My experience as an intern of the Wealth Management Department in Hwabao Securities

October 30, 2022 by Wenxuan HU

My experience as an intern of the Wealth Management Department in Hwabao Securities

In this article, Wenxuan HU (ESSEC Business School, Global BBA, 2021-2023) shares her internship experience as an intern in the Wealth Management Department in Hwabao Securities in China.

The Company

Hwabao Securities is a securities company of China Baowu Steel Group, one of the world’s top 500 companies. With the strong support of shareholders, Hwabao Securities adheres to the business purpose of “creating value for customers, opportunities for employees, returns for shareholders and benefits for society” and continues to provide professional, high-quality and personalized comprehensive financial services for investors.

Logo of Hwabao Securities
Logo Hwabao Securities
Source: Hwabao Securities.

Wealth Management is an important business unit of Hwabao Securities. From 2019 to 2021, approximately 80% of Hwabao Securities’ revenue is derived from wealth management business and securities proprietary business.

Headquarters of Hwabao Securities
Headquarters Hwabao Securities
Source: Hwabao Securities.

My Internship

My missions

I worked as an intern in the Wealth Management Department of Hwabao Securities. I was mainly responsible for supporting the department staff in business analysis and compliance management.

I coordinated and analyzed the company’s 2021 interim brokerage business operation. In practice, I used the Vlookup function and pivot table, etc. to count the market share of the sales department, business revenue, commission breakdown, etc., and created data visualization charts to report to the company president and other managers in the interim meeting. In addition, I calculated the performance of the marketing staff. Based on their performance I adjusted their rank.

Moreover, I was responsible for investor eligibility management (a review system that requires institutions to Know Your Customer( KYC) and identify the customer’s risk tolerance) and branch compliance training and participated in developing compliance test questions for branch heads. In addition, I created a PowerPoint presentation for the training of new regulations of positive repo risk control and compliance management work report to assist the compliance officer in personnel training. I also assisted the compliance officer in completing the company’s risk compliance management work, preparing and integrating multi-departmental internal control compliance checklists, and formulating branch compliance cross-check work plans. I wrote an article about typical case of compliance to improve the construction work of the company’s compliance system. The article was appreciated by the department manager and the staff of the Shanghai Stock Exchange (SSE).

Required skills and knowledge

The Wealth Management Department is an important department of a financial institution, directly managing all branches and sales staff, and an important line of defense to ensure business compliance. Working in the Wealth Management Department requires less computer skills and mathematical abilities but requires financial knowledge and legal background. Interns are required to keep an eye on changes in market regulations to assess the risk of financial transactions between the company and its clients. Interns also need to have strong communication skills and be willing to give advice to colleagues in different departments. In addition, departmental staff should also have a high level of ethics and self-discipline and adhere to the legal bottom line.

What I have learnt

My internship at Hwabao Securities gave me a good understanding of the composition of the entire financial institution and the operation of the financial market. This experience allowed me to master many financial terms and trading processes and raised my awareness of compliance and the different types of risks related to investments. The knowledge I learned in class was also applied during the internship, such as money and credit, macroeconomics, credit management, bank management, risk management, compliance management, and law.

While writing the article about the revision of SSE’s investor eligibility management regulations, I also found areas where compliance management could be improved. My article was called “Dispute over account opening for the visually impaired – enhancing investor satisfaction with personalized services”. In the article, the investor’s application for online account opening at the company was rejected due to the investor’s visual impairment and the company’s lack of corresponding hardware facilities. In order to effectively protect the rights and interests of vulnerable groups, the company developed a personalized off-site account opening business process applicable to the visually impaired investor. The company took several measures to take care of the physical conditions of special groups while achieving compliance. For example, the company let the investor open the account offline with professional staff, rather than online. To ensure compliance, the company informed the investor of the investment risks in detail and made a recording.

The article was adopted and commended by SSE as China’s management methods for special group investors are not yet perfect. For special groups, under the condition of meeting the requirements of regulatory laws and regulations, providing better and more humane services in a targeted manner can better protect the interests of investors. Actively fulfilling social responsibility can reflect the social responsibility of enterprises.

Three key financial concepts

Here are three useful financial concepts I learned in Wealth Management Department.

Anti-money-laundering

Money laundering is the process by which monetary gains are cleansed from their illegal origins. The money laundering process has three stages and often incorporates an important international dimension: placement, layering, and integration. Financial institutions are often used, wittingly or unwittingly, by criminals in this cleansing process.

For securities firms, there are usually a variety of measures in place to fight money laundering:

Establish various anti-money laundering systems.
Establish internal working mechanism, staff with professional personnel and improve operation process.
Improve business systems to meet the needs of AML work and ensure accurate and efficient information collection.
Identify customers and reasonably classify and adjust customer risk levels. Strengthen identification and supervision for high-risk customers or accounts.
Manage customer information, including identity information and transaction records.
Establish abnormal transaction detection indicators and models to identify large or suspicious transactions.
Conduct anti-money laundering assessments to provide system-wide risk prevention capabilities.
Organize anti-money laundering training and strengthen training for personnel in key positions to effectively communicate the latest regulatory requirements.

