Indirect Taxes: Definition, Examples, Types, Importance, Meaning, vs. Direct Taxes

Indirect taxes are very common no matter where you are from. These taxes are charged on goods and services instead of directly on income or wealth.

They are usually collected by the government from businesses or producers, who then pass on the tax to the consumers in the form of higher prices.

It's an important part of a country's revenue system and is used to fund public services and infrastructure.

What are Indirect Taxes?

Indirect taxes are essentially the additional costs added to the price of goods and services that consumers purchase.

Unlike direct taxes, which individuals pay straight to the government (like income tax), indirect taxes are collected by businesses at the point of sale and then forwarded to the government.

This mechanism makes them less visible to consumers, as they are integrated into the total price rather than listed as a separate charge.

Common examples include sales tax, value-added tax (VAT), and excise duties on specific items such as alcohol and tobacco.

Indirect taxes serve as a crucial revenue stream for governments, funding various public services and infrastructure projects without requiring direct payments from citizens.

How Indirect Taxes Work

Indirect taxes work by adding a cost to goods and services at different stages of production and distribution, ultimately paid by the consumer. When a product is made, each step from manufacturing to reaching the store shelf may involve a tax.

The final price you see includes these taxes, making them somewhat hidden within the total cost.

For example, when you buy a bottle of soda, the price includes taxes charged on the production and sale of that soda.

The company making the soda pays taxes on the materials and processes, then sets the final price high enough to cover these costs plus a profit.

This way, the government collects tax revenue indirectly through your purchases, without you directly sending money to them.

Examples of Indirect Taxes

Here are some of the most common indirect taxes that can be found in different countries around the world

1. VAT or Value-added Tax: This is a consumption-based tax that is added to the final price of goods and services at each stage of production.
2. Sales Tax: Sales tax is another common form of indirect tax that is added to the final price of goods and services at the point of sale.
3. Excise Duty: This is a tax charged on specific goods, such as alcohol, tobacco, and gasoline.
4. Customs Duty: Also known as import or export duty, this is a tax charged on goods crossing international borders.
5. Stamp Duty: This tax is charged on legal documents such as property deeds, contracts, and agreements.

Importance of Indirect Taxes

Indirect taxes play a crucial role in generating revenue for the government and funding public services. These taxes are often seen as fairer than direct taxes, as they are based on consumption rather than income or wealth.

Indirect taxes are also used to regulate the market by influencing consumer behavior and controlling the demand for certain goods and services.

Another important aspect of indirect taxes is that they can be used to promote social and environmental objectives. For example, higher taxes on cigarettes and alcohol can help reduce consumption and improve public health.

Conclusion

In conclusion, indirect taxes are an essential part of the government's taxation system. They not only provide a significant source of revenue but also serve as a tool for economic regulation and social change. By understanding how different types of indirect taxes work, people and businesses can better manage their finances and contribute to the overall development of society.

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Term Structure of Expected Stock Returns

In the financial literature and media, we often encounter the concept of term structure, such as the term structure of volatility and the term structure of interest rates. Reference [1] introduced the concept of term structure of expected stock returns.

Essentially, the authors utilize options data to first calculate a risk-neutral PDF using the Black-Scholes model. Then, the risk-neutral PDF is risk-adjusted to arrive at the "real" probabilities. To do this, the author assumes power utility preferences for the investors. This approach allows us to obtain the real-world PDF and derive the option-implied expected returns. The paper pointed out,

This paper has proposed a new method to derive the term structure of expected stock returns. Using option prices, a forward‐looking term structure was derived, which we call the “equity curve”. We described how the shape of the equity curve has empirically evolved and analysed its predictive power for future stock returns. Three main results have emerged from the analysis of the US stock market over the period between 1997 and 2017. First, a higher level of the equity curve is associated with higher future stock returns. Second, the slope of the equity curve is also related to future stock returns in a theoretically expected manner. A positive slope (i.e., short‐term expected stock returns are lower than long‐term returns) is followed by future realised returns which are lower in the short term (1 month) than in the long term (1 quarter or 1 year). Third, a steeper slope (either positive or negative) is associated with a larger absolute difference between short‐term and long‐term returns. Thereby, the method used to derive the equity curve from option prices has economically and statistically reasonable properties.

This paper proposed an important concept that should be incorporated into modern portfolio management.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Olaf Stotz, The Equity Curve and Its Relation to Future Stock Returns, J. Risk Financial Manag. 2020, 13, 19

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Cash Sweeping System: Definition, Advantages, Disadvantages, Meaning, Business vs. Personal

Companies aim to maximize the returns on any excess funds they have. Sometimes, they invest these funds, but it may not always be an option. Nonetheless, companies may use automated ways to maximize those returns. One of these is a cash sweeping system.

What is a Cash Sweeping System?

