Producing a product requires a firm to make several decisions. These include how much labor and capital will be devoted to the production process, what level of quality is required, and how much product should be produced in order to maximize profit. One important factor that contributes to the profitability of a given product is marginal cost. Marginal cost gives an idea of the total cost of a product at a certain stage of production. It helps to set up the production level to maximize profit. In this article, we will be looking at what marginal cost is, how to calculate it, and some examples. What is Marginal CostIn economics, marginal cost is the change in total cost that arises when the quantity produced changes by one unit. It is also known as incremental cost or differential cost. Marginal costs basically come from the production of one more unit. It is the cost of producing one extra item or an additional item to a group of items. For example, if 5 pens are being produced, the marginal cost will be the expense of producing the 6th pen. Marginal cost can also refer to serving customers as well. Importance of Marginal CostMarginal cost is an important factor when it comes to making decisions related to production. It helps the firm decide how much product should be produced in order to maximize profit. This is because marginal cost provides information about the additional costs of producing another unit, e.g., raw material costs or labor costs. It is one of the most integral concepts in economic analysis. It is a very real cost associated with the production of a good or service, which has to be considered during business decision-making. How to Calculate Marginal CostIn order to calculate marginal cost, you will need two things
The formula of marginal costMarginal cost is calculated as follows, Marginal Cost = Change in total cost/Change in quantity produced As you can see it is not that difficult at all. All you need is the difference in total cost and change in quantity produced, and you can easily calculate the marginal cost. Marginal Cost ExampleLet's say that you own a car manufacturing company. This is your first year and you have manufactured 10 cars for $100,000, which cost you $50,000 to make. So the next year you change your plans and want to manufacture 15 cars for $150,000, which will cost you $75,000. So what will be your marginal cost for this year? Marginal Cost = Change in total cost/Change in quantity produced = $25,000/5 So your marginal cost will be $5,000 ConclusionMarginal cost is indeed an important factor for any company that offers goods and services. It helps a firm decide what level of production it should achieve to maximize the profit they make. It provides information about the additional costs of producing another unit, e.g., raw material costs or labor costs. The formula is not hard at all and once you have understood the concept, you'll find it very easy to apply in several situations. Post Source Here: Marginal Cost: Definition, Formula, Example
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The world of finance is a complex one, and to be successful in it you need to make smart decisions based on accurate data. Data science is the process of extracting insights from data, and when applied to finance it can be used to make sound business decisions. In this article, we will discuss how data science can be used in finance, and we will give some examples of real-world applications. We will also show you how to get started with data science in finance so that you can start making informed decisions today. The role of data science in financeThere are many different applications of data science in finance, but some of the most common include:
Each of these applications can help you make better financial decisions and increase your chances of success in the finance world. How data science can help with risk assessment and fraud detectionRisk assessment is a key part of finance, and data science can be used to identify and mitigate risk factors. One common application of data science in finance is fraud detection. By analyzing large amounts of financial data, it is possible to detect patterns that may indicate fraudulent activity. This can help banks and other financial institutions protect themselves from losses due to fraud. How data science can help with investment decisionData science is used in finance to help make informed investment decisions. By analyzing data, data scientists can identify trends and patterns that would otherwise be difficult to see. This information can then be used to predict future market movements and make more informed choices about where to invest your money. The use of machine learning in finance and tradingThe finance industry is under pressure as never before to find new ways to cut costs, and machine learning (ML) is seen as one of the