Trading Strategies - Impact of News on market

7/29/2019 Trading Strategies - Impact of News on market

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TRADING STRATEGIES PROJECT

Impact of expected & unexpected news in financial markets


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Introduction

In this project, we have analyzed reaction of the markets to the following types of

news:

Reaction to positive surprises in expected news Reaction to negative surprises in expected news Reaction to positive unexpected news Reaction to negative unexpected news

As a primer, lets look at some of the very recent events of expected and unexpected

news affecting the markets:

1. Reaction of the E-mini S&P500 futures to negative surprises in BenBernankes scheduled press release:

2. Strengthening of the Swiss Franc (CHF) and gold prices after the negativesurprise of S&Ps downgrade of US credit rating:

USD/CHF


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XAU/USD

3. Reaction of BANK NIFTY index to negative surprise of RBI hiking interestrates to more than expected levels:


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Literature Review

NBER: Stock Prices & Economic News (Douglas K. Pearce & V. Vance Roley)

We find daily interpretations of various economic & financial news in the media. But

there has been no quantitative model that provides systematic evidence on price

movements. According to the Efficient Market Hypothesis, the current prices

incorporate all existing and expected information. Thus, on arrival of news, any

change in stock price can be attributed solely to unexpected news. It is also assumed

that stock prices change instantaneously on arrival on any new information. Based on

these hypotheses, Pearce and Roley built an economic model in an attempt to model

price changes to unexpected information.

In order to assess the impact of new economic news on stock prices, Pearce and Roley

used the following model:


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If we were to follow the EMH, each value in c should be zero as current stock prices

reflect expected economic data and hence cannot contribute to change in stock prices.

Since stock prices change immediately on arrival of new information, surprises that

have occurred on previous day cannot contribute to change in stock prices on the

current day. Therefore, each element of di is zero as well. So effectively, the model

can be described as:

The key findings of this model were as follows:

1. Surprises related to monetary policies significantly affect stock prices2. There was limited evidence of impact from surprise in inflation figures3. There was almost no impact of surprises in economic activity4. There was limited evidence of stock price responses to surprises beyond the

announcement day

In academic literature, we found that the economists were actually interested in the

actual rise in price level to different news and finance professional were more

interested in the volatility impact of the news. We summarize briefly below different

academic articles that addresses these topics.

Impact of Scheduled news on volatility

Patell and Wolfson (1981), ex-ante and ex post effects of quarterly earnings

announcement reflected in option and stock prices.


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In this study, Patell and wolfson studied the effect of investors anticipation of

impending informative disclosures on the behavior of option and stock prices. They

observed the option prices prior to and immediately after the scheduled news

announcements. They noticed that while the market expectancy suggests that the price

of the stock should not be affected until the actual news is out and digested, the

(implied) volatility of the stocks reflected in option prices is likely to increase as theinvestors are unsure and are anticipating the news. They noticed that the variability of

the stock price would increase on the days of the announcement rising up to the actual

announcement and decreasing after that. The empirical test verifies four interrelated

phenomena

Realized increase stock price variability at the announcement date Anticipation of increased stock variability evident in preannouncement option

prices, i.e. ex-ante content

Confirmation of anticipated information content reflected in announcementdate option prices

Ordinal correspondence between the magnitudes of the first two effects.Nikkinen and Sahlstrm (2001), Global stock market reactions to scheduled U.S.

macroeconomic news announcements.

In this study, Nikkinen and Sahlstrm extend the previous study to observe the effects

of U.S macroeconomic news announcements and its impact on various stock markets.