The Eligibility Management of Investors

The Eligibility Management of Investors is an obligation that sell-side institutions should fulfill for investors. (A sell-side institution is a party that sells its own products or services. Unlike the physical industry, sell-side institution in the financial industry sell virtual products, such as industry research reports, liquidity services, financing services, etc.) The investor eligibility management system was established by the China Securities Regulatory Commission (CSRC) and the China Financial Futures Exchange (CFFEX), taking into account the characteristics of the stock index futures market. The system requires financial institutions to understand their customers, objectively and comprehensively measure their risk appetite and risk-taking ability, and adhere to the principle of “providing the right products to the right investors”. The eligibility obligation was first introduced in the U.S. to regulate misconduct by securities firms. In recent years, the content of the appropriateness obligation has been gradually enriched and improved, and has played an increasingly important role in the trials of Chinese courts at all levels.

The eligibility management of investors has become a direct legal basis for investors to seek remedies in financial disputes. The purpose of investor suitability management is to ensure that customers can make investment decisions and bear the resulting benefits and risks on the basis of a full understanding of the risks of the relevant financial products. In essence, the investor eligibility management system is the investor protection system.

Repurchase Agreement and Reverse Repurchase Agreement

In my internship, I was responsible for compliance training for staff. I produced PowerPoints on the new regulations for Repurchase Agreement risk control.
Repurchase Agreement (Repo) is a transaction in which a party pledges a certain size of bond to raise funds and promises to repurchase the pledged bond at a later date. It is also one of the open market instruments frequently used by The People’s Bank Of China (PBC), which can achieve the effect of repatriating funds from the market by using positive repo operations. Compared with PBC bills, Repurchase Agreement will reduce operating costs, while locking in funds more effectively and reducing liquidity.

Reverse Repurchase Agreement is a transaction in which the PBC purchases marketable securities from a primary dealer and agrees to sell the marketable securities to the primary dealer on a specific date in the future. Reverse Repurchase Agreement is an operation in which the PBC puts liquidity into the market, and the expiration of Reverse Repurchase Agreement is an operation in which the PBC takes back liquidity from the market, called Repurchase Agreement. Simply put, a Reverse Repurchase Agreement is a transaction in which the investor actively lends funds and obtains a bond pledge is called a Reverse Repurchase Agreement transaction, at which time the investor is the financier who accepts the bond pledge and lends the funds.

Useful resources

Hwabao Securities

Shanghai Stock Exchange

The People’s Bank Of China (PBC)

China Securities Regulatory Commission (CSRC)

China Financial Futures Exchange (CFFEX)

About the author

The article was written in October 2022 by Wenxuan HU (ESSEC Business School, Global BBA, 2021-2023).

My internship experience as an industry research assistant in Industrial Securities

October 30, 2022 by Wenxuan HU

My internship experience as an industry research assistant in Industrial Securities

In this article, Wenxuan HU (ESSEC Business School, Global BBA, 2021-2023) shares her internship experience as an industry research assistant in Industrial Securities which is a securities company in China.

The Company

Industrial Securities is a integrated, innovative, conglomerate and international Chinese securities company approved by the China Securities Regulatory Commission. In May 2022, Industrial Securities was listed in the Forbes 2022 Global 2000 list of companies. As of the end of June 2022, the Group had total assets of 238.2 billion RMB and over 10,000 employees at home and abroad. The company has developed into a securities and financial holding group covering securities, funds, futures, asset management, equity investment, alternative investment, industrial finance, offshore business, regional equity market and other professional fields. The company’s core businesses rank among the top in the industry.

Logo of Industrial Securities
Logo Industrial Securities
Source: Industrial Securities.

Industrial Securities adheres to the “industry-oriented” driving force, creates a unique financial ecological alliance, forming a complete ecological chain that runs through the life cycle of enterprises and industries.

Headquarters of Industrial Securities
Headquarters Industrial Securities
Source: Industrial Securities.

My Internship

My missions

During my internship, I work in the Home Appliance Group, which belongs to Industrial Securities. Home Appliance Group is composed of research analyst firm focusing in the home appliance industry.

I was mainly responsible for writing company reports and medium-term strategy reports about firms in the home appliance industry. I was also exposed to how to value companies with Excel and various databases.

I used Wind, Euromonitor and other databases, combined with expert interviews, to analyze the development dynamics of companies like Bear which produces small appliances like blenders, kettles, air fryer, etc. I wrote reports that compared the company with its peers from the perspective of products, channels and marketing, and I found out the competitive advantages of the company. I created over 20 charts in the report to demonstrate its high cost-performance ratio, multiple segmentation categories, and mature online channels. I also tracked the interim reports of leading companies in mature foreign markets, such as Electrolux, in terms of revenue and profit by region, to compare and analyze with major domestic home appliance brands.

I also studied the characteristics of the long-tail small home appliance market where it is located. Long-tail small home appliances refer to home appliances with small demand and sales scale (contrary to large home appliances like dish washers and laundry machines).

In addition, I independently collected information to analyze the market size, financial indicators, and the company’s product channels of XGIMI, a leading company in the Chinese projector industry. I assisted the analyst to create a 55-page roadshow PowerPoint.

