The News Impact Curve (Modified)

It is not news that news moves financial markets. This blog will publish research on how, when, why, and which news moves what financial markets.

News drives the market. Good news lifts the market. Bad news dampens growth. The effect, however, is not symmetric: good news does not lift the market as much as bad news depresses it; good (bad) news does not lift (depress) a bull market as much as a bear market. Positive and negative stock return innovations have different impact on the volatility, as found in the literature by many researchers, for example Campbell and Hentschel (1992) and Engle and Ng (1993). Volatility following bad news is found to be higher than following good news. This is the well documented predictive asymmetry effect in stock market, which is sometimes called the leverage effect. Besides that good and bad news influences volatility differently, good news in a bull market may not lift up the market as much as in a bear market or vice versa. Intuitively, given a continuous downward market movement, bad news may drag down the market more than if there has been upward market movement. In other words, in a bear market, the market is waiting for bad news and bad news shakes market confidence more than if it has been a bull market. Although some volatility models, mainly in the regime switching context, have allowed for a bull and bear market effect, for example the switching regime GARCH models by Hamilton and Susmel (1994) and Cai (1994), the literature so far has not considered the asymmetric impact of good and bad news in bull and bear markets, which I found to be significant in a paper entitled "How Bad is Bad News; How Good is Good News?". Rob Engle got a Nobel prize for his work on volatility models. Volatility tends to be clustered together, that is there are periods of volatility storms and calms, in which, large squared returns tend to be followed by large squared returns. Engle and Ng (1993) have suggested, for these conditional volatility models, a standard measure of how news influences stock volatility, a news impact curve. Given information up to current time, the news impact curve examines the relationship between the news and future volatility. The news impact curve plots news scenarios, that is a range of bad and good news, on the horizontal axis, against the resulting volatility. Here are some News Impact Curves for the Dow Jones (estimated daily from 1915-2001), showing how volatility responds to good (right side of the chart) and bad news (left side of the chart):

Figure 2 (which, in Firefox at least, can be expanded by clicking on it and then you can zoom in for a closer view) shows the general characteristics of news impact curves, that GARCH model is symmetric around zero, whereas TARCH and EGARCH are asymmetric, with different slopes.

An alternative to the differing slopes of the EGARCH and TARCH models is the asymmetric GARCH (AGARCH) model by Engle (1990) (The AGARCH model is called Quadratic GARCH in Campbell and Hentschel (1992)).

OK, so much for the build up, but this is not really news. It has been well documented that news creates volatility. But here is the news - news moves returns. Using the right market expectations measure and the right time horizon for returns, we can show that returns (as well as squared returns) move predictably when the market is surprised. Here is a news impact curve for the EUR returns (rather than for volatility) responding to non-farm payrolls news: While the news impact curve for volatility was defined some time ago, I think the same curve for the first moment is more interesting than that for the second moment. Don't you? I'll be concentrating on the movement in the first moment, that is returns in financial markets. And in coming posts I'll show how some very simple, naive, rules can be used in combination with the (modified) news impact curve.

References

Cai, J. (1994). A Markov Model of Switching-Regime ARCH. Journal of Business & Economics Statistics, 12, 309-316.

Campbell, J.Y. and L. Hentschel (1992). No news is good news: an asymmetric model of changing volatility in stock returns, Journal of Financial Economics, 31, 281-318.

Engle, R.F. (1990). Discussion: stock market volatility and the crash of 87. Review of Financial Studies, 3, 103-106.

Engle, R.F. and V.K. Ng (1993). Measuring and testing the impact of news on volatility. Journal of Finance, 48(5), 1749-78.

Hamilton, J.D. and Susmel, R. (1994). Autoregressive Conditional Heteroskedasticity and Changes in Regime. Journal of Econometrics, 64, 307-334.

