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Articles on Trending Technologies
Technical articles with clear explanations and examples
When should Capital Cash Flow (CCF) approach be used in evaluating a project?
The choice of using Capital Cash Flow (CCF) in evaluating an investment project is related more to convenience than theoretical grounds. CCF is not the only approach for evaluating an investment project. It is used to evaluate a project when some certain conditions are present. In this article, we will discuss the conditions that should be met in order to choose CCF as an evaluation tool for an investment project.The evaluation of a project rests more on whether debt is fixed, or the debt-to-equity ratio is fixed in an investment. The fact is that, calculations of a project can be ...
Read MoreWhat is Asset Beta or Unlevered Beta?
The asset beta or unlevered beta of the assets of a company is a representation of the systematic risks of the assets. The asset beta is the weighted average of debt beta and equity beta of the assets. It is also called unlevered beta because it can be determined from the equity beta.To determine the unlevered beta, the equity beta has to be divided by a factor 1 plus (1 minus tax rate) times the debt-to-equity ratio of the company. That is, $$\mathrm{Unlevered \:Beta\:=\:\frac{Equity\: Beta}{1+[(1-Tax \:rate)\times(\mbox{Debt-Equity} \:Ratio)]}}$$Asset Beta and Systematic RiskAsset beta also has a direct impact on the systematic ...
Read MoreFinancial flexibility Vs Operating flexibility
Financial FlexibilityFinancial flexibility refers to the capability of a company to respond to its cash flow or an investment opportunity set in a timely and value-maximizing manner. The concept of financial flexibility is not new, but the most corporate approach has been via the Miller and Modigliani model of capital structure where the corporate performance is judged in a perfect capital market. Such capital markets are frictionless, and they perform in a costless manner where the firms can enjoy complete flexibility of arranging their capital structure.Financial Flexibility is Important in Uncertain TimesFinancial flexibility plays a critical role only when the ...
Read MoreWhy Free Cash Flows are called Unlevered Cash Flows?
Free Cash Flow (or FCF) is a widely used metric in finance and it is sometimes known as the unlevered cash flow. But before we dive deeper into why FCFs are called so, let's begin with what FCFs are.What is Free Cash Flow?Free Cash Flow is a cash component that a company retains after investing and distributing money to all kinds of debt outstanding in the market. FCF is a measure of the wellbeing of a company and so, it is of interest to the lenders and debt-holders of the company.Simply put, FCF is the funds that remain after repaying ...
Read MoreWhat is Capital Cash Flow method?
In Free Cash Flow (FCF) method, the interest tax shield is adjusted in the discount rate which is also called weighted average cost of capital (WACC). The adjustment is not done in cash flows of the firm. We can take an alternate measure to adjust the cash flow where the adjustments are not made in tax shields of the business. This is known as Capital Cash Flow (CCF) approach.In this approach, interest tax shields are adjusted in cash flows rather than in discount rates. This method of adjusting tax shields in cash flow is known as capital cash flow. In ...
Read MoreHow to upsample a given multi-channel temporal, spatial or volumetric data in PyTorch?
A temporal data can be represented as a 1D tensor, and spatial data as 2D tensor while a volumetric data can be represented as a 3D tensor. The Upsample class provided by torch.nn module supports these types of data to be upsampled. But these data must be in the form N ☓ C ☓ D (optional) ☓ H (optional) ☓ W (optional), Where N is the minibatch size, C is the numberchannels, D, H and W are depth, height and width of the data, respectively. Hence, to upsample a temporal data (1D), we need it to be in 3D in ...
Read MoreHow to adjust saturation of an image in PyTorch?
The saturation of an image refers to the intensity of a color. The higher the saturation of a color, the more vivid it is. The lower the saturation of a color, the closer it is to gray.To adjust the saturation of an image, we apply adjust_saturation(). It's one of the functional transforms provided by the torchvision.transforms module. adjust_saturation() transformation accepts both PIL and tensor images. A tensor image is a PyTorch tensor with shape [C, H, W], where C is the number of channels, H is the image height, and W is the image width.This transform also accepts a batch ...
Read MoreHow to apply linear transformation to the input data in PyTorch?
We can apply a linear transformation to the input data using the torch.nn.Linear() module. It supports input data of type TensorFloat32. This is applied as a layer in the deep neural networks to perform linear transformation. The linear transform used −y = x * W ^ T + bHere x is the input data, y is the output data after linear transform. W is the weight matrix and b is biases. The weights W have shape (out_features, in_features) and biases b have shape (out_features). They are initialized randomly and updated during the training of a Neural Network.Syntaxtorch.nn.Linear(in_features, out_features)Parametersin_features - It ...
Read MoreHow to flatten an input tensor by reshaping it in PyTorch?
A tensor can be flattened into a one-dimensional tensor by reshaping it using the method torch.flatten(). This method supports both real and complex-valued input tensors. It takes a torch tensor as its input and returns a torch tensor flattened into one dimension.It takes two optional parameters, start_dim and end_dim. If these parameters are passed, only those dimensions starting with start_dim and ending with end_dim are flattened.The order of elements in the input tensor is not changed. This function may return the original object, a view, or copy. In the following examples, we cover all the aspects of flattening the tensor ...
Read MoreHow to compute the cross entropy loss between input and target tensors in PyTorch?
To compute the cross entropy loss between the input and target (predicted and actual) values, we apply the function CrossEntropyLoss(). It is accessed from the torch.nn module. It creates a criterion that measures the cross entropy loss. It is a type of loss function provided by the torch.nn module.The loss functions are used to optimize a deep neural network by minimizing the loss. CrossEntropyLoss() is very useful in training multiclass classification problems. The input is expected to contain unnormalized scores for each class.The target tensor may contain class indices in the range of [0, C-1] where C is the number ...
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