case study on financial leverage

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Understanding Financial Leverage

• Karen Firestone

The key to borrowing effectively is to have a keen understanding of what might go wrong.

“Leverage” is one of the more interesting and difficult concepts to fully grasp in all of finance, but it’s important for anyone that borrows or plans to borrow money to understand. Much of the confusion stems from the contrasting meanings embedded in the same word. Merriam-Webster’s dictionary includes two very different definitions. The first suggests strength: “power, effectiveness.” The other, on face value, has little to do with control: “the use of credit to enhance one’s speculative capacity.” Combining the two suggests that the party which borrows has the leverage — they have the power and advantage over others. Does that mean that the borrower is dominant over the lender? Somehow, that flies in the face of what many of us learned at an early age. The gambler who can’t pay his bookie ends up with a right hook to the gut. Yet many people jump into risky financial situations without considering the potential consequences.

• Karen Firestone is the President and CEO of Aureus Asset Management, an asset management firm which serves as the primary financial advisor to families, individuals, and nonprofit institutions. She cofounded Aureus after 22 years as a fund manager and research analyst at Fidelity Investments. She’s the author of Even the Odds: Sensible Risk-Taking in Business, Investing, and Life (Bibliomotion, April 2016) .

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What Is Financial Leverage, and Why Is It Important?

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master's in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology. He is a CFA charterholder as well as holding FINRA Series 7, 55 & 63 licenses. He currently researches and teaches economic sociology and the social studies of finance at the Hebrew University in Jerusalem.

What Is Financial Leverage?

Financial leverage is the concept of using borrowed capital as a funding source. Leverage is often used when businesses invest in themselves for expansions, acquisitions, or other growth methods.

Leverage is also an investment strategy that uses borrowed money—specifically, the use of various financial instruments or borrowed capital —to increase the potential return of an investment.

Key Takeaways

• Leverage refers to using debt (borrowed funds) to amplify returns from an investment or project.
• Companies can use leverage to invest in growth strategies.
• Some investors use leverage to multiply their buying power in the market.
• There is a range of financial leverage ratios used to gauge a company's financial strength, with the most common being debt-to-assets and debt-to-equity.

Investopedia / Lara Antal

Understanding Financial Leverage

Leverage is using debt or borrowed capital to undertake an investment or project. It is commonly used to boost an entity's equity base. The concept of leverage is used by both investors and companies:

• Investors use leverage to significantly increase the returns that can be provided on an investment. They leverage their investments using various instruments, including options , futures, and margin accounts.
• Companies can use leverage to finance their assets. In other words, companies can use debt financing to invest in business operations to influence growth instead of issuing stock to raise capital.

Investors who are not comfortable using leverage directly have a variety of ways to access leverage indirectly. They can invest in companies that use leverage in the ordinary course of their business to finance or expand operations—without increasing their outlay.

The point and result of financial leverage is to multiply the potential returns from a project. At the same time, leverage will also multiply the potential downside risk in case the investment does not pan out. When one refers to a company, property, or investment as "highly leveraged," it means that the item has more debt than equity.

How to Calculate Financial Leverage

There is an entire suite of leverage financial ratios used to calculate how much debt a company is leveraging in an attempt to maximize profits. Here are several common leverage ratios.

You can analyze a company's leverage by calculating its ratio of debt to assets. This ratio indicates how much debt it uses to generate its assets. If the debt ratio is high, a company has relied on leverage to finance its assets. A ratio of 1.0 means the company has $1 of debt for every $1 of assets. If it is lower than 1.0, it has more assets than debt—if it is higher than 1.0, it has more debt than assets.

Debt Ratio = Total Debt ÷ Total Assets

Keep in mind that when you calculate the ratio, you're using all debt, including short- and long-term debt vehicles.

Debt-to-Equity (D/E) Ratio

Instead of looking at what the company owns, you can measure leverage by looking strictly at how assets have been financed. The debt-to-equity (D/E) ratio is used to compare what the company has borrowed to what it has raised from private investors or shareholders.

Debt-to-Equity (D/E) Ratio = Total Debt ÷ Total Equity

A D/E ratio greater than 1.0 means a company has more debt than equity. However, this doesn't necessarily mean a company is highly leveraged. Each company and industry typically operates in a specific way that may warrant a higher or lower ratio.

For example, start-up technology companies may struggle to secure financing and must often turn to private investors. Therefore, a debt-to-equity ratio of .5 ($1 of debt for every $2 of equity) may still be considered high for this industry.

Debt-to-EBITDA Ratio

You can also compare a company's debt to how much income it generates in a given period using its Earnings Before Income Tax, Depreciation, and Amortization (EBITDA). The debt-to-EBITDA ratio indicates how much income is available to pay down debt before these operating expenses are deducted from income.

A company with a high debt-to-EBITDA carries a high degree of debt compared to what the company makes. The higher the debt-to-EBITDA, the more leverage a company is carrying.

Debt-to-EBITDA Ratio = Debt ÷ Earnings Before Interest, Taxes, Depreciation, and Amortization

An issue with using EBITDA is that it isn't an accurate reflection of earnings. This is because it doesn't include expenses that must be accounted for. It is a non-GAAP measure some companies use to create the appearance of higher profitability.

Equity Multiplier

Debt is not directly considered in the equity multiplier . But it is inherently included, as total assets and total equity each have a direct relationship with total debt. The equity multiplier attempts to understand the ownership weight of a company by analyzing how assets have been financed. A company with a low equity multiplier has financed a large portion of its assets with equity, meaning they are not highly leveraged.

Equity Multiplier = Total Assets ÷ Total Equity

DuPont analysis uses the equity multiplier to measure financial leverage. One can calculate the equity multiplier by dividing a firm's total assets by its total equity. Once figured, multiply the total financial leverage by the total asset turnover and the profit margin to produce the return on equity.

For example, if a public company has total assets valued at $500 million and shareholder equity valued at $250 million, the equity multiplier is 2.0 ($500 million ÷ $250 million). This shows the company has financed half its total assets with equity. But if it had $500 million in assets and equity of $100 million, its equity multiplier would be 5.0. Hence, larger equity multipliers suggest that further investigation is needed because there might be more financial leverage used.

