• The seminar places will be allocated in one application round. The allocation is done manually, the time of application (within one application round) is not important. As criteria, we take into account both grades and previous knowledge in the area in Finance (from lectures, seminars, etc.). In addition, we also take into account the priority of the application as stated in the wiwi portal.
• When applying, you have the possibility to indicate your desired topics. We will try to take your preferences into account as far as possible. The final allocation of topics will only be communicated after the second application round has been completed.
• Please note that we consider the acceptance of an allocated seminar place via the wiwi portal as a binding registration for the seminar. After that, a withdrawal from the seminar is only possible in justified exceptional cases after prior consultation.
• As an official start of the seminar we will hold a joint kick-off.
• Relevant for grading are an individual thesis and a group presentation in a block seminar.

Content of the seminar

Momentum strategies in the stock market are fascinating and have challenged capital market researchers for decades. The usual and very robust finding states that stocks with relatively high returns over the past 11 months (excluding the most recent month) also tend to exhibit relatively high returns in the following month. The success of the investment strategy based on this finding raises the question of whether capital markets can truly be information-efficient. One explanation is based on biased earnings expectations by analysts. In this seminar, some of the most important empirical studies on this topic will be replicated and confronted with new data.

Deliverables

The seminar language is English. Every participant will work on a separate topic and has to hand in a separate seminar thesis of around 12 pages, written in English. In addition, each participant is supposed to produce a well documented python code (in English, preferably in the form of a Jupyter notebook), replicating the (main) results of the paper.

After about half of the semester (see schedule below), there is a status update-meeting with the whole group, where participants are supposed to present their plans for their seminar theses and replication code and a rough roadmap for the remaining semester.

At the end of the semester, we will have a meeting where participants present their results in a 20-minute presentation.

The final grade will depend primarily on the thesis, the code, and the final presentation, but also on the midterm presentation and the participation in the discussions in the two meetings.

Individual topics

1. Analysts’ forecast change (main article: Hawkins, Chamberlin, and Daniel (1984)) Abstract: Can generally available information about consensus earnings expectations be used to generate risk-adjusted excess returns? If the market is truly efficient, such information should be discounted instantaneously and offer no profit opportunity. If the market is inefficient, however, and discounts new information only gradually, then it should be possible to demonstrate a relation between current consensus forecasts and subsequent stock price behavior. Using a data base that contained earnings estimates for over 2,400 stocks madeby more than 70 brokerage firms, the authors examined month-to-month percentage changes in consensus estimates to determine whether large positive revisions in earnings expectations can predict changes in stock prices. Their findings indicate that this information can be used to achieve returns significantly above the market’s return. Furthermore, the returns remain superior after risk adjustment and after transaction costs.

2. Earnings surprise (main article: Foster, Olsen, and Shevlin (1984)) Abstract: A common finding in the literature is that systematic post-announcement drifts in security returns are associated with the sign or magnitude of unexpected earnings changes. This paper examines proposed explanations for these drifts. The paper also documents that the systematic drifts in security returns are found foronly a subset of earnings expectations models. For a class of expectations models based on the time series of reported quarterly earnings, variables coding (1) the sign and magnitude of the earnings forecast error and (2) firm size independently explain 81 percent and 61 percent, respectively, of the variation in post-announcement drifts. The joint explanatory power of (1) and (2) is 85 percent, indicating that the effect of these two variables is highly collinear.

3. Revisions in analysts’ earnings forecasts (main article: Chan, Jegadeesh, and Lakonishok (1996)) Abstract: We examine whether the predictability of future returns from past returns is due to the market’s underreaction to information, in particular to past earnings news. Past return and past earnings surprise each predict large drifts in future returns after controlling for the other. Market risk, size, and book-to-market effectsdo not explain the drifts. There is little evidence of subsequent reversals in the returns of stocks with high price and earnings momentum. Security analysts’ earnings forecasts also respond sluggishly to past news, especially in the case of stocks with the worst past performance. The results suggest a market that responds only gradually to new information.