In the process of writing the report, I not only honed my analytical and presentation skills and learned to be graphic in the report, but also learned about the market situation of China’s home appliance industry. For example, I found that the two waves of the Covid pandemic in 2020 and 2022 showed different dynamics in terms of impact on the growth of demand for home appliances in China. The first wave of the pandemic increased the home cooking scenario; young consumers purchased basic, just-needed small appliances. The first wave of the pandemic led to an outbreak of live e-commerce, with online sales becoming the main channel for home appliance consumption, which drove rapid growth in demand for small appliances (like blenders and nutri-pots). The second wave of the pandemic has hampered logistics in some areas, and after 2020, the category of basic small home appliances was gradually saturated, and the demand was not fully released. The pandemic pushed consumers to form healthy living concepts and home cooking habits, demand shifted from basic appliances to advanced appliances. This pushed the industry product structure. So, I really felt the impact of the pandemic (a Black Swan) on the market in practice. For investors and companies, any major event means both challenges and opportunities.

Required skills and knowledge

When starting out, interns usually need to learn to write meeting minutes quickly and use Excel to do some simple calculations and data summaries. This is more of a test of the student’s information gathering skills and basic computer skills.

As we become familiar with the work, we need to apply our financial knowledge, understand industry dynamics, develop market insight and learn to express our opinions clearly. This includes being able to read company financial reports, fully analyze company operations, and make predictions about the future.

What I have learnt

During my internship I worked with valuation models. Valuation modeling has always been an important section in company research or industry research reports. For investors, financial projections provide a visual representation of the underlying company’s operations and future state of development. Also, students looking for jobs in investment banks, equity research analyst firms and even consulting firms, need solid modeling skills.
The steps of valuation modeling financial projections are as follows:

Forecast operating income and split revenue (different products and business, domestic vs foreign, etc.). Then forecast costs and expenses to complete the income statement forecast.
Forecast the balance sheet and complete the forecast for all accounts except for the reserved matching items (money funds and financing gap).
Prepare the cash flow statement, and calculate the monetary funds and financing gap on this basis.
Fill in the vacant monetary funds and financing gap in the balance sheet, and match the balance sheet.

In fact, the complete financial modeling requires a lot of financial accounting knowledge and requires to be careful and conscientious, otherwise, the data can easily be wrong. On specific financial items, analysts need to mobilize financial knowledge to fill in the numbers. For example, depreciation and amortization are calculated with the fixed assets and intangible assets in the balance sheet forecast. Then we can go back to the income statement to fill in the two vacant cells. In the internship, I found that financial modeling is closely related to the financial management and financial statement courses we studied in university, so we still need to firmly grasp the basics of finance before seeking employment.

Key financial concepts

The discounted cash flow (DCF) model is a standard valuation method, which aims to calculate the value of a company based on the projected future cash flows of the company discounted to the present at the discount rate (weighted-average cost of capital or WACC).

Basic Formulas

Entreprise value formula

Where EV means the enterprise value, FCF free cash flows, WACC the weighted-average cost of capital, and TV the terminal value.

Free Cash Flow

Free cash flow

We can predict future turnover, expenses, tax rates, etc. by extrapolating the past or imagining the future of the company). Although this part of the formula is relatively complex, usually in practice the analysts will use the Excel formula or Visual Basic for Applications (VBA) to collate the various subjects, greatly simplifying the steps of financial modeling.

WACC

wacc formula

Where D represents the market value of the company’s debt, E the market value of equity capital, and t the income tax rate.
The Cost of equity can be calculated by CAPM model:

cost equity formula

Where:

Risk free rate is the rate of return that can be obtained by investing money in an investment object without any risk.
β, also known as the beta coefficient, is a risk index that measures the price volatility of an individual stock or stock fund relative to the overall stock market.
Market risk premium, also known as equity risk premium or market risk return, refers to the difference between the return on a market portfolio and the risk-free rate of return. It measures the rate at which investors are paid for taking risk.
The Risk free rate can be the yield of the country’s national debt and β can be queried through the Wind database, such as the last three years of β.
Market risk premium is sometimes a forecast value in practice.
Cost of debt is the after-tax cost of debt. It is necessary to multiply the pre-tax cost by (1-t).

Terminal Value Calculation

To calculate the terminal value, we can use the Gordon Growth method to estimate the value based on its growth rate into perpetuity.

The Gordon Model, also known as the constant-growth model, is a special case of the dividend discount model, which reveals the relationship between the stock price, the expected base period dividend, the discount rate and the fixed growth rate of the dividend. The model has three assumptions:

1. The dividend payment is permanent in time;
2. The dividend growth rate is a constant;
3. The discount rate in the model is greater than the dividend growth rate.

The terminal value is extrapolated from the Gordon model:

cost equity formula

Where g is perpetual growth rate which means that the company has perpetual growth rate and return on invested capital. The perpetual growth model assumes stable and sustainable growth in the long term. In practice, g is usually a conservative figure.

Useful resources

Industrial Securities

Wind Database

China Securities Regulatory Commission

About the author

The article was written in October 2022 by Wenxuan HU (ESSEC Business School, Global BBA, 2021-2023).

Time Series Forecasting: Applications and Artificial Neural Networks

October 21, 2022 by Micha FISCHER

Time Series Forecasting: Applications and Artificial Neural Networks

In this article, Micha FISHER (University of Mannheim, MSc. Management, 2021-2023) discusses on the applications of time series forecasting and the use of artificial neural networks for this purpose.

This article will offer a short introduction to the different applications of time-series forecasting and forecasting in general, will then describe the theoretical aspects of simple artificial neural networks and finish with a practical example on how to implement a forecast based on these networks.

