Impact of foreign exchange on the revenue and profit of selected IT companies

CHAPTER 1: INTRODUCTION

1.1 Currency Fluctuation

1.1.1 Currency Fluctuation: Preamble

A currency has value, or worth, in relation to other currencies, and those values change

constantly. For example, if demand for a particular currency is high because investors

want to invest in that country's stock market or buy exports, the price of its currency will

increase. Just the opposite will happen if that country suffers an economic slowdown, or

investors lose confidence in its markets.

While some currencies fluctuate freely against each other, such as the Japanese yen and

the US dollar, others are pegged, or linked. They may be pegged to the value of another

currency, such as the US dollar or the euro, or to a basket, or weighted average, of

currencies.

Currency fluctuations are simply the ongoing changes between the relative values of the

currency issued by one country when compared to a different currency. These changes

are something that occur every day and affect the relative rate of exchange between

various currencies on a continual basis. It is these fluctuations that investors in currency

exchange deals look to closely in order to generate a profit from their investments.

It is important to note that currency fluctuations may appear as both upward and

downward movements. When currencies that are purchased by an investor demonstrate

an upward movement in comparison to the currencies used to make the purchase, there is

opportunity to realize a significant return on the transaction. At the same time, if the rate

of exchange remains somewhat flat, or if the base currency actually increases in relative

value, the investor stands to realize no return or actually lose money in the deal.

There are a number of factors that can lead to such fluctuations. One key factor is the

current state of the economy associated with a given country. If the general perception is

that a country is going through a phase where severe conditions will exist for an extended

period of time, the currency of that country is likely to lose value in comparison to other

countries. When it appears that the currency of a country will only remained depressed

for a limited amount of time, and an investor can afford to hold on to the currency in the

interim, he or she may realize a substantial profit when the country recovers and the

relative value of the currency rises.

Political issues may also affect the nature of currency changes, in that a lack of

confidence in a new government may temporarily diminish the value of the nation’s

currency on the open market. Once confidence is restored, the currency will tend to rise

and investors can consider it to be a worthwhile investment once again. When currency

fluctuations are due to political factors, the impact is often short term, although it can be

long term as well.

1.1.2 The Global Influence of Currencies – Empirical Evidences

“The global forex market is by far the largest financial market with its daily trading

volume of over $5 trillion - far exceeding that of other markets including equities, bonds

and commodities. Despite such enormous trading volumes, currencies stay off the front

pages most of the time. However, there are times when currencies move in dramatic

fashion; during such times, the reverberations of these moves can be literally felt around

the world. Following is list of few such examples:

The Asian crisis of 1997-98:

A prime example of the havoc that can be wreaked on an economy by adverse currency

moves, the Asian crisis began with the devaluation of the Thai baht in July 1997. The

devaluation occurred after the baht came under intense speculative attack, forcing

Thailand’s central bank to abandon its peg to the U.S. dollar and float the currency. This

triggered a financial collapse that spread like wildfire to the neighboring economies of

Indonesia, Malaysia, South Korea and Hong Kong. The currency contagion led to a

severe contraction in these economies as bankruptcies soared and stock markets plunged.

China’s undervalued Yuan:

China held its Yuan steady for a decade from 1994 to 2004, enabling its export

juggernaut to gather tremendous momentum from an undervalued currency. This

prompted a growing chorus of complaints from the U.S. and other nations that China was

artificially suppressing the value of its currency to boost exports. China has since allowed

the yuan to appreciate at a modest pace, from over 8 to the dollar in 2005 to just over 6 in

2013.

Japanese yen’s gyrations from 2008 to mid-2013:

The Japanese yen has been one of the most volatile currencies in the five years to mid-

2013. As the global credit intensified from August 2008, the yen – which had been a

favored currency for carry trades because of Japan’s near-zero interest rate policy –

began appreciating sharply as panicked investors bought the currency in droves to repay

yen-denominated loans.

As a result, the yen appreciated by more than 25% against the U.S. dollar in the five

months to January 2009. In 2013, Prime Minister Abe’s monetary stimulus and fiscal

stimulus plans – nicknamed “Abenomics” – led to a 16% plunge in the yen within the

first five months of the year.

Euro fears (2010-12):

Concerns that the deeply indebted nations of Greece, Portugal, Spain and Italy would be

eventually forced out of the European Union, causing it to disintegrate, led the euro to

plunge 20% in seven months, from a level of 1.51 in December 2009 to about 1.19 in

June 2010. A respite that led the currency retracing all its losses over the next year

proved to be temporary, as a resurgence of EU break-up fears again led to a 19% slump

in the euro from May 2011 to July 2012.”( http://finance.yahoo.com/news/effects-

currency-fluctuations-economy-150000568.html)

1.2 Factors that Cause Fluctuations in the Currency Market

The currency (Forex) market is subject to frequent fluctuations. The first question that

comes to mind is “What causes these fluctuations?” The primary cause of these

fluctuations is, of course, a shift in demand and supply. But, what causes this shift?

The demand and supply pattern in the currency market is primarily governed by the

following broad category of factors:

1.2.1 Economic Data

Economic data of a country, such as gross domestic product (GDP), industrial production

(IP), consumer price index (CPI), unemployment numbers, Manufacturing Index of the

Institute for Supply Management (ISM), retail sales, international trade and housing

statistics, directly impacts the value of the currency. This data is regularly released by a

government or a private organization that keeps track of these economic performances.

This economic data reflects a country's economic health. If a country’s economy is on the

downswing, the value of its currency is most likely to fall vis-à-vis the currencies of other

nations.

1.2.2 Interest Rates

Any change in the interest rates of a country directly impacts the value of its currency. If

a country raises its interest rates, the demand for and, consequently, the value of its

currency will rise in relation to other currencies. When a country lowers its interest rates,

people will start earning lower interest on their deposits and investments, reducing the

incentive of holding this currency. They will then tend to buy other currencies of

countries that offer higher interest rates. This will reduce the demand for the particular

currency.

1.2.3 News

A change in the nation’s political conditions and other such news could lead to severe

fluctuations in the value of the currency. If a country’s government modifies its trade

policies such that they adversely impact traders and businessmen, the demand for the

currency would decline immediately. A national calamity, such as earthquakes,

hurricanes and floods, can have a negative impact on the value of a currency.

1.2.4 Market Sentiments

The way people perceive a national or international event has a direct bearing on the

currency market. Even if an event occurring in a country is not of a high risk category

and might not impact the country’s currency, traders may go ahead and sell the currency

for a safer investment.

1.3 The Risks of Currency Value Fluctuation

From political turmoil to a natural disaster, there are plenty of factors that cause a

currency's value to rise and fall. When a business engages in international business

transactions — such as importing, exporting and paying foreign employees — swings in

currency value can have a significant impact on the bottom line.

Market fluctuations can impact everything from purchasing power to operating costs,

making it difficult for businesses to predict profits and losses. If exchange rates take an

unfavorable turn, an international business may end up paying more or receiving less

from its partners and overseas customers.

Here are three approaches that can help business owners plan for swings in currency

value.

1.3.1 Acknowledge that Unpredictability Is Predictable

It’s crucial that business owners take steps to understand where a country’s currency

stands. But it’s just as important to acknowledge that foreign currency values can change

on a dime. Take, for example, the 2011 natural disasters that impacted Japan.

Immediately after the 8.9-magnitude earthquake and 13-foot tsunami hit Japan’s Eastern

coast on March 11, 2011, the Japanese yen began to slide. But within a few days, the

currency value strengthened as Japanese business owners and citizens created a high

demand for the currency by purchasing medical supplies and other essential items.

The Japanese government began pumping money into the economy to guard against an

overly strong yen. Yet the yen continued to soar, and as it did, it became harder for

Japanese exporters to compete with the prices offered by exporters in other countries.

Adding to the problem, when customers made international payments in their native

currencies, Japanese exporters ended up with less money after currency conversion.

1.3.2 Consider Currency Risk at the Outset

“A lot of business owners think of an exotic spot to do business, and they just move there

and start a business,” says Dmitry Dragilev, a Boston native and designer of the Currency

Exchange Fee Calculator app.

Dragilev says it’s important for business owners to consider the economic climate and

fluctuations in the exchange market before setting up shop in another country. While

many economic and political turns can’t be predicted, understanding the variables can

help business owners steer clear of markets on the verge of instability.

1.3.3 Minimize Risk

There are a few things small business owners can do to help minimize fluctuation risks in

the market. For example, business owners can employ strategies such as crafting contract

terms that ensure payment will be submitted in their domestic currency. And when

businesses make international payments, they may consider locking in a fixed rate on

their currency exchanges by using forward contracts.

1.4 Why currency fluctuates and its impact on business

The rupee movement against major world currencies is in the limelight again since the

last few weeks. The rupee appreciated sharply against the US dollar this month, by

almost four percent, from Rs 48 per dollar to Rs 46 per dollar, in a span of four weeks.

One of the main reasons for the appreciation of the rupee against the dollar, and other

major currencies, is the funds coming in from large global players.