Degree of Financial Leverage (DFL)

Fundamental analysts can also use the degree of financial leverage (DFL) ratio. The DFL is calculated by dividing the percentage change of a company's earnings per share (EPS)  by the percentage change in its earnings before interest and taxes (EBIT) over a period.

Degree of Financial Leverage = % Change in Earnings Per Share ÷ % Change in EBIT

The goal of DFL is to understand how sensitive a company's EPS is based on changes to operating income. A higher ratio will indicate a higher degree of leverage, and a company with a high DFL will likely have more volatile earnings.

Consumer Leverage Ratio

The formulas above are used to evaluate a company's use of leverage for its operations. However, households can also use leverage. By taking out debt and using personal income to cover interest charges, households may also use leverage.

Consumer Leverage is derived by dividing a household's debt by its disposable income. Households with a higher calculated consumer leverage have high degrees of debt relative to what they make and are, therefore, highly leveraged.

Consumer Leverage = Total Household Debt ÷ Disposable Income

Consumers may eventually find difficulty in securing loans if their consumer leverage gets too high. For example, lenders often set debt-to-income limitations when households apply for mortgage loans.

Financial ratios hold the most value when compared over time or against competitors. Be mindful when analyzing leverage ratios of dissimilar companies, as different industries may warrant different financing compositions.

Advantages and Disadvantages of Financial Leverage

Some investors and traders use leverage to amplify profits. Trades can become exponentially more rewarding when your initial investment is multiplied by additional upfront capital . Using leverage also allows you to access more expensive investment options that you wouldn't otherwise have access to with a small amount of upfront capital.

Leverage is best used in short-term, low-risk situations where high degrees of capital are needed. For example, during acquisitions or buyouts, a growth company may have a short-term need for capital, resulting in a strong mid-to-long-term growth opportunity. As opposed to using additional capital to gamble on risky endeavors, leverage enables smart companies to execute opportunities at ideal moments with the intention of exiting their leveraged position quickly.

Disadvantages

If investment returns can be amplified using leverage, so too can losses. Using leverage can result in much higher downside risk, sometimes resulting in losses greater than your initial capital investment. On top of that, brokers and contract traders often charge fees, premiums, and margin rates and require you to maintain a margin account with a specific balance. This means that if you lose on your trade, you'll still be on the hook for extra charges.

Leverage also has the potential downside of being complex. Investors must be aware of their financial position and the risks they inherit when entering into a leveraged position. This may require additional attention to one's portfolio and contribution of additional capital should their trading account not have a sufficient amount of funding per their broker's requirement.

Can amplify returns, creating potential for big profits

Reduces barriers to entry by allowing investors to access more expensive trading opportunities

A strategic way for companies to meet short-term financing needs for acquisitions or buyouts

Can amplify downside by creating potential for losses and increased debt

More expensive than other types of trading

Results in fees, margin rates, and contract premiums regardless of the success of the trade

More complex as trading may require additional capital and time based on portfolio needs

Financial Leverage vs. Margin

Margin is a special type of leverage that involves using existing cash or securities as collateral to increase one's buying power in financial markets.  Margin allows you to borrow money from a broker for a fixed interest rate to purchase securities, options, or futures contracts in anticipation of receiving substantially high returns.

You can use margin to create leverage, increasing your buying power by the total amount in your margin account . For instance, if you require $1,000 in collateral to purchase $10,000 worth of securities, you would have a 1:10 margin or 10x leverage.

Example of Financial Leverage

Consider a company formed with a $5 million investment from investors. This equity is the money the company can use to operate. If the company uses debt financing and borrows $20 million, it now has $25 million to invest in business operations and more opportunities to increase value for shareholders. However, it would have a high debt-to-equity ratio. Depending on its industry and its average ratios, a ratio this high could be either expected or concerning.

These types of leveraged positions occur frequently. For example, Apple ( AAPL ) issued $4.7 billion of Green Bonds for the third time in March 2022. By using debt funding, Apple could expand low-carbon manufacturing and create recycling opportunities while using carbon-free aluminum. This type of leverage strategy can work when more revenue is generated than the debt created by issuing bonds.

Financial leverage is the strategic endeavor of borrowing money to invest in assets. The goal is to have the return on those assets exceed the cost of borrowing the funds. The goal of financial leverage is to increase profitability without using additional personal capital.

What Is an Example of Financial Leverage?

An example of financial leverage is buying a rental property. If the investor only puts 20% down, they borrow the remaining 80% of the cost to acquire the property from a lender. Then, the investor attempts to rent the property out, using rental income to pay the principal and debt due each month. If the investor can cover its obligation by the income it receives, it has successfully utilized leverage to gain personal resources (i.e., ownership of the house) and potential residual income.

How Is Financial Leverage Calculated?

Financial leverage can be calculated in several different ways. There is a suite of financial ratios referred to as leverage ratios that analyze the level of indebtedness a company experiences against various assets. The two most common financial leverage ratios are debt-to-equity (total debt/total equity) and debt-to-assets (total debt/total assets).

What Is a Good Financial Leverage Ratio?

In general, a debt-to-equity ratio greater than one means a company has decided to take out more debt as opposed to finance through shareholders. Though this isn't inherently bad, the company might have greater risk due to inflexible debt obligations. The company must be compared to similar companies in the same industry or through its historical financials to determine if it has a good leverage ratio.

Why Is Financial Leverage Important?

Financial leverage is important as it creates opportunities for investors and businesses. That opportunity comes with high risk for investors because leverage amplifies losses in downturns. For businesses, leverage creates more debt that can be hard to pay if the following years present slowdowns.

The Bottom Line

There are several ways that individuals and companies can boost their equity base. Financial leverage is one of these methods. For businesses, financial leverage involves borrowing money to fuel growth. It allows investors to access certain instruments with fewer initial outlays.

Because of the risks of using leverage, it's important to compare the advantages and disadvantages and determine whether financial leverage truly makes sense for your financial circumstances and goals.

