Valuation methods and issues that arise while conducting valuation

Valuation of a Business is conducted in order to arrive at an estimation of the Economic Value of an Owner’s Interest in a certain Business under the guidance of a certain set procedures. Valuation may be computed for a business in order to arrive at an accurate snapshot of the Financial Standing of the business which is presented to Current or Potential Investors. Valuation is generally conducted when a company is looking to merge with another company or acquire another company or sell off the entire or a fragment of its operations to another company. Some other reasons to conduct Valuation include establishing partner ownership, taxation, analysing the financial strength of the business i.e. determining solvency, planning for future growth and profitability of the business or even divorce proceedings. Further is a brief description of the approaches to Valuation Models and the issues that arise when conducting Valuation under those methods.

There are Three Different Approaches which are commonly used in Valuation:

  1. Income Approach
  2. Asset Based Approach
  3. Market Approach

Further is a brief description of the approaches to Valuation Models and the issues that arise when conducting Valuation under those methods.

I. INCOME APPROACH

  • Under the Income Approach, Valuation is based on the Economic Benefit expected from the investment and the level of risk associated with the investment.
  • There are several different Income Methods which include Capitalisation of cash flow or earnings, Discounted Future Cash Flows which is commonly known as DCF and the excess earnings method.
  • DCF is the Net Present Values of the Cash Flows projected by the company. The Value of an asset is intrinsically based on its ability to generate Cash Flows is the underlying principle of this approach.
  • This method relies more on the fundamental forthcoming expectations of the business rather than on the public market factors.

ISSUES THAT ARISE WHEN CONDUCTING VALUATION USING INCOME APPROACH:

1. USING ACCOUNTING PROFITS INSTEAD OF CASH FLOW

  • The value of a business depends largely on the profitability, financial health and earning power. Accounting Profits and Cash flows are two means to measure it.
  • Free cash flow is a better means to analyse profitability as compared to accounting profit because the Revenues and expenditures of the business are accounted for at the right time and the cash flows of a business cannot be manipulated as much as earnings.

2. OVERLY OPTIMISTIC REVENUE FORECASTS

  • At times while projecting the forecasts, the revenue is shown to be shooting up in numbers during the forecast period. This results when taking a hypothetical high growth rate.
  • What the valuer fails to notice is whether the growth of the company is aligned with the industry, what the market size of the company is or even whether the company has a strategy to achieve the desired growth goal.

3. NARROW FORECAST HORIZON

  • What should be taken as the optimal length of the Financial Forecasts is one of the key choices that need to be made.
  • If a shorter forecast period is considered, it fails to give the effect of different parameters on the business in the upcoming years. For example in case of a company under FMCG sector, it would not be right to prepare financial forecasting for a period of just two to three years.
  • On the other hand is the length considered is too long, the valuation could result as misleading. This is because in the long run, risks associated with the business cannot be anticipated easily.
  • Thus, it is essential to consider an explicit time frame while conducting valuation that is neither too short nor too long. A time frame ranging from 5 to 7 years is generally considered when performing DCF Valuation.

4. INCORRECT BETA

  • Beta comes into consideration when deriving the Cost of Equity of a company.
  • When Valuation of a company is done under the circumstances of a merger or an acquisition, majority of the times, the Beta is taken to be that of the Acquiring Company. This is done under the assumption that the Target Company is a smaller company when compared to its bidder, thus the Target Company would have no influence on the resulting Capital Structure as well as the riskiness of the New Company.
  • Other times, the Beta considered is an estimation of the emerging company’s Beta with respect to a Market Index. But just using the historical beta is very risky when the company or its future risk prospects are not analysed.
  • At times, when levering and unlevering the Beta to arrive at the estimate, incorrect formulae are used. The levering should depend on the amount of debt prospect of the company in future.

5. HIGH COST OF EQUITY

  • Along with Beta, another problem that arises in deriving the Cost of Equity is the Risk free Rate.
  • Majority of the times the Risk free rate considered is just the 10 year Government Bond Yield.
  • What one fails to consider in this is the Country Risk. If this view is taken into consideration, the Cost of Equity of a company in United States would be same as that of a company in Bolovia, which is highly incorrect.
  • Thus, the Country Risk Premium needs to be deducted to arrive at an accurate Risk Free Rate.