Foreign investors have invested around 13 billion dollars this year. These funds coming

in since the middle of September have triggered an appreciation in the rupee against

major world currencies.

Here are some of the major reasons behind large foreign players pumping funds into the

markets:

1.4.1 Liquidity

There is surplus liquidity in most of the large economies of the world. One of the main

reasons for this liquidity is the large economic stimulus packages given by governments

and the liberal monetary policy adopted by central banks. In the recent G-20 nations'

meet, it was decided to continue the liberal monetary measures. This has increased the

money flow from large financial players into emerging markets which have better

potential for growth in the medium term. Some expect the Reserve Bank of India (RBI)

to hike policy rates in this quarter to control inflation. This is another reason why funds

are flowing into the domestic markets. The softer interest rate regimes in developed

countries are attracting foreign funds to the domestic debt market.

Another reason for currency fluctuations is speculation. Some large funds and major

traders are pumping money into emerging markets to make arbitration profits due to

currency fluctuations.

1.4.2 Impact of currency fluctuations

As more businesses are expanding their operations around the globe there is a widespread

impact of sharp currency fluctuations. In simple terms, it shrinks the receivables of

exporters and makes life easier for importers as the prices of imports get cheaper. A sharp

fluctuation in the currency hits the small and mid-cap companies harder than their larger

peers, as the larger players can manage the situation through actively managing (hedging)

the currency and working with the scale.

The rupee appreciation would also have an impact on investors in international

commodities funds (for example, gold exchange-traded funds). Although gold prices are

rising in international markets in terms of dollar, it translates to lower gains due to rupee

appreciation against the dollar.

1.4.3 RBI action

Theoretically, currency movements should be driven by the economic fundamentals and

progress of the economy. But modern communication systems and globalisation have

made active management of large funds easy which in turn has increased the short-term

currency volatility. This short-term currency volatility (upward or downward) is not good

for business and hence the central banks in many countries allow controlled/managed

currency movements by actively intervening from time to time. Here, the RBI smoothens

the short-term currency fluctuations by buying/selling dollars in the market.

1.4.4 Outlook

Analysts expect capital inflows to continue and even increase in the short to medium

terms due to the huge increase in global liquidity conditions. There are large financial

institutions and funds that are accessing offshore markets for debt and equity. Analysts

believe there is a huge amount of money waiting to come into the domestic markets. This

will ensure a steady stream of dollars and hence keep the rupee moving in an upward

direction.

1.5 Evaluation of changes in the exchange rate on business

The effect of the exchange rate on business depends on several factors.

1.5.1 Elasticity of demand

If there is depreciation in the value of the Pound, the impact depends on elasticity of

demand. If UK firms are selling goods which are price inelastic, then the fall in their

foreign price will only have a relatively small increase in demand. If exports are price

sensitive, then there will be a bigger percentage increase in demand. Evidence suggests

that British goods are increasingly price inelastic and after a depreciation there is a

relatively small increase in demand.

1.5.2 Economic growth in other countries.

In 2009/10, there was a significant depreciation in the value of the Pound, however, the

global economy (and EU in particular) were in recession. Therefore, demand for UK

exports was weak – despite the lower price.

1.5.3 Depends on percentage of raw materials imported.

If a UK firm imports raw materials and sells to the domestic market, it may lose out from

a depreciation. If a firm imports only a small percentage of raw materials from abroad

and sells to Europe, then it will benefit more from a depreciation.

1.5.4 It depends why there was an appreciation / depreciation.

If there is an appreciation in the Pound because UK labour productivity is increasing,

then firms are likely to be able to absorb the stronger Pound. However, if the Pound rises

due to speculation or weakness of other countries (e.g. Euro crisis in 2011) then firms

may become uncompetitive because the rise in the value of Pound is not related to

increased productivity and competitiveness.

1.5.5 Inflation?

One possible problem of a depreciation is that it could cause inflation. (for more details

see whether depreciation causes inflation) If inflation does result, then firms could face

costs, such as greater uncertainty.

1.5.6 Fixed contracts.

Many business use fixed contracts for buying imported raw materials. This means

temporary fluctuations in the exchange rate will have little effect. The price of buying

imports will be set for up to 12 or 18 months ahead. Exporters may also use future

options to hedge against dramatic movements in the exchange rate. These fixed contracts

help to reduce the uncertainty around exchange rate movements and mean there can be

time lags between changes in the exchange rate and changing costs for business.

1.6 How exchange rate fluctuations affect companies

Most investors will be familiar with the concept of currency exposure, with constantly

changing exchange rates affecting the cost of investing in international stocks. These

same issues also affect companies that operate internationally. So what effect do currency

fluctuations have on company profits, and what are they doing to insulate themselves? In

this extract from the Modern Wealth Management blog, we take a look at this issue.

1.6.1 International firms vs international currency

Companies with overseas branches, or those that trade internationally, is at the mercy of

global currency fluctuations. As is the case with private investments, changes in

conversion rates can wipe out profits or increase gains.

When a firm has shareholders to report to, and the figures can run into millions, then it

can have a serious impact on profits and losses. The rapidly changing currency landscape

can have the potential to make businesses reluctant to set firm figures in contracts months

before a deal takes place. If a US-based firm makes EUR 10 million, they can end up

with much more or less than they thought depending on the movement of the EUR/USD

exchange rate. For example, in June 2011 it would have been worth $14.4 million, but in

June 2012 it would have been worth $2 million less.

These issues also exist when discussing contracts with international clients. Although

something may seem like a good deal when it is first written down, it can turn bad a few

months later when the contract is fulfilled.

A study by SunGard Data Systems polled 275 US businesses of various sizes. It found

that 59 per cent of those surveyed had seen a loss or gain of more than five per cent as a

result of currency fluctuations in the previous year.

"The majority of corporations are in the business of doing business, producing and

manufacturing, not hedging currencies," said Paul Bramwell, a senior vice president of

Treasury solutions at the AvantGard unit of SunGard. "A lot of companies were caught

unawares by volatility."

He explained that looking at where the exposure lies instead of waiting for quarterly

results to discover the impact of fluctuations was a better approach, although he conceded

that this is a stance more and more firms are taking.

1.6.2 The impact on real businesses

Therefore, organizations have to evaluate the risks of doing business on an international

level. But it doesn't always work in their favour. For instance, McDonald's saw sales in

Europe increase in 2011, but the yearly profits were actually down as a result of a

weakening euro. Indeed, some experts think investors should be cautious this year too

given that the US dollar has strengthened so much recently and is expected to continue

doing so. As McDonald's generate nearly three quarters of its profits overseas, this could

be an issue if they have not hedged.

Another recent example of this happened at eBay, with CFO Bob Swan admitting that

currency fluctuations will hit the bottom line by around three points in 2012. Ralph

Lauren reported that although currency changes have gone in its favour so far in 2012, it

expects a turnaround in fortunes in 2013.

"Foreign currency effects are estimated to negatively impact net revenue growth by

approximately 200-300 basis points in the first quarter," the company stated.

1.6.3 Currency Effects are Far-Reaching

“While the impact of a currency’s gyrations on an economy is far-reaching, most people

do not pay particularly close attention to exchange rates because most of their business

and transactions are conducted in their domestic currency. For the typical consumer,

exchange rates only come into focus for occasional activities or transactions such as

foreign travel, import payments or overseas remittances.

A common fallacy that most people harbor is that a strong domestic currency is a good

thing, because it makes it cheaper to travel to Europe, for example, or to pay for an

imported product. In reality, though, an unduly strong currency can exert a significant

drag on the underlying economy over the long term, as entire industries are rendered

uncompetitive and thousands of jobs are lost. And while consumers may disdain a weaker

domestic currency because it makes cross-border shopping and overseas travel more

expensive, a weak currency can actually result in more economic benefits.

The value of the domestic currency in the foreign exchange market is an important

instrument in a central bank’s toolkit, as well as a key consideration when it sets

monetary policy. Directly or indirectly, therefore, currency levels affect a number of key

economic variables. They may play a role in the interest rate you pay on your mortgage,

the returns on your investment portfolio, the price of groceries in your local supermarket,

and even your job

prospects.”(http://www.investopedia.com/articles/forex/080613/effects-currency-

fluctuations-economy.asp)

1.6.4 What can firms do?

As with private investors, business essentially has four options to counteract their

currency exposure.

The simplest approach is just to monitor the changes, and this can be the best

option if companies do not think that they are at a particularly high risk from

exchange rate fluctuations.

Another option is to lock into an exchange rate for a fixed period of time by

setting up a forward contract. If the exposure estimates are correct, this can be a

beneficial approach. Some businesses will also purchase currency in advance if

they know that they will be making big purchases and are concerned about

volatility.

A third option is to hedge against this exposure via derivatives. Although this may

be the most complicated option, it can be effective in limiting exposure to

volatility. It can also give a clearer picture of how a company's overseas

operations are really performing.

Finally, firms can choose to manage their currency exposure through business

practices. Having a truly international company can help with this as,

theoretically, losses made when one currency falls will be recovered when another

rises. Where contracts are concerned businesses can also set up clauses that

reduce this exposure. In many cases this comes in the form of an agreement to

protect the client and the company should exchange rate movements exceed the

agreed-upon level. Some businesses also agree on setting all contracts in their

core currency, protecting them from any exposure as they will always be paid the

same relative amount.