A cash sweeping system is a financial tool to manage cash flow effectively. It involves the automatic transfer of excess cash from one account to another, such as from a checking account to an interest-bearing account or a debt repayment account. This system helps companies minimize idle cash balances, maximize interest earnings, and reduce interest expenses by ensuring that surplus funds get utilized actively.

By implementing a cash sweeping system, companies can optimize their liquidity management and improve their overall financial efficiency. The system streamlines managing cash balances across different accounts, allowing companies to maximize the returns from their available funds. Additionally, cash sweeping enhances cash flow forecasting accuracy and provides greater control over cash resources, which is particularly beneficial for companies with fluctuating cash flows.

How does a Cash Sweeping System work?

A cash sweeping system operates through automated transfers of excess cash from one account to another within a company's financial structure. It begins by identifying surplus funds in a specified account, usually a checking account, surpassing a predetermined threshold. Once this excess is detected, the system automatically moves the surplus to another designated account, such as an interest-bearing account or one allocated for debt repayment.

This automated process optimizes cash utilization by putting idle funds to work, either earning interest in investment accounts or reducing interest costs by paying down debt. It enhances financial efficiency by ensuring that cash is actively managed and deployed where it can generate value for the company. Additionally, the system provides real-time monitoring and control over cash balances, supporting better liquidity management and improved overall financial performance.

What is the difference between a Business and Personal Cash Sweeping System?

A personal cash sweeping system typically involves individuals managing their personal finances by automatically transferring excess funds from one account to another within their own banking structure. For example, an individual might set up a cash sweeping arrangement to move surplus funds from a checking account to a high-yield saving or an investment account.

On the other hand, a business cash sweeping system helps companies to manage their corporate finances more effectively. It involves automated transfers of excess cash from operational accounts, such as checking accounts, to designated accounts for investment, debt repayment, or other strategic purposes. The objective is to improve liquidity management, minimize idle cash balances, reduce interest expenses, and maximize returns on available funds.

What are the advantages and disadvantages of a Cash Sweeping System?

A cash sweeping system offers several advantages for businesses, including improved liquidity management, optimized cash flow, and enhanced financial efficiency. By automatically transferring excess cash to designated accounts for investment or debt repayment, companies can minimize idle cash balances, maximize interest earnings, and reduce interest expenses.

However, there are also some disadvantages to consider with a cash sweeping system. One potential drawback is the complexity of implementation and maintenance, especially for businesses with multiple accounts and varying cash flow patterns. Setting up and managing the rules, thresholds, and transfers within the system requires careful planning, monitoring, and coordination.

Conclusion

A cash sweeping system is an automated process that moves excess cash from one account to another. Usually, these transfers happen between non-interest- and interest-bearing accounts. This system follows a threshold that the user can set up. A cash sweeping system can work for individuals and businesses with some advantages and disadvantages.

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Profitability of Cross-Sectional Momentum Strategy

Cross-sectional momentum is an investment strategy that involves ranking and selecting assets based on their past performance relative to their peers. Unlike time-series momentum, which looks at an individual asset's past performance in isolation, cross-sectional momentum compares multiple assets to each other. Investors buy the top-performing assets and sell the underperformers, capitalizing on the persistence of trends within the market. This strategy exploits the tendency of winners to continue winning and losers to keep losing over medium-term periods, typically ranging from three to twelve months.

Reference [1] examines the profitability of cross-sectional momentum strategy over the past decades and concludes that its profitability has diminished. The author subsequently proposes an approach to enhance the strategy's returns,

In my exploration of momentum portfolio strategies, I demonstrated that it is possible to enhance both the Sharpe ratio and abnormal returns, for example through the Crash-WML (CWML) approach. This strategy significantly mitigates momentum crashes, showcasing resilience in periods of market downturns. Also, I discovered that how one calculates portfolios—whether equal weighted or value weighted, or volatility scaled—results in different sensitivities to financial factors, influencing Sharpe ratios, alphas, and skewness distinctly. I tried to do a good job documenting the process of how I constructed my portfolios which is not always present in the literature.

In short, by applying techniques such as volatility scaling and adjusting long and short positions based on the market state we can significantly enhance the efficacy of the momentum strategy, restoring its former robustness.

This study is another contribution to a growing body of research examining how to use volatility to size positions in an investment portfolio.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Pyry Pohjantähti, Revisiting (Revitalizing) Momentum, 2024, Aalto University School of Business

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Ex-Ante vs. Ex-Post: Definition, Meaning, Explanation, Example

Ex-ante and Ex-Post are Latin phrases that are commonly used in economics and financial analysis to describe different types of approaches or perspectives.

These terms can seem a bit intimidating at first but they are both important and useful concepts to understand when dealing with economic or financial decisions.

By understanding how they work businesses and companies can better assess their strategies and make more informed decisions.

What is Ex-ante?

Ex-ante means "before the event". In financial terms, this means making predictions or estimates about future outcomes based on available information and assumptions.