The analyzed the GARCH volatilities around ten important scheduled macroeconomic

news announcements on thirty five local stock markets in six regions. One interesting

observation is the relation between a countries development status and the correlation

with U.S. news. While the G7 countries and Asian countries were correlated to theU.S. economic data, transition countries like Latin America were not affected. The

macroeconomic news announcements investigated are consumer confidence,

consumer price index, employment cost index, employment situation, gross domestic

product, import and export price indices, NAPM(National Association of Purchasing

Management report): manufacturing and non-manufacturing, producer price index

and retail sales. The local stock markets represent the G7 countries, the European

countries other than G7 countries, developed and emerging Asian countries, the

countries of Latin America and countries from Transition economies.

In this paper, it is hypothesized that uncertainty associated with the announcements ofthe U.S. economic indicators is reflected differently in volatilities of local stock

exchanges. This is to be expected, given that the stock markets differ considerably in

terms of size, industrial diversity, and proportion of foreign ownership. The

magnitude of reaction is therefore hypothesized to depend on the degree of integration

and development of the particular market. The degree of economic integration affects

stock market reactions in two main ways. First, it affects the performance of

companies from small and medium-sized enterprises (SMEs) to large multinational

companies (MNCs). For example, MNCs are not dependent on the situation on one

particular market but the worldwide economic situation affects their performance.

Similarly, the success of SMEs can either depend directly on the worldwide economic

situation or indirectly, for example, through their multinational customers. Second,


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stocks of local exchanges are owned by both local and foreign investors and the

proportion of foreign ownership varies across different exchanges and over time.

They also reinforced the findings of Patell and Wolfson (1981). They observe the

jumps in implied volatility are sudden on the day of the announcement and do not last

after that. This provides a significant finding that the effect of scheduled news isusually digested in a day.

Impact of unscheduled news

Fang, L. and Peress, J. (2009) Media coverage and the cross-section of stock returns

In this study, Fang L and Peress J investigated the hypothesis that by reaching a broad

population of investors, mass media can alleviate informational frictions and affect

security pricing even if it does not supply genuine news. Mass Media Outlets, such as

newspapers, play an important role in disseminating information to a broad audience,

especially to individual investors. Every weekday, some 55 million newspaper copies

are sold to individual readers in the United States, reaching about 20% of the nations

population. If we consider online subscriptions and multiple readers per copy, theactual readership of the printed press is even larger, and certainly far broader than

other sources of corporate information such as analyst reports. Given mass medias

broad reach, one might expect it to affect securities markets. Interest in the relation

between media and the market has been on the rise among both researchers and

practitioners.

They found that stocks not covered by the media earn significantly higher future

returns than stocks that are heavily covered, even after accounting for widely accepted

risk characteristics. A portfolio of stocks with no media coverage outperforms a

portfolio of stocks with high media coverage by 3% per year following portfolio

formation after adjusting for market, size, book-to-market, momentum, and the

Pastor-Stambaugh (2003) liquidity factor. The return difference is particularly large

among small stocks, stocks with low analyst coverage, stocks primarily owned by

individuals, and stocks with high idiosyncratic volatility. In these subsamples, the

no-media premium ranges from 8% to 12% per year after risk adjustments. Thus,

the return premium for stocks with no media coverage is economically significant.

They also provided empirical evidence of two phenomena because of which the above

observation may be possible. First, it may be a liquidity-related phenomenon.

Alternatively, the no-media premium may represent compensation for imperfect

diversification.

Tetlock, P. C. (2007) Giving content to investor sentiment: The role of media in the

stock market,

He quantitatively measures the interactions between the media and the stock market

using daily content from a popular Wall Street Journal column. He finds that high

media pessimism predicts downward pressure on market prices followed by a

reversion to fundamentals, and unusually high or low pessimism predicts high market

trading volume. Casual Observation suggests that the content of news about the stock

market could be linked to investor psychology and sociology. However, it is unclear

whether the financial news media induces, amplifies, or simply reflects investors

interpretations of stock market performance. This paper attempts to characterize therelationship between the content of media reports and daily stock market activity,


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focusing on the immediate influence of the Wall Street Journals (WSJs) Abreast of

the Market column on U.S. stock market returns.