Dealing with currency exposure is all about managing risk, as fluctuations are by their

very nature unpredictable. However, while private investors only have their own savings

to worry about if they fail to manage this risk appropriately, businesses face angry

shareholders and a drop in share value - as well as a drop in profits.

Chapter 2: Literature Review

“Among the documents on the impact of exchange rate fluctuations upon multi-

national enterprises, Jorion (1990) indicated that all firms were susceptible to exchange

rate exposure; however, the number of firms affected by exchange rate fluctuations was

not high. Also, empirical tests of the sensitivity of stock returns to exchange rate changes,

Jorion(1990) failed to find a significant link between exchange rate changes and the stock

returns of US firms, and US and Canadian firms respectively. Why the rate of exchange

exposure for individual multi-national enterprises was significantly higher in the real

world than that in the empirical results of Jorion (1990) was possibly due to exchange

rates between the currencies of overseas branches against US dollars. The reason is that

Jorion (1990) used the exchange rate of average weighted volume of trade based on the

exchange rates between the currencies of major trading countries of America against US

dollars. Meanwhile, Jorion (1990) indicated that exchange rate exposure varied with

time.”( http://www.jgbm.org/page/23%20Yaw-Yih%20Wang.pdf)

There have been several empirical studies of the foreign exchange rate exposure

of U.S. firms (for example, Jorion (1990), Bodnar and Gentry (1993), Amihud (1994),

Choi and Prasard (1995), Griffin and Stulz (1997), and Allayannis (1997)). Most of the

studies have typically found low or negligible levels of exposure for most firms, even

when the firms examined have significant foreign operations. None of these studies were

based explicitly on a model of firm behavior, however, so it is difficult to interpret their

findings of low exposure in terms of economic behavior. For example, Jorion (1990)

examined U.S. multinational firms and found that only 5% of them exhibit significant

exposures.

Although the evidence for firms domiciled in other countries was somewhat

stronger, it was still relatively weak. For example, He and Ng (1998) and Glaum, Brunner

and Himmel (2001) investigated Japanese and German firms, respectively, and found a

greater relation between stock returns and exchange rate movements. But even in these

countries, where presumably the large firms have relatively more foreign trade than do

their U.S. counterparts, the percentage of firms with significant return exposures was still

less than would be expected. Several possible explanations have been offered for such

small exposures for U.S. firms. First, the small observed exposures may be due to the

offsetting nature of currency exposures.

Since researchers generally lack complete data on individual firms’ imports,

exports and business competitors, they cannot identify which firms are exposed to a given

currency. For example, Brown’s (2001) study of the hedging practices of a U.S. firm

found that the firm hedged twenty-four different currencies due to both extensive foreign

sales and the importation of a major portion of their manufacturing inputs. As a result,

some studies have chosen to examine exchange rate exposure at the industry level where

it is more appropriate to proxy for exchange rate movements with changes of a trade-

weighted index.

Second, the small observed exposures may be due to the complexity of the firms’

foreign exchange exposures since exchange rate risk can vary over time as well as cross-

sectionally. For example, it can vary with the level of a firm’s foreign trade, the demand

elasticity of the firm’s product, or the competitive reactions of other firms in the same

industry. Allayannis (1997), Bodnar, Dumas, and Marston (2002), Allayannis and Ihrig

(2001), and Francis, Hasan and Hunter (2005) examined time-varying exposure at the

industry level. They provided evidence that exchange rate exposures increase with the

level of foreign trade and decrease with firms’ ability to mark up prices and pass through

the impact of exchange rate movements to customers. These studies indicated that it is

important to measure exposure in a specification that allows it to vary both cross-

sectionally and over time.

However, a survey of derivative usage by Bodnar, Hayt and Marston (1998)

indicated that although many firms engaged in currency hedging, they hedged selectively.

Further, Guay and Kothari (2003) found that the potential effects of hedging with

derivatives were small compared to firm size. Hentschel and Kothari (2001) found no

differences in risk for firms that hedge with derivatives versus those that do not. Given

these evidences, it is unlikely that hedging can completely insulate firms from currency

risk. Bodnar and Marston (2000) developed a simple model of exposure that, when

calibrated, provided estimates of exposure consistent with the previous findings of low

exposure. The model was that of a monopoly firm whose revenues and expenses are

exposed to changes in exchange rates. It demonstrated that exposures were related to net

foreign currency revenues and profit margins, and that firms that developed operational

hedges can shield themselves from the large scale effects of exchange rate changes.

Bodnar, Dumas, and Marston (2002) provided an explicit theoretical model, and

they found relatively high levels of exposure. But their model was estimated for a group

of Japanese firms that have been chosen because they were likely to have high levels of

exposure. Other theoretical studies of exposure include Adler and Dumas (1984),

Hekman (1985), Shapiro (1975), Flood and Lessard (1986), von Ungern-Sternberg and

von Weizsacker (1990), Levi (1994) and Marston (2001). None of these studies have

attempted to provide empirical estimates of their models.

Sparks and Wei (2003) argued that exchange rate movements are more likely to

affect a firm through direct effects on short-term cash flows, which in turn depend on the

firm’s sensitivity to short-term cash flow volatility. For example, if a firm’s liquidity is

already low, then a large fluctuation in its cash flows due to an exchange rate movement

can push the firm into financial distress, and as a result, lead to changes in its

fundamental value. Similarly, when a firm has substantial growth opportunities, exchange

rate movements can have greater effects on firm value due to the firm’s larger

underinvestment costs.

Chow, Lee & Solt(1995) proved that the significance of exchange rate exposure

increased as duration of the sample extended and exposure type also varied with time.

For the studies on the relationship between MNC companies and exchange rate risks,

Aggarwal (1981) and Ajayi & Mougoue (1996) indicated that exchange rate fluctuations

affected stock prices significantly in a reverse direction because an apparent positive

relationship between stock prices and US dollars existed (depreciation of US dollars

leading to decreased stock prices in America).

Results of the research conducted by Aggarwal (1981) supported J curve effect

because the effect of depreciated local currency on increased export would not be shown

after two to three years and domestic impact in the beginning was increase in import

costs. Results of the research conducted by Ajayi & Mougoue(1996) show that an

increase in aggregate domestic stock price has a negative short-run effect on domestic

currency value. However, in the long run, increases in stock prices have a positive effect

on domestic currency value. On the other hand, currency depreciation has a negative

short-run and long-run effect on the stock market.

Ajayi & Mougoue (1996) used cointegration analysis and error correction analysis

and error correction models, found evidence in favor of dynamic linkages between stock

prices and exchange rates for eight industrialized economies. They found evidence that

exchange rates changes exerted significant dynamic influence on stock returns for eight

industrialized countries.

Empirical results of the research conducted by Abdalla (1997) also revealed there

was a cause and effect relationship between exchange rates and stock prices. He claimed

a negative correlation existed between exchange rate fluctuations and stock price indexes

for those four industrial countries studied. That is, when the US dollars are appreciated,

foreign stock prices corresponding to US stock prices fell except the Philippines; i.e.,

exchange fluctuations affected export of the corporations and then corporate stock prices

at the end.

Agarwal (2012), in article, “Effect of devaluation of Indian currency in Indian

economy” say that devaluation happens as a method to rectify BOP imbalance. He

mentions in his paper that that rather than devaluation depreciation of the currency is

favorable which would enhance the export and in turn increase the economy by increase

in employment helping the economy to grow. Mark Frankena, Devaluation, Recession,

and Nontraditional Manufactured Exports from India Author-Mark Frankena, University

of Western- Ontario-This paper analyzes the role of industrial recession, changes in

export subsidy schemes, and devaluation in explaining the rapid expansion of exports of

iron and steel, engineering goods, and tires from India during the 1960s.From this study

he was able to state that changes in trade policies and domestic demand let to an increase

in the level of export of these commodities.

Bhawna Kalra(2012),Devaluation of INR vs USD : an historical perspective -

Bhawna Kalra - 2012 -This paper talks about the reasons for devaluation pre & post

liberalization. Author is trying to study the effect of the INR depreciation on the India‟s

economy .He is also mentioning that in the continuing weaker INR is more of concern

than being favorable. Author suggests that central govt active participation is required to

keep stable currrency.

Dash & Madhava (2008) in his research found that the last twelve months have

seen the Indian Rupee (INR) soaring to new highs against the Dollar (USD), and

subsequently falling to new lows. This has been a key concern, with the INR rising

notably from around INR 47/$ level in July 2007 to its level around INR 38/$ in

September 2007, and back to around INR 43/$ currently. This is expected to have had an

impact on the Information Technology (IT) sector, which mainly depends on the earnings

from exports of software and hardware products, and also from the services of the Indian

IT workforce in the US. The study analyses the impact of this INR/USD exchange rate

fluctuation on the Indian IT sector as a whole, and surveys the different types of

measures/strategies adopted by IT companies to mitigate this impact. The analysis is

performed on a random sample of fifty major IT companies, and uses the concept of

foreign exchange exposure to assess the same. The results of the study showed that

FOREX exposure was especially alarming for a small fraction of small-cap IT

companies. The mid-cap and large-cap IT companies had relatively low/moderate

exposure levels. The majority of large-cap companies had already hedged their FOREX

risk, and were not significantly affected by their respective FOREX exposures.