4. Revenue surprise (main article: Jegadeesh and Livnat (2006)) Abstract: This paper examines the relation between revenue surprises and contemporaneous and future stock returns. It also investigates whether analysts update their earnings forecasts in response to revenue surprises in atimely and unbiased fashion. Stock price reaction on the earnings announcement date is significantly related to contemporaneous as well as past revenue surprises. After controlling for earnings surprises, we find significant abnormal returns in the post-announcement period for stocks that have large revenue surprises. Although analysts revise their forecasts of future earnings in response to revenue surprises, they are slow to incorporate fully the information in revenue surprises.

5. Tax expense surprise (main article: Thomas and Zhang (2011)) Abstract: We investigate the joint hypothesis that (1) tax expense contains information about core profitability that is incremental to reported earnings and (2) that information is reflected in stock prices with a delay. We find that seasonally differenced quarterly tax expense, our proxy for tax expense surprise, is related positively to future returns. This anomaly is separate from previously documented pricing anomalies based on financial and tax variables. Additional investigation reveals that tax expense surprise is related positively to changes in future quarterly earnings and tax expense, and both those future changes are related positively to future returns. While the returns to investing in predictable future earnings changes has been documented before, these results suggest that predicting changes in future tax expense also generates incremental future returns.

6. Number of consecutive quarters with earnings increases (main article: Barth, Elliott, and Finn (1999)) Abstract: This study demonstrates that firms with patterns of increasing earnings have higher price-earnings multiples than other firms, and that this relation persists after controlling for growth and risk using proxies identified in previous research. However, price-earnings multiples decline significantly when earnings decreaseafter a previous pattern of increasing earnings.

Literature

Besides the six papers listed above, there are typically several studies that the papers build upon. A part of the work is tofind these papers, for example by using google scholar. The six main articles can also be found via google scholar:

Barth, Mary E, John A Elliott, and Mark W Finn, 1999, Market rewards associated with patterns of increasing earnings, Journal of Accounting Research 37, 387–413.

Chan, Louis KC, Narasimhan Jegadeesh, and Josef Lakonishok, 1996, Momentum strate-gies, The Journal of Finance 51, 1681–1713.

Foster, George, Chris Olsen, and Terry Shevlin, 1984, Earnings releases, anomalies, and the behavior of security returns, Accounting Review pp. 574–603.

Hawkins, Eugene H, Stanley C Chamberlin, and Wayne E Daniel, 1984, Earnings expec-tations and security prices, Financial Analysts Journal 40, 24–38.

Jegadeesh, Narasimhan, and Joshua Livnat, 2006, Revenue surprises and stock returns, Journal of Accounting and Economics 41, 147–171.

Thomas, Jacob, and Frank X Zhang, 2011, Tax expense momentum, Journal of Accounting Research 49, 791–821.

Preliminary Schedule

Please let us know if one of the dates does not work for you.

Reaching out

There are neither fixed dates for nor a maximum number of meetings with the supervisors. If you have any questions, please feel free to arrange a meeting by e-mailing us at

Julian Thimme (thimme∂kit.edu)

Fanchen Meng (fanchen.meng∂kit.edu)

Content of the seminar

Momentum strategies in the stock market are fascinating and have challenged capital market researchers for decades. The usual and very robust finding states that stocks with relatively high returns over the past 11 months (excluding the most recent month) also tend to exhibit relatively high returns in the following month. The success of the invest-ment strategy based on this finding raises the question of whether capital markets can truly be information-efficient. One explanation is based on biased earnings expectations by analysts. In this seminar, some of the most important empirical studies on this topic will be replicated and confronted with new data.

Deliverables

The seminar language is English. Every participant will work on a separate topic and has to hand in a separate seminar thesis of around 12 pages, written in English. In addition, each participant is supposed to produce a well documented python code (in English, preferably in the form of a Jupyter notebook), replicating the (main) results of the paper.

After about half of the semester (see schedule below), there is a status update-meeting with the whole group, where participants are supposed to present their plans for their seminar theses and replication code and a rough roadmap for the remaining semester.