most promising areas where this can be done. The finance sector has been slower than other industries to adopt ML, but that is starting to change. Machine learning can process large amounts of data much faster than humans can. This makes it a powerful tool for finance, where the need to make decisions quickly is often paramount. Getting started with data science in financeIf you want to start using data science in finance, there are a few things you need to know. First, you need to have some basic knowledge of statistics and machine learning. Next, you need to be familiar with the tools and software used for data analysis. Finally, it is helpful to have some knowledge of finance and investment. Once you have the basics down, you can start learning about specific applications of data science in finance. There are many resources available online, and there are also courses offered by universities and other institutions. With a little effort, you can become a data scientist in finance and start making smart decisions that will help you succeed in the world of finance. Final thoughtsThe finance industry is changing, and data science will be a key part of that change. Data science can help finance professionals make better decisions and stay ahead of the competition. As more and more data becomes available, the role of data science in finance will only become more important. So if you want to succeed in finance, it is essential to learn data science. Post Source Here: Data Science in Finance Companies usually receive compensation for their products and services after delivery. In some cases, they may also charge the customer simultaneously as they deliver. However, some companies also receive advances for products and services that they will supply later. Accounting standards require companies to record these revenues as unearned revenue. Before discussing the accounting for unearned revenues, it is crucial to understand what it is. What are Unearned Revenues?Unearned revenues are proceeds received from customers before delivering products or services. Another name used for these revenues is deferred revenues. In business terms, these revenues can also be called advances or deposits. When customers pay upfront for a product or service, the revenues will remain unearned. Once the company completes its side of the transaction, it becomes earned. Unearned revenues aren’t actual revenues. Companies record these separately to conform to the accounting standards. Usually, these revenues stay in the accounts for a short time. Unearned revenues do not become a part of the income statement. Since these revenues do not constitute earned proceeds, they stay on the balance sheet. Once the company delivers the products or services, it can report them in the income statement. Is Unearned Revenue asset or liability?Unearned revenue in the balance sheet falls under liabilities. Usually, they are short-term obligations and, therefore, constitute current liabilities. Unearned revenues do not fall under the asset category for the company receiving the advance. However, the customer who pays this amount may record it as a prepayment. For that customer, the unearned liability will be an asset. However, they must term it prepayment. Unearned revenues stay in the balance sheet until the company delivers the product or service. Once it does so, the company can transfer the amount out of the balance sheet. In that case, the unearned revenues will become a part of the income statement as net sales. If the company does not deliver the goods or services, the unearned revenues will continue appearing in the balance sheet. What are the journal entries for Unearned Revenues?Companies record the journal entries for unearned revenues in two cases. The first involves the receipt of the advance from the customer. In that event, the company must create a liability in its balance sheet termed unearned revenues. This accounting treatment is crucial under accounting standards. These standards require companies to record revenues only when they meet performance obligations. When a company receives an advance from a customer, the journal entries will be as below.
The other journal entries for unearned revenues occur when the company delivers its products or services. As mentioned, the company must transfer this amount to recognize it as sales. In this case, the journal entries will be as below.
ExampleA company, Red Co., receives $10,000 in cash as advance from a customer. The company promises to deliver its products and services after a month. At the time of this transaction, Red Co. must record the advance as unearned revenues. Therefore, the journal entries will be as below.
One month later, Red Co. delivers its products to the customer. At that time, the company must transfer the liability to revenues. Thus, the journal entries will be as below.