Bartov & Bodnar (2012) claimed that consistent with previous research, they fail

to find a significant correlation between the abnormal returns of our sample firms with

international activities and changes in the dollar. We investigate the possibility that this

failure is due to mispricing. Lagged changes in the dollar are a significant variable in

explaining current abnormal returns of our sample firms, suggesting that mispricing does

occur. A simple trading strategy based upon these results generates significant abnormal

returns. Corroborating evidence from returns around earnings announcements as well as

errors in analysts' forecasts of earnings is also provided.

Francesco Nucci & Alberto F. Pozzolo (2001) This paper investigates the

relationship between exchange rate fluctuations and the investment decisions of a sample

of Italian manufacturing firms. The results support the view that a depreciation of the

exchange rate has a positive effect on investment through the revenue channel, and a

negative effect through the cost channel. The magnitude of these effects varies over time

with changes in the firm's external orientation, as measured by the share of foreign sales

over total sales and the reliance on imported inputs. Consistent with the predictions of our

theoretical framework, the effect of exchange rate fluctuations on investment is stronger

for firms with low monopoly power, facing a high degree of import penetration in the

domestic market, and of a small size. We also provide evidence that the degree of

substitutability between domestically produced and imported inputs influences the effect

through the expenditure side.

Ki-ho Kim and Charles T. Davidson (1996) this paper reports evidence that the

aggregate profit of the U.S. manufacturing industry is affected by fluctuations in the real

exchange rate of the dollar. Using quarterly data covering the 1975Q1-1993Q4 period,

regression of the manufacturing profit on the exchange rate and other key variables

reveals a significant negative relationship between the dependent variable and the

exchange rate. The estimated regression coefficient indicates that a 1% change in the

dollar value will change the profit in the opposite direction by 0.56%. It was also found

that the manufacturing unit labor cost and raw materials price are negatively related to the

manufacturing profit and that the manufacturing capacity utilization rate and the

aggregate income level are positively related to the profit level. Unit root and

cointegration tests ascertained that the variables have long-run equilibrium relationships.

A vector autoregressive analysis further confirmed the relationship

Anuradha Sivakumar and Runa Sarkar (2008) attempted to evaluate the various

alternatives available to the Indian corporates for hedging financial risks. By studying the

use of hedging instruments by major Indian firms from different sectors, the paper

concludes that forwards and options are preferred as short term hedging instruments

while swaps are preferred as long term hedging instruments. The high usage of forward

contracts by Indian firms as compared to firms in other markets underscores the need for

rupee futures in India. In addition, the paper also looks at the necessity of managing

foreign currency risks, and looks at ways by which it is accomplished. A review of

available literature results in the development of a framework for the risk management

process design, and a compilation of the determinants of hedging decisions of firms. The

paper concludes by pointing out that the onus is on Reserve Bank of India, the apex bank

of the country, and its Working Group on Rupee Futures to realise the need for rupee

futures in India and the convertibility of the rupee.

If exchange rate changes have pronounced effects on fundamental values

primarily when the resulting short-term cash flow fluctuations force the firm into

financial distress or cause it to forsake positive NPV investment opportunities, the

magnitude of exposures would vary cross-sectionally with the expected cost of financial

distress in terms of both the probability of distress and the cost related to it, so that firms

that have greater expected costs of financial distress should be more exposed to exchange

rate risk.

The literature addressing foreign exchange rate exposure is almost entirely

focused on firms operating in developed economies. Foreign exchange rate exposure is

perhaps all the more important for developing economies, as movements in the exchange

rate can affect export sectors, and perhaps even the entire economy.

The present study addresses the issue of foreign exchange rate exposure in the

Indian information technology (IT) sector. Foreign exchange revenues are the component

of the total revenue of the Indian IT players, and the sector’s performance depends

greatly on the FOREX trend.

OBJECTIVES OF THE STUDY

To evaluate the impact of exchange rate volatility on the revenues of the IT sector

companies from 2009 to 2014.

To evaluate the impact of exchange rate volatility on the profit of IT sector

companies from 2009 to 2014.

Chapter 3: Research Methodology

Research methodology process includes a number of activities to be performed. These are

arranged in proper sequence of timing for conducting research. One activity after another

is performed to complete the research work. Research methodology includes the

following steps:

Type of Research

The topic for the research study is Impact of Foreign Exchange on Revenues and Profits

of selected IT Companies and the nature of the topic is general naturel and exploratory.

So the conduct the research study the type of research suitable is Exploratory research

only. The data are collected from the annual reports and as well as from quarterly reports

of the companies performing in IT sector in India. Further, for the exchange rate price of

various currencies, we are depending upon investment websites. The exploratory research

has met the requirement of research study.

Exploratory research has been defined as” it is most commonly unstructured, informal

research that is undertaken to gain background information about the general nature of

the research problem” and “it is usually conducted when the researcher does not know

much about the problem and needs additional information or desires new or more recent

information.”(Pearson, 2003, p.122)

The best suitable method for conducting exploratory research is Case Studies. Case

studies have been defined as a “examining similar situations in the past, called case

studies.” (Pearson, 2003, p.124)

DATA SOURCES AND COLLECTION

For collecting the data of time period 2009 to 2014, secondary data has been used. The

sources used include mainly company annual reports as well as quarterly reports,

newspaper articles, journals and magazines, research bulletins, and other accessible

publications on websites.

These are explained below:

Secondary Data:-

Secondary data are the data collected by a party not related to the research study

but collected these data for some other purpose and at different time in the past. If

the researcher uses these data then these become secondary data for the current

users.

These may be available in written, typed or in electronic forms. A variety of

secondary information sources is available to the researcher gathering data on an

industry.

Secondary data is also used to gain initial insight into the research problem.

Secondary data is classified in terms of its source – either internal or external.

Internal, or in-house data, is secondary information acquired within the

organization where research is being carried out. External secondary data is

obtained from outside sources. There are various advantages and disadvantages of

using secondary data.

Advantages of Secondary Data:

The primary advantage of secondary data is that it is cheaper and faster to access.

Secondly, it provides a way to access the work of the best scholars all over the

world.

Thirdly, secondary data gives a frame of mind to the researcher that in which

direction he/she should go for the specific research.

Fourthly secondary data save time, efforts and money and add to the value of the

research study.

Disadvantages of Secondary data:

The data collected by the third party may not be a reliable party so the reliability

and accuracy of data go down.

Data collected in one location may not be suitable for the other one due variable

environmental factor.

With the passage of time the data becomes obsolete and very old

Secondary data collected can distort the results of the research. For using

secondary data a special care is required to amend or modify for use.

Secondary data can also raise issues of authenticity and copyright.

Sampling

The research is a systematic study to examine or investigate the issue or problem and find

out the relevant information for solution. For study data are to be collected from the

respondents. It is not possible to collect data from every one of the population.

Population is a very large number of persons or objects or items which is not feasible to

manage. A population is a group of individuals, persons, objects, or items from which

samples are taken for measurement. For research purpose a part of the population is to be

selected.

Sampling is the process in which a representative part of a population for the purpose of

determining parameters or characteristics of the whole population is selected. This is

called a sample. It is easier to contact a smaller part of the population for data collection.

It can be done within a limited time, efforts and with minimum cost.

For selection of a sample special care should be taken that the sample is proper

representative of the whole population. Every segment of the population should be

included but the number should not be very large which may become difficult to manage

within time and cost limits. For this research study purpose out of different sampling

methods the stratified random sampling has been selected.

The universe includes each and every business firm which is dealing with foreign

currency and is located everywhere in India. Selected the right or correct business firm

for the research study, we have taken the data of best five IT companies from the

Bombay Stock Exchange on the basis of market capitalization on the date of 1st of March

2015. Where it is observed that there are five IT companies registered in Bombay Stock

Exchange and there market capitalization are being mentioned which is very helpful for

the research study.

The name of those five IT companies and their market capitalization are as given below

on the date of first of March, 2015:-

Ranking Company Name Market capitalization ( Cr.)

1 TATA CONSULTANCY

SERVICES LTD.

524018.5

2 INFOSYS LTD. 263557.17

3 WIPRO LTD. 162768.06

4 HCL TECHNOLOGIES LTD. 141985.1

5 TECH MAHINDRA LTD. 68788.58

Data Analysis Techniques

To measure the impact of exchange rate volatility on the segment wise revenue and

profitability of the selected IT sector companies, segment wise revenue and PBIT of five

companies is correlated with exchange rate, the regression technique of data analysis is

going to use for our research.

Regression analysis refers to the statistical technique of modelling the relationship

between two or more variables.

In general sense, regression analysis means the estimation or prediction of the unknown

value of one variable from the known value(s) of the other variable(s). It is one of the

most important and widely used statistical techniques in almost all sciences - natural,

social or physical.