At the end of the semester, we will have a meeting where participants present their results in a 20-minute presentation.

The final grade will depend primarily on the thesis, the code, and the final presentation, but also on the mid-term presentation and the participation in the discussions in the two meetings.

Individual topics

1. Analysts’ forecast change (main article: Hawkins, Chamberlin, and Daniel (1984)) Abstract: Can generally available information about consensus earnings expectati-ons be used to generate risk-adjusted excess returns? If the market is truly efficient, such information should be discounted instantaneously and offer no profit oppor-tunity. If the market is inefficient, however, and discounts new information only gradually, then it should be possible to demonstrate a relation between current consensus forecasts and subsequent stock price behavior. Using a data base that contained earnings estimates for over 2,400 stocks madeby more than 70 brokerage firms, the authors examined month-to-month percentage changes in consensus estimates to determine whether large positive revisions in earnings expectations can predict changes in stock prices. Their findings indicate that this information can be used to achieve returns significantly above the market’s return. Furthermore, the returns remain superior after risk adjustment and after transaction costs.

2. Earnings surprise (main article: Foster, Olsen, and Shevlin (1984)) Abstract: A common finding in the literature is that systematic post-announcement drifts in security returns are associated with the sign or magnitude of unexpected earnings changes. This paper examines proposed explanations for these drifts. The paper also documents that the systematic drifts in security returns are found foronly a subset of earnings expectations models. For a class of expectations models based on the time series of reported quarterly earnings, variables coding (1) the sign and magnitude of the earnings forecast error and (2) firm size independently explain 81 percent and 61 percent, respectively, of the variation in post-announcement drifts. The joint explanatory power of (1) and (2) is 85 percent, indicating that the effect of these two variables is highly collinear.

3. Revisions in analysts’ earnings forecasts (main article: Chan, Jegadeesh, and Lakonishok (1996)) Abstract: We examine whether the predictability of future returns from past returns is due to the market’s underreaction to information, in particular to past earnings news. Past return and past earnings surprise each predict large drifts in future re-turns after controlling for the other. Market risk, size, and book-to-market effectsdo not explain the drifts. There is little evidence of subsequent reversals in the returns of stocks with high price and earnings momentum. Security analysts’ earnings fore-casts also respond sluggishly to past news, especially in the case of stocks with the worst past performance. The results suggest a market that responds only gradually to new information.

4. Revenue surprise (main article: Jegadeesh and Livnat (2006)) Abstract: This paper examines the relation between revenue surprises and contempo-raneous and future stock returns. It also investigates whether analysts update their earnings forecasts in response to revenue surprises in atimely and unbiased fashion. Stock price reaction on the earnings announcement date is significantly related to contemporaneous as well as past revenue surprises. After controlling for earnings surprises, we find significant abnormal returns in the post-announcement period for stocks that have large revenue surprises. Although analysts revise their forecasts of future earnings in response to revenue surprises, they are slow to incorporate fully the information in revenue surprises.

5. Tax expense surprise (main article: Thomas and Zhang (2011)) Abstract: We investigate the joint hypothesis that (1) tax expense contains infor-mation about core profitabilitythat is incremental to reported earnings and (2) that information is reflected in stock prices with a delay. We find that seasonally differenced quarterly tax expense, our proxy for tax expense surprise, is related positively to future returns. This anomaly is separate from previously documented pricing anomalies based on financial and tax variables. Additional investigation re-veals that tax expense surprise is related positively to changes in future quarterly earnings and tax expense, and both those future changes are related positively to future returns. While the returns to investing in predictable future earnings changes has been documented before, these results suggest that predicting changes in future tax expense also generates incremental future returns.

6. Number of consecutive quarters with earnings increases (main article: Barth, Elliott, and Finn (1999)) Abstract: This study demonstrates that firms with patterns of increasing earnings have higher price-earnings multiples than other firms, and that this relation persists after controlling for growth and risk using proxies identified in previous research. However, price-earnings multiples decline significantly when earnings decreaseafter a previous pattern of increasing earnings.