EURGBP Case Study

It is not news that news moves financial markets. This blog will publish research on how, when, why, and which news moves what financial markets. The Euro against the Pound (EURGBP) exchange rate accounts for only 5% of FX trading according to the BIS. It is not a major currency pair, but today I thought I'd give it a quick once over to demonstrate some issues that have been showing up so far. Also the U.S. International Trade Balance (ITB) is a long-runnning announcement and an important one for foreign exchange rates however it the economic derivatives data for it has a short history.

ITB is the monthly estimate of the balance of payments on U.S. International trade in goods and services, expressed in billions of current U.S. Dollars, for the calendar month which is two months prior to the month in which such estimate is scheduled by the U.S. Department of Commerce to be released. ITB is available from February 2005.

So what can I show you when the currency is not one of the most-traded and there is not a lot of derivative-based expectations data. Quite a lot actually. I start with the news (measured as the expected number from derivatives prices minus the actual): Then using this to explain the cumulative returns for the EURGBP for the minutes following the ITB announcement days I find that the best fit (in terms of R-squared is 8 minutes after the 8:30am annnouncement - shown as 9 in the chart below since the first return is from 8:29am to 8:30am). There is also a peak at 1 minute, but the best fit is at 9. Notice how sensitive the fit of the model is to the cumulative return window, choose a window of 30 minutes the R-squared is 0.05 as opposed to 0.56 at 8 minutes: The same pattern can be seen in the news coefficient and its significance (shown by the t-Statistic): So the return window is critical to finding a significnat news effect. The EURGBP has relatively symmetric response to good and bad news. This can be seen in the plot of news against the 8-minute cumulative return: If we split the good and bad news we get similar coefficients:

VARIABLE COEFFICIENT STDERROR T STAT P-VALUE

ITB_PosNews -0.0111799 0.00356628 -3.135 0.00604 ***

ITB_NegNews -0.0117322 0.00343696 -3.414 0.00331 ***

So it is not worth distinguishing between good and bad news. What about higher moments of the expectations distribution? It turns out the skewness of the expectation matters (marginally):

VARIABLE COEFFICIENT STDERROR T STAT P-VALUE

ITB_CalcNews -0.0111298 0.00240619 -4.626 0.00024 ***

ITB_CalcSkew -0.0178708 0.0158213 -1.130 0.27436

There is a bit of a skew to the cumulative return distribution too (compared to the normal): With the addition of the skewness of expectations I am now explaining 59% of the return in the EURGBP return after a U.S. Trade Balance announcement. And what confidence can I attach to the significance of these two coefficients affecting EURGBP returns? Here's the confidence intervals for the two parameters, you'll notice that the skewness parameter confidence interval includes zero so it is debatable whether it should be kept. Because it lowers the root mean squared error (t-stat greater than 1.0) it was kept: So while there is still not a lot of data on economic derivatives for the Trade Balance and the EURGBP is not a currency you would expect to be moved by the data nonetheless there is a strong statistical relationship that can be modeled (and exploited?).

Findings to Date

It is not news that news moves financial markets. This blog will publish research on how, when, why, and which news moves what financial markets. So what can I conclude from thre research done to date and why is this research new(s)?

The three contributions of this research are, first, the market expectation is derived from economic derivative prices that allow a full distribution for the market expectation to be derived. Economic derivatives data better predict financial market movements and also allow for testing whether there is information in the high moments of the distribution. Second, high frequency financial data allows us to test for the optimal window and discover how long it takes financial markets to digest and react to news. Finally, by using a U.S. and a European economic announcement and a wide range of financial markets, this paper compares announcements to show which are important for which markets.

I have found that high frequency financial data leads to a much bigger and more significant news announcement effect over previous studies that used end-of day data. Further financial markets react very quickly to news. Unlike other studies that have assumed a 25-30 minute window, I have demonstrated that the announcement window is often just one minute. Using the richness of the economic derivatives-based expectations data I determine when higher moments of the expectations distribution are useful in determining the announcement effect. I also show in which markets, and for which announcements, good news and bad news have asymmetric effects; and, in which markets are most responsive to which announcements. Finally, I have highlighted some of the interesting results that traders or risk managers might want to delve into in more detail.