In this research study we will focus only on simple regression –linear regression

involving only two variables: a dependent variable and an independent variable.

The following regression equations are used:

Revenue = a + b (Exchange Rate)

PBIT = a + b (Exchange Rate)

Where a and b are constants and Exchange rate is independent variable which affects

both revenue as well as profit of the companies. In other words, Revenue and PBIT are

dependent variables.

http://www.bseindia.com/stock-share-price/tata-consultancy-services-ltd/tcs/532540/

http://www.bseindia.com/stock-share-price/tata-consultancy-services-ltd/tcs/532540/

http://www.bseindia.com/stock-share-price/infosys-ltd/infy/500209/

http://www.bseindia.com/stock-share-price/wipro-ltd/wipro/507685/

http://www.bseindia.com/stock-share-price/hcl-technologies-ltd/hcltech/532281/

http://www.bseindia.com/stock-share-price/tech-mahindra-ltd/techm/532755/

Hypothesis

For conducting our research on above given topic, we have multiple hypotheses. These

are given as per our objectives first part fulfil our first objective and another part fulfil

our second objective, which are as follows:-

For Tata Consultancy Service Ltd

Ho1: Exchange rate volatility does not affect North America revenue of the Tata

Consultancy Service.

Ho2: Exchange rate volatility does not affect Ibero/Latin America revenue of the Tata

Consultancy Service.

Ho3: Exchange rate volatility does not affect Europe revenue of the Tata Consultancy

Service.

Ho4: Exchange rate volatility does not affect ROW revenue of the Tata Consultancy

Service.

For INFOSYS Ltd

Ho21: Exchange rate volatility does not affect North America revenue of the INFOSYS

Ltd.

Ho22: Exchange rate volatility does not affect Europe revenue of the INFOSYS Ltd.

Ho23: Exchange rate volatility does not affect ROW revenue of the INFOSYS Ltd.

For WIPRO Ltd

Ho31: Exchange rate volatility does not affect America revenue of the WIPRO Ltd.

Ho32: Exchange rate volatility does not affect Europe revenue of the WIPRO Ltd.

Ho33: Exchange rate volatility does not affect Japan revenue of the WIPRO Ltd.

Ho34: Exchange rate volatility does not affect ROW revenue of the WIPRO Ltd.

For HCL Technology Ltd

Ho41: Exchange rate volatility does not affect the US of the HCL Technology Ltd.

Ho42: Exchange rate volatility does not affect the Europe of the HCL Technology Ltd.

Ho43: Exchange rate volatility does not affect the ROW of the HCL Technology Ltd.

For Tech Mahindra Ltd

Ho51: Exchange rate volatility does not affect North America revenue of the Tech

Mahindra Ltd.

Ho52: Exchange rate volatility does not affect Europe revenue of the Tech Mahindra Ltd.

Ho53: Exchange rate volatility does not affect ROW revenue of the Tech Mahindra Ltd.

For another objective hypothesis are as follows:-

Ho1: Exchange rate volatility does not affect the PBIT of the Tata Consultancy Service

Ltd.

Ho2: Exchange rate volatility does not affect the PBIT of the INFOSYS Ltd.

Ho3: Exchange rate volatility does not affect the PBIT of the WIPRO Ltd.

Ho4: Exchange rate volatility does not affect the PBIT of the HCL Technologies Ltd.

Ho5: Exchange rate volatility does not affect the PBIT of the Tech Mahindra Ltd.

Here,

PBIT means profit before interest and tax

ROW means rest of the world

DATA COLLECTION:-

Exchange rates of different currencies like US Dollar, Euro currency and Japanese Yen,

from the time period of Jun 2009 to March 2014, are collected from the website on the

monthly basis. Which are as given below:-

Quarterly exchange rate of US Dollar into INR

Time Period Price at Date Average Price

Jun '09 47.75 49.138

Sep '09 47.735 48.088

Dec '09 46.41 47.065

Mar '10 44.825 46.213

Jun '10 46.445 45.155

Sep '10 44.57 46.638

Dec '10 44.712 44.898

Mar '11 44.535 45.267

Jun '11 44.7 44.616

Sep '11 49.02 44.903

Dec '11 53.015 49.948

Mar '12 50.875 50.546

Jun '12 55.51 53.193

Sep '12 52.885 55.531

Dec '12 54.995 53.656

Mar '13 54.285 54.271

Jun '13 59.533 54.85

Sep '13 62.59 62.301

Dec '13 61.81 62.204

Mar '14 60.015 62.096

Quarterly exchange rate of EURO into INR


Jun '09 67.022 66.496

Sep '09 69.856 69.264

Dec '09 66.445 68.451

Mar '10 60.556 62.44

Jun '10 56.833 57.588

Sep '10 60.763 60.341

Dec '10 59.819 60.367

Mar '11 63.093 62.768

Jun '11 64.841 65.073

Sep '11 65.618 65.038

Dec '11 68.609 68.717

Mar '12 67.888 66.029

Jun '12 70.275 69.755

Sep '12 67.951 68.717

Dec '12 72.403 70.904

Mar '13 69.596 71.555

Jun '13 77.453 73.896

Sep '13 84.659 84.164

Dec '13 84.964 84.893

Mar '14 82.647 84.161

Quarterly exchange rate of JPY into INR


Jun '09 0.4958 0.4982

Sep '09 0.5316 0.52

Dec '09 0.4995 0.5196

Mar '10 0.4795 0.503

Jun '10 0.5252 0.5017

Sep '10 0.534 0.5432

Dec '10 0.5505 0.5497

Mar '11 0.5355 0.5491

Jun '11 0.5551 0.5508

Sep '11 0.6361 0.6032

Dec '11 0.6889 0.6612

Mar '12 0.6143 0.6229

Jun '12 0.6956 0.6901

Sep '12 0.6784 0.6998

Dec '12 0.6324 0.6549

Mar '13 0.5762 0.5805

Jun '13 0.6005 0.5715

Sep '13 0.6372 0.6428

Dec '13 0.5869 0.6075

Mar '14 0.5815 0.6009

For achieving our objectives which considers the revenue of the selected IT companies

which are listed in Bombay Stock Exchange as well as in National Stock Exchange. The

data of following years with regards to segmented geographical revenue and Profit

before interest and tax(PBIT) are given below:-

Geographical segment wise revenue of HCL (amount in crores)

Time Period US Europe ROW Total

2009-10

Jun '09 674.72306 326.4789 144.338 1,145.54

Sep '09 732.17684 361.7228 153.42 1,247.32

Dec '09 691.8147 358.04445 163.851 1,213.71

Mar '10 765.83045 343.65837 177.621 1,287.11

2010-11

Jun '10 818.32515 327.33006 184.955 1,330.61

Sep '10 869.0256 400.05144 229.243 1,498.32

Dec '10 941.61 438.64996 268.797 1,649.06

Mar '11 921.87 458.3871 317.476 1,697.73

2011-12

Jun '11 1060.45184 528.27656 360.632 1,949.36

Sep '11 1,147.95 528.45174 302.821 1,979.22

Dec '11 1251.16378 582.85388 357.162 2,191.18

Mar '12 1175.44296 584.4744 404.803 2,164.72

2012-13

Jun '12 1399.2224 697.0391 475.839 2,572.10

Sep '12 1504.76976 717.32752 474.623 2,696.72

Dec '12 1626.53736 741.34696 398.336 2,766.22

Mar '13 1669.07223 812.45844 462.159 2,943.69

2013-14

Jun '13 2343.3783 1253.91295 513.899 4,111.19

Sep '13 2198.65932 1160.83062 484.32 3,843.81

Dec '13 2182.80349 1200.73373 452.673 3,836.21

Mar '14 2241.43414 1286.59938 517.876 4,045.91

Quarterly data of HCL(amount in crores)

Time

Period

Quarterly

Revenue

Quarterly

PBIT

Jun '09 1,145.54 230.93

Sep '09 1,247.32 312.46

Dec '09 1,213.71 305.36

Mar '10 1,287.11 313.34

Jun '10 1,330.61 275.15

Sep '10 1,498.32 246.91

Dec '10 1,649.06 334.1

Mar '11 1,697.73 298.69

Jun '11 1,949.36 426.58

Sep '11 1,979.22 448.97

Dec '11 2,191.18 597.33

Mar '12 2,164.72 489.95

Jun '12 2,572.10 815.31

Sep '12 2,696.72 888.5

Dec '12 2,766.22 962.11

Mar '13 2,943.69 997.42

Jun '13 4,111.19 1,679.63

Sep '13 3,843.81 1,612.98

Dec '13 3,836.21 1,582.02

Mar '14 4,045.91 1,762.06

Geographical segment wise revenue data of INFOSYS (amount in crores)

