semir fss 2015 – attenti a fi ial markets · pdf filechair of international finance...

Chair of International Finance

Seminar FSS 2015 – Attention and Financial MarketsStefan Ruenzi, Anja Kunzmann, Pavel Lesnevski, Michael Ungeheuer

What are the prerequisites?

• You are a master student.

• You have successfully completed at least one finance course (Albrecht, Maug, Niessen-

Ruenzi, Ruenzi, Terberger, Theissen, Weber).

• Some knowledge of statistics and econometrics is useful and participants should be

motivated to undertake empirical work.

• You are available in the time period from January to March.

2Chair of International Finance

Schedule

• Monday, November 24, 2014: Topics announcement

• Thursday, November 27, 2014: Topics presentation

• Monday, December 1 - Friday, December 5, 2014: Submission of priority lists

• Friday, January 2, 2015: Topics allocation announcement

• Friday, January 2, 2015: Starting date

• Friday, January 2 - Wednesday, January 7, 2015: Registration/Withdrawal period

• Friday, February 27, 2015, 12pm (noon): Submission of seminar papers (hardcopy and

electronic version, which should include the data and do-files necessary to replicate

your work)

• Beginning/Middle of March: Presentation of seminar papers


Stata and database tutorial

• Short crash-course on how to write an empirical paper using Stata and the databases

offered at the University of Mannheim.

• Not a mandatory prerequisite for writing a seminar paper or master thesis.

• No ECTS

• Schedule:

– January 5 (9-12am): Getting to know Stata

– January 5 (1-2.30pm): Databases at the University of Mannheim

– January 7 (10-12am, 1-3pm): Database manipulation and estimation

– January 8 (10-12am, 1-3pm): Basic programming structures

– January 9 (9am-1pm): Case study

• Detailed information on the tutorial is available on the website of the Chair of Prof.

Theissen.

• We strongly recommend that you attend this course as it answers many of the basic

questions that you will encounter during your seminar thesis.


How to apply?

• Submit your priority list online (link on our website) .

• You can combine topics from different chairs.

– E.g., first preference: “3rd Topic, Chair of Prof. Weber”; second preference: “10th

Topic, Chair of Prof. Theissen”; third preference: “4th Topic, Chair of Prof. Ruenzi”

• Please only choose topics you are really willing to work on.

• The allocation of topics is based on the average grade of your finance exams.

• Due to time constraints it is unlikely that we can take into account the grades of the

exams you are going to write in December.


How do we grade?

• Supervision of the seminar paper by Prof. Ruenzi and the assigned advisor.

• Grading:

– 50% seminar paper

– 25% presentation of the seminar paper

– 15% discussion of an assigned seminar paper of a fellow student

– 10% oral participation in the discussion

• Own empirical contribution will be rewarded.

– Participate in the Stata and database tutorial!

• Plagiarism: no excuse policy


How should your paper look like?

• ~12 pages (without appendix)

• Language: English

• Detailed formal requirements: see guidelines of the Chair of Prof. Maug and/or

guidelines of the Chair of Prof. Weber


R1: Does Media Coverage of Stocks Affect Mutual Funds’ Trading and

Performance?

• Advisor: Pavel Lesnevski

• Idea:

– Barber and Odean (2008) show that stocks with salient characteristics, such as extreme

one-day returns or high turnover, initiates trading of retail investors. � Limited

attention.

– Fang and Peress (2009) find that stocks which are barely covered by the media earn

higher returns than stocks which are in the focus of the news media. This effect could

be due to lack of arbitrageurs’ attention to these stocks.� Are professional investors

also subjected to limited attention?

– Fang, Peress, and Zheng (2014) find evidence in support of this hypothesis.

• Aim of seminar thesis:

– Replicate the findings of Fang, Peress, and Zheng (2014).

– Extend the results of the paper by obtaining other attention-grabbing characteristics

and analyzing their impact on mutual funds’ trading and performance.

• Requires extensive use of statistical software (e.g. Stata) and manipulation of complex data.

• Main Reference:

Fang, Lily H., Joel Peress, and Lu Zheng. “Does Media Coverage of Stocks Affect Mutual

Funds’ Trading and Performance?” Review of Financial Studies, September 2, 2014.


R2: Fund Manager Use of Analyst Recommendations


• Idea:

– Sell-side equity research has a positive value for investors (see, e.g., Jegadeesh et

al. 2004)� Do mutual fund managers following stock analysts’ recommendations

outperform their peers?

• Kacperczyk and Seru (2007) show that funds relying on analysts’ recommendations

perform worse than their competitors.


– Replicate the results of Kacperczyk and Seru (2007).

– Determine fund characteristics that are associated with higher reliance on analyst

recommendations .

• Requires extensive use of statistical software (e.g. Stata) and manipulation of complex

data.

• Main Reference:

Kacperczyk, Marcin, and Amit Seru. “Fund Manager Use of Public Information: New

Evidence on Managerial Skills.” The Journal of Finance 62, no. 2 (April 1, 2007): 485–528.


R3: Analyst Recommendations, Mutual Fund Herding and Stock Returns


• Idea:

– Kacperczyk and Seru (2007) show that managers whose trading is less sensitive to analyst recommendation outperform their peers.�What is the source of underperformance?