ConclusionUnearned revenues represent advances from customers for which a company has not delivered goods or services. These revenues constitute liability for the company and are usually short-term. Once the company completes its obligation, the amount gets transferred to the revenues account. Originally Published Here: Unearned Revenue: Journal Entry, Examples, Asset or Liability? Despite the growing popularity of passive investing, active hedge fund managers are still adding value to their investors' portfolios. While passively managed funds can be a great option for some investors, active management can provide added benefits for those who are looking for better risk-adjusted returns and diversification, especially during market downturns. A recent study [1] showed that the correlation between market-neutral hedge funds and the stock market changes according to the market regimes. Specifically, the correlation is negative in bear markets and positive in bull markets. This result is unique to market-neutral hedge funds, as others exhibit similar characteristics in terms of trading strategies. In this paper, we explore dependence between hedge funds and the market portfolio. As opposed to previous papers, we study this question conditional on financial cycles. This dimension is important, as it has been shown that hedge funds are one of the most dynamic investment vehicles, and thus, their performance is affected by market conditions. We find evidence that market neutral and other hedge fund styles exhibit tail dependence during bull periods, but not during bear periods. Moreover, we find that as opposed to other hedge fund styles, the correlation between market-neutral hedge funds and the stock market changes with the economic state. We link this behavior to the ability of hedge fund managers to time market regimes, and find evidence that market neutral hedge funds are able to adjust their strategies according to the financial cycles. We illustrate how disregarding changes in dependence might lead to inaccurate risk management practices. Finally, we find that our results on dependence and state timing hold in individual fund data. The evidence that we find underscores the importance of understanding and incorporating financial cycle conditions in asset management and investment decision making. The result from this study showed that investing in active funds is beneficial. Active hedge fund managers add value to the portfolio by executing market-neutral strategies. Hedge funds performance was shown to be negatively correlated with the market during bear cycles. Thus they could be great diversifiers to investors' portfolios. References [1] Crego, Julio and Galvez, Julio, Cyclical Dependence in Market Neutral Hedge Funds (2021). Banco de Espana Working Paper No. 2141, https://ssrn.com/abstract=3970564 Article Source Here: Do Hedge Funds Add Value? There is a lot of confusion surrounding the terms "artificial intelligence" and "machine learning." Some people use them interchangeably, while others see them as two very distinct concepts. In this blog post, we will discuss the differences between artificial intelligence and machine learning, and explain why you should care about both. What is Artificial Intelligence?Artificial intelligence is a field of computer science that deals with the creation of intelligent agents, which are systems that can reason, learn, and act autonomously. Artificial intelligence applications can be used to solve complex problems or automate tasks. What is Machine Learning?Machine learning is a subfield of artificial intelligence that involves teaching computers to learn from data without being explicitly programmed. Machine learning algorithms can be used to make predictions or identify patterns in data. Differences Between Artificial Intelligence and Machine LearningThere are a few key differences between artificial intelligence and machine learning:
Overall, artificial intelligence applications tend to be more complex and require more data to train properly, while machine learning algorithms are more versatile and can be used with smaller datasets. However, both artificial intelligence and machine learning have the potential to make significant impacts in a variety of industries. Why Should You Care about Artificial Intelligence and Machine Learning?Artificial intelligence and machine learning are two of the most important technologies of our time. They have the potential to change the way we live and work, and they are already being used in a number of industries. Here are just a few examples:
The applications of artificial intelligence and machine learning are endless, and the potential benefits are enormous. ConclusionArtificial intelligence and machine learning are two of the most important technological innovations that have shaped our world in recent history. The applications for both artificial intelligence and machine learning are nearly endless, but they continue to be used mainly in data analysis-oriented tasks - an area where their capabilities can yield tremendous value. Machine learning is expected to take over jobs from human beings fairly soon, so it’s worth figuring out how you can benefit from these technologies sooner rather than later! Have you tried using AI or ML yet? We want to know what your thoughts on this topic are. Let us know below. Article Source Here: Artificial Intelligence vs. Machine Learning: What’s the Difference? Do you know the difference between data science and computer science? Many people don't realize that these two disciplines are actually quite different. In this blog post, we will compare data science vs. computer science and discuss the benefits of each field. We will also provide some resources for those who are interested in learning more about data science and computer science. What is data science?Data science is the process of extracting insights from data. This involves using data mining techniques, statistics, and machine learning algorithms to analyze data and uncover patterns and trends. Data scientists also use visualization tools to present their findings in a clear and concise manner. What is computer science?Computer science is the study of computers and how they work. This includes topics such as programming, algorithms, data structures, and software engineering. Computer scientists also study theory of computation and how to design efficient computer systems. Differences between data science and computer scienceThere are several key differences between data science and computer science.