Time Period North America Europe India Rest of The World Total

2009-10

211.2318483 80.6402883 2.9383101 31.6684533 326.4789

3427.459 1206.632 62.412 504.497 5,201.00

3553.11 1168.365 64.02 549.505 5,335.00

3635.5 1237.5 77 550 5,500.00

2010-11

3875.134 1168.874 97.886 616.106 5,758.00

4,227.65 1400.65 134.925 661.775 6,425.00

4227.498 1424.412 143.748 738.342 6,534.00

4247.516 1473.628 180.036 766.82 6,668.00

2011-12

4433.01 1470.765 179.53 821.695 6,905.00

4877.91 1531.35 164.34 896.4 7,470.00

5539.352 1965.296 182.616 1008.736 8,696.00

5495.728 1646.589 213.51 827.169 8,183.00

2012-13

5710.669 1897.617 178.18 1069.08 8,909.00

5833.431 1999.251 146.064 1150.254 9,129.00

5732.78 2255.52 206.756 1202.944 9,398.00

6124.12 1873.612 302 1029.722 9,329.00

2013-14

6114.826 2350.324 258.934 1234.916 9,959.00

7061.43 2755.68 275.568 1389.322 11,482.00

6920.4 2871.966 299.884 1441.75 11,534.00

7866.344 1822.03 443.62 1234.012 11,366.00

Quarterly result of Infosys (amount in crores)

Time Period

Quarterly

Revenue

Quarterly

PBIT

Jun '09 5,104.00 1,572.00

Sep '09 5,201.00 1,592.00

Dec '09 5,335.00 1,689.00

Mar '10 5,500.00 1,700.00

Jun '10 5,758.00 1,682.00

Sep '10 6,425.00 1,989.00

Dec '10 6,534.00 1,993.00

Mar '11 6,668.00 2,010.00

Jun '11 6,905.00 1,883.00

Sep '11 7,470.00 2,158.00

Dec '11 8,696.00 2,724.00

Mar '12 8,183.00 2,502.00

Jun '12 8,909.00 3,047.00

Sep '12 9,129.00 3,155.00

Dec '12 9,398.00 3,050.00

Mar '13 9,329.00 3,022.00

Jun '13 9,959.00 3,076.00

Sep '13 11,482.00 2,705.00

Dec '13 11,534.00 3,123.00

Mar '14 11,366.00 3,085.00

Geographical segment wise revenue data of TCS (amount in crores)

Time

Period

North

America

Ibero/Latin

America Europe India

Rest of The

World Total

2009-10

2933.8208 258.0416 1565.0784 510.474 342.1856 5,609.60

3067.5096 287.22 1556.7324 419.341 413.5968 5,744.40

3088.77975 288.28611 1553.21496 500.088 453.02103 5,883.39

3135.8124 255.51064 1463.37912 516.828 435.5295 5,807.06

2010-11

3526.0225 275.67085 1538.628 564.164 506.46505 6,410.95

3902.62065 283.43055 1773.2578 719.478 588.66345 7,267.45

4080.5841 236.44506 1929.69678 701.708 678.82614 7,627.26

4255.8465 255.032 2024.3165 701.338 733.217 7,969.75

2011-12

4556.57324 267.02036 2170.61712 801.061 818.2882 8,613.56

4925.83872 317.19416 2481.57784 699.693 904.93628 9,329.24

5546.15978 379.58508 2804.71198 801.346 1012.22688 10,544.03

5403.66091 363.00985 2717.38802 912.71 974.94074 10,371.71

2012-13

6104.69775 376.55145 3035.2329 810.156 1084.01175 11,410.65

6296.69568 405.46904 3172.19896 894.417 1156.77932 11,925.56

6505.0157 445.2102 3289.6087 939.888 1187.2272 12,366.95

6590.3374 442.729 3314.1428 1113.15 1189.0436 12,649.40

2013-14

7543.74728 334.65792 3750.95752 1059.75 1254.9672 13,944.08

8835.30704 381.97756 4733.2002 1145.93 1511.30252 16,607.72

8797.02655 383.93095 4857.56115 1051.64 1602.4944 16,692.65

9102.86046 383.64546 5214.09057 1081.18 1656.65085 17,438.43

Quarterly result of TCS (amount in crores)

Time Period

Quarterly

Revenue

Quarterly

PBIT

Jun '09 5,609.60 1,441.03

Sep '09 5,744.40 1,553.41

Dec '09 5,883.39 1,729.96

Mar '10 5,807.06 1,477.92

Jun '10 6,410.95 1,746.75

Sep '10 7,267.45 2,032.44

Dec '10 7,627.26 2,205.84

Mar '11 7,969.75 2,240.68

Jun '11 8,613.56 2,276.80

Sep '11 9,329.24 2,486.52

Dec '11 10,544.03 3,078.23

Mar '12 10,371.71 2,856.00

Jun '12 11,410.65 3,270.06

Sep '12 11,925.56 3,378.28

Dec '12 12,366.95 3,469.35

Mar '13 12,649.40 3,350.40

Jun '13 13,944.08 3,943.40

Sep '13 16,607.72 5,486.71

Dec '13 16,692.65 5,498.83

Mar '14 17,438.43 5,152.95

Geographical segment wise revenue data of Tech Mahindra (amount in

crores)

Time Period North America Europe Rest of The World Total

2009-10

307.4667 646.7403 106.023 1,060.23

310.7552 677.0024 122.0824 1,109.84

344.97 643.944 160.986 1,149.90

349.161 663.4059 151.3031 1,163.87

2010-11

349.9424 601.4635 142.1641 1,093.57

372.9425 641.4611 477.3664 1,491.77

377.9936 637.8642 165.3722 1,181.23

383.6576 623.4436 191.8288 1,198.93

2011-12

394.9952 629.5236 209.8412 1,234.36

418.1991 595.6169 253.454 1,267.27

455.7201 621.4365 303.8134 1,380.97

462.5428 625.7932 272.084 1,360.42

2012-13

687.7 493.35 313.95 1,495.00

678.5415 512.6758 316.6527 1,507.87

646.0836 525.882 330.5544 1,502.52

628.53 493.845 374.125 1,496.50

2013-14

1598.796 1172.4504 781.6336 3,552.88

1828.7456 1371.5592 955.9352 4,156.24

1981.4166 1306.8918 927.4716 4,215.78

1966.608 1354.7744 1048.8576 4,370.24

Quarterly result of Tech Mahindra (amount in

crores)

Time Period

Quarterly

Revenue Quarterly PBIT

Jun '09 1,060.23 234.79

Sep '09 1,109.84 248.47

Dec '09 1,149.90 229.85

Mar '10 1,163.87 238.57

Jun '10 1,093.57 157.21

Sep '10 1,491.77 238.36

Dec '10 1,181.23 212

Mar '11 1,198.93 183.11

Jun '11 1,234.36 184.77

Sep '11 1,267.27 131.69

Dec '11 1,380.97 167.36

Mar '12 1,360.42 197.86

Jun '12 1,495.00 274.88

Sep '12 1,507.87 252.74

Dec '12 1,502.52 260.07

Mar '13 1,496.50 233.66

Jun '13 3,552.88 654.99

Sep '13 4,156.24 884.58

Dec '13 4,215.78 851.11

Mar '14 4,370.24 743.72

Geographical segment wise revenue data of Wipro (amount in crores)

Time

Period Americas Europe Japan

India & Middle

East Business ROW Total

2009-10

3156.8763 1348.4145 95.1822 417.7441 269.6829 5,287.90

3420.9552 1552.317 93.7248 474.4818 316.3212 5,857.80

3364.8459 1549.8327 94.2864 524.4681 359.4669 5,892.90

3481.947 1615.083 92.115 540.408 411.447 6,141.00

2010-11

3427.8006 1519.4788 89.733 538.398 406.7896 5,982.20

3665.3071 1737.5785 98.3535 583.5641 472.0968 6,556.90

3589.8828 1874.4222 99.351 589.4826 470.2614 6,623.40

3869.1576 2009.952 107.676 653.2344 538.38 7,178.40

2011-12

3874.989 2091.0318 80.4243 658.017 606.8379 7,311.30

4035.0816 2247.7824 101.4624 725.8464 694.6272 7,804.80

4366.1625 2345.253 108.1145 756.8015 740.1685 8,316.50

4360.9784 2318.6008 92.0744 803.5584 795.188 8,370.40

2012-13

4533.7824 2468.9784 773.2032 1010.436 8,786.40

4643.137 2542.4556 775.3588 1054.8486 9,015.80

4626.5783 2744.4232 815.9096 1084.7889 9,271.70

4282.8486 2436.351 803.5684 1025.832 8,548.60

2013-14

4339.9531 2532.367 768.4424 1091.5375 8,732.30

4775.571 2771.3655 795.9285 1246.635 9,589.50

4984.9601 2957.0104 849.1415 1198.788 9,989.90

5169.15 3101.49 909.7704 1157.8896 10,338.30

Quarterly result of Wipro (amount in crores)