First and foremost, He finds that high levels of media pessimism robustly predict

downward pressure on market prices, followed by a reversion to fundamentals.

Second, unusually high or low values of media pessimism forecast high markettrading volume. Third, low market returns lead to high media pessimism. These

findings suggest that measures of media content serve as a proxy for investor

sentiment or non-informational trading. By contrast, statistical tests reject the

hypothesis that media content contains new information about fundamental asset

values and the hypothesis that media content is a sideshow with no relation to asset

markets.

This study systematically explores the interactions between media content and stock

market activity. He constructs a straightforward measure of media content that

appears to correspond to either negative investor sentiment or risk aversion.

Pessimistic media content variables forecast patterns of market activity that areconsistent with the DeLong et al. (1990a) and Campbell et al. (1993) models of noise

and liquidity traders. High values of media pessimism induce downward pressure on

market prices; unusually high or low values of pessimism lead to temporarily high

market trading volume. Furthermore, the price impact of pessimism appears

especially large and slow to reverse itself in small stocks. This is consistent with

sentiment theories under the assumption that media content is linked to the behavior

of individual investors, who own a disproportionate fraction of small stocks.

Regression of price change with earnings surprises

Listed companies in India and US report their earnings quarterly. Expected earnings

of firms are already incorporated in the stock prices in accordance with efficient

market hypothesis. Earnings estimates are obtained by taking average of the opinion

from analysts belonging to different brokerage houses and are available on databases

such as Bloomberg. However, prices of individual stocks show large fluctuations if

the quarterly earnings reported by firms deviate significantly from the expectations in

either negative or positive direction. Both institutional investors and retail investors

take appropriate actions to factor in the new surprise information coming in.

We have tried to analyze the fluctuations in stock prices near earnings results

announcement dates. The data for earnings estimates and stock price movement wasdownloaded from Bloomberg for both Indian and US firms. However, the data for

earnings estimates available for Indian firms was scanty since only yearly data

starting from 2007 was available. Running regression on insufficient data would have

led to inconclusive results. Therefore, leading US stocks were considered for analysis

due to lack of sufficient data points for Indian stocks on Bloomberg.

Correlation between stock returns from T-5 to T and T to T+1

A correlation was obtained between stock price holding period returns for a periodstarting from five days prior to earnings announcement to the announcement date and


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the returns on the date the earnings was announced. The following results were

obtained.

Stock Correlation

Google -0.192

IBM -0.01

Agilent -0.051

Yahoo 0.097

HP -0.067

Microsoft -0.242

GE -0.426

There is no significant positive correlation in the above data, which indicates that the

stock returns after earnings announcements differed significantly from past returns.

This proves the fact that insider information did not have a role to play and that

earnings estimates were fairly accurate and the prices fluctuated due to genuine

surprises in the reported earnings.

Choice of independent variables (using Google)

Estimates were available for a myriad of parameters such as Sales, EBITDA, Net

Profit, and EPS. Returns on the day of results announcement were regressed

separately with surprises in Sales, EBITDA, Net Profit, and EPS to find out the

surprise variable which would best predict the stock price movement.

Parameter Adjusted R square

Sales Surprise 0.0006

EBITDA Surprise 0.1122

Net Profit Surprise -0.05811

EPS Surprise 0.1757

All 4 0.0755

Therefore, EPS was chosen as a proxy for earnings surprise since it has the highest

predictive power. The same results were confirmed for IBM.

Regression results

Adjusted R square values for regression between percentage change in price and

percentage surprise in EPS estimates was found to be low (0.176 for google and 0.123for IBM). To our surprise, we found several data points where the earnings surprises


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and price changes seemed to be moving in opposite direction. While the reported

earnings significantly beat expectations yet the stock price moved down and vice

versa.