It is a forward-looking approach that focuses on forecasting and planning for potential scenarios. It's basically a prediction method where potential outcomes are analyzed before a decision is made.

How Ex-ante Works

Ex-ante is as simple as it sounds. It involves examining potential future outcomes before taking action.

This can be applied in various areas of economics and finance, such as investment decisions, budgeting, risk management, mergers, and policy-making.

For example, a company may use ex-ante analysis to forecast sales for the upcoming year based on current market trends and consumer behavior.

This information can then be used to make decisions on production levels, inventory, and marketing strategies.

What is Ex-Post?

Ex-post is the exact opposite of ex-ante, meaning "after the event". It refers to analyzing outcomes after looking at the actual past results. This approach is based on historical data and is used to make evaluations and assessments.

It can be used to evaluate a security's performance after an investment or to analyze the effectiveness of a policy after its implementation.

How Ex-Post Works

To calculate the ex-post value of an asset, start by looking at its initial and final values over a specific period, often less than a year. This involves assessing how much the asset’s value increased or decreased and any income it generated.

The initial value is its price at the beginning of the purchase price within this timeframe. The final value is its current market price.

This process helps evaluate how an investment performed and compare actual returns against expected ones to measure investment risk.

Key Differences Between Ex-ante and Ex-post

The main difference between ex-ante and ex-post is the timing of analysis. While ex-ante looks at potential outcomes before an event, ex-post analyzes actual outcomes after the event has occurred.

Ex-ante is often used for decision-making purposes, while ex-post is more commonly used for evaluation and assessment purposes.

Another key difference is that ex-ante is based on forecasts and predictions, while ex-post is based on historical data and actual results.

These two are very important concepts in business, finance, and economics. Understanding the differences between them is crucial for making informed decisions and evaluating performance.

Conclusion

Both ex-ante and ex-post approaches are important tools for analyzing investment performance. While ex-ante helps with decision-making, ex-post provides a more accurate evaluation of an investment’s performance. Both of them are valuable in their own ways and should be used together for a comprehensive understanding of investments.

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An Options Pricing Model for Non-Frictionless Markets

The traditional option pricing model assumes that the market is frictionless. However, a body of research has developed theories that do not make this assumption. Reference [1] utilizes the Stochastic Arbitrage (SA) approach to derive price bounds within which the admissible risk-neutral option prices, which are determined by using the frictionless market assumption, should lie.

In this approach, two generic identical risk-averse investors are compared: one holds the index and the riskless asset, referred to as the index trader (IT), and the other, the option trader (OT), adds a zero net cost option position to the IT holdings. OT should not dominate IT to the second degree. This absence of dominance creates a region, with upper and lower bounds, within which the risk-neutral option prices should lie. The SA approach uses only the index return dynamics without involving the option market data.

The article pointed out,

Our study provides evidence that extracting the frictionless Q-dynamics from the observed bid-ask midpoint in the short-term index option market is a futile effort on both theoretical and empirical grounds. The observed option market data is fundamentally inconsistent with the frictionless option price format as implied by the underlying index returns. There exists a clear segmentation of the markets for SPXW call and put options and strong moneyness effects in these options. More importantly, for the highly liquid ATM calls/puts that represent more than 70% of the SPXW trading volume, not only the bid-ask midpoint but also the entire width of the quotes lay above their SA option bounds in most of the cross sections. These non-overlapping (mispriced) options were used to generate zero-net-cost portfolio returns that are statistically and economically significant in the frictionless world. However, such portfolio returns disappear when trading options at the appropriate bid and ask prices. This frictionless mispricing of the SPXW options is consistent with the documented mispricing in the few studies that recognized frictions and with the even fewer theoretical studies that compared option pricing without and with frictions

In short, the authors developed an option pricing model for a market that is not frictionless. They created a trading strategy based on the price bounds determined by their Stochastic Arbitrage approach. The strategy yielded favourable returns.

This article presents an important option pricing theory that we should be aware of and follow closely.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Hamed Ghanbari and Stylianos Perrakis, Stochastic Arbitrage and the Valuation of Weekly Options: Theory and Empirical Evidence

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Manufacturing Throughput Time: Definition, Calculation, Example, Improvement

Companies strive to create efficient processes to maximize profits. However, they may come across some challenges that may prevent it. Therefore, companies must track manufacturing throughput time.

What is Manufacturing Throughput Time?

Manufacturing throughput time represents the total duration needed to convert raw materials into finished products, covering all stages from production initiation to product readiness for delivery. Shorter throughput times not only streamline production processes but also enable businesses to adapt quickly to changes in demand, optimize resource utilization, and stay agile in a dynamic market environment.

Manufacturing throughout time helps evaluate manufacturing efficiency, production capacity, and inventory management, directly impacting a company's ability to meet customer demand and maintain competitiveness. Reducing manufacturing throughput time is a strategic objective for many companies as it leads to cost savings, faster delivery times, improved customer satisfaction, and enhanced responsiveness to market demands.