Time

Period

Quarterly

Revenue

Quarterly

PBIT

Jun '09 5,287.90 1,216.50

Sep '09 5,857.80 1,309.30

Dec '09 5,892.90 1,332.70

Mar '10 6,141.00 1,321.00

Jun '10 5,982.20 1,254.60

Sep '10 6,556.90 1,223.50

Dec '10 6,623.40 1,321.40

Mar '11 7,178.40 1,401.10

Jun '11 7,311.30 1,366.40

Sep '11 7,804.80 1,323.20

Dec '11 8,316.50 1,403.10

Mar '12 8,370.40 1,574.90

Jun '12 8,786.40 1,651.70

Sep '12 9,015.80 1,770.90

Dec '12 9,271.70 1,772.50

Mar '13 8,548.60 1557.9

Jun '13 8,732.30 1,697.90

Sep '13 9,589.50 2,157.80

Dec '13 9,989.90 2,307.60

Mar '14 10,338.30 2,538.30

Chapter-4: DATA ANALYSIS & INTERPRETATION

SUMMARY OUTPUT _ US revenue of HCL

Regression

Statistics

Multiple R 0.888602025

R Square 0.789613559

Adjusted R Square 0.777925424

Standard Error 265.0466258

Observations 20

ANOVA

df SS MS F Significance F

Regression 1 4745848.996 4745848.996 67.55684 1.66381E-07

Residual 18 1264494.85 70249.71387

Total 19 6010343.845

Coefficients

Standard

Error t Stat P-value Lower 95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept

-

2930.00022 519.350829

-

5.6416589

2.37E-

05 -4021.1158 -1838.88 -4021.1 -1838.9

X Variable

1 83.106188 10.1111065 8.2192971

1.66E-

07 61.8635415 104.3488 61.8635 104.349

Interpretation:-

Meaning of Significance F (1.66381E-07 = 0.000000166281) is less than 0.05

So Null hypothesis rejected and alternative hypothesis is accepted

R square shows that US revenue i.e. 78.96% is a variation in context of the

Foreign exchange i.e. in USD currency.

Revenue = a + b (Exchange rate)

Revenue = -2930.00022 + 83.1016188(Exchange rate)

SUMMARY OUTPUT_Europe revenue of

HCL

Regression

Statistics


R Square 0.776237



Observations 20

ANOVA

df SS MS F

Significance

F

Regression 1 1574470.971 1574471 62.44224 2.92E-07

Residual 18 453867.0726 25214.84

Total 19 2028338.044

Coefficients

Standard

Error t Stat P-value

Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -1892.805 324.427219 -5.8343

1.59E-

05 -2574.4 -1211.209 -2574.402

-

1211.209131

X

Variable 1 36.914464 4.67151047 7.90204

2.92E-

07 27.09998 46.728944 27.099985 46.72894356

Interpretation:-

Meaning of Significance F (2.92E-07 = 0.000000292) is less than 0.05.

So Null hypothesis rejected and alternative hypothesis is accepted.

R square shows that Europe revenue i.e. 77.62% is a variation in context

of the Foreign exchange i.e. in Euro currency.


Revenue = -1892.805426 + 36.91446425(Exchange rate).

SUMMARY OUTPUT_ROW revenue of HCL

Regression Statistics


R Square 0.572678947



Observations 20

ANOVA

Df SS MS F

Significance

F

Regression 1 188343.393 188343.4 24.1229 0.000112482

Residual 18 140537.9009 7807.661

Total 19 328881.2939

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -502.5841 173.140644 -2.90275 0.009489 -866.339 -138.829 -866.339 -138.83

X Variable

1 16.555854 3.37083025 4.911506 0.000112 9.474003 23.63771 9.474003 23.6377

Interpretation:-

Meaning of Significance F (.000112482) is less than 0.05.


R square shows that ROW revenue i.e. 57.26%, is a variation in context of the




SUMMARY OUTPUT_ PBIT of HCL



R Square 0.851247439



Observations 20

ANOVA

Df SS MS F

Significance

F

Regression 1 4628178.339 4628178 103.0063 7.11E-09

Residual 18 808758.2423 44931.01

Total 19 5436936.581

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -3458.9189 415.347554

-

8.32777

1.38E-

07 -4331.53 -2586.31 -4331.53 -2586.31

X Variable 1 82.069436 8.08629371 10.1492

7.11E-

09 65.08076 99.05811 65.08076 99.05811

Interpretation:-

Meaning of Significance F (7.11E = 0.00000000711) is less than 0.05.


R square shows that PBIT i.e. 85.12%, is a variation in context of the Foreign

exchange i.e. in USD currency.

PBIT = a + b (Exchange rate)

PBIT = -3458.918921 + 82.06943564(Exchange rate).

SUMMARY OUTPUT_North America revenue of INFOSYS

Regression

Statistics


R Square 0.53320392



Observations 20

ANOVA

Df SS MS F

Significance

F

Regression 1 28588111 28588111 20.56074 0.000257

Residual 18 25027607 1390423

Total 19 53615719

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept

-

5452.667688 2310.532 -2.35992 0.029772 -10306.9 -598.42 -10306.9 -598.42

X Variable

1 203.9713345 44.98315 4.534395 0.000257 109.4653 298.4774 109.4653 298.4774

Interpretation:-



R square shows that US revenue i.e. 53.32%, is a variation in context of the

Foreign exchange i.e.in USD currency.



SUMMARY OUTPUT_Europe revenue of INFOSYS



R Square 0.458179338

Adjusted R

Square 0.42807819


Observations 20

ANOVA

Df SS MS F

Significance

F

Regression 1 3390782 3390782 15.22132 0.001046

Residual 18 4009775 222765.3

Total 19 7400557

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -2059.5413 964.3017 -2.13579 0.046689 -4085.46 -33.6187 -4085.46 -33.6187

X Variable

1 54.172539 13.88523 3.901452 0.001046 25.00076 83.34432 25.00076 83.34432

Interpretation:-



R square shows that Europe revenue i.e. 45.82%, is a variation in context of the

Foreign exchange i.e. in Euro currency.



SUMMARY OUTPUT_ ROW revenue of INFOSYS



R Square 0.568127149



Observations 20

ANOVA

Df SS MS F

Significance

F

Regression 1 1314049.343 1314049 23.67893 0.000124156

Residual 18 998900.0472 55494.45

Total 19 2312949.39

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -1345.2701 461.5973574

-

2.91438 0.009253 -2315.05 -375.49 -2315.05 -375.49

X Variable

1 43.730278 8.986719131 4.8661 0.000124 24.84988 62.61067 24.84988 62.61067

Interpretation:-







SUMMARY OUTPUT_PBIT of INFOSY


Multiple R 0.793771

R Square 0.630073



Observations 20

ANOVA

df SS MS F

Significance

F

Regression 1 4482601 4482601 30.65823 2.95E-05

Residual 18 2631816 146212

Total 19 7114417

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -1733.67 749.2556 -2.31385 0.032697 -3307.8 -159.541 -3307.8 -159.541

X Variable

1 80.76839 14.58706 5.536988 2.95E-05 50.12211 111.4147 50.12211 111.4147

Interpretation:-

Meaning of Significance F (2.95E = 0.0000295) is less than 0.05.





PBIT = -1733.67 + 80.76839(Exchange rate)

SUMMARY OUTPUT_North America revenue of TCS


Multiple R 0.911778

R Square 0.83134

Adjusted R

Square 0.82197


Observations 20

ANOVA

df SS MS F

Significance

F

Regression 1 64293225 64293225 88.72338 2.22429E-08

Residual 18 13043665 724648

Total 19 77336890

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -10199 1668.023 -6.11445

8.92E-

06

-

13703.43067 -6694.66 -13703.4 -6694.66

X

Variable 1 305.8856 32.47431 9.419309

2.22E-

08 237.6595869 374.1116 237.6596 374.1116

Interpretation:-






Revenue = -10199 + 305.8856(Exchange rate)

SUMMARY OUTPUT_Ibero /Latin America revenue of TCS



R Square 0.551750389



Observations 20

ANOVA

df SS MS F

Significance

F

Regression 1 46237.25 46237.25 22.1562 0.00017572

Residual 18 37563.78 2086.877

Total 19 83801.03

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -87.5584659 89.51317 -0.97816 0.340958 -275.619 100.5017 -275.619 100.5017

X Variable

1 8.202994147 1.742709 4.707037 0.000176 4.541699 11.86429 4.541699 11.86429

Interpretation:-







SUMMARY OUTPUT_Europe revenue of TCS



R Square 0.816861273

Adjusted R

Square 0.806686899


Observations 20

ANOVA

df SS MS F

Significance

F

Regression 1 21455531 21455531 80.28615 4.70404E-08

Residual 18 4810289 267238.3

Total 19 26265819

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -6659.5244 1056.181 -6.30528