In the coming weeks I plan to explore some more financial markets (especially more currency pairs), focusing on the most promising economic announcements, and working on finding the best models for explaining movements in returns following economic news releases.

Does Bad News Matter More Than Good?

It is not news that news moves financial markets. This blog will publish research on how, when, why, and which news moves what financial markets.

Bad news tends to have a bigger impact than good. Running a regression of the returns from futures on the S&P 500 index for 5 minutes before to 25 minutes after the data release on the derivatives-based non-farm payroll news gives:

S&P 500 Index	News Coefficient	Standard Error	t-Statistic	R2
Derivatives-Based News	0.00311	0.00082	3.78	0.3091

The same intraday regression as above is run but splitting the news effect into two (one when the news is a positive surprise and the other when the released number is less than expected):

S&P 500 Index	News Coefficient	Standard Error	t-Statistic	R2
Negative News	0.00331	0.00110	3.01	0.3108
Positive News	0.00285	0.00128	2.22

There is a slight overall improvement in fit (although the standard error increases and adjusted R² falls). Depending on the application of the results the difference in the estimated average effect for positive and negative of 0.00311 and the 0.00331 for good and 0.00285 for bad may be enough to justify the differentiation.

U.S. Employment Affects Which Financial Markets?

It is not news that news moves financial markets. This blog will publish research on how, when, why, and which news moves what financial markets.

The U.S. employment report is an important release. The change in Non-Farm Payrolls ("NFP") as determined and published by the U.S. Department of Labor, Bureau of Labor Statistics, estimating the monthly change in the total number of employees on non-agricultural payrolls is published here. NFP is available from January 1994 with Economic Derivatives being sold for this release since October 2002. From October 2002 to July 2003, one auction was held for each release, then two auctions for each release until May 2005. Since June 2005 there have been three auctions for each release. In October 2006 the CME announced that a fifth economic derivative auction on the U.S. non-farm payroll was be added. How does NFP news affect different financial markets and instruments? Well, the Euro (EUR/USD) moves the most in response to the surprise component of the release. The following chart shows how other markets react, relative to the Euro. Currency markets clearly move the most, however there are still some significant relationships amongst other markets. Also there has been no attempt to optimize these relationships to account for differential effects of good and bad news, recent good/bad news, or a non-symmetric market opinion. The U.S. Trade Balance is also important for currencies as is, but to a lesser extent, the ISM Manufacturing PMI Index.

The Swissy and the Fiber - An Interesting Couple

It is not news that news moves financial markets. This blog will publish research on how, when, why, and which news moves what financial markets.

The chart below shows the relationship between the returns on the Fiber or Euro (EUR/USD) and Swissy or Swiss Franc (USD/CHF) right after the U.S. Trade Balance is released.

At 8:30am (Eastern Time), on a known date every month, this relationship has held.

I have made refinements in terms of differential effects of good and bad news, the volatility and shape of market expectations and so on, but as shown here this is a very simple relationship, the EUR and the CHF move in opposite directions when the trade news is announced.

Points in the bottom right quadrant above tend to be when bad news is released, points in the upper left is when a positive surprise hits the markets. By adding in the U.S. Trade Balance news as another dimension the relationship of these two currency pairs to the news can be seen. (There are less data points because this chart only shows the most recent announcements). Around economic news announcement times there are lots of correlations such as the one illustrated here. The underlying stable relationship that give rise to the moves is between financial markets and news. Do you have a comment on this relationship? Please post below any feedback you may have.

How Might a Trader Use the Announcement Effect?

It is not news that news moves financial markets. This blog will publish research on how, when, why, and which news moves what financial markets.