How does Manufacturing Throughput Time work?

Manufacturing throughput time measures the complete duration needed to transform raw materials into finished products. It encompasses stages like acquiring raw materials, processing them through manufacturing processes, conducting quality checks, assembling components, and packaging for delivery. This metric is vital for evaluating how efficiently a company's production processes convert inputs into outputs, affecting production capacity and customer satisfaction.

Companies often focus on streamlining workflows, optimizing production schedules, and investing in automation technologies to improve manufacturing throughput time. By reducing idle time between processes, minimizing setup times, and enhancing overall operational efficiency, businesses can shorten throughput time, meet customer demands more promptly, and maintain a competitive edge in the market.

How to Calculate Manufacturing Throughput Time?

Calculating manufacturing throughput time involves identifying the key stages in a production process. Next, companies must record the time spent at each stage, adding up the durations to get the total time it takes for raw materials to become finished products. For instance, if procurement takes two days, production three days, quality checks one day, and assembly/packaging two days, the total throughput time would be eight days.

This calculation helps companies understand how efficiently they convert raw materials into final products. By streamlining processes, reducing idle time, and optimizing workflows, companies can shorten throughput time. Tracking and analyzing throughput time allows for continuous improvement and better operational performance in manufacturing operations.

There isn't a specific formula for calculating manufacturing throughput time because it depends on the stages and activities involved in a company's production process. However, companies can calculate it by summing up the time spent at each stage, from raw material procurement to finished product completion.

How to improve Manufacturing Throughput Time?

Improving manufacturing throughput time involves strategic measures to enhance operational efficiency and reduce delays in the production process. One approach is to identify and address bottlenecks or areas where production slows down frequently. Streamlining workflows by eliminating unnecessary steps, optimizing the sequence of operations, and reducing handoffs between departments can also contribute to shorter throughput times.

Similarly, adopting lean manufacturing principles, investing in automation technologies, and optimizing production scheduling are effective strategies to improve throughput time. By reducing setup times, empowering employees, leveraging technology solutions, and fostering a culture of continuous improvement, businesses can achieve faster production cycles, increase productivity, and meet customer demands more effectively.

Conclusion

Manufacturing throughput time is the period it takes to convert raw materials to finished goods. It consists of different stages along this process. Ideally, companies want this time to be the lowest possible. However, various bottlenecks cause it to go higher. Improving manufacturing throughput time can be challenging, though it is still achievable.

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Volatility Risk Premium Is a Reward for Bearing Overnight Risk

The volatility risk premium (VRP) represents the difference between the implied volatility of options and the realized volatility of the underlying asset. Essentially, it reflects the compensation that investors demand for bearing the risk associated with the uncertainty of future volatility. Typically, implied volatility is higher than realized volatility, indicating that options are often overpriced relative to the actual market movements. Understanding and exploiting the VRP can be crucial for designing effective trading strategies and managing risk in financial portfolios.

Reference [1] examines the asymmetry in the VRP. Specifically, it investigates the VRP during the day and overnight sessions. The authors pointed out,

We find a significant difference in the returns from overnight and intraday short option positions, which is not explained by a weekend effect. The return asymmetry declines with increase in option moneyness and maturity. A systematic relationship is found between the day-night option returns and the option Greeks. We extend the results of MN by observing that average postnoon returns are significantly negative (positive) for short positions in call (put) options, while prenoon returns are largely insignificant, suggesting that the VRP changes across the trading day, but differently for calls and puts. We also provide evidence that a significant jump in the underlying index has a dampening effect on the day-night disparity in option returns. We hypothesize that the spike in implied volatilities consequent to a significant jump in the underlying causes an increase in both intraday and overnight option returns, thus reducing their day-night variation. The strong positive and significant overnight returns imply that the VRP embedded in prices of Nifty options is a reward mainly for the overnight risk. Our results show that a strategy of selling index options at the end of the trading day and covering the same at the beginning of the following trading day yields positive returns before accounting for transaction costs, although this strategy is not profitable after factoring in the explicit and implicit transaction costs.

In short, the article concluded that the VRP is negative during the day and positive during the night. It is worth noting that this research was conducted in the Nifty options market, but previous studies in the S&P 500 market reached the same conclusion.

This finding has implications for portfolio managers who use options to manage the risks of their portfolios. It helps investors to better manage risks.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Aparna Bhat, Piyush Pandey,  S. V. D. Nageswara Rao, The asymmetry in day and night option returns: Evidence from an emerging market, J Futures Markets, 2024, 1–18

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Restrictive Covenant: Definition, Contract, Meaning, Agreement, Example

When it comes to business and property, the term "restrictive covenant" often pops up. This might sound like complex legal jargon, but it's actually a simple concept with significant implications.

It plays a crucial role in how properties are used and businesses operate, which affects decision-making and strategies.

