purchasing of 30 stocks with lowest combined ranking holding the portfolio for one year before selling and then repeating the process. The study illustrated that the expected return is 30.8% per year of the period from 1988 to 2004. This means that such investment would have turned $10,000 into over $960,000 while the return would be only $71,000 across the same period assuming that the market returns 12.3% annually. (cf. Grey/Carlisle 2013, p.38).

Finding Quality, Academically

The definition of gross profitability to total assets (GPA) is introduced.

GPA equals: (Revenue-Cost of Goods sold) / Total Assets. It is noted that the higher the stock’s GPA, the higher the quality of the stock. As advised by Novy-Marx, the gross profitability is believed to be the cleanest measure of true economic profitability rather than other factors such as earnings or EBIT. As noted by Novy-Marx, the income statements could provide misleading profitability measures sometimes. As example; a firm with both lower production costs and higher sales apparently considered more profitable than other companies. However, this could be not the real case when taking into consideration marketing expenses and sales force commission. (cf. Grey/Carlisle 2013, p.45).

Finding Price, Academically

The definition of book value to market capitalization (BM) is introduced.

BM equals: Book Value / Market Price. BM is preferred to be used rather than price to book value (P/B) because it is more directly comparable with the Magic Formula’s EBIT / TEV. As advised by Eugene Fama and Ken French, BM capitalization is considered as superior matric because it has less variance against time compared to other measures based on income. (cf. Grey/Carlisle 2013, p.48).

Reference

Grey, Wesley ; Tobias Carlisle (2013): Quantitative Value. A Practioners guide to automating intelligent investment and eliminating behavioral errors. John Wily & Sons. Hoboken, New Jersey.

Introduction into the Quantitative Analysis and Evaluation of Enterprises

Huang/Luo/Xu/Zhou (2018) refer to the quantitative analysis and evaluation method of the banking industry, combined with the data characteristics of the financial company’s industry. The scientists carry out the quantitative analysis of the efficiency of 79 Chinese enterprise group finance companies from 2011 to 2016 through the data envelopment analysis (DEA) model and the Malmquist index model (MIM). Their results are as follows: From the static point of view (based on the DEA model), the overall efficiency of the chinese financial companies is low. And the efficiency is less affected by scale efficiency than that of pure technical efficiency. From the dynamic point of view (based on the MIM), the overall efficiency of financial companies has been slightly improved and the efficiency is easily influenced by the change of scale efficiency. (cf. Huang et al. 2018, p.1).

Underlying Research Methodology

Due to the similarity in main business activities, the scholars’ research methods of the banking efficiency is implemented for the research methods of industry efficiency of financial companies entailing two main factors; DEA and stochastic frontier approach (SFA). The SFA is a parametric method concerned with determining the unknown parameters in the frontier cost function. DEA is non-parametric method concerned with evaluating the relative efficiency of entities with the same input factors and the same output with the same function. The evaluated entity becomes the Decision Making Unit. The DEA method can obtain the quantitative index of each Decision Making Unit (DMU) comprehensive efficiency, and then classify each DMU according to this. Compared to SFA, DEA method can handle multiple input and output items simultaneously, does not required sample size, more flexible in dealing with data. (cf. Huang et al. 2018, p.6).

Reference

Huang, Yanni ; Luo, Sumei ; Xu, Guohu ; Zhou, Guanyou (2018): Quantitative Analysis and Evaluation of Enterprise. Group Financial Company Efficiency in China. University Shanghai et al.

https://www.mdpi.com/2071-1050/10/9/3210/pdf

The VRS model is mainly used to study the efficiency evaluation in the case of variable scale income. This paper is dealing with the input-oriented VRS model. The model assumes that there are K DMUs and each DMU can obtain Y output with the use of X input. The input vector of j DMU is Xj, and the output vector is Yj. OS is the output relaxation of DMU and is the input relaxation of DMU. (Constant vector. Through the K linear programming solver, the relative technical efficiency value of each DMU can be obtained v, the value range of v is from 0 to 1. If v is 1, the DMU is just on the frontier, indicating that the technique is effective. Namely, at the current input level, the output of DMU is optimal; if v < 1, it indicates that the technology has not reached the effective level. That is, there is a gap between the actual output and the optimal output of DMU). VRS’s linear programming model is as follows(cf. Huang et al. 2018, p.6 )

The technical efficiency is then decomposed in two parts. The first is pure technical efficiency  induced by management level, the second is scale efficieny induced by enterprise size. The closer the efficiency value is to 1, the more ineffective DMU is. (cf. Huang et al. 2018, p.6).

Malmquist Index Model

Malmquist index is the geometric average of total factor productivity index in t + 1 relative to t period. The model is as follows (cf. Huang et al. 2018, p.7 ).

(xt, yt) is the input and output vector in the period of t, while (xt+1, yt+1) is the vector in t + 1 period; DtO. (xt, yt) is the output distance function of the input-output vector in t period with the technology in the period of t as a parameter, and DtO (xt+1, yt+1) is the output distance function in t + 1 period. The Malmquist index can be transformed into (cf. Huang et al. 2018, p.7).

The left part of formula (3) represents the change of technical efficiency, and the left part represents technological progress. the Malmquist index (M) can be decomposed into technical progress and technical efficiency change index (TECH). The latter can be decomposed into pure technical efficiency change index (PTECH) and scale efficiency change index (SECH). The following formula is generated (cf. Huang et al. 2018, p.7).

M> 1 means that the total factor productivity of DMU shows an increasing trend from the t period  to t + 1 period, while M < 1 means that the total factor productivity of DMU shows a declining trend from the t period to t + 1 period (cf. Huang et al. 2018, p.8)

Reference

Huang, Yanni ; Luo, Sumei ; Xu, Guohu ; Zhou, Guanyou (2018): Quantitative Analysis and Evaluation of Enterprise. Group Financial Company Efficiency in China. University Shanghai et al.

https://www.mdpi.com/2071-1050/10/9/3210/pdf

Literature on the Efficiency of the Banking Industry Using the DEA Method

Based on the data of 18 big international Banks, Tang Qiming and Wen Fu (2011) [34] used the DEA model to conduct empirical research on the efficiency, risk and technological progress of Chinese commercial Banks. The whole factor productivity decreases because of technological change in Chinese and foreign Banks (cf. Huang et al. 2018, p.8).

Literature on the Dynamic Change of Banking Efficiency by Using the MIM

In terms of the dynamic change of efficiency, Zhu Chao (2006) [38] used the Malmquist Productivity index to study the dynamic efficiency changes of 13 commercial banks in China from 2000 to 2004, and found that the total factor productivity of China’s commercial banking industry has dropped slightly (cf. Huang et al. 2018, p.8 ).

Reference

Huang, Yanni ; Luo, Sumei ; Xu, Guohu ; Zhou, Guanyou (2018): Quantitative Analysis and Evaluation of Enterprise. Group Financial Company Efficiency in China. University Shanghai et al.

https://www.mdpi.com/2071-1050/10/9/3210/pdf