Traders often develop triggers for major economic announcements. They place bets on the upside and downside. Here is how a trader, armed with information on how the Euro moves in response to the non-farm payrolls announcement, might see the opportunity. First here is how news has been distributed in the past (news is measured as actual minus the expectation derived from the econoomic derivative prices from an auction just before the announcement: Now, based on how the Euro has reacted to the news, here is the buy/sell picture: So, on negative news, buy the EURUSD pair, on positive news sell the EURUSD. Often traders will take both positions. Here is the news distribution for the retail sales excluding automobiles (RSX) announcement: And here is the buy/sell chart: Looking at the buy/sell charts one can see how often in the past the announcements have caused big moves in the exchange rate. Returns are calcualted as continuously compounded returns (the ratio of the natural logs of the exchange rate over the rate a minute ago times 100). So you can see that a surprise of +/- 150 on the NFP will give a 0.5% return on the EUR . Similarly , news of +/- 0.5 on the RSX will generate a a bit less than 0.1% return. You might also see that good and bad news are not always symmetric. More on this to come ... *Sting, in his song An Englishman in New York, wrote "If manners make the man as someone said". Well it was William of Wykeham (1324-1404) who said it. The "Manners Makyth Man" crest above is from New College Oxford, the college he founded.

Standardized News

It is not news that news moves financial markets. This blog will publish research on how, when, why, and which news moves what financial markets.

Comparing Different News Consistently

Timing of the announcements matter, some are more important than others.

Andersen et. al.* show that some announcements “… are to some extent redundant, and the market then only reacts to those released earlier.’ (p. 13).

Andersen et. al. note that “although closely timed news events are highly correlated, the correlation does not create a serious multicollinearity problem except in a few specific instances. For example, industrial production and capacity utilization are released at the same time, and they are highly correlated (0.64). In general, however, the event that two announcements within the same category (e.g., real activity) are released simultaneously is rare.” (p. 11)

I investigate which announcements matter and confirm that some announcements matter a lot; some seem to have a marginal impact, while others do not matter.

To do this it is helpful to follow Andersen et. al. and define standardized news (S) as the surprise divided the sample (for each announcement) standard deviation of the news (σ):

N_t = A_t - E_t-δ(A_t)

S_t = N_t / σ

Standardized news allows for comparisons of responses of different asset prices to different news.

There is a lot of news. Even if we restrict our attention to those announcements for which there are economic derivative prices there were 231 announcements in the last four years (note that this is not a traditional line chart as there are sometimes two announcements on the same day):

Here is what standardized news looks like (for 7 announcements all together) if you do a frequency plot of the data against a normal distribution:

• An aside - This is a plug for a nice little FREE econometrics package that produced the above chart. The package is called Gretl (Gnu Regression, Econometrics and Time-series Library).

With news standardized we can compare how a big non-farm payrolls (NFP) surprise compares to a big retail sales news announcement. And, with them both on the same footing, we can say which one is more important.

Of course importance depends on your perspective. If NFP news moves the U.S. 30-Year Treasury bond futures contract but not the foreign exchange market and you are an FX trader then why should you care?

So to be relevant I need to address which news is important (relative to other news), but also which news is important for which markets.

Here is the reference:

*Andersen, Torben G., Bollerslev, Tim, Diebold, Francis X., Vega, Clara, (2002) “Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange” NBER Working Paper Series, NBER Working Paper No. 8959, May 2002. pdf link.

How Quickly Do Markets Respond to NFPs?

It is not news that news moves financial markets. This blog will publish research on how, when, why, and which news moves what financial markets.

How quickly do financial markets react to news - specifically the Non-Farm Payrolls ("NFP") news?

To answer this, I started with the U.S. non-farm payrolls data as this was found by others to be a significant announcement. For a group of commodities, exchange rates, bond, and equity prices regressions were run of the form:

X_i,j,t =β_i,jN_j,t+ε_i,j,t

Where i is an index of how many periods are included in the cumulative return calculation from 1 to 31; j is an index of financial markets from 1 to 14 (where the markets are: Gold; AUD; CAD; CHF; EUR; GBP; Heating Oil; JPY; Natural Gas; S&P 500; 2-Year T-Bond; 5-Year T-Bond; 10-Year T-Bond; 30-Year T-Bond), N the news or surprise in the announcement as measured by the difference of the actual from the mean of the economic derivative-based expectation.

