Warren Buffet and his basket of eggs

In this article, Rayan AKKAWI (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022) analyzes the two following quotes “Do not put all eggs in one basket” and “Put all your eggs in one basket and watch that basket” often used by Warren Buffet to describe his investment strategy.

“Do not put all eggs in one basket”

I particularly liked this quote first because it is said by the world’s greatest investor and one of the richest people on the planet, Warren Buffet. I aspire this man due to his great investment philosophy which is to invest in great businesses at value for money prices and then by using the “buy and hold strategy” keep the stocks over the long term. He has bought great brands such as Coca Cola, Microsoft, and American Express. Second, I like this quote particularly because it is dedicated to any person who has little or no knowledge in investment, so it is easy to implement.

Analysis

If we analyze the wealthiest people in the world, they are entrepreneurs who have created companies that grew exponentially in value. For example, Bill gates who is the founder of Microsoft (1975), Jeff Bezos who is the founder of Amazon (1994), and Mark Zuckerberg who is the founder of Facebook (2004). And as we continue to analyze these founders, we come to realize that they have made their wealth by putting all their eggs in one basket at least early in their lives. However, not all of us have this entrepreneurial spirit and business success such as these brilliant men. Thus, when Warren Buffet said “do not put all eggs in one basket” he was referring to an average person who has little knowledge in investments. Therefore, he advocates investment into index tracker or passive funds which have the benefit of low charges, better performance, and large diversification than most active managed funds. This involves a buy and hold strategy which keeps share dealing charges low. Thus, it is always recommended to have 80% of investments in passive funds which are low cost, predictable, and conservative funds and 20% of investments in satellite which usually involve higher charges with greater volatility and greater returns.

Another way of looking at it is the following. One might decide to invest a certain number of personal wealth in a new business or in crypto. This would be a risky type of investment because another competitor might release a better and more attractive or even more affordable version of the product or service. Eventually, this might put you out of business if a customer writes a bad review of your product or business or if the bitcoin value drops.

So before you invest more time and money in your business, consider how you can manage your risk. First, you must think about your risk tolerance which depends on your age and current financial obligations. Second, you need to keep sufficient liquidity in your portfolio by setting aside an emergency fund that should be equal to 6 to 8 months’ expenses. For ensuring that there is easy accessibility to emergency funds, you should have low-risk investment options like Liquid Funds and Overnight Funds in your accounts. Then you need to determine an asset allocation strategy that works which refers to investing in more than one asset class for reducing the investment risks and this strategy also provides you with optimal returns. You can invest in a perfect mix of key asset classes like Equity, Debt, Mutual Funds, real estate, etc. One of the asset allocation strategies is to invest in a combination of asset classes that are inversely correlated to each other. After you have found the best mix of asset classes for your portfolio, you can reduce the overall investment risk by diversifying your investment in the same asset class. Think about diversifying by offering more than one product or service. To avoid liquidity risk, it is always better to stay invested in blue chip stock or fund. Investors should check the credit rating of debt securities to avoid default risk.

“Put all your eggs in one basket and watch that basket”

At the same time, Warren Buffet believes that diversification makes little sense if a person doesn’t know exactly what he or she is doing. Diversification is a protection against ignorance and is for people who do not know how to analyze businesses. Sometimes it is enough to invest in two or three companies that are resistant to competition rather than fifty average companies due to less risk. That is why it is as critical for a person to invest in a company where its values and vision are similar to that of the investor and to be able to watch closely the performance of that business and its stocks.

Thus, Warren Buffet believes that it is extremely crucial to be able to “watch your basket” or your stocks closely to better understand the stock market. For example, when the stock market is going down, it is the best way to start buying stocks because businesses will be selling at a discount.

Why should I be interested in this post?

One would be interested to read this post because it introduces the basics of investing in stock markets for an average person who has little knowledge in investments or for a student studying business. As a student, it is crucial and important to be able to have at least a general idea of the basic rules of investments and especially those stated by one of the most famous investors in the world such as Mr. Warren Buffet. Whether you are interested in buying stocks yourself or whether you are not, as a business student, you might be asked about investments and the financial market one time in your life and knowing some useful information about investments will be impressive for you. It will allow you to understand the bigger picture of financial markets, give some recommendations for your family and friends, and help you invest yourself in the safest and most successful way.

Useful resources

Berkshire Hathaway

About the author

The article was written in May 2022 by Rayan AKKAWI (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022).

Big data in the financial sector

In this article, Rayan AKKAWI (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022) explains the role of big data in the financial sector.

Big data is a term used for contemporary technologies and methodologies that are used to collect, process, and analyze complex data. Today, data is being created at an exponential rate. In fact, and according to a 2015 IBM study, 90% of the data in the world has been created in the past two years. As big data gets bigger, it becomes even more important and essential for executives in the financial sector to stay ahead of the curve. Also, it is expected that data creation will continue to grow moving forward in time.

Big Data in The Financial Sector

For decades, financial analysts have relied on data to extract insights. Today, with the rise of data science and machine learning, automated algorithms and complex analytical tools are being used hand in hand to get a head of the curve in diferetn areas of the financial sector.