Understanding restrictive covenants is important for property owners, businesses, and individuals interested in purchasing properties.

What are Restrictive Covenants?

Restrictive covenants are special rules set out in contracts that limit or set conditions on what a person or organization can do.

These rules are often found in agreements related to property and real estate. For example, they can decide what type of activities can be carried out in a particular property or development.

For example, the kind of color scheme can be used for exterior painting or whether pets are allowed on the property.

Now when it comes to finances, these covenants can also have implications on businesses and individuals.

For instance, a restrictive covenant might be included in a loan agreement between an individual or business and a bank, which outlines certain restrictions on how the borrowed money can be used.

This could include limiting the amount of debt that can be taken on or requiring certain financial ratios to be maintained.

How Restrictive Covenants Work

Restrictive covenants are basically rules and regulations that everyone agrees to follow. For example, when someone buys a house, they might agree to a rule that says they can only paint it certain colors.

This rule sticks with the house, so even if it's sold again, the new owner must follow the same rule.

In business, especially when companies borrow money by selling bonds, these rules make sure the company doesn’t spend too much money in a way that could be risky.

If someone breaks these rules, they could get fined or end up in court. It’s all about making sure everyone plays fair and sticks to the agreed-upon rules.

In simple words, restrictive covenants are like a set of guidelines or restrictions that individuals or organizations must accept in order to maintain the integrity of a property, business, or financial agreement.

Importance of Restrictive Covenants

Restrictive covenants serve several important purposes for both individuals and businesses.

1. Protecting Property Values: By placing restrictions on property use, restrictive covenants help to maintain the value of properties within a neighborhood or community. This can benefit both current and future property owners.
2. Maintaining Quality Standards: In some cases, restrictive covenants may outline certain standards for the construction or maintenance of properties. This helps to ensure that all properties in the area maintain a certain level of quality, which can also help to maintain property values.
3. Ensuring Consistency: By setting rules and regulations for businesses, restrictive covenants promote consistency and fair competition in the market. This helps to prevent one company from gaining an unfair advantage over others.
4. Mitigating Risk: In financial agreements, restrictive covenants help lenders mitigate risk by setting limits on how much a borrower can spend or what they can do with the borrowed funds. This helps to protect the lender's investment and minimize potential losses.
5. Promoting Fairness: Restrictive covenants also promote fairness among parties involved in a transaction, ensuring that everyone is held accountable and no one is given preferential treatment.

Conclusion

It's very important to understand the role of restrictive covenants in different aspects of business and finance. It can cause significant legal and financial consequences if they are not properly understood and followed. It's important to carefully review and consider any restrictive covenants before agreeing to them, as they can have long-term effects on property values, business operations, and financial agreements.

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Can Hypothesis Testing Reduce Data Mining Risks?

A significant challenge in designing trading strategies is the data mining problem, which arises from the vast amount of data available and the potential for spurious correlations. With an abundance of historical market data, traders may inadvertently identify patterns or relationships that appear significant but are merely coincidental. This can lead to overfitting, where a strategy performs well on historical data but fails to generalize to new market conditions.

To mitigate this issue, rigorous testing procedures, such as out-of-sample testing and cross-validation, are essential to validate the effectiveness and robustness of trading strategies and guard against data mining biases.

Reference [1] presents a method for minimizing data mining risks using hypothesis testing without requiring out-of-sample data. Specifically, it employs the false discovery rate (FDR) method to address this issue. The authors pointed out,

In this paper we study if the use of simple technical trading rules can outperform buying and holding bitcoins. We attempt to do this by first selecting outperforming rules, from a set of 75,360 possible trading rules, then combining them in different portfolios and finally assessing their performance out-of-sample after ’realistic’ transaction costs. Compared to earlier research, which generally concludes that trading rules can outperform a buy-and-hold strategy in the bitcoin market, we apply much more restrictive conditions (transaction costs, out-of-sample performance, data mining corrections) and search over a higher amount of technical trading rule classes, parameterizations and trading frequencies. Unlike in Hudson and Urquhart (2021), we find that our rules still can significantly outperform the buy-and-hold strategy out-of-sample, mainly risk-return wise.

In short, the study utilized 75,360 simple technical trading rules. The best-performing rules are selected after factoring in transaction costs using a multiple hypothesis procedure. Portfolios are then formed by combining the selected rules, and their out-of-sample performance is shown to be superior to Buy and Hold’s.

We find it interesting that through in-sample hypothesis testing alone, we can significantly reduce data mining risks and achieve favorable out-of-sample results. We remain open-minded and continue to monitor developments in this area of research.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Niek Deprez1, Michael Frömmel, Are simple technical trading rules profitable in bitcoin markets?, International Review of Economics & Finance, Volume 93, Part B, June 2024, Pages 858-874

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Sales Margin: Definition, Formula, Calculation, Example, Accounting

Gross profit represents a company's earnings from its products and services before considering operating and other expenses. It comes after subtracting a company's cost of goods sold from its revenues. However, it does not provide insights into a company's profitability. Therefore, some stakeholders may prefer sales margin as a better metric.

