Illuminating the dark side of valuation using a Vanilla LSTM Recurrent Neural Network

One of the major problems faced by investors is to try and distinguish between “Price” and “value”, This problem arises because there are several behavioral biases as well as unethical practices that play a role while doing valuation. Sometimes, the valuation company is given a figure by their client and they do a backward calculation to keep up the pretense. The root cause for there even being a “Dark Side” of valuation is that in the DCF model, we make projected cash flows; These, projected cash flows are based on assumptions made by the valuation company. Now, based on their assumptions the valuation company could grow the cash flows at 100% or 1%, it’s perfectly acceptable as long as they have a story to back them up. Due to this ambiguity in projections, the valuation company provides their client with any valuation they want.

In this article, I have solved this problem by implementing an Artificial Recurrent Neural Network (Vanilla LSTM) to predict future cash flows. This gives us an unbiased prediction and takes away the ambiguity from a valuation that caused “The Dark Side”. The Artificial Recurrent Neural Network is able to detect interrelationships between thousands of diverse market variables and therefore is the perfect analytical tool for the financial forecast. In this article, I have used the Vanilla LSTM to forecast cash flows and arrived at a valuation of Maruti Suzuki Ltd and avoided any biases that usually pay a role while valuating a company.

What is a Recurrent Neural Network?

Recurrent Neural Network is a type of Neural Network where the output of the previous step is fed as an input of the current step. Usually in neural networks (feed-forward), the outputs and inputs are independent; But, in situations where you must predict the next word of a sentence, the previous word is essential. Recurrent Neural Networks solved this issue using hidden layers. The hidden layer remembers some of the information about a sequence. A Recurrent Neural Network remembers all the information over a period. It is considered as a powerful tool because of its ability to remember previous inputs.  An RNN remembers each information through time. It is useful in time series prediction only because of the feature to remember previous inputs as well. This is called Long Short-Term Memory (LSTM). A Vanilla LSTM is an LSTM model that has only one hidden layer of LSTM units, and an output layer used to make a prediction.

  • For forecasting Time series data Via a Vanilla LSTM Recurrent Neural Network, we are taking 10 data points or historical data. These data points are nothing but Net cash flows we arrive at while doing FCFE. Therefore, we start with getting Income statement and Profit and loss from FY 2010 to FY 2019.
  • For each of these 10 years, we take the Profit after tax add the depreciation, Changes in Debt (Current year Debt – Previous years debt) and subtract the Capital expenditure and changes in working capital (Excluding cash). Using this, we arrive at Net Cash Flows of 10 years.
Source
  • These Net Cash Flows are used as Inputs for the Vanilla LSTM Recurrent Neural Network and are used to predict the future cash flows for the next 5 years. The code for this is written on Python by Dr. Jason Brownlee. Each time the neural network is used to predict the future net cash flows, it gives only one output (the net cash flow of the next year). This output is included in the input for the next year. This is how even though the neural network is capable of forecasting only 1 year, I have forecasted for the next 5 years. The Vanilla LSTM is as follows:
  • The terminal growth rate has been assumed at 10% because that is the rate at which the automobile sector is expected to grow as per Research Cosmo. However, to avoid any biases I have done a sensitivity analysis with terminal growth rates varying from 9% to 11%. To find the terminal value we increase the Net Cash flow value of the 5th year by the terminal growth rate and divide this by the difference between the Cost of Equity (Ke) and the terminal Growth rate.
  • These discounted cash flows are added to arrive at Equity Value of the company. This equity value is divided to arrive at Expected market price of the company.

Conclusion

The objective of this article was to remove the assumptions made while valuation because these assumptions create a window for ambiguity which can be used to unethically inflate the valuation of the company. In this article no assumptions were made except the terminal growth rate (taken from a Research Cosmo Report). To tackle this, I have done a sensitivity analysis with terminal growth rates varying from 9% to 11%. Using this new model, we got a valuation at 10% terminal growth rate of Rs 8,417.22. Therefore, using the method I suggested in this article, you can find the “Value” of the company and not “price”. This way you save up on the money you would have paid the valuation company and also get a unbiased valuation.

Author
Neil Jha
Team Leader – Fintech
(M.Sc. Finance, NMIMS – Mumbai. Batch 2018-20)

Connect with Neil on LinkedIn
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