6. INCORRECT DISCOUNT RATES

  • It is a wrong notion to consider a higher discount rate when there are higher risk cash flows, on the basis that the discount rate on cash flows should reflect the riskiness.
  • Generally Book Values of Debt and Equity for arriving at the Weighted Average Cost of Capital (WACC). But this violates the Basic Principle of Valuation which is to arrive at a Fair Value
  • Thus, when valuing an on-going business, the market values of debt and equity should be taken into consideration to derive the WACC.

7. HIGH LONG TERM GROWTH RATE

  • There is a Material Impact created on the value of a company when a long term growth rate is used.
  • This is considered when arriving at the Terminal Value. The Terminal Value is the Present Value of all the Cash Flows at a future point in time, when the cash flows are expected to be at a stable growth rate.
  • These Long Term growth rates generally lie in the range of 5% to 6%.
  • They depend on the growth rate of the economy and never exceed that figure. This is because, a higher growth rate than the GDP rate of the economy would imply that the company would grow larger than the economy. Applying such a high rate would result in overvaluation.

II ASSET BASED APPROACH

  • Under this approach, the value of a business is derived as a sum of its parts. This method takes into account all the assets and liabilities of the Business.
  • The Value of the Business is the difference between value of all relevant assets of the business and value of all the relevant liabilities.

III MARKET APPROACH

  • This approach is used to derive the appraisal value of the business, intangible asset, security or business ownership interest by considering market prices of comparables which have been sold recently or are still available.
  • There are two main Valuation Methods under this approach-
    1. Comparable Companies Method – This method entails the use of valuation multiples of companies which are traded publically.
    2. Comparable Transactions Method – This method entails the use of valuation figures of observed transactions of companies in the same industry as that of the Target Company.
  • Certain common multiples considered for Relative Valuation are – P/E Ratio, PEG Ratio, EV/Sales, EV/EBITDA, EV/ Sales.

ISSUES THAT ARISE WHEN CONDUCTING VALUATION USING MARKET APPROACH:

1. INCORRECT PEER SELECTION

  • The industries in a market are often loosely defined. Making is difficult to select optimum peers to conduct Comparable Company Analysis.
  • Some of the major factors to be considered while selecting peers are product line, geography, seasonality, revenue, etc.
  • Another way to identify peers it to check the annual report of the companies, in case the company is a listed one, where the peers would be mentioned.
  • The same could apply for a Comparable Transaction Analysis. Where Multiples of an extra ordinary Transaction are considered for conducting Valuation.

2. INCORRECT MULTIPLES

  • There are a number of Multiples available to value the worth of a business. Each of these Multiples relate to a specific extent of the financial performance to the potential selling price of the business.
  • If a multiple is based on the Net Cash Flow, it should not be applied to the Net Profit.
  • For valuing new companies, which have small sale and negative profits, using multiples such as Price-to-Sales or Enterprise Value to EBITDA Multiples can be misleading. In such cases, Non-Financial Multiples can be helpful.
  • Certain common multiples considered for Relative Valuation are – P/E Ratio, PEG Ratio, EV/Sales, EV/EBITDA, EV/ Sales.

3. NOT ADJUSTING THE ENTERPRISE VALUE TO EBITDA MULTIPLE FOR NON-OPERATING ITEMS

  • The Enterprise Value should not include excess cash. Also the Non-Operating Assets must be evaluated separately.
  • Operating leases must be considered in the Enterprise Value, the interests costs associated to such operating leases must also be added back to the EBITDA Value.
  • This is because though the Value of Lease and the Interest Cost of the lease, affect the ratio in the same direction, the effect is not of the same magnitude.

4. TAKING AVERAGE INSTEAD OF MEDIAN

  • While conducting Relative Valuation, it is a common practice to consider the Average value of the PEER’s multiples instead of Median value.
  • The middle element of the data is the Median Value. Taking Median Value enables the extremely high or low values to be disregarded.

5. USING RELATIVE VALUATION AS PRIMARY VALUATION METHODOLOGY

  • Valuation should not be derived by depending on just one methodology, especially just Relative Valuation. Relative Valuation is a considerably good method to validate the value derived from other Valuation Methods.
  • One issue of relying on Relative Valuation is that getting data of a privately owned business is difficult. Also the shares of a public company are more liquid than that of a private company.