Benefits of data scienceThere are several benefits of data science:
Benefits of computer scienceThere are also several benefits of computer science:
Final thoughtsSo, what’s the verdict? Is data science better than computer science or vice versa? The answer is that it depends on your goals and interests. Both fields are incredibly valuable in their own ways and can lead to great career opportunities. If you want to work with data and find insights in large datasets, then data science is the right choice for you. If you want to design and build software systems, then computer science is a good fit. We hope this article has helped you understand the differences between these two exciting fields and given you some things to think about as you make your decision about which one to study. What do you think? Let us know in the comments below. Article Source Here: Data Science vs. Computer Science: What’s the Difference? Buying and selling companies is the most common method of financing growth for many business organizations. Generally, companies that are sold are smaller than the purchaser company, but it does not mean that large companies cannot buy other large companies because there are times when this happens. When it comes to buying and selling companies, Purchase Price Allocation or PPA plays a major role. Let's find out more about it. What is Purchase Price AllocationWhenever a company is sold, the price that is paid for it must be allocated properly to reflect the fair value of every single asset and liability transferred. This process is called Purchase Price Allocation or PPA. The main goal of PPA is to fairly allocate the purchase price among all business assets acquired so that they can be identified and valued appropriately. In simple words, Purchase price allocation (PPA) is used when one company (acquirer) is purchasing another company (target) and assigns the purchase price to various assets acquired. PPA is also used when one company (acquirer) is purchasing land and building of another company (target). Main components of Purchase Price AllocationThere are three main components of PPA: Identifiable assets, Liabilities, and Goodwill, let's take a look at them:
Identifiable assets are the tangible and intangible things that you can see and touch. It basically refers to the assets that have a physical form and some value at the time of acquisition. Identifiable assets represent the book value of the company. Identifiable assets can also be called "net identifiable assets" because they are those with a certain value at a given point in time and whose benefits are recognizable.
If the book value of the identifiable assets acquired is less than their fair market value (FMV), then an amount equal to the difference between book value and FMV becomes goodwill. It is calculated as the difference between the FMV and the purchase price of identifiable assets acquired. This goodwill is classified as an intangible asset because it has no physical form, but it does have value to shareholders.
When the book value of an asset is lower than its fair market value, a write-up is used to adjust it so that the carrying amount equals the actual price paid. This increase in the book value of an asset is called a "write-up." The purpose of this exercise is to ensure that assets acquired are recorded at their FMV. Example of Purchase Price AllocationLet's say company A acquired company B for $5 billion. Company A bought all the assets of Company B, i.e., plant, machinery, furniture buildings, etc. All these are recorded as an asset on company A's balance sheet. If the market value of these assets is greater than their book value, then company A records them at their market values and does not make any changes in the book value of company B's assets. But if the market value is below the book value, then company A increases the book value of Company B's assets and decreases its cash account by that amount. This way, company A values all of Company B's assets on its books at their fair market value. This increase in assets and decrease in cash is recorded as a capital transaction and is known as an "adjusting entry." The company may also record a gain or loss on bargain purchases. ConclusionPurchase Price Allocation or PPA is very important for any company that is buying another one, whether small or large. It plays a major role in the financial statements of both companies because it affects their balance sheet and income statement numbers. So PPA plays an essential role in making sure that assets are recorded at their fair value which ultimately helps the company make a more informed decision. Originally Published Here: Purchase Price Allocation: Definition, Example, Formula, Valuation There are a lot of benefits to buying a home. You can build equity, have a place to call your own, and get tax breaks. But one of the biggest benefits is that you can often buy a home with zero down payment. In this blog post, we will discuss how to get a mortgage with zero down payment. We will cover the different types of mortgages available, as well as the requirements for each type. So whether you are a first-time homebuyer or you are looking to refinance your mortgage, this blog post has everything you need. What is a zero-down mortgage and how does it work?A zero-down mortgage is a mortgage where the homebuyer does not have to make a down payment. This type of mortgage is available from a number of different lenders, and it can be used for both