Charting, in the Figure below, the Adjusted R² (which is the same as the R² in this case), a couple of findings become clear.

R² For Announcement Effect Regressions For Various Financial Markets for Non-Farm Payrolls – Cumulative Returns

1. The maximum correlation is often immediate, one minute after the announcement at 8:31am.(Note that on the chart this corresponds to the interval 2 since 1 represents the cumulative return from the 8:29am close to the 8:30am close).
2. The news effect is often very significant in these regressions.
3. The markets group quite distinctly into:

• Commodities (excluding Heating Oil) equities and bonds that have an R² of 0.1 or less. Heating Oil that rises to an R² of 0.12 after 15 minutes.
• Foreign exchange rates that have R² ’s that peak between 0.35 and 0.5 1 minute after the announcement and decline thereafter.

The 30-Year Treasury has the lowest correlation, the EUR/USD exchange rate the highest.

It is clear from the above that researchers using a 25-30 minute announcement window, or 5-minute returns, will find a relationship but that higher frequency data narrowing the window maximizes the news effect.

The flowers? Non very seasonal but it was pooring rain this a.m. and I thought some spring flowers might brighen up the site on a gloomy, wet, autumn day.

What's News?

It is not news that news moves financial markets. This blog will publish research on how, when, why, and which news moves what financial markets.

The announcement effect is defined as the impact of news on financial markets. News is defined as the difference between the market’s expectation of the release and the actual release (before any revision).

Theory tells us that it is only news or the surprise component of a release that moves financial markets. The definition and measurement of news is therefore critical.

News (N) or surprise at time t is the actual released data (A) at time t minus the market expectation (E(A)) close to, but before, time t (δ> 0 but small, so the expectation is measured perhaps a few hours or at most a day or two before the announcement).

N_t = A_t – E_t-δ(A_t)

In the case of economic derivatives, since 2003 the auction data I have used has come from the same day as the announcement. So for example the auctions on U.S. GDP will take place on Friday, October 27th (from 7 - 8am Eastern Time) and the release will be the same day at 8:30am.

In the early days of studying the announcement effect (early to mid-80's) researchers used model-based measures of market expectations. They used autoregressive models, modelling expectations as a weighted average of lagged actual data. Then came the theory of rational expectations and people realized that these models assumed irrational behaviour.

When I was writing my thesis I was exploring the idea of a compromise between rational expectations as an equilibrium condition and an autoregressive mechanism as a learning method to get to this equilibrium. A Kalman filter provided a useful mechanism to describe how the market learns.

Later people took existing surveys that asked people whow they felt about inflation or economic growth and turned this into quantitative information that could be used in econometric models.

Then firms began to survey market participants specifically for a number, a forecast of an upcoming release.

Since 1980 MMS International surveyed around 40 market participants weekly (in the U.S., 20 for the Canadian survey) for their forecasts of major economic indicators. The forecast medians are sold by Haver Analytics under a lease they signed with MMS. MMS no longer exists. MMS’s successor company is Action Economics.

For the Retail Sales (excluding autos) April 2005 release the following information was available from the survey: mean (and median) 0.5, standard deviation 0.3986. The standard deviation and mean are from Gürkaynak and Wolfers (2006). MMS published only the medians. Action Economics, from December 2003, provide mean, median, high, low, number of participants and standard deviation.

Survey data are available for the U.S. Canada Europe for a wide range of forecasts including: Policy Indicators; National Accounts Data: GDP, Consumption & Income; Industrial Production, Capacity Utilization; Housing Indicators; Consumer, Producer, Import, and Export Prices; Employment & Earnings; Manufacturing & Trade; International Trade; and Leading Indicators.

Since October of 2002 Goldman Sachs (Deutsche Bank is also listed as originators of these auctions) have held auctions for Economic Derivatives, the name they give to options on scheduled macro-economic statistics. The auctions are held with the Chicago Mercantile Exchange (CME) and recent auction results are published on the Goldman Sachs and CME web sites.