CERTAIN OTHER COMMON ISSUES THAT ARISE WHEN CONDUCTING VALUATION:

1. CONSIDERING VALUATION IS A SCIENTIFIC FACT

  • Most of the times it is asserted that Valuation is a Scientific Fact rather than an Opinion.
  • A logical process is followed to reach a Valuation Figure or Opinion, thus there is the role of Science.
  • But what is forgotten is that the Value arrived at from any Valuation Method, is contingent to a set of assumptions and expectations. These expectations include future prospects of the company, industry or even the country. Another thing which is factored in is the Valuer’s appraisal of Company Risk.
  • Hence, valuation is more of an Art than a Science.

2. ASSUMING THAT EVERY ESTABLISHED BUSINESS HAS A POSITIVE GOODWILL

  • Business Goodwill is actually directly related to the earning power of the business.
  • If the Business earnings fall below the return on assets, then the business has a negative goodwill.

3. FAILING TO ASSESS COMPANY SPECIFIC RISK

  • When conducting Business Valuation, risk assessment plays a very important role.
  • Each company has different financial and operational factors which contribute to its risk profile.
  • Thus, each company has different Discount and Capitalisation rates which need to be taken into consideration.

4. REDUNTANT ASSETS ARE NOT ADDED TO COMPANY VALUE

  • Redundant assets are those which are not required for the day to day operations of the business. The value of such assets should be added to the value of the business or company.

5. THINKING THAT THE BUSINESS PURCHASE PRICE AND PROJECT COST ARE THE SAME

  • Many a times the project cost is considered to be the same as the purchase price. But that is not correct.
  • In order to arrive at the Purchase price s=certain adjustments need to be made to the project cost.
  • One such adjustment is that the buyer of the business also needs to inject certain working capital.
  • If there is any deferred equipment, its maintenance cost also needs to be adjusted.
  • There are certain investments which are needed to maintain the income stream such as hiring staff replacements, licences, regulatory compliances, etc. Such costs also need to be adjusted.

The method which has the capability to incorporate all the significant factors which have a material effect on the Fair Value is the Most Appropriate Method of Valuation.

Furthermore, one must keep in mind the above issues which can arise while deriving Valuation for a Business, Stock or Company in order to avoid any misleading valuation figures.

Author
Vhabiz Lala
Volunteer – Equity Research & Valuation (M.Sc. Finance, NMIMS – Mumbai. Batch 2018-20)

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China is coming up with cryptocurrency – shock or surprise?

The meeting of finance and technology, widely known as fintech, is changing the landscape of investment management. As the saying goes, it’s tough to make predictions, especially about the future events. But it’s manifestly worth the effort because catching big trends is how fortunes are made and catastrophic losses are avoided.

Blockchain-related topics are extremely hot nowadays and cryptocurrencies are one of those. So, what is a cryptocurrency? From the word itself you can see that it has something to do with cryptography and currency. For its part, cryptography is the process of converting ordinary plain text into unintelligible text and vice-versa. Modern cryptography deals with confidentiality: information cannot be understood by anyone, integrity: information cannot be altered, and authentication: sender and receiver can confirm each other.

Putting all the pieces together, cryptocurrency is a medium of exchange value (just like ordinary money) that exists in the digital world and relies on encryption, which makes transactions secure. A cryptocurrency is an alternative form of payment to cash, credit cards, and cheques. The technology behind it allows you to send it directly to others without going through a third party like a bank. In short, cryptocurrencies are like virtual accounting systems.

As you can find, there are many exciting use cases for this. You can send money back to your family without incurring large international fees if you’re working in a different country. Merchants no longer have to worry about payment fraud because people can only spend what they have. Summing up, Cryptocurrency is a radically new way of paying that makes all the transactions secure and helps to get rid of intermediaries represented by banks, which also contributes to a significant reduction in the commission fee.

The cryptocurrencies can either be based on blockchain technology or can be centrally issued, circulated within a community or geographic location, or tied to fiat currency. Blockchain is a revolutionary ledger technology, with a wide array of potential applications from smart contracts to healthcare systems, but it did not catch the attention of speculators and the media until Bitcoin surged from $0.009 to more than $11,000 per coin. There are more than 869 cryptocurrencies, but without fundamentals, they are little more than “trust machines” and, as such, are nearly unanalyzable. They generate no cash flow, making discounted valuation approaches inapplicable, but this criticism applies to gold as well.

Although it is cheaper to invest in the early stages, during a new cryptocurrency’s initial coin offering, doing so may overlook the network effect that favors older altcoins (alternative cryptocurrencies other than bitcoin).