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Leverage Analysis: A Study on Whirlpool Ltd

Finance is the life blood of every economic activity. Leverage, as a business term, refers to debt or to the borrowing of funds to finance the purchase of a company's assets. Business owners can use either debt or equity to finance or buy the company's assets. Using debt, or Leverage, increases the company's risk of bankruptcy. It also increases the company's returns; specifically its return on equity. This is true because, if debt financing is used rather than equity financing, then the owner's equity is not diluted by issuing more shares of stock. The analysis has shown that Whirlpool has financed its activities mainly from its Net Worth and the amount of Debt has fallen over the years. It is suggested that Whirlpool must increase its Debt funding to take the advantage of Tax Shield. Moreover, Cost of Debt is cheaper than Cost of Equity. Therefore Whirlpool has to revise its capital structure so that financial leverage will help to maximize the shareholders wealth

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This research paper has tried to measure the relationship between leverage and profitability of firms in the cement industry of Pakistan. Debt to equity is used to measure the leverage of the companies in the cement industry in Pakistan. Short term debt to equity (STD/E) and long term debt to equity (LTD/E) are considered as leverage variables .Return on equity (ROE) and return on assets (ROA) are used to measure the financial performance of the companies. For this research paper 10 cement companies are considered, listed in the Karachi Stock Exchange during the time period 2008-2012. To measure the relationship between leverage and profitability of firms in the cement industry of Pakistan regression model and descriptive statistics have been used. Our results found negative and significant relationship between leverage and profitability of the firm. 1. Introduction Importance of the leverage can be seen from its presence in the capital structure of the organizations. It is important for the organizations to take decision of leverage portion in the capital structure. Debt financing not only minimizes the risk of the organization but also provide tax exemptions to the organization. Capital structure theory highlights the real importance and significance of the debt financing in the organizations. Leverage of the firm influence its capital structure. According to Myers (1984) leverage defines capital structure of the firms. Capital structure consists of debt and equity financing. It is one of the most difficult decisions for the management of the organizations to opt the mixture of debt and equity. Debt portion represent the other's claim and it reduces the risk of the owners (Eckbo, B.E., (1986). In this global world management is not an easy job because it has to take difficult decisions. Management of the company's remains conscious about the debt portion of the organizations because it affects the financial performance of the firms and the performance of the management is measured through the financial performance of the organizations. According to Black et al, (1973) Leverage affects not only the performance of the organizations but also it affects the market value of the organizations as well. Management of the debt financing is very crucial in the organization because companies are using the funds of creditors which have to be returned with interest. Financial leverage and operational leverage are the part of total leverage which affects the profits of the organizations, market value and stock price of the organizations (Denis et al, 2012). Financial leverage is cost saving and it also reduces the risk of the owners but it becomes costly when organizations are unable to use it efficiently. Companies have to pay financial charges on the leverage. If companies fail to use leverage effectively than they have to suffer from many problems because they have to return the amount of leverage with interest expenses. Profitable companies prefer to use leverage because it reduces the risk of owners and more cost saving for the shareholders of the organizations. Leverage affects the profitability which has direct impact on the management performance, capital structure, stock price, wealth of shareholder and all the stakeholders. Cost and benefits of the leverage are better discussed by different researchers whereas it is also explained in the trade-off theory. Combination of debt and equity and its impact on the organizational performance also need consideration because organizations are well familiar with its importance. Trade off theory is helpful for the top management of the organizations because they can take decisions through evaluating the cost and benefits of the organizations in different perspectives. Operational and financial both types of leverage can be assessed and used for taking better decisions. Optimum capital structure is necessary for achieving success and increasing financial performance of the organizations.

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The Impact of Asset Management Efficiency on the Financial Leverage and Return on Investment in Small Production Enterprises in View of the Covid-19 Pandemic. Case Study: The Jordanian Ministry of Social Development - Zarqa Governorate

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• Ziad Al-Theebeh 11 ,
• Riham Alkabbji 11 ,
• Ala’ Alrazim 11 &
• Ibtisam Abu-Bakr 12  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 495))

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The study aimed to determine the impact of the asset management efficiency of small production enterprises on financial leverage and the return on investment in view of the Covid-19 pandemic. The Jordanian Ministry of Social Development - Zarqa Governorate was selected as a case study. The descriptive-analytical approach was used to achieve the study objectives. The data related to the study variables were collected from the statements of Social Development Directorates in Zarqa Governorate, as these directorates are the case study of the study population represented by 40 directorates of the Ministry of Social Development in Jordan. The study concluded that there is a statistically significant negative impact of the asset management efficiency on the financial leverage in small production enterprises in the Ministry of Social Development - Zarqa Governorate. Also, there is a statistically significant positive impact of the asset management efficiency on the return on investment in small production enterprises in the Ministry of Social Development in Zarqa Governorate. The study recommended providing training courses for these enterprises’ ownership to empower them to face economic crises, especially in view of global economic crises such as the Covid-19 pandemic and increase the ability to reduce financial leverage and raise the return on investment for these enterprises.

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Dabić, M., Stojčić, N., Simić, M., Potocan, V., Slavković, M., Nedelko, Z.: Intellectual agility and innovation in micro and small businesses: the mediating role of entrepreneurial leadership. J. Bus. Res. 123 , 683–695 (2021)

Article   Google Scholar  

Farwana, H., Aliwa, K.: The impact of legal regulation on the banking system in financing small projects contributing to economic development in the Gaza strip. Adm. Econ. Res. 14 (5), 45–64 (2019)

Google Scholar  

Humphries, J.E., Neilson, C., Ulyssea, G.: The evolving impacts of COVID-19 on small businesses since the CARES act (2020)

Ilhiti, Y.H.: Economic importance of production loan in supporting small projects. J. Islamic Sci. 1 (19) (2018)

Jabur, S.A.H., Hasan, A.M.: The role of government loans in the development of small enterprises “an analytical study of small enterprises in Iraq.” Coll. Mag. Univ. Sci. City 11 (2), 147–161 (2019)

Mirhag, M., Ali, M.: Evaluation of mechanisms to support small and medium enterprises in Syria - field study on small and medium enterprises in Latakia Governorate. Tishreen Univ. J. Econ. Leg. Sci. Ser. 41 (5) (2019)

Ministry of Planning and International Cooperation: Local Development and Productivity Enhancement Programs, Amman – Jordan (2020)

Ministry of Social Development: The Concept of Corporate Social Responsibility and its Practical Applications from the Ministry of Social Development in Jordan, Amman – Jordan (2020)