What is the Sales Margin?

Sales margin is a critical financial metric used to assess the profitability of a company's sales revenue. It represents the percentage of earnings that remains as gross profit after deducting the direct costs associated with producing or acquiring the goods or services sold. This metric is essential for businesses as it provides insights into the efficiency and effectiveness of their core business activities in generating profit from sales.

A higher sales margin percentage indicates better profitability, as more profit gets retained from each sales dollar. Comparing sales margins over time or against industry benchmarks helps businesses evaluate their financial performance, pricing strategies, cost management efforts, and overall profitability. Sales margin is a critical tool for decision-making and financial analysis, enabling companies to make informed decisions about pricing, production, sales strategies, and resource allocation.

How to Calculate Sales Margin?

Companies must follow various steps to calculate sales margins. It starts by identifying the total revenue generated from sales during a specific period. It consists of all sales of goods or services. Next, companies calculate the cost of goods sold, which includes all direct costs associated with producing or acquiring the goods or services sold. It typically includes costs, such as raw materials, labour, and manufacturing expenses.

Once companies calculate the COGS, they must subtract the COGS from the total revenue to find the gross profit. It represents the profit generated from sales before deducting other expenses such as operating expenses, taxes, and interest. Finally, they divide the gross profit by the total revenue and multiply by 100 to express the sales margin as a percentage.

What is the formula for Sales Margin?

The formula for sales margin is straight and as below.

Sales margin=[(Revenue - Cost of goods sold​) / Revenue]×100

The above formula provides a percentage of the sales margin or gross profit a company generates from its products or services. The resulting sales margin figure indicates the profitability of sales after considering the direct costs of production or acquisition. As mentioned above, A higher sales margin percentage signifies better profitability.

On the other hand, a lower percentage may indicate higher production costs relative to revenue. Comparing sales margins over time or against industry benchmarks helps businesses assess their financial performance and make informed decisions about pricing, cost management, and overall profitability.

Example

Blue Co. is a manufacturing company that produces microchips. The company had total revenues of $500,000 last year. The cost of goods sold the company reported on its income statement for the period was $300,000. Based on the above figures, the sales margin for Blue Co. is as follows.

Sales margin = [(Revenue - Cost of goods sold) / Revenue] × 100

Sales margin = [($500,000-$300,000) / $500,000] × 100

Sales margin = 40%

While the sales margin is high, it is crucial to view it comparatively to analyze it better.

Conclusion

Sales margin measures a company's gross profits against its revenues. In other terms, it shows the percentage of profits a company makes from its products before considering operating and other expenses. The sales profit margin formula is also straightforward. The components for the calculation come from the income statement alone.

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Do Calendar Anomalies Still Exist?

Calendar anomalies in the stock market refer to recurring patterns or anomalies that occur at specific times of the year, month, or week, which cannot be explained by traditional financial theories. These anomalies often defy the efficient market hypothesis and provide opportunities for investors to exploit market inefficiencies. Some well-known calendar anomalies include the January effect, where stock prices tend to rise in January, and the day-of-the-month effect, where returns are higher on certain days of the month.

The calendar anomalies were discovered long ago. Reference [1] examines whether they still persist in the present-day stock market. Specifically, the author investigates the turn-of-the-month (TOM), turn-of-the-quarter (TOQ), and turn-of-the-year (TOY) effects in the US stock market. They pointed out,

This thesis presents several valuable findings. The TOM, TOQ, and TOY effects are all present in the US stock market, with the TOY effect being the most substantial. Second, the TOY effect remains primarily confined to small stocks with volatile prices, strengthening the hypothesis that individual investors sell their losses for tax purposes before the year-end. Additionally, the TOY effect is stronger in stocks with low momentum, which reinforces the idea that institutional investors sell stocks that negatively influence performance. These results suggest that the TOY effect can be explained by both the practice of window dressing and performance hedging by institutional investors. Third, we find that the calendar effects have evolved considerably over time. In recent decades, the TOM and TOY effects have resurfaced and continue to exist. The absence of a significant TOQ effect in the past decade suggests that increased disclosure regulations have reduced portfolio pumping in the US stock market. Fourth, companies with low Google search volumes are significantly more affected by all three effects.

In short, calendar anomalies continue to exist in the US stock market. Furthermore, they can be exploited to gain abnormal returns. For instance, every four-day TOY window yields an average profit of 1.66% when holding all stocks exclusively over the TOY windows. Similarly, an average profit of 0.55% is generated every four-day TOM window by exclusively holding all stocks over the TOM windows.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Idunn Myrvang Hatlemark and Maria Grohshennig, Calendar Effects in the US Stock Market: Are they still present?,  2022, Norwegian University of Science and Technology

Originally Published Here: Do Calendar Anomalies Still Exist?