Cryptocurrencies are going to play a major role in the coming years and China has decided to be part of that future, in a big way. China’s official digital currency is nearly ready. As much as China frowns on cryptocurrency, it’s happy to introduce its cryptocurrency. There is a great deal of confusion and misunderstood facts surrounding the legal status of cryptocurrency in China. Various headlines like China Bans Bitcoin, China Bans Crypto Exchanges, China Bans Bitcoin Mining, and many more make most people unclear on where China stands on cryptocurrency and whether that has any real impact on how its citizens behave.

The People’s Bank of China has revealed that its digital currency, “can now be said to be ready” after five years of research work. Don’t expect it to mimic crypto, however. According to payments Deputy Chief Mu Changchun, it’ll use a more complex algorithm and structure. This project of coming up with own cryptocurrency of China was started by the former governor of China’s central bank, Zhou Xiaochuan, who retired in March. He decided to come up with the digital currency which will protect China from having to adopt a technology standard, like Bitcoin, designed and controlled by others. 

Facebook Inc.’s push to create cryptocurrency Libra has caused concerns among global central banks, including the People’s Bank of China (PBOC), which said the digital asset must be put under central bank’s supervision to prevent potential foreign exchange risks and protect the authority of monetary policy. Sun Tianqi, an official from China’s State Administration of Foreign Exchange, said, “Libra must be seen as a foreign currency and be put under China’s framework of forex management”. Dave Chapman, executive director at BC Technology Group Ltd also said on similar lines that, “It is without a doubt that with the announcement of Libra, governments, regulators and central banks around the world have had to speed up their plans and approach to digital assets. They have to consider the possibility that non-government issued currencies could dramatically disrupt finance and payments.”

How the cryptocurrency issued by China will be different from other cryptocurrencies, might be one of the questions coming to your mind. To begin with, in launching the new cryptocurrency, referred to as DC/EP for Digital Currency/Electronic Payment, the People’s Bank of China (PBOC) has stolen a march on both Facebook and other central bankers who have been discussing the possibility of a cryptocurrency and how it’s the implication. What sets China’s DC/EP apart from libra and Mark Carney’s(Bank of England’s Governor) “synthetic hegemonic currency” (SHC), according to Paul Schulte(The founder and editor of Schulte Research, a company does research on banks, financial technology, bank algorithms, and credit algorithms), is that while libra is little more than early-stage computer code and the SHC doesn’t appear to have gone much further than Carney’s mind, the Chinese cryptocurrency is ready to launch. “China is barreling forward on reforms and rolling out the cryptocurrency,” says Schulte, who now runs a research firm. PBOC will be the first central bank to come up with its cryptocurrency. Unlike the decentralized blockchain-based offerings, this one could give Beijing more control over its entire financial system. It would increase the PBOC’s ability to root out risks and crackdown on money laundering. It could also give the government an unprecedented window into individuals’ private lives.

Deputy Chief Mu Changchun described the central bank’s “two-tiered” system, wherein the bank would create the cryptocurrency and a small group of trusted commercial businesses would “pay the central bank 100% in full” to be allowed to distribute it. This dual delivery system is suitable for national conditions of China. It can not only use existing resources to mobilize the enthusiasm of commercial banks but also smoothly improve the acceptance of the digital currency across China. If China’s leaders agree on with this idea of a legal cryptocurrency for the whole country, its introduction will likely be gradual. Early adopters would be barred from using it on investment products, a person familiar with the central bank’s plans says, which would make the impact on monetary policy negligible. 

“China’s strategic plan is to integrate more closely with the rest of the world. Cryptocurrency is just one of the means to have a more internationalized renminbi. It’s all strategic. It’s all long term”, said Charles Liu, chairman of HAO International, a private equity firm investing over $700 million in Chinese growth companies. Finally, the Chinese government said that the cryptocurrency could launch as soon as November 11, China’s busiest shopping day, known as Singles Day.

Author
Pratik Jaju
Team Member– Fintech
(M.Sc. Finance, NMIMS – Mumbai. Batch 2019-21)

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Co-author
Omkar Pawar
Team Member– Fintech
(M.Sc. Finance, NMIMS – Mumbai. Batch 2019-21)

Connect with Omkar on LinkedIn

Who is liable for cyber fraud?