Overview

The American economist and diplomat John Kenneth Galbraith once said: “The function of economic forecasting is to make astrology look respectable”. Certainly, the failure of mainstream economics to predict several financial crises is testimony to this quote.

However, on a smaller scale, forecast can be very useful in different applications and this article describes several use cases for the forecasting of time series data and a special method to perform such analyses.

Different Applications of Time Series Forecasting

Different methods of forecasting are used in various settings. Central banks and economic research institutes use complex forecasting methods with a vast amount of input factors to forecast GDP growth and other macroeconomic figures. Technical analysts forecast the evolution of asset prices based on historical patterns to make trading gains. Businesses forecast the demand for their products by including seasonal trends (e.g., utility providers) and economic developments.

This article will deal with the latter two applications of forecasting that is focused on the analysis of historical patterns and seasonality. Using different input factors to come up with a prediction, like for example a multivariate regression analysis does, can be a successful way of making prediction. However, it also inherently includes the problem of determining those input factors as well in the first place.

The practical methods described in this article circumvent this problem by exclusively using historical time series data (e.g., past sales per month, historical electricity demand per hour of the day, etc.). This makes the use of those methods easy and both methods can be used to predict helpful input parameters of DCF models for example.

Artificial neural networks

Artificial Intelligence (AI) is a frequently used buzzword in the advertising of products and services. However, the concept of artificial intelligence is going back to the 1940s, when mathematicians McCulloch and Pitts first presented a mathematical model that was based on the neural activity of the human brain.

Before delving into the practical aspects of an exemplary simple artificial neural network, it is important to understand the terminology. These networks are one – although not the only one – of the key aspects of “Machine Learning”. Machine Learning itself is in turn a subtopic of Artificial Intelligence, which itself employs different tools besides Machine Learning.

Figure 1. Neural network.

Source: internet.

To give a simple example of an artificial neural network we will focus on a so-called feedforward neural network. Those networks deliver and transform information from the left side to the right side of the schematic picture below without using any loops. This process is called Forward Propagation. Historic time series data is simply put into the first layer of neurons. The actual transformation of the data is done by the individual neurons of the network. Some neurons simply put different weights on the input parameter. Neurons of the hidden layers then use several non-linear functions to manipulate the data given to them by the initial layer. Eventually the manipulated data is consolidated in the output layer.

This sounds all very random and indeed it is. At the beginning, a neural network is totally unaware of its actual best solution and the first computations are done via random weights and functions. But after a first result is compiled, the algorithm compares the result with the actual true value. Of course, this is not possible for values that lye in the future. Therefore, the algorithm divides the historic time series into a section used for training (data that is put into the network) and into a section for testing (data that can be compared to the transformed training data). The deviation between compiled value and true value is then minimized via the process of so-called backpropagation. Weights and functions are changed iteratively until an optimal solution is reached and the network it sufficiently trained. This optimal solution then servers to compute the “real” future values.

This description is a very theoretical presentation of such an artificial neural network and the question arises, how to handle such complex algorithms. Therefore, the last part of this article focuses on the implementation of such a forecasting tool. One very useful tool for statistical forecasting via artificial neural networks is the programming language R and the well-known development environment RStudio. RStudio enables the user to directly download user-created packages, to import historical data from Excel sheets and to export graphical presentations of forecasts.

A very easy first approach is the nnetar function of R. This function can be simply used to analyze existing time series data and it will automatically define an artificial neural network (number of layers, neurons etc.) and train it. Eventually it also allows to use the trained model to forecast future data points.

The chart below is a result of this function used on simulated sales data between 2015 and 2021 to forecast the sales of 2022. In this case the nnetar function used one layer of hidden neurons and correctly recognized a 12-month seasonality in the data.

Figure 2. Simulated sales data.

Source: internet.

Why should I be interested in this post?

Artificial neural networks are a powerful tool to forecast time-series data. By using development environments like RStudio, even users without a sophisticated background in data science can make apply those networks to forecast data they might need for other purposes like DCF models, logistical planning, or internal financial modelling.

Useful resources

RStudio Official Website

Rob Hyndman and George Athanasopoulos Forecasting: Principles and Practice

About the author

The article was written in October 2022 by Micha FISHER (University of Mannheim, MSc. Management, 2021-2023).

My job in the Investors Relations department at SAP

October 21, 2022 by Micha FISCHER

My job in the Investors Relations department at SAP

In this article, Micha FISHER (University of Mannheim, MSc. Management, 2021-2023) shares his experience as an employee in the Investors Relations department at SAP, Europe’s largest software company.

SAP

SAP is a curious case within the DAX 40 index. Unlike many of the well-known German enterprises, it is not a company built around the automotive sector, machinery, or chemicals. Instead, SAP is one of the very few European software companies, that can match the dominant players from the USA.

SAP
Logo SAP
Source: SAP.

However, SAP is not known for its consumer products, and its business is purely focused on the business-to-business (B2B) sector. As one of the leading providers of Enterprise Resource Planning (ERP) systems, SAP provides other companies with the opportunity to transform themselves into intelligent enterprises with integrated processes. Applications cover all possible business processes from supply-chain management to finance through supporting functions like human resources.