Kabdani, A.: The impact of economic growth on the equity distribution of income in Algeria compared to the Arab countries, analytical and analytical study. PhD thesis in Economic Sciences, University of Abu Bakr Belkaid - Tlemcen, University Year (2012–2013)

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Al-Theebeh, Z., Alkabbji, R., Alrazim, A., Abu-Bakr, I. (2023). The Impact of Asset Management Efficiency on the Financial Leverage and Return on Investment in Small Production Enterprises in View of the Covid-19 Pandemic. Case Study: The Jordanian Ministry of Social Development - Zarqa Governorate. In: Alareeni, B., Hamdan, A. (eds) Explore Business, Technology Opportunities and Challenges ‎After the Covid-19 Pandemic. ICBT 2022. Lecture Notes in Networks and Systems, vol 495. Springer, Cham. https://doi.org/10.1007/978-3-031-08954-1_85

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Artificial intelligence in strategy

Can machines automate strategy development? The short answer is no. However, there are numerous aspects of strategists’ work where AI and advanced analytics tools can already bring enormous value. Yuval Atsmon is a senior partner who leads the new McKinsey Center for Strategy Innovation, which studies ways new technologies can augment the timeless principles of strategy. In this episode of the Inside the Strategy Room podcast, he explains how artificial intelligence is already transforming strategy and what’s on the horizon. This is an edited transcript of the discussion. For more conversations on the strategy issues that matter, follow the series on your preferred podcast platform .

Joanna Pachner: What does artificial intelligence mean in the context of strategy?

Yuval Atsmon: When people talk about artificial intelligence, they include everything to do with analytics, automation, and data analysis. Marvin Minsky, the pioneer of artificial intelligence research in the 1960s, talked about AI as a “suitcase word”—a term into which you can stuff whatever you want—and that still seems to be the case. We are comfortable with that because we think companies should use all the capabilities of more traditional analysis while increasing automation in strategy that can free up management or analyst time and, gradually, introducing tools that can augment human thinking.

Joanna Pachner: AI has been embraced by many business functions, but strategy seems to be largely immune to its charms. Why do you think that is?

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Yuval Atsmon: You’re right about the limited adoption. Only 7 percent of respondents to our survey about the use of AI say they use it in strategy or even financial planning, whereas in areas like marketing, supply chain, and service operations, it’s 25 or 30 percent. One reason adoption is lagging is that strategy is one of the most integrative conceptual practices. When executives think about strategy automation, many are looking too far ahead—at AI capabilities that would decide, in place of the business leader, what the right strategy is. They are missing opportunities to use AI in the building blocks of strategy that could significantly improve outcomes.

I like to use the analogy to virtual assistants. Many of us use Alexa or Siri but very few people use these tools to do more than dictate a text message or shut off the lights. We don’t feel comfortable with the technology’s ability to understand the context in more sophisticated applications. AI in strategy is similar: it’s hard for AI to know everything an executive knows, but it can help executives with certain tasks.

When executives think about strategy automation, many are looking too far ahead—at AI deciding the right strategy. They are missing opportunities to use AI in the building blocks of strategy.

Joanna Pachner: What kind of tasks can AI help strategists execute today?

Yuval Atsmon: We talk about six stages of AI development. The earliest is simple analytics, which we refer to as descriptive intelligence. Companies use dashboards for competitive analysis or to study performance in different parts of the business that are automatically updated. Some have interactive capabilities for refinement and testing.

The second level is diagnostic intelligence, which is the ability to look backward at the business and understand root causes and drivers of performance. The level after that is predictive intelligence: being able to anticipate certain scenarios or options and the value of things in the future based on momentum from the past as well as signals picked in the market. Both diagnostics and prediction are areas that AI can greatly improve today. The tools can augment executives’ analysis and become areas where you develop capabilities. For example, on diagnostic intelligence, you can organize your portfolio into segments to understand granularly where performance is coming from and do it in a much more continuous way than analysts could. You can try 20 different ways in an hour versus deploying one hundred analysts to tackle the problem.

Predictive AI is both more difficult and more risky. Executives shouldn’t fully rely on predictive AI, but it provides another systematic viewpoint in the room. Because strategic decisions have significant consequences, a key consideration is to use AI transparently in the sense of understanding why it is making a certain prediction and what extrapolations it is making from which information. You can then assess if you trust the prediction or not. You can even use AI to track the evolution of the assumptions for that prediction.

Those are the levels available today. The next three levels will take time to develop. There are some early examples of AI advising actions for executives’ consideration that would be value-creating based on the analysis. From there, you go to delegating certain decision authority to AI, with constraints and supervision. Eventually, there is the point where fully autonomous AI analyzes and decides with no human interaction.

Because strategic decisions have significant consequences, you need to understand why AI is making a certain prediction and what extrapolations it’s making from which information.

Joanna Pachner: What kind of businesses or industries could gain the greatest benefits from embracing AI at its current level of sophistication?

Yuval Atsmon: Every business probably has some opportunity to use AI more than it does today. The first thing to look at is the availability of data. Do you have performance data that can be organized in a systematic way? Companies that have deep data on their portfolios down to business line, SKU, inventory, and raw ingredients have the biggest opportunities to use machines to gain granular insights that humans could not.

Companies whose strategies rely on a few big decisions with limited data would get less from AI. Likewise, those facing a lot of volatility and vulnerability to external events would benefit less than companies with controlled and systematic portfolios, although they could deploy AI to better predict those external events and identify what they can and cannot control.

Third, the velocity of decisions matters. Most companies develop strategies every three to five years, which then become annual budgets. If you think about strategy in that way, the role of AI is relatively limited other than potentially accelerating analyses that are inputs into the strategy. However, some companies regularly revisit big decisions they made based on assumptions about the world that may have since changed, affecting the projected ROI of initiatives. Such shifts would affect how you deploy talent and executive time, how you spend money and focus sales efforts, and AI can be valuable in guiding that. The value of AI is even bigger when you can make decisions close to the time of deploying resources, because AI can signal that your previous assumptions have changed from when you made your plan.

Joanna Pachner: Can you provide any examples of companies employing AI to address specific strategic challenges?