With the rise in digital transactions ad their spread to the interiors of the country, cyber frauds are on the rise. In this situation, the question arises who is actually liable for the same. Subscribe to Areesha Fatma Channel on YouTube
Areesha Fatma
(M.Sc. Economics, NMIMS – Mumbai 2018-20)

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on LinkedIn

Non-Performing Assets (NPA) of India: Journey so far and the road ahead!

“The failure of a loan usually represents miscalculations on both sides of the transaction or distortions in the lending process itself.”

— Radelet, Sachs, Cooper and Bosworth (1998)


In the recent times the newspapers have been filled with some or the other news, issues, policies, regulation or resolution of NPAs. The NPA ratio has come down to 9.3% in March, 2019 from 11.5% in March,2018 according to mention by RBI Governor Shaktikanda Das. 

Source: SCB’s GNPA Ratio,Financial Stability Report, RBI

According to RBI, the definition of NPA is: ‘An asset, including a leased asset, becomes non-performing when it ceases to generate income for the bank.’

A non-­performing asset (NPA) is a loan or an advance where the payment of principal/interest is due (in default) for 90 days or above.First, when there is a default of payment, till 90 days, the accounts are subsequently classified as Special Mention Accounts (SMA): SMA 0/1/2. Then after 90 days, these accounts are classified as NPAs.Further NPAs are classified into sub­standard,doubtful and loss assets.Any income for standard assets is recognized on accrual basis, but income from NPAs is recognized only when it is actually received.

Reasons for accumulation of NPAs:

Increasing cases of wilful defaults and frauds are often considered as the primary reason behind the accumulation of bad loans in the Indian banking system.

When an economy experiences healthy GDP growth, a substantial part of it is financed by the credit supplied by the banking system. As long as the GDP keeps growing, the repayment schedule does not get substantially affected. However, when the GDP growth slows down, the bad loans tend to increase due to macroeconomic factors, primarily among them are interest rate, inflation, unemployment and change in the exchange rates.Hence, bad loans accumulate as borrowers are unable to repay due to stalling/closure of the big development projects

Bank-related micro indicators such as capital adequacy, size of the bank, the history of NPA and return on financial assets also contribute to the accumulation of bad loans. NPAs, specifically in the Public Sector Banks (PSBs), have adverse effects on credit disbursement. Increasing amounts of bad loans prompt the banks to be extra cautious. This in turn has caused drying up of the credit channel to the economy, particularly industries, making economic revival more difficult.

Need for Solution

Reviving industrial credit is crucial for the health of the overall economy, because industry (particularly manufacturing) tends to create more employment.

Mounting bad loans suggests vulnerability in the system, wherein short-term deposit-taking banks have to extend credit for long-term big development projects. And this model is visibly failing. Hence NPAs put several small depositors of the banks, particularly in the PSB, at risk.

Also an improvement in the recovery rate and reduction in timeline for resolution for insolvent companies will increase investor confidence in Indian Bond Market.

Recognition of the problem and the solution:

NPAs story is not new in India and there have been several steps taken by the GOI on legal, financial and policy level reforms. In the year 1991, Narsimham committee recommended many reforms to tackle NPAs.

SICA Act, The Debt Recovery Tribunals (DRTs) – 1993, CIBIL: Credit Information Bureau (India) Limited-2000, LokAdalats – 2001, One-time settlement or OTS- compromise settlement-2001, SARFAESI Act- 2002, Asset Reconstruction Company (ARC), Corporate Debt Restructuring – 2008, 5:25 rule – 2014, Joint Lenders Forum – 2014, Mission Indradhanush – 2015, Strategic debt restructuring (SDR) – 2015, Asset Quality Review- 2015, Sustainable structuring of stressed assets (S4A)- 2016 were some of the techniques applied to tackle the problem by government and RBI.

Every method was entangled, rules were not that clear, there were lot of cases pending in front of DRTs owing to limited infrastructure, not enough field experts and hence, it took years for creditors to recover their money. India needed a structured process; thereby Insolvency and Bankruptcy Code (IBC) -2016 came into existence.

It sets a time limit of 180 days which can be extended by another 90 days to complete the entire process. Some of the features of the code include the allocation of a new forum to carryout insolvency proceedings, setting up a dedicated regulator, creating a new class of insolvency professionals and another new class of information utility providers.

The forum where corporate insolvency proceedings can be initiated is the National Company Law Tribunal (NCLT) and appeals against its decisions can be made in the (National company Law Appellate Tribunal) NCLAT. The IBC vests the NCLT with all the powers of the DRT.