– Brown, Wei, and Wermers (2013) find that funds’ simultaneous trading leads to herding in particular stocks. Such herding results in positive returns due to price pressure in the short run but in return reversal (underperformance) over the long-run.


– Replicate the results of Brown, Wei, and Wermers (2013) using an alternative database of analyst recommendations (IBES database instead of Zacks).

• Requires extensive use of statistical software (e.g. Stata) and manipulation of complex data.

• Main Reference:

Brown, Nerissa C., Kelsey D. Wei, and Russ Wermers. “Analyst Recommendations, Mutual Fund Herding, and Overreaction in Stock Prices.” Management Science 60, no. 1 (September 3, 2013): 1–20.


R4: Investor Attention and Economic Uncertainty


• Advisor: Anja Kunzmann

• Idea:

– Investors dislike uncertainty about the economic state and therefore intensify their search for information to dissolve the situation. Da et al. (2011) find that Google search volume is a good proxy for the effort of investors' attempt to gain information.

– Based on this, Dzielinski (2012) uses Google search volume of economic terms to proxy for the uncertainty of investors. He finds that increased uncertainty also impacts stock markets (returns and volatility).


– Replicate the findings of Dzielinski (2012) with stock market data, data from Google trends and further uncertainty measures available online.

– Extend the analysis to additional search terms and larger time series of the data.

• Main Reference:Dzielinski, M. (2012): Measuring economic uncertainty and its impact on the stock market, Finance Research Letters, 9, pp. 167-175.

R5: Investor Attention and Index Performance


• Idea:

– Attention is a scarce cognitive resource. When it comes to investment decisions, investors cannot process the vast amount of information available due to limited attention. Peng et al. (2006) show that investors therefore focus on market-wide rather than firm-specific information.

– Vozlyublennaia (2014) builds on these findings and uses Google search volume forindices to measure investor attention and investigates its effect on indexperformance.


– Replicate the findings of Vozlyublennaia (2014) with data from Google trends and data on indices.

– Extend the analysis to larger time series of the data.

• Main Reference:Vozlyublennaia, N. (2014): Investor attention, index performance, and return predictability, Journal of Banking & Finance, 41, pp. 17-35.


R6: Investor Attention and Earnings Announcements


• Idea:

– Drake et al. (2012) provide evidence that Google search queries reduce

information asymmetries in the pre-announcement period of earnings news and

thus reduce surprises when earnings are eventually announced.

– Based on this, Fricke et al. (2014) show that the findings also apply to the post-

announcement period: the post earnings announcement drift (PEAD) is reduced if

pre-announcement Google search volume for the corresponding firm is high.


– Replicate the findings of Fricke et al. (2014) with data from Google trends and data

on firm earnings.

– Extend the analysis to larger time series of the data.

• Main Reference:

Fricke, E.; Fung, S.; Goktan, M. (2014): Google Search, Information Uncertainty, and

Post-Earnings Announcement Drift, Journal of Accounting and Finance, 14, pp. 11.


R7: Investor Attention and the Cross-Section of Stock Returns

• Advisor: Michael Ungeheuer

• Idea:

– Attention is a scarce resource, particularlarly for retail investors’ investment

decisions.

– Barber/Odean model: Attention increases & short-sale constraints� buy-sell

imbalances� short-term price increases and eventual reversal.


– Measure investor attention by google search volume for ticker symbols (e.g. AAPL).

– Analyze the impact of attention shocks on stock returns. Do stocks with higher

investor attention have predictably higher returns than stocks with lower investor

attention? Replicate Da et al. (2011) for 2004-2008. Are findings from Da et al.

valid for the years after 2008?

• Main Reference:

Da/Engelberg/Gao (2011): In Search of Attention, Journal of Finance.


R8: Driven to Distraction?


• Idea:

– If distracted by other news, investors underreact to new information

– Use earnings announcements to test this: Is there a weaker direct reaction and a

stronger post-announcement drift if there are many earnings announcements on

the same day?


– Replicate findings of Hirshleifer et al. (2009) and extend it up to 2013.

– Analyze how investor attention – measured by google search volume for ticker

symbols – changes around earnings announcements. Is this pattern attenuated on

distraction-prone announcement days?

• Main Reference:

Hirshleifer/Lim/Teoh (2009): Driven to Distraction: Extraneous Events and

Underreaction to Earnings News, Journal of Finance.


R9: Product Market Advertising and Investor Attention


• Idea:

– Product market advertising has been found to influence financial markets, e.g.

returns and liquidity. It is usually assumed that investor attention provides the link

between advertising and financial markets.

– The impact of changes in advertising on investor attention can be measured

directly: Does google search volume for ticker symbols react to product market

advertising?


– Use google SVI for firms’ ticker symbols to measure the impact of different

advertising channels (newspapers, tv, internet…) on investor attention for this firm

and its peers.

– Compare findings to Madsen/Niessner, 2014.

• Main Reference:

Madsen/Niessner (2014): Is Investor Attention for Sale? The Role of Advertising in

Financial Markets, Working Paper.


Final Remarks

• Visit our website (http://intfin.bwl.uni-mannheim.de/). There you will find a detailed

description of every topic.

• Pay attention to the deadlines (submission of priority list).

• Apply only for topics you really want to work on.

• In case of questions, do not hesitate to contact us.

– Anja Kunzmann: [email protected]

– Pavel Lesnevski: [email protected]

– Michael Ungeheuer: [email protected]


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