Yuval Atsmon: Some of the most innovative users of AI, not coincidentally, are AI- and digital-native companies. Some of these companies have seen massive benefits from AI and have increased its usage in other areas of the business. One mobility player adjusts its financial planning based on pricing patterns it observes in the market. Its business has relatively high flexibility to demand but less so to supply, so the company uses AI to continuously signal back when pricing dynamics are trending in a way that would affect profitability or where demand is rising. This allows the company to quickly react to create more capacity because its profitability is highly sensitive to keeping demand and supply in equilibrium.

Joanna Pachner: Given how quickly things change today, doesn’t AI seem to be more a tactical than a strategic tool, providing time-sensitive input on isolated elements of strategy?

Yuval Atsmon: It’s interesting that you make the distinction between strategic and tactical. Of course, every decision can be broken down into smaller ones, and where AI can be affordably used in strategy today is for building blocks of the strategy. It might feel tactical, but it can make a massive difference. One of the world’s leading investment firms, for example, has started to use AI to scan for certain patterns rather than scanning individual companies directly. AI looks for consumer mobile usage that suggests a company’s technology is catching on quickly, giving the firm an opportunity to invest in that company before others do. That created a significant strategic edge for them, even though the tool itself may be relatively tactical.

Joanna Pachner: McKinsey has written a lot about cognitive biases  and social dynamics that can skew decision making. Can AI help with these challenges?

Yuval Atsmon: When we talk to executives about using AI in strategy development, the first reaction we get is, “Those are really big decisions; what if AI gets them wrong?” The first answer is that humans also get them wrong—a lot. [Amos] Tversky, [Daniel] Kahneman, and others have proven that some of those errors are systemic, observable, and predictable. The first thing AI can do is spot situations likely to give rise to biases. For example, imagine that AI is listening in on a strategy session where the CEO proposes something and everyone says “Aye” without debate and discussion. AI could inform the room, “We might have a sunflower bias here,” which could trigger more conversation and remind the CEO that it’s in their own interest to encourage some devil’s advocacy.

We also often see confirmation bias, where people focus their analysis on proving the wisdom of what they already want to do, as opposed to looking for a fact-based reality. Just having AI perform a default analysis that doesn’t aim to satisfy the boss is useful, and the team can then try to understand why that is different than the management hypothesis, triggering a much richer debate.

In terms of social dynamics, agency problems can create conflicts of interest. Every business unit [BU] leader thinks that their BU should get the most resources and will deliver the most value, or at least they feel they should advocate for their business. AI provides a neutral way based on systematic data to manage those debates. It’s also useful for executives with decision authority, since we all know that short-term pressures and the need to make the quarterly and annual numbers lead people to make different decisions on the 31st of December than they do on January 1st or October 1st. Like the story of Ulysses and the sirens, you can use AI to remind you that you wanted something different three months earlier. The CEO still decides; AI can just provide that extra nudge.

Joanna Pachner: It’s like you have Spock next to you, who is dispassionate and purely analytical.

Yuval Atsmon: That is not a bad analogy—for Star Trek fans anyway.

Joanna Pachner: Do you have a favorite application of AI in strategy?

Yuval Atsmon: I have worked a lot on resource allocation, and one of the challenges, which we call the hockey stick phenomenon, is that executives are always overly optimistic about what will happen. They know that resource allocation will inevitably be defined by what you believe about the future, not necessarily by past performance. AI can provide an objective prediction of performance starting from a default momentum case: based on everything that happened in the past and some indicators about the future, what is the forecast of performance if we do nothing? This is before we say, “But I will hire these people and develop this new product and improve my marketing”— things that every executive thinks will help them overdeliver relative to the past. The neutral momentum case, which AI can calculate in a cold, Spock-like manner, can change the dynamics of the resource allocation discussion. It’s a form of predictive intelligence accessible today and while it’s not meant to be definitive, it provides a basis for better decisions.

Joanna Pachner: Do you see access to technology talent as one of the obstacles to the adoption of AI in strategy, especially at large companies?

Yuval Atsmon: I would make a distinction. If you mean machine-learning and data science talent or software engineers who build the digital tools, they are definitely not easy to get. However, companies can increasingly use platforms that provide access to AI tools and require less from individual companies. Also, this domain of strategy is exciting—it’s cutting-edge, so it’s probably easier to get technology talent for that than it might be for manufacturing work.

The bigger challenge, ironically, is finding strategists or people with business expertise to contribute to the effort. You will not solve strategy problems with AI without the involvement of people who understand the customer experience and what you are trying to achieve. Those who know best, like senior executives, don’t have time to be product managers for the AI team. An even bigger constraint is that, in some cases, you are asking people to get involved in an initiative that may make their jobs less important. There could be plenty of opportunities for incorpo­rating AI into existing jobs, but it’s something companies need to reflect on. The best approach may be to create a digital factory where a different team tests and builds AI applications, with oversight from senior stakeholders.

The big challenge is finding strategists to contribute to the AI effort. You are asking people to get involved in an initiative that may make their jobs less important.

Joanna Pachner: Do you think this worry about job security and the potential that AI will automate strategy is realistic?

Yuval Atsmon: The question of whether AI will replace human judgment and put humanity out of its job is a big one that I would leave for other experts.

The pertinent question is shorter-term automation. Because of its complexity, strategy would be one of the later domains to be affected by automation, but we are seeing it in many other domains. However, the trend for more than two hundred years has been that automation creates new jobs, although ones requiring different skills. That doesn’t take away the fear some people have of a machine exposing their mistakes or doing their job better than they do it.

Joanna Pachner: We recently published an article about strategic courage in an age of volatility  that talked about three types of edge business leaders need to develop. One of them is an edge in insights. Do you think AI has a role to play in furnishing a proprietary insight edge?

Yuval Atsmon: One of the challenges most strategists face is the overwhelming complexity of the world we operate in—the number of unknowns, the information overload. At one level, it may seem that AI will provide another layer of complexity. In reality, it can be a sharp knife that cuts through some of the clutter. The question to ask is, Can AI simplify my life by giving me sharper, more timely insights more easily?

Joanna Pachner: You have been working in strategy for a long time. What sparked your interest in exploring this intersection of strategy and new technology?