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Accrued Payroll: Definition, Accounting, Journal Entry, Example, Meaning

Companies pay their employees after predefined intervals. However, accounting requires them to recognize any liability incurred for those expenses regardless of the payment. Therefore, companies use accrued payroll to track it.

What is Accrued Payroll?

Accrued payroll is a fundamental concept in accounting that represents the accumulated amount of employee compensation earned but not yet paid by a company as of the end of an accounting period. It includes various components such as wages, salaries, bonuses, commissions, benefits, and payroll taxes that employees have earned but have not received in cash or check.

The accrual of payroll occurs because of the time lag between when employees perform work and when they receive their compensation. This time difference is common in many businesses, especially those with regular pay periods such as weekly, bi-weekly, or monthly. From an accounting perspective, accrued payroll gets recorded as a liability on the company's balance sheet.

What is the accounting for Accrued Payroll?

Accrued payroll accounting involves recognizing the wages, salaries, bonuses, benefits, and taxes employees have earned but not yet received as a liability. After every accounting period, an adjustment records the accrued payroll expense and increases the accrued payroll liability. This adjustment ensures that the income statement accurately reflects the expenses incurred during the period, regardless of the payment.

The accounting process for accrued payroll helps businesses maintain accurate financial statements by reflecting the true obligations and expenses related to employee compensation. It also facilitates effective cash flow management and budgeting by ensuring that payroll expenses are properly recorded and accounted for in the appropriate periods.

What is the journal entry for Accrued Payroll?

The journal entry for accrued payroll involves recording an increase in salaries and wages expenses. This expense includes all labor-related costs that a company bears. On the other hand, it also increases the accrued payroll liability. The journal entry is as follows.

Dr	Salaries expense
Cr	Accrued payroll

The above journal entry for accrued payroll only recognizes labour-related expenses incurred. However, it falls under the accrued category because the company has not paid for it yet. Once the company compensates its workers, it must reduce the liability using the following journal entry.

Dr	Accrued payroll
Cr	Cash or bank

Example

At the end of January, Green Co. calculates its total salaries and wages expenses to be $5,000. At the time of the calculation, the has incurred the cost and must record it using the following journal entry.

Dr	Salaries expense	$5,000
Cr	Accrued payroll	$5,000

After five days, Green Co. pays all its employees through cash. The company records this transaction as follows.

Dr	Accrued payroll	$5,000
Cr	Cash	$5,000

What is the importance of Accrued Payroll?

Accrued payroll ensures that financial statements accurately reflect the company's financial position by recognizing the liabilities associated with employee compensation that have been earned but not yet paid. It helps present a true and fair view of the company's obligations and expenses at the end of an accounting period.

Secondly, accrued payroll is crucial for effective cash flow management and budgeting. By recording accrued payroll expenses, businesses can plan and budget for future cash outflows related to employee compensation more accurately. It allows companies to allocate resources efficiently, anticipate financial commitments, and avoid cash flow shortages or surprises.

Conclusion

Accrued payroll is an account that records labour-related expenses that a company has incurred but not yet paid. It appears as a liability on the balance sheet until the company compensates its workers. On the other hand, it also increases the expenses for costs incurred. Accrued payroll is crucial in accurately reporting expenses and liabilities.

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Volatility Spillover Between Developing Markets

Volatility spillover refers to the transmission of volatility shocks from one market or asset to another, leading to increased volatility in the receiving market. These spillovers can occur within the same asset class or across different asset classes. For instance, a sudden increase in volatility in one stock market may trigger similar movements in other stock markets around the world. Similarly, volatility shocks in the currency market can spill over into the equity market.

Reference [1] investigates the volatility spillover effect between the Nigerian exchange rate and the exchange rates of the BRICS countries. The authors pointed out,

This study focuses mainly on examining the volatility spillover between the economies of Nigeria and the BRICS nations, using MGARCH time series models; VECH, DBEKK and CCC. Thus, to study some features of good volatility modeling on FTS. It is shown that not all series are stationary since trend components exist which must be transformed prior to modeling. These trend components have been appropriately handled, having noted that some periods pose more threats than the others. In addition, risky periods are spread out at random and a certain degree of autocorrelation exists in the series, implying that big changes are likely to follow big changes and small to follow small, which is called volatility clustering. The result of the VECH model shows that all parameters are significant at 5% level (p< 0.05) and this clearly indicates that there are positive effects of ER shocks in Nigeria on the ERV of BRICS markets. In addition, the VECH model was able to capture volatility spillover (own and across) with both parameters are on negative directions for Nigerian market, suggesting a causal relationship between past volatility shocks of Nigeria and current volatility in the BRICS markets (which clearly revealed that Nigeria has better advantage in being with the BRICS nation).