Fraud prevention

First, data has helped with fraud prevention such as identity theft and credit card schemes. Abnormally high transactions from conservative spenders and out of region purchases often signal credit card fraud. Whenever this happens, the card is automatically blocked, and a notification is sent out to the card owner. This protects users, insurance companies, and banks from huge financial loses in a small period. This also made things even easier and more practical avoiding the hassle of having to call and cancel the card. Data science comes in the form of tool like random forests that can detect a certain suspicion. In addition, and to lower the chance of identity theft, data has helped ease this process through 3D passwords, text messages, and PINT code which have backed up the safety of online transactions.

Anomaly detection

Second, data has helped the financial sector through anomaly detection. Data analysis is not only created to avoid a problem but also to detect it. For example, data today helps with catching illegal insider traders. To do so, data analysts created anomaly detection algorithms that can analyze history in trading patterns and thus detect and catch abnormal transactions of illegal traders.

Customer analytics

Third, data has helped with improving customer analytics. Data analyzes previous behavioral trends of consumers based on historical transactions and then makes future predictions of how consumers are likely to act. With the help of socioeconomic characteristics, we can create clusters of consumers and group customers based on how much money we expect to gain or lose from each client in the future. Following that, we can come up with decisions to focus on a certain type of clients to make profits and cut on other customers to make savings. Thus, financial institutions minimize human errors by utilizing data science. To achieve that, first, by identifying uncertain interactions and then monitor them going forward. Finally, prioritizing the investments most vulnerable at a given time. For example, banks use this approach to create adaptive real risk score time models to identify risky clients and those who are suitable for a mortgage or a loan.

Algorithmic trading

Fourth and most importantly data has created algorithmic trading. Machines make trading based on algorithms multiple times every second with no need for approval by a stand-by analyst. These trades can be in any market and even in multiple markets simultaneously. Thus, algorithmic trading has mitigated opportunity costs. Thus, there are algorithmic rules that can help in identifying if there is a need to trade or not to trade and reinforces business models where errors are highly penalized and then adjust hyper parameters. We can see algorithms that exploit arbitrage opportunities where they can find inconsistencies and make trades which can cause problems. The huge upside is that it is high frequency trading; whenever it will find an opportunity to make a trading, it will. However, the downside is that imprecision could lead to huge losses due to lack of human supervision. That is why sometimes human interventions are needed.

Conclusion

Thus, we can say that data has become the hottest commodity that results in getting an edge over competition. Financial institutions spend a huge amount of money to get exclusive rights to data. By having more information, they can construct better models. The most valuable commodities are not analysts but the data itself. That is how the data science has revolutionized finance.

Characteristics of Big Data

When talking about Big Data, four main characteristics need to be considered to understand the why Big Data plays a transformational role in the financial sector: volume, variety, velocity, and value.

Volume

First, the amount also known as volume of data being produced on daily basis by users has been increasing exponentially by users. This large output of data has helped create Zettabytes (10¹² Gigabyte) and Yottabytes (10¹⁵ Gigabyte) of datasets in which companies can benefit by extracting knowledge and insights out of it. However, this amount of data cannot be processed using regular computers and laptops. Since they would require a lot of processing power.

Variety

Second, as the massive amount of data is being generated by multiple sources, the output of this data is unstructured making it hard to organize the data extract insights. Raw data extracted from the source without being processed does not provide any value to business as it does provide stakeholders with the ability to analyze it.

Velocity

Third, to address the issue of processing technological advancements have brought us to the tipping point where technologies such as cloud computing have enabled companies to process this large amount of data by utilizing the ability to share computational power. Furthermore, cloud platforms have not only helped in the processing part of data but by the emergence or cloud solution such as data lakes and data warehouses. Businesses are able to store this data in its original from to make sure that they can benefit from it.

Value

Finally, this brings us to the most important aspect of Big Data and that in being able to extract insights and value out of the data to understand what it is telling us. This process is tedious and time consuming however with ETL tool (Extract Transform Load) the data in its raw format is transformed so that standardized data sets can be produced. Insights can be extracted through Business Intelligence (BI) tools to create visualization that help business decisions. As well as predictive artificial intelligence models that help business predict when to take a strategic decision. In the case of financial markets, these decisions are when to buy or sell assets, and how much to invest.

Challenges Solved by Big Data in the Financial Industry

Utilizing Big Data in the finance industry presents a lot of benefits and helps the industry to overcome multiple challenges.

Data Quality

As previously mentioned, the multiple data sources present a huge challenge from a data management standpoint. Making it an ongoing and a tedious effort to maintain the integrity and the reliability of the records collected. Therefore, adding information processing systems and standardizing the data gathering and transformation processes helps improve the accuracy of the decision-making process, especially in financial services companies where real-time data enables fast decision making and elevates the performance of companies.

Data Silos

Since financial data comes from multiple sources (applications, emails, documents, and more), the use of data integration tools help simplifies and consolidate the data of the institution. These technologies facilitate processes and make them faster and more agile, which are important characteristics in the financial markets.

Robo-Advisory

Big Data and analytics have had a huge impact on the financial advisory sector. Where financial advisors are being replaced by machine learning algorithms and AI models to manage portfolio and provide customers with personalized advice and without human intervention.

Why should I be interested in this post?

This article is just an eye opener on the trends and the future state of the financial industry.

Like many other industries, the financial sector is becoming one of the most data driven field. Therefore, as future leaders it is vital to keep track and push towards data driven solutions to excel and succeed within the financial sector.

Useful resources

The Future of Cognitive Computing

Five Ways to Use RPA in Finance

About the author

The article was written in May 2022 by Rayan AKKAWI (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022).