Yuval Atsmon: I have always been intrigued by things at the boundaries of what seems possible. Science fiction writer Arthur C. Clarke’s second law is that to discover the limits of the possible, you have to venture a little past them into the impossible, and I find that particularly alluring in this arena.

AI in strategy is in very nascent stages but could be very consequential for companies and for the profession. For a top executive, strategic decisions are the biggest way to influence the business, other than maybe building the top team, and it is amazing how little technology is leveraged in that process today. It’s conceivable that competitive advantage will increasingly rest in having executives who know how to apply AI well. In some domains, like investment, that is already happening, and the difference in returns can be staggering. I find helping companies be part of that evolution very exciting.

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Backtesting a Trading Strategy in Python With Datalore and AI Assistant

Over lunch the other day, a friend mentioned his brother, a professional asset manager, swears by a simple mean reversion trading strategy. His strategy consists of buying the 10 biggest losers in the stock market each day and selling them at the close of the following trading session. I asked him if he knew which index or exchange his brother used to pick his losers from, and he told me that he wasn’t certain. As a curious casual investor, I decided to put this strategy to the test using historical data and backtest the trading strategy with Python. 

Disclaimer: This article is for informational and educational purposes only and is not intended to serve as personal financial advice.

What you will learn from this backtesting tutorial

In this article, I’ll walk through the process of backtesting a daily Dow Jones mean reversion strategy using Python in Datalore notebooks . To make it accessible even for those with limited coding experience, I’ll leverage Datalore’s AI Assistant capabilities. I’ll also show how intuitive prompts can be used to create the key components of the backtest, and demonstrate Datalore’s interactive charting and reporting features to effectively analyze and share the backtest results. 

To make things more challenging for myself (and easier for you), I won’t write a single line of code myself. Every line of code in this tutorial will be generated by AI as shown below:

Still, building a comprehensive backtesting system does require significant Python expertise. But for those who don’t yet possess strong Python skills, this is where Datalore’s AI code assistance comes in. With Datalore you can:

• Generate the needed code from natural language prompts, putting backtesting in reach for Python beginners.
• Leverage a cloud-hosted Jupyter environment, eliminating the need to manage your own setup.
• Create interactive, self-documenting reports to share methodology and results with stakeholders.

If you have experience with Python, you can access the example notebook of the implemented backtesting strategy here .

Open Datalore Notebook

Understanding the basics of backtesting

Before diving into the specific strategy we’re exploring in this article, let’s take a moment to understand what backtesting is and why it’s a critical tool for any trader or investor looking to validate their trading strategies using historical data.

Backtesting is a process by which traders simulate a trading strategy on past data to see how it would have performed. This method allows traders to evaluate and refine their strategies before applying them in real market conditions. By backtesting a strategy, one can get insights into its potential profitability, risk, and other performance metrics, without risking actual capital.

The concept is based on the assumption that historical market behavior can provide insights into future market movements. While not foolproof, backtesting offers a way to statistically analyze the likelihood of a strategy’s success based on past performance.

The mean reversion strategy: a case study in backtesting

The specific trading strategy we will backtest in this article is based on the principle of mean reversion. This financial theory suggests that asset prices and returns eventually revert back to their long-term mean or average level. Our strategy involves:

• Identifying the 10 biggest losers : At the close of each trading day, we identify the 10 stocks within the Dow Jones Industrial Average (DJIA) that have declined the most in percentage terms from the previous day.
• Executing trades : We then purchase an equal dollar value of each of these 10 stocks and hold them until the close of the following trading day, at which point we sell all positions. Immediately afterward, we repeat the process by purchasing the 10 biggest losers of that day.
• Performance evaluation : To assess the viability of this strategy, we compare its performance to that of the DJIA itself, providing an “apples-to-apples” comparison to see if our mean reversion strategy would have outperformed the broader stock market over time.

The DJIA, a stock market index that tracks 30 large, publicly-owned companies trading on the New York Stock Exchange and the Nasdaq, serves as our testing ground. By applying our strategy to the constituents of the DJIA, we aim to explore the potential of mean reversion in a real-world scenario.

To objectively evaluate this strategy, we’ll use 10 years of daily price data for all current DJIA constituents. Given the complexity of accurately modeling changes in the index’s composition over time, we’ll assume that the DJIA’s constituents have remained unchanged over the past 10 years. In our evaluation, we’ll calculate common performance metrics, including:

• Annualized return
• Annualized volatility
• Sharpe ratio
• Maximum drawdown

In the sections that follow, we’ll dive deeper into the process of implementing this strategy, from retrieving historical price data to calculating performance metrics, all with the help of Python and the Datalore AI assistant.

Retrieving historical Dow Jones stock prices 

Here’s a concise prompt you could provide to the Datalore AI to retrieve the constituents of the DJIA and their historical returns:

The following code was produced by the AI using this prompt to effectively complete the task:

Implementing the mean reversion strategy

Now that we have the historical price data for the DJIA constituents, we can proceed with implementing the mean reversion strategy. The steps involved are as follows:

• Calculate the daily returns for each stock.
• Identify the 10 stocks with the lowest returns (biggest losers) for each trading day.
• Simulate buying an equal amount of each of these 10 stocks at the close of the trading day.
• Simulate selling all 10 positions at the close of the following trading day.
• Repeat this process for the entire 10-year period.

Step 1: Calculate daily returns

To begin implementing the mean reversion strategy, we first need to calculate the daily returns for each stock in our data_filled DataFrame.

We can use the following prompt to generate the code for this step:

The AI generates the following code:

Step 2: Identify biggest losers

Next, we will identify the 10 stocks with the lowest returns (biggest losers) for each trading day.

Step 3: Simulate trades

Now, we will simulate buying an equal amount of each of the 10 biggest losers at the close of each trading day and selling all positions at the close of the following trading day. We’ll assume an initial capital of $100,000.

Step 4: Calculate performance metrics

Finally, we will calculate the strategy’s annualized return, annualized volatility, Sharpe ratio (assuming a risk-free rate of 0), and maximum drawdown.

Step 5: Compare with the Dow Jones index

To determine if our mean reversion strategy outperformed the market, we’ll compare its Sharpe ratio with that of the DJIA. We’ll use the SPDR Dow Jones Industrial Average ETF Trust (DIA) as a proxy for the Dow Jones. The point here is to find out if betting on the losers of the Dow Jones, rather than the Dow Jones itself, is a more profitable strategy in hindsight.