In the 2020s, SAP’s current main challenge is to transform its business and its large and international client base from mainly locally managed systems (on premise) to remotely managed systems (cloud services). This presents a great opportunity and comes with many benefits not only for SAP’s customers but also for SAP shareholders, as cloud contracts provide the business with stable and more recurring revenues.

My Work Experience

As a multinational enterprise, SAP offers various jobs in areas like development, consulting, or sales. Due to my proclivity for Finance and Communication, I choose to work for SAP’s Investor Relations department. This department works closely with the CFO and CEO of the company to facilitate an ongoing dialogue with the investor community, to prepare the publication of quarterly results and to manage the annual general meeting of shareholders.

While some colleagues deal with matters of retail shareholders or with matters of ESG investors specifically, I was mostly supporting the institutional side of the team. This means listening to the sell-side analysts of the large investments firms that are covering the company (UBS, GS, JPM, etc.), preparing meetings with those analysts or with portfolio managers and in general keeping an eye on the current sentiment of the market.

Knowledge and skills needed

A good Investor Relations Officer should have a diverse and broad background. Of course, financial knowledge and the skills to analyze financial statements is key, as those topics are part of the daily discussions with external analysts as well as with internal stakeholders.

However, a good general understanding of the industry and of the product landscape is necessary as well. And finally, sufficient communication skills are a must: it is not enough to advertise the company to future potential shareholders, it is also critical to listen to the concerns of existing shareholders and to relay this information back into the board room of the company.

What I learned

The market is always right. This is a very confrontative statement and I suppose not everyone would agree with this initially. However, in my experience, an honest and transparent approach to financial communication is the most successful one in the long term. Investor Relations should not sugarcoat its messages to the market. At the end, the value of the company is fundamentally decided by its potential to generate cash flows (and especially cash flows for shareholders with dividends). Changing the messaging can only delay a change of the stock price. One of my colleagues with a lot of experience loves to quote President Abraham Lincoln on this matter (although nobody knows if he really said that): “You can fool some of the people all of the time, and all of the people some of the time, but you cannot fool all of the people all of the time.”.

Financial concepts

To work in Investor Relations, you should be aware of several financial concepts: Firm valuation and modelling are at the heart of the job. General knowledge about M&A activities and divestitures can also be very helpful. But the most important concept is to understand the different players on the equity market:

Sell side

The sell side represents all the third-party analysts from investment banks or independent research firms that do not actually trade the stock of the company but sell their reports and insights to those who do. These analysts have a very deep understanding of the industry and the business model and there are excellent at modelling firm valuations.

Buy side

The buy side consists of large private funds, insurance companies and sovereign state funds. These are the actual shareholders of the company and often the portfolio managers of these companies are generalists with various industries in their portfolios. They are a diverse group of firms and while some of them are very passive investors, others are actively trying to influence the decision processes within the company.

Proxy advisors

Proxy advisors provide advisory services to institutional investors. They advise the buy side investors on how to vote during the annual general meeting of a corporation. As the market for proxy advisory is heavily concentrated, it is of utmost importance for Investor Relations to keep an ongoing dialogue with these firms. Well-known proxy advisors are “Glass, Lewis & Co” and “Institutional Shareholder Services (ISS)”.

Why should I be interested in this post?

Investor Relations is a developing function in public companies and the discipline must be better studied in the academic field. It is a key function within every publicly traded company to minimize the information asymmetries between investors and management and thus in my opinion a very interesting area to work in.

Useful resources

SAP

SAP Investor Relations

National Investor Relations Institute (US-focused association)

About the author

The article was written in October 2022 by Micha FISHER (University of Mannheim, MSc. Management, 2021-2023).

Simple interest rate and compound interest rate

October 16, 2022 by Sébastien PIAT

Simple interest rate and compound interest rate

In this article, Sébastien PIAT (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2024) explains the difference between simple interest rate and compound interest rate.

Introduction

When dealing with interest rates, it can be useful to be able to switch from a yearly rate to a period rate that is used to compute interests on a period for an investment or a loan. But you should be aware that the computation is different when working with simple interests and compounded interests.

Below is the method to switch back and forth between a period rate and a yearly rate.

With simple interests

If you think of an investment that generates yearly incomes at a rate of 6%, you might want to know what your monthly return is.

As we deal with simple interests, the monthly rate of this investment will be 0.5% (=6/12).

With simple interests, the interests on a given period are computed with the initial capital:

Interests computed a simple rate

Assuming that the interests are computed over p periods during the year, the capital of the investment at the end of the year is equal to

Interests computed a simple rate

The equivalent yearly rate of return R_y gives the same capital value at the end of the year

Interests computed a simple rate

By equating the two formulas for the capital at the end of the year, we obtain a relation between the period rate R_p and the equivalent yearly rate R_y:

Formula to switch from a period rate to the equivalent yearly rate with simple interests

Formula to switch from a yearly rate to the corresponding period rate with simple interests

With compound interests

Things get a little trickier when dealing with compound interests as interests get reinvested period after period.

Compounded interests can be considered by the following equation:

Interests computed a compound rate

Where R_p is the period rate of the investment and C_n is your capital at the end of the n_th period.