Insolvency professionals will have the task of monitoring and managing the business so that neither the creditors nor the debtor need worry about economic value being eroded by the other.On acceptance of the application by NCLT for proceeding for Corporate Insolvency Resolution Process (CIRP), Board of Directors of the company has to step down and Insolvency Professional takes the charge and the plan for revival or liquidation of the company, approved by majority of creditors is put in the action according to the IBC rules and timeframe.

It is predicted that the NCLT is focused on the legal process while the insolvency professional is focused on business matters.RBI listed out the 12 major accounts in India, which has the largest share of NPAs in the country.

Source : ICRA

Some great results have fared in: Ranking for ‘Resolving Insolvency’ But still there is a long way to go: Suggestions

As mentioned above, there is a mismatch of assets and liability for the banks. Banks’ assets are long term loans, whereas banks liabilities are short term deposits, which have landed banks in failures. Hence, it makes sense to say that commercial banks should be focusing on short term assets to match their short term liabilities. And for Long term projects, special purpose vehicles (SPV) should be created to fund a particular sector project and financial institution should be created to fund these SPVs and should be given incentives and proper regulation from the government.

Also, as recapitalization of PSBs is going on, a bank should first divide its assets into good and bad, meaning viable and unviable asset. Banks should be recapitalized according to viable assets to revive with its positive core rather than just giving out public money. By this, banks can also focus on their core business rather than managing NPAs and not contribute to slowing of the economic growth.

SICA Act in India was a ‘Debtor in Possession’ (DIP) Model just like U.S. Chapter 11. But there were flaws in the act compared to the U.S.model. There was also a problem in the assessment of viability of the company as only a few accounts were revived. ‘Another relevant fact is the definition of insolvency or ‘sickness’ under the SICA. The N.L. Mitra committee criticized the definition provided by SICA i.e. ‘at the end of any financial year, accumulated losses equal or exceed its entire net worth’ stating that this is the end rather than the initial point where the company’s problems begin.’

Time has changed, India made a comeback with ‘Creditor in Possession’ (CIP) Model of IBC inspired by U.K. owing to similarities in the judicial process and SMEs culture, but there is one problem. In SICA, debtors were made liable to take the proceeding to court if it is identified by them that company is in trouble. Under IBC there is no such amendment and hence there is a ‘problem of initiation’ which was clearly seen in the case of Jet Airways. Just because directors didn’t want to step down, they dragged the process, rejected lot of revival bids in early insolvency phase. And be it any reason, even the financial or operational creditor did not initiate the process.

Australia also followed CIP model, but faced the same problem and added the amendment to make directors liable for any default under their directorship, directors became scared to default and didn’t take any risky decision to grow the company making them stagnant. This also should not happen with India. But then Australia laid ‘Safe Harbor’ provision to ease out the rules. Hence still amendment in the IBC is required to make directors take help from outside professional for the revival of their company in the early insolvency stage itself.

On June 7,2019, RBI laid provision pertaining to rules for creditors to enter into a ‘review period’ in the first 30 days of default by the debtor account, and make a resolution plan for the concerned account and apply the plan in next 180 days to revive it. If the plan is not put into implementation, provision for this account is required to be increased more and more as days pass. This might lead the banks to initiate the CIRP of the account under IBC and may overcome the ‘Initiation Problem’ from the side of creditors. According to this new frame work for stressed assets, the above mentioned rule is now applicable to Small Finance Banks and NBFCs, as they have become an integral part of the economy and needs to be properly regulated to retain the trust of investors.

There can be a solution to mitigate the problem of NPA by forming a‘Bad bank’. But this is a very risky model as it requires extensive research and cross-country analysis as the taxpayers’ money is on table.

In India Secondary Market for Corporate Loans, particularly distressed loan is in the making, taking inspiration from U.S. and European market. But there is a problem of transfer pricing of these distressed assets. India will have to design a proper mechanism, a platform and regulation of valuation techniques using DCF method, so that there isn’t much of a gap between the bid and the ask price of the assets and so the market remains active and transparent.

India and the banking system requires a major turn around and all the financial professional will have to put in the work.

Author
Vishwa Parekh
Volunteer – Fixed Income & Risk Management
(M.Sc. Finance, NMIMS – Mumbai. Batch 2018-20)

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Neural Networks for Finance

Let’s start with, what is an Artificial Neural Network?