Step 6: Compare our mean reversion strategy’s performance to that of the Dow Jones ETF

To better understand the performance of our mean reversion strategy compared to investing in the Dow Jones, we will visualize the annual returns, standard deviations, and Sharpe ratios of both strategies. Let’s break down these metrics and why they are relevant to this analysis:

• Annualized return: The average annual return of an investment. It allows for easy comparison of returns across different time frames and investments. We compare the annualized returns of our strategy and the Dow Jones ETF to see which generated higher returns on average.
• Annualized volatility: A measure of the dispersion of returns around the average return. Higher volatility indicates greater risk. Comparing the annualized volatility of our strategy and the Dow Jones ETF shows which had more stable returns.
• Sharpe ratio: A risk-adjusted performance measure comparing excess return to volatility. It reveals whether returns are due to smart decisions or excessive risk. A higher Sharpe ratio indicates better risk-adjusted returns. We compare the Sharpe ratios to determine which offered better returns relative to risk.

Examining these metrics side by side provides insights into the risk-return characteristics of our strategy and the Dow Jones ETF, allowing us to assess whether our strategy can outperform the market on a risk-adjusted basis.

Backtesting results:

The results of our analysis show that the mean reversion strategy outperformed the Dow Jones ETF in terms of both annualized returns and risk-adjusted returns. The mean reversion strategy generated higher annual returns while also achieving a higher Sharpe ratio, indicating that it provided better returns relative to the risk taken compared to the Dow Jones ETF.

Step 7: Visualize portfolio growth

To better understand the performance of our mean reversion strategy compared to investing in the Dow Jones, let’s visualize the growth of a hypothetical $100,000 portfolio over time for both strategies.

When we run the code, we get the following output:

The visualization of the portfolio growth over time provides a clear and compelling illustration of the superior performance of our mean reversion strategy compared to investing in the Dow Jones ETF. Starting with an initial investment of $100,000, the mean reversion strategy’s portfolio value grew to over $350,000 by the end of the 10-year period, demonstrating a significant return on investment.

In contrast, the portfolio value of the Dow Jones ETF, represented by the DIA, only reached a level below $300,000 over the same time frame. This stark difference in portfolio growth highlights the potential of the mean reversion strategy to outperform the broader market, as represented by the DJIA.

The divergence in portfolio values between the two strategies is particularly evident in the later years of the analysis, where the mean reversion strategy’s portfolio continues to climb at a faster rate compared to the Dow Jones ETF. This observation underscores the mean reversion strategy’s ability to capitalize on short-term overreactions in the market and generate superior returns over the long run.

However, it is essential to note that past performance does not guarantee future results. While historical analysis suggests that the mean reversion strategy has outperformed the Dow Jones ETF, it is crucial for investors to consider their own risk tolerance, financial objectives, and conduct thorough research before making any investment decisions.

Fine-tuning and optimization

While our mean reversion strategy has demonstrated impressive performance compared to the Dow Jones ETF, there are several areas where the analysis could be further refined and optimized:

• Lookback period: In this analysis, we identified the 10 biggest losers based on a single day’s returns. Experimenting with different lookback periods, such as using the average returns over the past 3, 5, or 10 days, could potentially improve the strategy’s performance by filtering out noise and focusing on more significant trends.
• Portfolio rebalancing: Our current strategy equally distributes capital among the 10 biggest losers. Exploring different portfolio weighting schemes , such as weighting stocks based on the magnitude of their losses or their market capitalization, could potentially enhance the strategy’s returns and risk management.
• Risk management: Implementing risk management techniques, such as setting stop-loss orders or dynamically adjusting position sizes based on market volatility, could help mitigate potential drawdowns and improve the strategy’s risk-adjusted returns.
• Transaction costs: Our analysis assumes no transaction costs. Incorporating realistic transaction costs, such as commissions and slippage, would provide a more accurate picture of the strategy’s net performance and help identify potential areas for optimization.
• Utilizing a Python backtesting library: While we implemented the mean reversion strategy from scratch, utilizing a Python backtesting library could streamline the process and provide additional features. Popular python backtesting libraries include Backtrader , which offers a simple and intuitive interface, and Zipline , which provides a comprehensive set of tools for complex strategies. These libraries differ in terms of performance, ease of use, and community support, so it’s essential to evaluate them based on the specific requirements of the backtesting project.
• Data cleaning with Datalore’s interactive tables: Instead of relying on AI to write the correct error handling code, we could leverage Datalore’s interactive tables for data cleaning tasks, such as dropping duplicates and columns. Datalore’s interactive tables make data cleaning easy and intuitive, allowing users to quickly identify and remove duplicates or unnecessary columns with just a few clicks. This feature streamlines the data preparation process and ensures that the data used for backtesting is clean and reliable.

By exploring these areas for fine-tuning and optimization, investors and analysts can further refine the mean reversion strategy and potentially unlock even greater performance potential. However, it’s essential to approach these optimizations with caution and thoroughly backtest any modifications to ensure they are robust and effective across different market conditions.

In conclusion, our exploration of a simple mean reversion strategy using the Dow Jones Industrial Average constituents has yielded compelling results. By leveraging the power of Python and the AI-assisted capabilities of Datalore notebooks, we were able to efficiently backtest the strategy and compare its performance with the broader market.

The results of our analysis demonstrate that the mean reversion strategy, which involves buying the 10 biggest losers in the Dow Jones Index each day and selling them at the close of the following trading day, outperformed the Dow Jones ETF in terms of both annualized returns and risk-adjusted returns. The visualization of the hypothetical portfolio’s growth over time further reinforces the potential of this strategy to generate superior returns compared to simply investing in the market index. 

However, it is crucial to emphasize that past performance does not guarantee future results, and investors should always consider their individual risk tolerance and financial goals before implementing any investment strategy. Nonetheless, this exercise serves as a powerful demonstration of how Python, coupled with AI-assisted tools like Datalore, can empower investors and analysts to test and refine trading strategies, ultimately leading to more informed and data-driven investment decisions.

If you would like to see an executive summary of the report in Datalore, you can visit this link .

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From stressed to secure: transforming financial well-being at work.