Assuming that the interests are computed over p periods during the year, the capital of the investment at the end of the year is equal to

Interests computed a compound rate

The equivalent yearly rate of return R_y gives the same capital value at the end of the year

Interests computed a compound rate

By equating the two formulas for the capital at the end of the year, we obtain a relation between the period rate R_p and the equivalent yearly rate R_y:

Formula to switch from a period rate to the equivalent yearly rate with compound interests

Formula to switch from a yearly rate to the corresponding period rate with compound interests

Excel file to compute interests of an investment

You can download below the Excel file for the computation of interests with simple and compound interests and the equivalent yearly interest rate.

You can download below the Excel file to switch from a period interest rate to a yearly interest rate and vice versa.

Why should I be interested in this post?

This post should help you switch between a period rate and the equivalent yearly rate of an investment.

This is particularly useful when we deal with cash flows that do not appear with a yearly frequency but with a monthly or quarterly frequency. With non-yearly cash flows, it is necessary to consider a period rate to compute the present value (PV), net present value (NPV) and internal rate of return (IRR).

Useful resources

longin.fr website Cours Gestion financière (in French).

About the author

The article was written in October 2022 by Sébastien PIAT (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2024) .

Why Berlin could be the new Silicon Valley for startups?

October 11, 2022 by Jessica BAOUNON

Why Berlin could be the new Silicon Valley for startups?

In this article, Jessica BAOUNON (ESSEC Business School, Executive Master in Direction Financière et Contrôle de Gestion, 2020-2022) explores the latest trends which is transforming the the startups world and the venture capital in Berlin, the capital of Germany.

These last decades, with the rise of internet and new technologies, startups rapidly boomed. Some of them became very famous. You may be familiar with AirBnb, Instagram or Uber; they all started from scratch in a grungy basement before flourishing at a global scale. BytheDance, with 2 billion users in 150 countries recorded 58 billion of dollars in revenues for 2021, an example of success story that creates a high interest of curiosity for tech-investors everywhere in the world. However, only a few cities are among the most attractive for entrepreneurs. Berlin is on the path to become the most popular city for startups. But first of all, let’s go back to the basics by defining what a startup is.

What is a startup ?

Startups are companies that are in the first stages of its business operations. They are often founded by young people. They work in a collaborative way to scale up quickly their innovative products or services. They are looking for disruption opportunities to change the world and industries. They want to bring new ideas. As a result, the startup ecosystem is known to be a very fast-paced environment but also valuable for investors that expect a high return on their investment. On the other side, corporate operate with a different approach. They are most of the time, well-established. They work with a pyramidal organization and focus on the productivity rather than taking risks to grow fast.

Startups costs are usually very high with a low revenue at the start. They need funds to finance innovation and their entry in the markets. This is the reason why they turn to venture capitalist, but it can also come from various sources. The venture capitalist is a private investor. He provides capital in exchange for equity stake when they don’t have access to equity markets. Startup can then graduate by going public with an initial public offering (IPO), making them purchasable on the stock exchange.

How is the Berlin Startup Ecosystem?

Berlin counts 4 500 startups among well-known organizations such as SoundCloud, Tier Mobility or Babbel and employ over 80 000 people. Software as a Service (Saas), FinTechs and healthcare startups represent most of the business models. For the next coming years, the forecast expects a shift towards the green industry with the new climate neutrality challenges.

Figure 1. Number of Berlin startups by “industries”.
Number of Berlin startups by industries
Source: Dealroom.co

With over 10 billion euros invested in total in startups in 2021, the city is also among the top 10 locations for startup investment worldwide (1). Berlin is leading the investments in Germany “Three out of five euros invested in start-ups in Germany (60%) were invested in Berlin startups in 2021” (2) and Berlin records the most financing rounds” (3) in 2021 and 2022. The funding is diverse coming from public, private and institutional actors. Startups are the engine of Berlin’s economy. The new government state has detailed the plan for the next four years. They want to pursue the development of Berlin’s startup ecosystem into one of the first technology location.

Figure 2. Berlin: leader for startups in Germany.
Berlin startups
Source: Ernst & Young Startup Barometer (2022)

Why Berlin is attractive for startups

Berlin offers a lot of favorable conditions. The startup ecosystem benefits from an important aspect of the capital: the diversity. The city offers a wide network of high-quality professional talents coming from all over the world. 44% of entrepreneurs are not German (4). Indeed, Berlin has a strong historical with several countries such as France, Great Britain, The United States. The geographical location, almost in the middle of the European Union, facilitates the connections between the north, south, east, and west side but also for people outside of the region.

Indeed, the diverse sources of financing from private to public actors enable a positive investment climate for entrepreneurs. They are business incubators, universities, technology centers, regular meetups, and the greatest number of coworking spaces in Germany to ensure an outstanding infrastructure. Most of the entrepreneurs don’t want to follow the classic corporate path. Berlin as a creative and dynamic city offers the opportunity to express their ideas and freedom. The city is constantly in transformation in all areas: technology, art, music, architecture which attract people who aspire to change and innovation. Although rental prices rise in Berlin, it keeps one of the most affordable in term of living cost. Berlin’s startups and the venture capital scene promises to grow at a high dynamic for the next coming years.

Why should I be interested in this post

If you are considering working abroad and interested to work for a startup or a capital venture, this article is for you. This article presents the Berlin startup scene and explains why Berlin is considered as one of the most attractive cities for entrepreneurs and venture capital.