We tried to identify the extraneous factors which might be distorting our results. One

possibility was that a company might be inflating its earnings artificially using corruptaccounting practices and thus analysts who read beyond the obvious might be

shorting the stock causing it to take a beating in market. This was unlikely since most

of the US stocks we considered were blue chip. Another possibility could be that the

estimates available from Bloomberg might be flawed and that the analysts look at

their own estimates obtained privately to take investment decisions. Chances of this

occurring were also very low since Bloomberg can be regarded as a reliable source

for US specific data.

Therefore, it was highly likely that the broad market movement on a particular day

might be the culprit. If the overall market is in a significant downtrend, even positive

earnings surprises might not be enough to catapult a stock to higher prices.

Regression approach

Therefore, we set out to eliminate the effect of market movement (S&P 500 index)

from our analysis.

Two kinds of regressions were carried out.

-Stock price movement was regressed with S&P movement and earnings

surprises as the independent variables.

-Excess return (Stock return S&P return) was regressed against earnings

surprises.

Regression results from IBM using both the approaches are as under:

Using market movement and EPS surprise

As is evident from Figure 1, both EPS surprise and market movement have a p value

less than 0.05, which indicates that both the variables are significant. The adjusted R

square is 0.50.


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Using excess returns

Excess returns of a stock over the market are again significantly explained by

EPS_Surprise at 95% confidence level, as is indicated by a small p value of 0.03.

Results (adjusted R square) from regression

A summary of adjusted R square values for different stocks under both kinds of

regressions is as under. While results for google, IBM, Agilent and Yahoo are in line

with expectations, the results from HP and Microsoft indicate that there might be

other subjective factors also which might not be captured by our regression e.g.

performance relative to peers.

Stock Using Excess Return

Using both S&P and Earnings

Surprise

Google 0.1967 0.3027

IBM 0.2027 0.5039

Agilent 0.0884 0.3137

Yahoo 0.1086 0.093

HP -0.038 0.1156

Microsoft -0.001 -0.013

GE -0.0614 -0.059

Dividend announcements

We also did regression analysis on dividends to find out how much of the stock returnis influenced by the expected part of dividends and how much by the unexpected


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(surprise) part of the dividend announcement. This analysis assumes the efficient

market hypothesis which means that the present stock price is not affected by the past

changes in the stock price. This would mean that the stock price at time t is

dependent on information available only at that moment and not on any influence

from data from t-1.

Methodology

The stock return on the day of a dividend announcement was regressed with the

expected part and the unexpected part of the dividends to find out the influences of

each on the stock return. 11 stocks from diverse sectors were taken (from Nifty) and

their dividend history for the past 10 years was analysed. The stocks that were used

for this are Airtel, Cipla, ICICI, ITC, Jindal Steel, JP Associates, L&T, NTPC, RIL,

Tata Motors and TCS.

The expected part of the dividend was assumed to be last years dividend multiplied

with the GDP growth rate of that year. The unexpected part of the dividend was theactual dividend announce minus the expected part. The stock returns on the date of

announcement were regressed with this data.

Example Cipla

Actual

Dividend

GDP growth

rate

Expected

Dividend

Unexpected

dividend

Return

Final 100.000% 8.500% 109.100% -9.100% 0.438%

Interim 40.000% 9.100% 0.000% 40.000% -3.246%

Final 100.000% 9.100% 104.900% -4.900% -0.882%Final 100.000% 4.900% 109.800% -9.800% -0.275%

Final 100.000% 9.800% 109.300% -9.300% 1.908%

Final 100.000% 9.300% 109.300% -9.300% -2.333%

Final 100.000% 9.300% 189.525% -89.525% 0.941%

Final 175.000% 8.300% 162.600% 12.400% 2.480%

Final 150.000% 8.400% 103.800% 46.200% -0.353%

Final 100.000% 3.800%

Average 107.222% 110.925% -3.703% -0.147%

Regression results of Cipla


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This was carried out for all the 11 stocks and their average was regressed with the

average of expected and unexpected returns to get an overall view of how market

reacts to expected and unexpected news.