purchase and refinance transactions. The requirements for getting a zero-down mortgage vary depending on the lender. However, most lenders require that you have a good credit score and that you be a responsible borrower. In addition, most lenders require that you purchase mortgage insurance. This is because the lender is taking on more risk by lending you money without any down payment. Mortgage insurance protects the lender in case you default on your mortgage. The benefits of getting a zero down mortgageThere are a few benefits to getting a mortgage with zero down payment. First, it can help you get into your dream home sooner. This is because you do not have to save up for a down payment. Second, it can help you avoid paying Private Mortgage Insurance (PMI). PMI is an insurance policy that protects the lender in case you default on your mortgage. It can be expensive, and it can add hundreds of dollars to your monthly mortgage payment. By getting a zero-down mortgage, you can avoid paying PMI altogether. Third, a zero-down mortgage can help you build equity faster. This is because when you put less money down, you are borrowing more money. And the more money you borrow, the faster you will pay off your mortgage. The different types of zero-down mortgagesThere are a few different types of zero-down mortgages available. The most common type is the FHA mortgage. An FHA mortgage is a mortgage that is insured by the Federal Housing Administration (FHA). This means that if you default on your mortgage, the FHA will pay the lender back. And because the FHA insures your mortgage, you can often get a mortgage with zero down payment. Another common type of mortgage with zero down payment is the USDA mortgage. The United States Department of Agriculture (USDA) offers a mortgage program that is designed for rural and suburban homeowners. This program allows you to buy a home with no money down, and it does not require mortgage insurance. However, there are some restrictions on who can qualify for this mortgage. If you do not qualify for the FHA or USDA mortgage, there are a number of other options available. You can get a mortgage from a bank, credit union, or other lenders. And most of these lenders offer mortgages with zero down payment. How to qualify for a zero-down mortgageThe requirements for getting a mortgage with zero down payment vary depending on the lender. However, most lenders require that you have a good credit score and that you be a responsible borrower. In addition, most lenders require that you purchase mortgage insurance. This is because the lender is taking on more risk by lending you money without any down payment. Mortgage insurance protects the lender in case you default on your mortgage. Final thoughtsWhile it is possible to get a mortgage with zero down payment, there are several things potential home buyers need to know before taking this route. We’ve outlined the pros and cons of getting a mortgage with no money down, as well as some tips for making the process go as smoothly as possible. So, if you’re considering buying a house and don’t have any cash saved up for a down payment, read on. And remember, if you still have questions after reading this post, check out other articles on our website. Post Source Here: How to Get a Mortgage with Zero Down Payment Financial transactions are the essence of a business. These transactions allow companies and other entities to acquire products or services. Once they do so, they can resell them to generate profits. While most transactions are straightforward, they may also require a legal form. For that purpose, sales and purchase agreements are prevalent. What is a Sales and Purchase Agreement?A sales and purchase agreement is a legally binding contract to support a transaction. With this agreement, a party known as the seller sells an item. On the other hand, the other party, the buyer, receives or purchases it. A sales and purchase agreement obligates a transaction between those two parties. This agreement is prevalent in most financial transactions. A sales and purchase agreement includes the terms and conditions for each transaction. Usually, it defines the compensation received and the items provided in exchange. Both parties entering the contract may go through various negotiations to reach those terms. However, those negotiations do not become a part of the final contract. Instead, only the agreed-upon terms get drafted into the sales and purchase agreement. Overall, a sales and purchase agreement sets out the details of a financial transaction. It allows both parties to understand their responsibilities in fulfilling that agreement. On top of that, it also acts as a legally binding contract in case of future disputes. A sales and purchase agreement is an essential document for most financial transactions. What are the uses for a Sales and Purchase Agreement?A sales and purchase agreement is crucial for all financial transactions. This agreement sets the expectation for every business deal. However, sales and purchase agreements are most common in real estate transactions. When acquiring real estate or properties, the buyer and seller negotiate the final price. Once they reach an agreed-upon amount, they prepare a sales and purchase agreement. On top of that, sales and purchase agreements are also prevalent for companies and businesses. Most companies use them with their suppliers and distributors. In those cases, these agreements act as a contract for revolving