An Artificial Neural Network or ANN is a software program that mimics the human brain’s ability to classify patterns or to make predictions or decisions based on past experiences. The human brain relies on inputs from the five senses, while the artificial neural network uses inputs from data sets. ANN have three or more layers of neurons. The first layer of neurons, called the input layer, has one neuron for each input to the network. Each neuron in the input layer is connected to every neuron in a hidden area. The hidden area occasionally consists of more than one layer, in which case each neuron in the first hidden layer will be connected to every neuron in the second hidden layer. The last layer in the hidden layer is connected to the outer layer. The strength of the connection between the various neurons varies with the weights allocated to each of the inputs. To start with a neural network, you would typically define a function that would map any value to a value between 0 and 1. This is called a “Sigmoid” function.

This function will be run at every neuron of our network, it is useful in converting probability out of numbers. Then after you add the input, you would like to seed them to make them deterministic as you would be adding random weights. Then you would add the synapses (synapses are the connection between each neuron in one layer to another neuron in another layer). Each synapse has a random weight attached to it. As you train this neural network, the error rate will go down as the allocation of weights which were initially random would autocorrect to a more accurate value.

Selection of appropriate inputs is one of the biggest challenges while designing a neural network. Selecting which inputs will impact NIFTY 50 is a relatively tougher job than selecting inputs for which mortgage application is most likely to default. Because both inputs and target outputs used in time series forecasting are very noisy (the data has a lot of random movement that has nothing to do with the trend). The data used in rating mortgage application are usually more generalized. If there is no income in that month, the person will default. However, in a time series forecast, the NIFTY 50 could go down today and come back up tomorrow and remain up for two months. Is it possible to claim that this fall in NIFTY 50 just noise? Credit Card customer search is another domain where neural networks are used because they require very specific customers to sustain. This ideal customer should spend heavily and not use revolving credit line. Hence, per card revenue will be below per card cost which will result in a lower breakeven point. This is crucial for a bank as the incidental & incremental exceed the revenue resulting in a non-profitable business. Therefore, implementing neural networks to distribute credit cards will maximize profits.

MJ futures claims that they achieved a return of 199.2% over a 2-year period using neural networks. Neural networks can identify trends in data that humans might not notice. For example, Dean Barr and Walter Loick at LBS Capital Management using a neural network with 6 inputs. One of these inputs is the ADX, which indicates the directional movement over the previous 18 days. Two more of these inputs are the current value of the S&P 500, and the net change in the S&P 500 value from 5 days prior. It has also been observed that with a network with 3 hidden layers and 20-40-20 neurons in hidden layers was the optimized network with an accuracy of 94.08% for validation dataset.

One of the most common mistakes that most traders make is taking Standard Deviation as their volatility in all their calculations. However, this is a backwards-looking figure and not an accurate anticipation of the future. To solve this figure an implied volatility could be used or volatility using an Artificial Neural Network. This was done in Shaikh A. Hamid and Abraham Habib’s paper on “Financial Forecasting with Neural Networks”, where they discovered that volatility forecasts as per ANN are more accurate than Barone-Adesi and Whaley (BAW) for pricing American options on futures.

  1. They started by selecting appropriate inputs. Technical price data on Treasury bonds was fed along with fundamental data that also effects the market could also be added like fed rates, GDP, money supply, Inflation rate, CPI& Inter-market inputs.
  2. Then they moved on to processing the input data by scaling it between 0 to 1 and normalizing the data.
  3. Specifying a network is the next step. Therefore, they used a feed forward back propagation network. For the input, they took 11 neurons and twice as many in the second layer (In total there are 3 layers).
  4. Then they trained the network with enough historical data.
Author
Neil Jha
Team Leader – Fintech
(M.Sc. Finance, NMIMS – Mumbai. Batch 2018-20)

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Artificial Intelligence in Finance: A Genesis

Artificial Intelligence (AI) started as the branch of computer science that aimed to make the computers follow logical steps to do some of the basic functions that the humans perform using common sense and has evolved from imitation, extension, augmentation and finally aims to reach human-level AI. In 1956 John McCarthy used the coinage ‘Artificial Intelligence’ and proposed a summer research project on the subject at the Dartmouth college involving scientists of psychology, mathematics, computer science and information theory; marking the beginning of AI as a science and engineering of making intelligent machines and recognizing it as a field of research. Technologies driving AI have since evolved from conventional programming using logical steps, heuristics using neural networks, machine learning using big data analytics to the ability of self-evolution. As a part of AI, machines simulate human intelligence processes like learning (acquire information and the rules for using that information), reasoning (using rules to reach approximate or definite conclusions) and self-correction to find application in expert systems, speech recognition and machine vision.