Ressources utiles

Startup Map Berlin

Startup Capital Berlin

Startup Barometer Germany E&Y

Startup Ecosystem

About the author

The article was written in October 2022 by Jessica BAOUNON (ESSEC Business School, Executive Master in Direction Financière et Contrôle de Gestion 2020-2022).

Enjeux de la pratique de la pleine conscience et de l’intelligence émotionnelle dans la fonction de contrôle de gestion

October 11, 2022 by Jessica BAOUNON

Enjeux de la pratique de la pleine conscience et de l’intelligence émotionnelle dans la fonction de contrôle de gestion

Dans cet article, Jessica BAOUNON (ESSEC Business School, Executive Master in Direction Financière et Contrôle de Gestion, 2020-2022) explique les enjeux de la pratique de la pleine conscience et de l’intelligence émotionnelle dans la fonction contrôle de gestion. Le monde de l’entreprise s’est considérablement transformé avec la crise du COVID-19. L’appel à l’intelligence émotionnelle n’a jamais été aussi important pour faire face aux situations les plus complexes.

La fonction contrôle de gestion est en pleine évolution. Ses missions ne portent plus uniquement sur la production et la communication d’indicateurs financiers. Son rôle consiste désormais à accompagner dirigeants et managers dans l’amélioration de la performance financière, c’est-à-dire à les conseiller sur les décisions d’orientations stratégiques.

La crise Covid-19 a projeté le contrôle de gestion davantage vers un rôle de « coach. En effet, en étant proche de ceux qui ont dû garantir la continuité des activités, le contrôle de gestion a dû se pencher sur l’empathie dans sa relation établie avec dirigeants et managers. On attend de lui une attitude d’écoute, de disponibilité, une capacité à se placer dans le contexte de son interlocuteur pour agir avec efficacité et désamorcer des situations de crise.

En d’autres termes, acquérir des compétences relationnelles et se doter d’un capital émotionnel sont aujourd’hui des qualités recherchées. L’action d’un contrôleur de gestion s’inscrit de plus en plus dans un état d’esprit collaboratif. Il remplit une fonction de business partner.

Or comment imaginer qu’un contrôleur de gestion puisse construire une relation de partenariat pérenne s’il n’est lui-même pas pleinement conscient de l’environnement dans lequel il évolue ? Sa prise de conscience de soi et des autres doit faciliter ses interactions sociales.

A ce titre, s’exercer à une pratique régulière de méditation de pleine conscience peut s’avérer efficace pour travailler son intelligence émotionnelle. En effet, l’exercice de la pleine conscience implique avant tout de ressentir et comprendre les émotions en portant une qualité d’attention sur une expérience vécue. C’est une attitude qui propose d’ouvrir un espace d’observation sans filtre, sans attente, de ses sensations, pensées, émotions d’une action, d’un évènement dans l’acceptation et sans jugement.

Ce processus d’observation permet ainsi de mieux aller vers l’autre en apportant une réponse adaptée et clairvoyante dans des dialogues de gestion. Elle permet notamment de reprendre possession de soi dans des situations de stress ou de gestion de conflit.

Origines et impact de la pratique de la pleine conscience dans la fonction contrôle de gestion

Jon Kabat Zin, professeur de médecine à l’Université du Massachussetts et docteur en biologie, est le père-fondateur de la méditation de pleine conscience. Intitulé Mindfullness-Based Stress Reduction (MBSR), ce programme laïque inspiré du bouddhisme, offre une initiation à la méditation sur une période de huit semaines.

Cette pratique, à l’origine millénaire, s’est progressivement répandue avec succès dans les écoles scientifiques, philosophiques et psychologiques. Elle émerge depuis quelques années dans les entreprises telle que chez EDF, Google ou L’Oréal au travers de formations certifiées.

Google, précurseur, propose à ses collaborateurs depuis 2007 un programme de méditation nommé « Search Inside Yourself ». Chade-Meng Tan, ingénieur chez Google, a réuni une équipe d’experts en technique de pleine conscience et intelligence émotionnelle pour construire cette formation. L’objectif est de développer des compétences d’intelligence émotionnelle pour créer une cohésion sociale favorable à l’épanouissement individuelle et collectif chez Google. Ces cours ont été dispensés auprès de plus de 10 000 personnes et dans plus de 50 pays.

Cette pratique se démocratise et est perçue de moins en moins comme une bizarrerie. Face à un contexte de crises successives, burn out, démotivation des collaborateurs, rééquilibrer les esprits pour évoluer dans un environnement sain devient un enjeu de performance cruciale. Plus que jamais, et en témoigne la récente crise du Covid-19, la responsabilité sociale d’une entreprise est de créer les conditions qui permettront une cohésion sociale durable.

En outre, face à l’ampleur d’imprévisibles changements, la mission du contrôle de gestion consistant à assurer la stabilité des processus de gestion doit s’accompagner d’une réflexion constante sur l’évolution des outils et systèmes d’information. Si les solutions d’automatisation des processus de gestion gagnent du terrain pour répondre à une volonté de rapidité d’exécution, elle ne doit pas pour autant conduire à un mode de pilotage automatique des taches d’un contrôleur de gestion.