Results

Stock Actual

Dividend

Expected

Part

Unexpected

Part

Return Correlation

with expected

Correlation

with

unexpected

Airtel 20.000% 14.267% 5.733% -0.132% -0.3321 0.3321

Cipla 107.222% 110.925% -3.703% -0.147% 0.0268 -0.0163

ICICI 100.000% 91.606% 8.394% -0.280% -0.0011 0.0078

ITC 377.778% 305.551% 72.227% -0.296% 0.002 0.0051

Jindal Steel 179.2857% 176.7511% 2.5346% -1.9965% -0.0023 0.0064

JP Ass 17.250% 14.491% 2.759% -0.875% -0.2314 0.1727

LT 585.000% 578.368% 6.632% 2.716% -0.0003 -0.0001NTPC 16.500% 14.952% 1.548% 0.161% 0.0305 -0.0766

RIL 84.500% 75.348% 9.152% -0.942% 0.013 -0.0094

Tata

Motors

108.500% 91.656% 16.845% 0.505% -0.0008 0.0172

TCS 367.857% 341.145% 26.713% 1.384% -0.0016 -0.0023


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Conclusion

Except JP associates, all stocks had correlation less than 0.1 indicating that theregression cannot be used to say much about the correlation of dividend

announcements with stock returns.

Most stocks (seven out of 11) are negatively correlated with the expecteddividend part while 6 stocks are positively correlated with the unexpected

dividend part

Correlations with the expected and unexpected dividend parts had oppositesigns for 10 out of 11 stocks indicating market views the expected and

unexpected parts in opposite manner for any particular stock.

The little influence that dividends have towards the returns are contributed bythe unexpected part in most of the cases (8 out of 11)

Some Examples of news and its reaction in Indian market

Case 1: Impact of a Macroeconomic news on Stock indexThe following is the sequence of news on RBI rate hike

[26-Jul-11 07:47] RBI hints rate hikes [26-Jul-11 11:10] RBI sharply raises repo rate by 50 bps as inflation risk

remains high [26-Jul-11 14:37] We were expecting a moderate hike of 25 bps but hiking

rates by 50 bps is going to dampen the growth

[26-Jul-11 17:12] IMC disapproves sharp hike in RBI's policy rates, says itwill retard growth and abet inflation

The Impact

The news was expected but the extent of hike was more than expected whichmade the market fall on T, T+1, T+2, T+3 days.

But on T+4 day the news was absorbed (other news were making effect) andNIFTY reversed.


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Case2: Impact of Better than expected profit for a single company

Headline : Mahindra Satyam jumps after turnaround Q1 result

Date : 10-Aug-11Mahindra Satyam jumped 9.85% to Rs 78.10 at 9:26 IST on BSE after the company

reported consolidated net profit of Rs 225.18 crore for Q1 June 2011 compared with a

net loss of Rs 327 crore for Q4 March 2011 . The results was announced after market

hours on Tuesday, 9 August 2011.Meanwhile, the BSE Sensex was up 322.22 points,

or 1.91%, to 17,180.13.

The Impact

NIFTY NIFTY

Ret

Satyam Comp Satyam Comp Ret

Close 9 Aug 2011 5072.85 71.05

Price 10 Aug 20111425Hrs

5139.95 1.3227% 78.7 10.7671%


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Clearly Satyam Comp market price had a positive impact of the news and the

magnitude can be interpreted by the unexpectedness of the news. A high open

indicates the over-reaction which was corrected instantly and again a 2nd

phase of

follower buying around 12:44Hrs was followed by a correction.