6.07E-

06

-

8878.48 -4440.57 -8878.48 -4440.57

X Variable

1 136.269566 15.20823 8.960254 4.7E-08 104.318 168.2209 104.3183 168.2209

Interpretation:-







SUMMARY OUTPUT_ROW revenue of TCS



R Square 0.762910844

Adjusted R

Square 0.749739224


Observations 20

ANOVA

df SS MS F

Significance

F

Regression 1 2392144 2392144 57.92081 4.95125E-07

Residual 18 743404.5 41300.25

Total 19 3135548

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -2085.608 398.2125 -5.23743

5.57E-

05

-

2922.22 -1249 -2922.22 -1249

X Variable

1 59.00245 7.752696 7.610572

4.95E-

07 42.7146 75.29026 42.71464 75.29026

Interpretation:-







SUMMARY OUTPUT_PBIT of TCS

Regression

Statistics

Multiple R 0.927381

R Square 0.860036



Observations 20

ANOVA

df SS MS F

Significance

F

Regression 1 27009261 27009261 110.6044 4.08973E-09

Residual 18 4395545 244197

Total 19 31404806

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -7183.15 968.2972 -7.41833

7.06E-

07

-

9217.46 -5148.83 -9217.46 -5148.83

X Variable

1 198.2589 18.85153 10.51686

4.09E-

09 158.653 237.8645 158.6533 237.8645

Interpretation:-



R square shows that PBIT i.e. 86%, is a variation in context of the Foreign




SUMMARY OUTPUT_North America revenue of Tech Mahindra

Regression

Statistics


R Square 0.80281358



Observations 20

ANOVA

df SS MS F

Significance

F

Regression 1 5297371 5297371 73.28418

9.21863E-

08

Residual 18 1301136 72285.34

Total 19 6598507

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -3753.2194 526.8217 -7.12427

1.23E-

06 -4860 -2646.41 -4860.03 -2646.41

X

Variable 1 87.8024455 10.25655 8.560618

9.22E-

08 66.2542 109.3507 66.25422 109.3507

Interpretation:-







SUMMARY OUTPUT_Europe revenue of Tech Mahindra



R Square 0.646682832

Adjusted R

Square 0.6270541


Observations 20

ANOVA

df SS MS F

Significance

F

Regression 1 1063729 1063729 32.94573 1.92842E-05

Residual 18 581171.4 32287.3

Total 19 1644900

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept

-

1352.579991 367.1175 -3.68432 0.001697 -2123.9 -581.295

-

2123.87 -581.295

X

Variable 1 30.34203085 5.286218 5.739837 1.93E-05 19.2361 41.44796 19.2361 41.44796

Interpretation:-







SUMMARY OUTPUT_ROW revenue of Tech Mahindra

Regression

Statistics


R Square 0.748151603



Observations 20

ANOVA

df SS MS F

Significance

F

Regression 1 1273917 1273917 53.47157

8.59964E-

07

Residual 18 428835.4 23824.19

Total 19 1702752

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept

-

1816.890143 302.4458 -6.00732

1.11E-

05

-

2452.305 -1181.48 -2452.31 -1181.48

X Variable

1 43.05731127 5.888239 7.312426 8.6E-07 30.68658 55.42804 30.68658 55.42804

Interpretation:-







SUMMARY OUTPUT_PBIT Of Tech Mahindra



R Square 0.740387079

Adjusted R

Square 0.725964139


Observations 20

ANOVA

df SS MS F

Significance

F

Regression 1 807942.7 807942.7 51.33399 1.13539E-06

Residual 18 283301 15738.94

Total 19 1091244

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -1420.787 245.8252 -5.77966

1.78E-

05

-

1937.246 -904.327 -1937.25 -904.327

X Variable

1 34.289938 4.785906 7.164774

1.14E-

06 24.23512 44.34475 24.23512 44.34475

Interpretation:-







SUMMARY OUTPUT_America's revenue of WIPRO



R Square 0.711128263

Adjusted R

Square 0.695079833


Observations 20

ANOVA

df SS MS F

Significance

F

Regression 1 4732692 4732692 44.31139 3.02131E-06

Residual 18 1922496 106805.3

Total 19 6655187

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -136.43241 640.3759

-

0.21305 0.833682 -1481.81 1208.947 -1481.81 1208.947

X Variable

1 82.9909087 12.46731 6.65668 3.02E-06 56.79806 109.1838 56.79806 109.1838

Interpretation:-







SUMMARY OUTPUT_Europe's revenue of WIPRO



R Square 0.596345896

Adjusted R

Square 0.573920668


Observations 20

ANOVA

df SS MS F

Significance

F

Regression 1 3065038 3065038 26.59263 6.62051E-05

Residual 18 2074661 115258.9

Total 19 5139699

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept

-

1367.20655 693.6278 -1.9711 0.064287

-

2824.464408 90.05131 -2824.46 90.05131

X Variable

1 51.5047394 9.987723 5.156805 6.62E-05 30.521311 72.48817 30.52131 72.48817

Interpretation:-







SUMMARY OUTPUT_ Japan's revenue of WIPRO 2009-12



R Square 0.199372645

Adjusted R

Square 0.11930991


Observations 12

ANOVA

df SS MS F

Significance

F

Regression 1 129.7594 129.7594 2.490205 0.145636287

Residual 10 521.0791 52.10791

Total 11 650.8385

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 59.479395 23.26283 2.556843 0.028527 7.6465778 111.3122 7.646578 111.3122

X Variable

1 66.249624 41.98226 1.578038 0.145636 -27.29269 159.7919 -27.2927 159.7919

Interpretation:-

Meaning of Significance F (.145636287) is greater than 0.05

So Null hypothesis is accepted and it means Foreign exchange does not affect the

Japan's revenue.

R square shows that Japan's revenue i.e. 19.94%, is a variation in context of the

Foreign exchange i.e. in JPY currency.


Revenue = 59.4794 + 66.24962(Exchange rate)

SUMMARY OUTPUT_ROW 's revenue of WIPRO



R Square 0.747674192

Adjusted R

Square 0.733656092


Observations 20

ANOVA

df SS MS F

Significance

F

Regression 1 1577269 1577269 53.33634 8.74991E-07

Residual 18 532298.1 29572.12

Total 19 2109567

Coefficients

Standard


Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -1697.2082 336.9611 -5.03681

8.57E-

05 -2405.14 -989.279 -2405.137 -989.28

X Variable

1 47.9103369 6.560208 7.303173

8.75E-

07 34.12785 61.69282 34.127851 61.6928

Interpretation:-







SUMMARY OUTPUT_PBIT of WIPRO



R Square 0.871898584

Adjusted R

Square 0.864781839


Observations 20

ANOVA

df SS MS F

Significance

F

Regression 1 2338904 2338904 122.5137 1.83184E-09

Residual 18 343637.4 19090.96

Total 19 2682541

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -1402.019 270.74 -5.17847

6.32E-

05 -1970.8228 -833.215 -1970.82 -833.215

X

Variable 1 58.342174 5.270967 11.06859

1.83E-

09 47.2682824 69.41607 47.26828 69.41607

Interpretation:-



R square shows that PBIT i.e. 87.19%, is a variation in context of the Foreign exchange

i.e. in USD currency.



FINDINGS OF THE STUDY

After carrying out the data analysis and interpretation, there are number of findings which

are as follows:-

Out of 22 cases, there is only one case which supports the null hypothesis and

other cases reject the null hypothesis and support the alternative hypothesis. It

means, only one case does not affect with the foreign exchange fluctuation in the

market. The name of that case is Revenue of Japan.

In 80% cases of US revenue, foreign exchange affects the revenue more than 70%

where is in only one case or remaining 20% cases, it is affected by more than

50%.

In context of Europe revenue, foreign exchange affects the revenue more than

70% in 2 cases which is 40% of total and another 40% or 2 cases affects more

than 50% of revenue and only in one case foreign exchange affects less than 50%.

The scenario of Rest of the World is no differ, foreign exchange also affects more

than 70% in 3 cases which is 60% of overall and in 2 cases or 40 % cases affects

by more than 50% through foreign exchange.

Profit before interest and tax or PBIT is also affected by foreign exchange. In

more than 60% cases it is affected by 80% and in another two cases or 40 %

cases, it is affected by more than 60%.

RECOMMANDATIONS

With the help of our research, we would like to recommend that:-

Make proper analysis of the market to avoid any unwanted risk in foreign

exchange volatility.

To protect from the foreign exchange risk, corporate must go for hedging in

foreign currency.

Limitations of the Study:-

To carry out the research study the following limitations were expected and faced

during the research study:

(a) Availability of required secondary data from the selected IT companies was

difficult.

(b) Geographical revenue data was available in percentage which needed to be

in Indian rupees.

(c) Time and cost become major difficulties in completion of research.

(d) Sample size was limited to only last five years of selected best five IT

companies which must be extended. This may cause of possibility of some error

to a limited extent.

However, to overcome the limitations and maintain the effectiveness of research

work sincere efforts were put.

SCOPE OF THE STUDY

For the further study on the given topic, there is a scope which we could not tap due the

time factor. The number of areas for further research study is as follows:-

The scope of the research is not limited to the IT industry but it may extend to

each and every industry or business firm which is dealing in International

business not only in India but also any other part of the world.

Time period of data collection could be extended to more than five years.

It is not necessary that there is only US geographical segment or European

geographical segment; it may also extend to the Africa, Australia or any other

geographical segment.

Researcher may use this data for the amount of hedging in the foreign exchange

because it not defined or given that at what extent corporate should go for

corporate hedging for reducing the foreign currency risk.

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Impact of foreign exchange on the revenue and profit of selected IT companies

Economy & Finance

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