Cette approche machinale de la fonction contrôle de gestion doit être signe d’alerte. En effet, le danger de cette posture est de se laisser gouverner, de ne plus observer activement les choses sous un regard nouveau et d’en perdre le sens. Dans un monde où l’humain rivalise de plus en plus avec les machines, développer un état d’esprit créatif et stimuler sa conscience d’esprit est un enjeu essentiel. La pleine conscience, en tant qu’outil, agit comme un accélérateur de créativité. Elle oblige à se libérer d’un mode de fonctionnement mécanique des processus en étant attentif à ce que l’on fait et à ce qui nous entoure pour cheminer vers des nouvelles idées. Avec la montée en puissance des technologies, cette qualité encore absente du langage courant, se retrouvera plus encore demain, dans les exigences de compétences requises en contrôle de gestion.

Innover avec un style de management durable

Dans cette même dynamique de changement, on assiste à une « reconnaissance accrue du rôle des émotions comme action et effet dans les organisations » (1). Celle-ci questionne les modèles de management classiques jugé trop bureaucratique et militaire « dans leur tentative de contrôler, supprimer toute émotion qui interférer la rationalité d’actions souhaitées » (1). L’essoufflement du modèle tayloriste est en train de laisser progressivement place à de nouveaux paradigmes. Cette transformation s’explique par une logique de revalorisation du capital humain subordonnée à celle de l’efficience productive. En outre, la montée en puissance de la Responsabilité Sociale des Entreprises (RSE) a donné lieu à d’importants renversements.

« La recherche de profit n’est pas en soi problématique, ce qui l’est c’est de ne souligner que le profit au détriment de la complexité de réalités humaines » (Bibard Laurent). En témoigne l’affaire Bhopal ou Orange qui ont eu pour effet de révéler une profonde dévalorisation des conditions de travail. Un renversement de rôle qui renvoie également à la question du sens, d’une humanité en prise de conscience sur ce qui ne fonctionne plus, sur la nécessité de l’entreprise à s’ancrer dans un monde durable et servir l’intérêt général.

Pour arriver à cet objectif de durabilité, reconstruire un modèle de management responsable en s’appuyant sur les acquis de la psychologie cognitive et sociale constitue une première solution. Les émotions ont été rejeté pendant très longtemps des visions managériales des entreprises. Or les récentes découvertes en psychologie démontrent que développer des compétences en intelligence émotionnelle permet de développer de réelles qualités relationnelles, de prendre de meilleures décisions et de se montrer bien plus créatif.

Dans un monde incertain rythmé par des crises financières, environnementales et sociales, chaque individu doit être en mesure de pouvoir se défaire de biais cognitifs, en se libérant de ses croyances limitantes pour contribuer à une vision d’un monde juste et responsable. La pratique de la pleine conscience et de l’intelligence émotionnelle contribue à mobiliser une connaissance de soi. Elle permet aux contrôleurs de gestion ainsi qu’à l’ensemble des collaborateurs de questionner la pertinence de leurs actions et décisions sous l’angle de leurs émotions. Cette pratique invite ainsi à nous rappeler ce que nous sommes : des êtres humains.

En quoi ça m’intéresse ?

Dans un monde où l’humain rivalise de plus en plus avec les machines, développer un état d’esprit créatif et stimuler sa conscience d’esprit est essentiel. Cet article présente les bénéfices de la pratique de la pleine conscience et de l’intelligence émotionnelle dans la fonction contrôle de gestion afin d’y apporter d’un éclairage sur ces nouvelles compétences recherchées.

Articles sur le blog SimTrade

▶ POUZOL Chloé Mon expérience de contrôleuse de gestion chez Edgar Suites

Ressources utiles

Teneau, Gilles, Empathie et compassion en entreprise, 2014, ISTE Editions.

Tan, Cheng-Made, Search Inside Yourself, 2015, Harper Collins Libri

Kotsou, Ilios – « Intelligence émotionnelle & management », 2016, De Boeck

Cappelletti, Laurent. Le management de la relation client des professions : un nouveau sujet d’investigation pour le contrôle de gestion, 2010, Revue Management et Avenir.

A propos de l’auteure

Cet article a été écrit en octobre 2022 par Jessica BAOUNON (ESSEC Business School, Executive Master in Direction Financière et Contrôle de Gestion 2020-2022).

Extreme Value Theory: the Block-Maxima approach and the Peak-Over-Threshold approach

February 4, 2024October 9, 2022 by Shengyu ZHENG

Extreme Value Theory: the Block-Maxima approach and the Peak-Over-Threshold approach

In this article, Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) presents the extreme value theory (EVT) and two commonly used modelling approaches: block-maxima (BM) and peak-over-threshold (PoT).

Introduction

There are generally two approaches to identify and model the extrema of a random process: the block-maxima approach where the extrema follow a generalized extreme value distribution (BM-GEV), and the peak-over-threshold approach that fits the extrema in a generalized Pareto distribution (POT-GPD):

BM-GEV: The BM approach divides the observation period into nonoverlapping, continuous and equal intervals and collects the maximum entries of each interval. (Gumbel, 1958) Maxima from these blocks (intervals) can be fitted into a generalized extreme value (GEV) distribution.
POT-GPD: The POT approach selects the observations that exceed a certain high threshold. A generalized Pareto distribution (GPD) is usually used to approximate the observations selected with the POT approach. (Pickands III, 1975)

Figure 1. Illustration of the Block-Maxima approach
BM-GEV
Source: computation by the author.

Figure 2. Illustration of the Peak-Over-Threshold approach

POT-GPD
Source: computation by the author.