Case3: Impact of announcement of new orders: Insider trading?[

10-Aug-11 12:14] Suzlon announces 85 MW of new orders in IndiaSuzlon Energy (SEL) has secured new orders worth Rs 483 crore for 85 MW

(megawatt) in India from a broad range of customers in corporate, PSU and

small/medium business segments. The key customers who have placed orders include

GAIL India - Asia's leading gas utility company and a 'Navaratna' PSU of the

Government of India. The 14.7 MW order from GAIL comprises seven units of

Suzlon's S88 - 2.1 MW wind turbines to be commissioned in Gujarat within six

months from the data of order. This is the second wind energy project order placed by

GAIL with Suzlon. Other key orders include Khatau Narbheram & Co, Oswal Group,

KRBL, and Varun Industries.The company made this announcement during the trading hours today, 10 August

2011.

The Impact

Buying in this particular scrip started in the morning and continued. A very small jump around the news publication Stock price fall after wards, although market continue to hold the price levels

price trend reversal from a bottom of 41.8, below the Opening price and thenstarted rising

Case4: Impact of possible dividend in a midcap stock


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[10-Aug-11 10:51] Garnet International to consider final dividendBoard meeting on 16 August 2011

The board meeting of Garnet International will be held on 16 August 2011 to consider

and approve the audited results of the company for the year ended 31 March 2011 and

to recommend final dividend, if any for the financial year ended 31 March 2011.

The Impact

Although dividend is uncertain the mere expectation boosted the price of thisilliquid stock by 2.86% in 2 minutes

The news came out at 10:51 but the price movement started at 10:53 (2 minutelag in diffusion)

Other Examples from Indian Market

According to efficient market hypothesis, price should adjust to the news information

immediately and prices do not increase or decrease subsequently for the same news.

But in actual market, reactions to news are not instantaneous.

Market tends to overreact to bad news and under react to good news. High volatility in stock price after the news reaches market. Although the major part of price fall may happen during one session, it may

take a couple of days for the market to completely factor in the news.

Slow response to news can be explained by behavioral finance asconservatism exhibited by the investors.


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Table showing price movement in some of Indian stocks due to + / - market news

Price fall due to negative news

Security Start Date Close Price End Date Close Price2 Trade Sessions % Price Fall News

Satyam 06-Jan-09 178.95 09-Jan-09 23.75 2 87% Corporate Governace Issue

GTL 16-Jun-11 408 20-Jun-11 127.95 2 69% Panic selling

RCOM 10-Nov-10 179.45 25-Nov-10 136.9 10 24% 2G Scam

SKS Micro Fin 12-Nov-10 919.85 18-Nov-10 639.4 3 30% Loan recovery issues

Sun TV 31-May-11 390.05 02-Jun-11 272.55 2 30% 2G Scam

Unitech 10-11-2010 89.8 19-11-2010 67.6 6 25% 2G Scam

HCC 23-Nov-10 57.2 26-Nov-10 40.15 3 30% Hurdle for major project

LIC Housing Fin 23-Nov-10 1308.75 26-Nov-10 931.15 3 29% Corporate Gove rnace Issue

MindTree 28-Jan-11 525.9 10-Feb-11 381.9 9 27% Founder quitting the board

NIFTY 01-Aug-11 5,516.80 08-Aug-11 5,118.50 5 7% US Credit Rating downgrade

Price rise due to positive news

Security Start Date Close Price End Date Close Price2 Trade Sessions % Price Jump News

Mahindra Satyam 09-Aug-11 71.1 10-Aug-11 78.35 1 10%

Posted profit against

loss in last quarter

Hero Honda 16-Dec-10 1696 20-Dec-10 1983 1 17% Unexpected jump in sales

ICICI Bank 14-May-09 536.55 19-May-09 745 3 39%

fall i n interest rate to

boost lending growth

It can be observed from the above table that impact of the news is not instantaneous.

For example in case of case of Satyam computers market took two trading sessions to

react to the negative news of corporate fraud. In case of RCOM we find that the pricedecrease was gradual over a period of 10 trading sessions.

Documents

Trading Strategies - Impact of News on market