In essence, there exists a spillover of volatilities between the Nigerian exchange rate and the exchange rate markets of the BRICS countries. The authors also offer economic explanations based on bilateral relations between these nations.

This paper demonstrates that seemingly unrelated markets can be interconnected and display lead-lag relationships. Such relationships are important in the design of trading and risk management systems, such as statistical arbitrage.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Ibrahim, M. K., Tasi’u, M. and Dikko, H. G., A study on the volatility spillover between Nigerian and BRICS  economies using multivariate GARCH models, FUDMA JOURNAL OF SCIENCES, 8(2), 170 - 179.

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Marginal Social Benefit: Definition, Cost, Formula, Equation, Meaning, Example

In decision-making, especially about economics or community projects, it's important to think about how choices impact everyone, not just a few.

There's a specific factor that helps measure this impact, showing how decisions can affect the whole society - which is known as Marginal Social Benefit (MSB).

It's crucial for businesses to understand the concept of MSB, as it can help them make more informed and responsible choices for their operations and strategies.

What is Marginal Social Benefit?

Marginal social benefit refers to the total gain appreciated by society when a product is consumed, accounting for both its direct benefits to consumers and its wider impacts on the environment and society.

Essentially, it combines the immediate satisfaction that users get with the broader societal and environmental costs or gains.

For instance, if consuming a product brings additional benefits to the community, like improved public health, then the marginal social benefits exceed the benefits that just the consumers themselves experience.

On the flip side, if the consumption of a product leads to negative outcomes, like pollution, the overall benefit to society is less than what the individual consumers perceive as their personal gain.

In simple words, marginal social benefit is all about considering the "bigger picture" and not just individual gains.

How Marginal Social Benefit Works

Imagine someone buys a bike and uses it instead of a car.

This decision doesn't just benefit the bike rider by saving money on gas - it also helps everyone else by reducing traffic and pollution. Marginal social benefit is like looking at this bigger picture.

It checks how one person's choice to buy and use something can be good for other people too. If the choice leads to nice things for others, like cleaner air, then the benefit is big.

But if it causes problems, like noise or waste, then the benefit isn't as great. It's about seeing how one action can ripple out and touch the community.

For a bigger picture like a business or an organization, this concept is important to consider when deciding on what products or services to offer.

By taking into account the potential impacts on society and the environment, companies can make more informed decisions that not only benefit themselves but also contribute positively to the world around them.

Equation for Marginal Social Benefit

The formula of marginal social benefit is quite simple

Marginal Social Benefit = External Benefits + Marginal Private Benefit

Where,

• External Benefits: These are the positive effects that are enjoyed by others in society as a result of an individual's consumption or production choices.
• Marginal Private Benefit: This refers to the personal gain or benefit experienced by the individual consumer.

In short, marginal social benefit takes into account not only the immediate benefits for individuals but also the potential benefits for society as a whole.

Conclusion

Social impact should be a major concern not only for individuals but also for businesses and organizations. By considering the concept of marginal social benefit, we can make more conscious choices that not only benefit ourselves but also contribute positively to society. It's good for both personal gains as well as the greater good.

Originally Published Here: Marginal Social Benefit: Definition, Cost, Formula, Equation, Meaning, Example

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Predicting Realized Volatility Using Skewness and Kurtosis

Realized volatility refers to the actual volatility experienced by a financial asset over a specific period, typically computed using historical price data. By calculating realized volatility, investors and analysts can gain insights into the true level of price variability in the market, which can be valuable for risk management, portfolio optimization, and trading strategies. Realized volatility is often used in conjunction with implied volatility to assess the accuracy of market forecasts and to adjust trading strategies accordingly.

Reference [1] examines whether realized volatility can be forecasted. Specifically, it studies whether realized skewness and kurtosis can be used to forecast realized volatility. The authors pointed out,

Given the important role of volatility forecasts accuracy in optimal portfolio designs, this study provides strong evidence that realized kurtosis is most useful when one to 22 day ahead forecasts are of interest by taking large and diverse set of data of 452 listed firms at PSX, and thus could assist in improving asset allocation decisions. Thus, the standard HAR model and its extensions containing realized kurtosis predicts the expected realized volatility as a linear function of yesterday’s realieed volatility and its mean over prior week and month as well as yesterday’s realieed kurtosis. Therefore, it is concluded that stocks’ own realieed kurtosis carries meaningful information for stocks’ future volatilities.

Briefly, the author extended the HAR-RV model to incorporate realized skewness and kurtosis. They found that realized kurtosis is most useful when forecasting one to 22 days ahead.

Note that this study was conducted in the Pakistan stock market, but the research framework can be applied to any stock market and asset class.

Let us know what you think in the comments below or in the discussion forum.

References

[1]  Seema Rehman, Role of realized skewness and kurtosis in predicting volatility, Romanian Journal of Economic Forecasting, 27(1) 2024

Article Source Here: Predicting Realized Volatility Using Skewness and Kurtosis

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