Kai Fu Lee classifies AI revolution in four waves. The first wave was the internet AI which began about 15 years ago but matured around 2012 and was mostly about using AI algorithms as recommendation engines. Some of the common applications of these are in recommending streaming videos in YouTube and likely next purchase on Amazon. Many users find internet experience getting better and becoming addictive often because of a successful AI recommending algorithm working in the background. Companies like Google, YouTube, Baidu, Amazon and Alibaba made substantial financial gains using internet AI. Another major application of internet AI is in using algorithms as editors giving real time news that is customised to a user, digital reporter and a virtual robot cop that reports fake news.

The use of neural networks, Deep learning and Machine learning in financial services has recently exponentially gone up, with there being “Robo-advisor’s”, such as “Wealthfront”. Traditional financial advisors have high fees, minimum account balances etc. However, in “Wealthfront”, they use risk assessment algorithms to ascertain risk and create highly individualized plans. The created portfolio is also continuously monitored and periodically churned to give the highest return.

The use of neural networks is also used to find cointegrated pairs for pairs trading. As after implementing a rolling Beta, the pair which was once cointegrated for a time period might not be so in the future and might not mean revert. And using machine learning on previously cointegrated pairs that stopped mean reverting.

Even Bank Of America Merril Lynch is implanting enterprise software fintech HighRadius’s Artificial Intelligence solution to speed up stock receivables reconciliation for the banks big business clients. While, some might look at Artificial Intelligence as a path to a brighter future with greater efficiency. There are others like Elon Musk, who refer to the future of Artificial Intelligence as an “immortal dictator”. This might sound hypocritical of him as he created an AI that defeated some of the best DOTA 2 players in the world. This is one of the greatest milestones that AI has ever crossed. IBM’s Deep blue computer defeated one of the best chess players in the world Garry Kasparov in 1997. And In 2016, AlphaGo defeated Lee Se-dol at the board game Go. This further pushed China to peruse AI as the board game GO which was the pride of China.These fears regarding AI are not applicable to the use in Finance; As they are not true while using “narrow AI”, such as in Financial Services.

The second AI wave was the business AI, that makes use of legacy systems data that was already being labelled and stored by traditional companies like the insurance companies covering data on accident claims and frauds, banks on loans and repayment rates, hospitals on diagnosis and survival rates etc. and structured corporate data like historic stock prices, credit card usage, mortgage defaults etc. Early instances of business AI have clustered heavily in the financial sector because it naturally lends itself to data analysis since it runs on structured information and has a clear metric that needs to be optimized. AI, therefore, is ideally applied for optimization for maximisation of the bottom line.

AI finds application in computational finance where an automated intelligent agent is applied for pattern recognition and use it to discover patterns in the stock prices for accurate prediction. Although major companies like Palantir and IBM offered big-data consultancy since 2004, major capabilities in the field emerged after the adoption of deep learning in 2013. Companies like Element AI of Canada and 4th Paradigm of China entered the competition offering algorithms that could mine the data of traditional companies and organizations to improve fraud detection, make smarter trades and uncover inefficiencies in supply chains to use AI for cost savings and profit maximisation.

Whilst there are several credit cards and mobile payment applications that are popular and prevalent, their core services limit spending. An AI powered micro finance app called Smart Finance in China relies exclusively on an algorithm to make millions of small loans. The deep learning algorithm uses a wide range of information including offbeat information like the speed at which the user types his date of birth or the amount of battery power left on the phone etc. of the user from his mobile to predict the repayment potential and authorise loans. In late 2017 the company was making more than 2 million loans per month with default rates in low single digits which outperform many traditional brick-and-mortar banks because it targeted a large user base of potential micro-finance seekers that was ignored by the traditional banking sector – the young and the migrant workers.

The other major applications in the second wave of AI are the use of algorithms for expert medical diagnostics and for legal advice to the judges based on deep learning. The 3rd AI wave is the perception AI where the distinction between the real and the virtual world are being increasingly blurred and digital assistants and Augmented as well as Virtual Reality applications are being introduced. The 4th AI wave is about autonomous vehicles like self-driving cars and autonomous robots to be employed in workplaces. The 3rd and the 4th waves are still in emerging phases but are fast expanding.

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

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