In these auctions, one can buy and sell options on economic data releases such as employment, retail sales, industrial production, trade balance, inflation, consumer sentiment and economic growth. The auctions typically last for about an hour and take place on the morning before or a few days before the release. To trade in the auctions one must have over $10 million in assets. There are some 120 participants (Estimates from a conversation with Goldman Sachs Economic Derivatives Group on September 11^th 2006) in these auctions. At any auction there are 40 or so participants, some 80% or so of these are large and small hedge funds. Large investment banks and a couple of corporations make up the rest. The investment banks, while accounting for less 20% of the participants make up for more than 20% of the auction volume. The participants are split almost equally between the U.S. and Europe (with the majority of European companies being U.K.-based).

Goldman Sachs’ Economic Derivatives provide an advantage over other expectations measures in that from the auction results one can construct a probability density function of the market’s expectation for the economic release. On the left below is an example from the Retail Sales (excluding autos) auction of May 12, 2005 (for the same April 2005 release for which the survey data is given above). The implied distribution for the retail sales announcement can be generated from the reported auction clearing prices for the digital puts, calls, or digital ranges.

Having expectations measured by a complete distribution has some advantages as we can test whether higher moments than the mean affect financial markets (such as the volatility, the skewness, and kurtosis).

As discussed above the expectation is taken just before the announcement (the closer the better). For the econoimc derivatives covered announcements, if δ is measured in days, the on average δ = 0.1 (excluding the HICP auctions which are 1 and 2 month options – as can be seen from the table below I have access to the 1-month option results).

Number of Announcements and Average Time to Announcement

	RSX	ISM	ITB	GDP	NFP	IJC	HICP	All	All (excl. HICP)
Number of Announcements	40	45	19	7	46	32	38	227
Average δ (days)	0.3	0.3	0.0	0.0	0.2	0.0	33.8	4.9	0.1
First Announcement	Sept. 04	Nov. 02	Feb. 05	Jan. 05	Nov. 02	Feb. 04	May 03
Last Announcement	Aug. 06	Sept. 06	Aug. 06	Jul. 06	Aug. 06	Sept. 06	Aug. 06

Summary – Expectations Data

Apart from proving more information the Economic Derivatives or market-based forecasts are found by Gürkaynak and Wolfers to outperform the survey data. They:

“… establish that the Economic Derivatives forecast dominates the survey forecast (although survey forecasts perform quite well) both in predicting outcomes and in predicting market responses to economic news.” (p. 13)

And,

· “… that central tendencies of market-based forecasts are very similar to, but more accurate than surveys. Further, financial market responses to data releases are also better captured by surprises measured with respect to market-based expectations than survey-based expectations, again suggesting that they better capture investor expectations. Some behavioral anomalies evident in survey-based expectations – such as forecastable forecast errors – are notably absent from market-based forecasts.” (p. 1)

The Federal Reserve Board of San Francisco (2006) (Wolfers) took data from the first 153 of these Economic Derivatives auctions and compared them with an alternative forecast aggregator: the survey of the expectations of financial market analysts taken on the Friday prior to the data release. They asked “which better predicts the actual data?” The Economic Derivatives forecasts were slightly (5%–10%) more accurate, although these differences were not statistically significant. They also found more interestingly, once one knows the Economic Derivatives forecast, there is no useful information in the survey-based forecast.

They also analyze the change in stock and bond prices from 5 minutes before the announcement to 25 minutes later for the two alternative measures of news. In each case, they confirm that the Economic Derivatives market better predicts financial market responses to economic data than does the alternative survey-based measure.

Derivatives data is available for 7 series with the longest history going back monthly to September 2002. Survey data is available for some 170 series with the longest history going back weekly to 1980.

While the Economic Derivatives data is superior in terms of information content and usefulness for measuring the announcement effect, the survey data has a longer history and broader coverage. I use the economic derivatives data.