A major telecommunications organization faced a challenge: their widely diverse employee population was stressed due to money strains, especially given the turbulent economic conditions. They needed a financial well-being program that would meet each employee’s unique needs and positively change behaviors around money without selling products or services.

After reevaluating their EAP and benefits, the client discovered they didn’t offer a benefit that would help employees manage financial health.

MSA’s Solution

MSA created an unbiased, holistic financial well-being solution, providing personalized coaching that’s rooted in behavioral change, a digital platform, and guided and self-help resources. They also offer many on-site trainings, events, and webinars for locations across the country.

The Results

With the support of C-Suite champions and MSA’s Customer Success Management team, program efforts have been outstanding:

• 14.3% utilization across all program features*
• 45% increase in financial well-being scores*
• 94 Net Promoter Score (NPS) for employees working with a coach*
• 21% increase in work productivity*
• $1,900+ decrease in debt*
• 50+ point improvement in credit score*
• 2% of salary increase in retirement contribution*

The Details

• MSA Client: Major Telecommunications Organization
• Employees: 70,000+
• Main Office: Bellevue, WA
• Locations: 6,400+

Challenge #1: Accessibility

Many locations and different work schedules made arranging benefit communications difficult.

Solution: MSA’s robust benefits communication guide and calendar solved the problem of what, who, and when.

Challenge #2: Benefit Integration

This client’s ideal partner would be able to integrate their benefit with other existing company benefits.

Solution: MSA’s program integrates an employer’s benefits package, boosting utilization of benefits like 401k, student loans, etc.

Challenge #3: On-site Events

This client’s many locations needed a more robust on-site education package.

Solution: MSA’s program allows for adding as many educational events as needed. MSA happily supports 20 events per year for this client.

The Feedback

Here’s what a major telecommunications employee and MSA member had to say:

“[My Money Coach] was great! He set clear expectations during the call, committed to those expectations, and wasted no time to dive right into educating me about strategies. I enjoyed the experience, and I have faith that I’ll be able to follow the practical steps he provided.”

To learn more about the MSA financial well-being solution and how you can help your workforce, call 800-984-6811.

* My Secure Advantage, Inc., 2023. Average based on MSA member self-reported data, when working with a coach on this specific issue, from 1/1/22 – 12/31/22.

Testimonial provided by member of MSA. They did not receive compensation of any kind for their statement.

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• Infotainment /

Know How To Be A Finance Guru While Studying Abroad

• Updated on  
• May 15, 2024

Don’t worry Champ, you are not alone in the financial juggle.

Almost every study abroad student faces financial issues once through their journey. And it is very normal too, as you were not used to doing so much financial planning while you were home. Most of the finances were managed by your parents then and you need not take all the money matters into your hands. But now as you are all by yourself and have to manage everything, you need to run ahead of the expenses and plan them before if you don’t want to run behind the overspending. 

Are you not sure how are you going to manage all this? Don’t worry. We will help you by giving you some tips that you can follow to manage your finances and be a finance guru while you study abroad.

This Blog Includes:

Make a budget , make a list of your spending priorities, rely on self for basic works, look for sales, take up part-time jobs , take public transports, use student card, financial tips to follow  .

Make note of all the tips that are mentioned below and remember to follow these, make your financial plan and in no time you will be a champ at managing finances:

Any financial planning starts with making a budget. If you are not a person who can run on a budget or financial scheduling, I am sorry bud but you can not succeed in becoming a financial guru. So if you really want to be better at this, take a pen and paper at the start of each month and pen down all your finances and spending that you will be required to make. Make a list of all the monthly expenses, and set a budget. This does not mean that you will have to strike out all your leisure expenses. You just need to manage them and systemize them in a way that they do not exceed the required or allowed limit. Make sure at the end of the month you have not ever spent, if you have, then re-plan your budget and limit the expenses.

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Segregate your priority spending from your non-important spending. Separate the spending that can wait. You will need to pay your rent and fees every month, also the expenses of food can not wait, but a trip can definitely wait if you are short on budget or a random shopping can wait another month. Similarly plan for yourself and first spend on what is important.

A big part of your money is going to go on your food expenses, cleaning expenses, laundry, etc. and this big part can also be saved and used at a better place if you try cooking for yourself. At first, you can start by cooking one meal and eating others our, later you can cook for yourself all the meals and save a whooping amount. Similarly, you can start by doing laundry on Sundays and save money on that too. Saving for these basic activities will give you a lot of money that can be used in your leisure.

Though the students do not have this much patience, if you can build then it will be of great help for you. Be patient in your shopping, wait for the seasonal sales. Shop when there are sales and you will end up saving a lot of money. If not offline, all the online shopping platforms always sell items at cheaper prices and also come up with many sales every couple of months, shopping online can also help you in your mission. 

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If you feel like your expenses are way greater than the amount that is available to you, then find an alternative to earn extra money. Most of the study abroad students take up part-time jobs along with studying. There are plenty of options available. Aptiona like teaching, data entry, being an agent, giving courses, etc. there are plenty of options, you just need to find the best for yourself and make a side income to support your expenses and also add experience to your resume.

You will need to travel a lot if your accommodation is far from the university or if you are an enthusiast. You definitely can not remove the cost of travelling completely, but it can be cut through. You can replace the private transport system with public transport. Public transport costs way less and can save you a handful of money each day.

If you are a student, you have an advantage. Advantages of playing your student card. You can save plenty of money by using your student card for travelling, sightseeing, and museums and some of the cafes also give discounts to the students. Every time you go to a new place, without shying away do ask if there are any student discounts. It will help you master your plan of savings.

It is a sure fact that if you can adopt these habits then your finances will never run out and you will have a very systematic and managed lifestyle. You may also help others in managing their budget and get inspired by you. So don’t wait for the next time you run out of money, start your planning now and be an expert at it.

Here are some amazing Travel hacks for study abroad students studying on a budget

Hope this blog on How to become a finance guru while studying abroad be of use to you and help you make your finances systematic. To read more such interesting blogs, kindly follow Infotainment and to read about studying abroad follow Leverage Edu .

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Anmol Makhija

Content writer at Leverage Edu with expertise in Blog writing, and editing. Write best about informative entertainment and study abroad blogs.

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