transactions. Similarly, they are common when shareholders acquire a company’s shares. Lastly, sales and purchase agreements are also prevalent for mergers and acquisitions. The sales and purchase agreement includes various crucial elements. They usually contain the parties involved, the agreement to sell and purchase, the consideration, and the product or service. On top of these, some sales and purchase agreements may also include warranties and indemnities or other conditions. Lastly, these agreements also include clauses related to the contract’s completion and post-completion. ExampleA company, Red Co., agrees to acquire another company. Red Co. drafts a sales and purchase agreement to support the transaction. This contract mentions a date, terms and conditions, and other elements. A sample sales and purchase agreement may include the following terms. “In accordance to the Sale and Purchase Agreement (SPA) dated XX/XX/XXXX, an agreement was entered into between Red Co. (the “Purchaser”) and the company (the “Seller”) which allows The Purchaser to acquire XX% of the Seller’s shares.” The above defines the sales and purchase agreement between the companies. On top of that, this agreement will also include terms related to the obligations for both parties. An example of these terms may include the following. "The price to be paid by the Purchaser to the Seller for the Shares will be $XXX million (the "Total Purchase Price")." Furthermore, it may elaborate on the form of the underlying payment as follows. “The Total Purchase Price for the Shares will be paid by the Purchaser in one lump sum payment to the Seller in the form of a check, a Teller’s check, or an electronic money transfer.” The sales and purchase agreement will also include clauses related to the transaction closing. The clause for it may include the following details. “The Closing of the purchase and sale of the Shares will take place on XX/XX/XXXX (the “Closing Date”) at XYZ location or at such other time and place as the Purchaser and Seller mutually agree.” Apart from these, a sales and purchase agreement will also include other details. However, the above are the most common terms and conditions for those contracts. ConclusionA sales and purchase agreement occurs between a buyer and a seller. It is a legally binding contract that obligates both parties to a transaction. Usually, these agreements are prevalent in real estate deals. However, they are also essential to business deals, including supply chain contracts, mergers and acquisitions, etc. Originally Published Here: Sales and Purchase Agreement The unpredictability of the markets is a well-known fact. Despite this, many traders and portfolio managers continue to try to predict market volatility and manage their risks accordingly. Usually, econometric models such as GARCH are used to forecast market volatility. In recent years, machine learning has been shown to be capable of predicting market volatility with accuracy. Reference [1] explored how machine learning can be used in this context. The findings are, First, we find that a limited number of predictors, namely, the current month realized volatility, idiosyncratic volatility, bid-ask spread and returns, respectively, account for most of the predictive power of the models. Indeed, these are the same variables that are empirically found in other studies to be most important for predicting expected returns confirming the risk-return trade-off in finance. Second, we show that machine learning methods not only properly can capture the order and magnitude of the impact for each predictor but also the direction of these impacts, as well as the interactions between predictors without any pre-specified interaction effects in the models. In particular, our results indicate that machine learning methods can account for the stylized facts about volatility. We find that large values of the current period volatility have large and positive impact on the next period volatility, whereas, small values of the current period volatility have small and negative impact on the next period volatility... Third, we show that an LSTM model, which captures the temporal dependence of predictors, can outperform the feedforward neural network and regression tree, which rely on the most recent information in the predictors.6 In particular, our LSTM model with only volatility and return as predictors but up to one year into the past, performs as good as an LSTM model with the full set of predictors and the same number of lags. One can think of our LSTM model as an alternative to the GARCH type models except that we do not need to impose any distributional assumption. However, similar to the GARCH type models we need to find the optimum number of lags through training different models across a range of lags. In short, the aim of the paper was to demonstrate the potential of machine learning for modeling market volatility. In particular, the authors have shown how the LSTM model can be used to predict market volatility and manage risks. The results suggest that this is a promising alternative approach to traditional econometric models like GARCH. References [1] Filipovic, Damir and Khalilzadeh, Amir, Machine Learning for Predicting Stock Return Volatility (2021). Swiss Finance Institute Research Paper No. 21-95, https://ssrn.com/abstract=3995529 Article Source Here: Using Machine Learning to Predict Market Volatility |
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