So "What's News?" Well I say it is the difference between what a financial market expects (and has built into prices) as measured by the implied forecast of an economic derivative auction and the actual release. A bit of a mouthful, but a useful definition nonetheless.

Cut to the Chase (for FX)

It is not news that news moves financial markets. This blog will publish research on how, when, why, and which news moves what financial markets.

Suppose you are a trader or a risk manager with exchange rates being a significant holding in, or a significant risk factor for, your portfolio. Which announcements should excite or worry you?

The table below provides the answer. By showing the R²’s in a heat map that defines strong correlations as R² > 0.5 (and shows them as red), medium correlations as 0.2 ² <>2 <>

Exchange rate are Australian Dollar (AUD), Canadian Dollar (CAD), Swiss Franc (CHF), Euro (EUR), British Pound (GBP), Japanese Yen (JPY). Euro is EUR/USD and GBP is GBP/USD. All of the others are defined as the converse, for example USD/CAD.

Heat Map of Correlations for Exchange Rate Responses to Announcements

	GDP	HICP	IJC	ISM	ITB	NFP	RSX
AUD
CAD
CHF
EUR
GBP
JPY

And here is the legend for the announcements (across the top):

• RSX - U.S. Retail Sales (excl. autos)
• IJC - U.S. Initial Jobless Claims
• NFP - U.S. Non-Farm Payrolls
• ITB - U.S. International Trade Balance
• ISM - ISM Manufacturing PMI Index
• HICP - Eurozone HICP Inflation Index
• GDP - U.S. Gross Domestic Product (Advance release)

The Advance GDP release is obviously very important in the foreign exchange market. It should be noted that being quarterly there are few observations for GDP announcements and so the results should be taken with some caution. The international trade balance (ITB) also has a very strong influence. Non-farm payrolls is also an important announcement.

W5 and How

Over the next while I hope to answer the W5 and How, the Who, What, Where, When, Why and How, of economic news and financial markets. Specifically, I'll try to answer:

• How quickly do financial markets react to news?
• Which announcements matter for which financial markets?
• Are the results stable over time?
• Does bad news matter more than good?
• Does a string of bad news affect financial markets?
• Does volatility, skewness, and kurtosis of expectations affect financial markets?

I am engaged in some research that should be of interest to risk managers, speculators, policy makers and academics. In getting to the answers there are a lot of issues related to data:

• Definition of News
• The Size of the Announcement Window
• The Timing of Announcements
• The Effect of Good News and Bad News
• News in Good Times and Bad

There are also econometric issues related to the estimation of the effect of news on financial markets. But I'll try not to dwell on the details, but rather concentrate on the journalistic approach of getting to the answers. The photos ... sk8brds as art.

News Moves Markets

It is not news that news moves financial markets.

Financial news is full of stories about how markets were surprised or anticipated an economic statistic and how the markets moved in response to this news.

Many people trade in financial markets around economic announcements. These traders like the volatility that surrounds the announcement, so people bet on good news, others on bad, and there is much speculation on what the market sentiment is before a new economic statistic such as the U.S. employment situation that is embodied in the monthly non-farm payrolls release.

Of course just as some want to profit from these market gyrations others see the market moves following news as a risk and would like to avoid them.

In the past surveys have been done of market forecasters before economic announcements. These have been used to gauge market sentiment and the extent to which the actual number differs from the survey is taken as the news component that drives the market in the minutes following the announcement.

New News

Recently two developments have occurred that have allowed us to quantify how much, when, and in what direction financial markets move in response to news.

Firstly, economic derivatives, auctions of puts and calls on economic releases allow us to get a much better read on market sentiment than comes from surveys. Participants in the economic derivatives market are putting their money where their mouth is.

Secondly real-time financial markets data has allowed the effect of the announcement to be isolated and separated from other influences.

The economic-news Blog

This blog will publish relevant research on how news moves financial markets.

The picture? Nice isn't it? More interesting photos to come ...