Literature

Besides the six papers listed above, there are typically several studies that the papers build upon. A part of the work is tofind these papers, for example by using google scholar. The six main articles can also be found via google scholar:

Barth, Mary E, John A Elliott, and Mark W Finn, 1999, Market rewards associated with patterns of increasing earnings, Journal of Accounting Research 37, 387–413.

Chan, Louis KC, Narasimhan Jegadeesh, and Josef Lakonishok, 1996, Momentum strate-gies, The Journal of Finance 51, 1681–1713.

Foster, George, Chris Olsen, and Terry Shevlin, 1984, Earnings releases, anomalies, and the behavior of security returns, Accounting Review pp. 574–603.

Hawkins, Eugene H, Stanley C Chamberlin, and Wayne E Daniel, 1984, Earnings expec-tations and security prices, Financial Analysts Journal 40, 24–38.

Jegadeesh, Narasimhan, and Joshua Livnat, 2006, Revenue surprises and stock returns, Journal of Accounting and Economics 41, 147–171.

Thomas, Jacob, and Frank X Zhang, 2011, Tax expense momentum, Journal of Accounting Research 49, 791–821.

Preliminary Schedule

Please let us know if one of the dates does not work for you.

Reaching out

There are neither fixed dates for nor a maximum number of meetings with the supervisors. If you have any questions, please feel free to arrange a meeting by e-mailing us at

Julian Thimme (thimme ∂does-not-exist.kit edu)

Fanchen Meng (fanchen meng ∂does-not-exist.kit edu)

Content of the seminar
The seminar covers four academic studies from the field of financial economics that are of great societal relevance and were recently published in one of the leading journals in the field. The specific topics are described in more detail below. Participants are expected to carefully read the main papers and related research articles and discuss the central economic findings in light of the current societal situation and within the context of the existing literature. Particular attention should be paid to potentially controversial claims of the studies and to the key new insights they provide.

Deliverables
The seminar language is English. Participants can work in groups of two on a topic. Importantly, however, every participant has to hand in a separate seminar thesis of around 12 pages, written in English. In addition, each group is supposed to produce a podcast episode of around 20 minutes about the research article. Technical support and help concerning the structure and content of the podcast episode will be provided in the kick-off meeting.
After about half of the semester (see schedule below), there is a status update-meeting with the whole group, where participants are supposed to present their plans for their seminar theses and podcast episode and a rough roadmap for the remaining semester.
At the end of the semester, we will have a meeting where we will listen to and discuss about the produced podcast episodes.
The final grade will depend primarily on the thesis and the podcast, but also on the mid-term presentation and the participation in the discussions in the two meetings.

Individual topics

• 1. The Rate of Return on Everything, 1870-2015 (main article: Jorda, Knoll, Kuvshinov, Schularick, and Taylor (2019) Abstract: What is the aggregate real rate of return in the economy? Is it higher than the growth rate of the economy and, if so, by how much? Is there a tendency for returns to fall in the long-run? Which particular assets have the highest long-run returns? We answer these questions on the basis of a new and comprehensive dataset for all major asset classes, including housing. The annual data on total returns for equity, housing, bonds, and bills cover 16 advanced economies from 1870 to 2015, and our new evidence reveals many new findings and puzzles.
• 2. Stocks for the Long Run? Evidence from a Broad Sample of Developed Markets (main article: Anarkulova, Cederburg, and O’Doherty (2022)) Abstract: We characterize the distribution of long-term equity returns based on the historical record of stock market performance in a broad cross section of 39 developed countries over the period from 1841 to 2019. Our comprehensive sample mitigates concerns over survivor and easy data biases that plague other work in this area. A bootstrap simulation analysis implies substantial uncertainty about long-horizon stock market outcomes, and we estimate a 12% chance that a diversified investor with a 30-year investment horizon will lose relative to in inflation. The results contradict the conventional advice that stocks are safe investments over long holding periods.
• 3. Empirical Asset Pricing via Machine Learning (main article: Gu, Kelly, and Xiu (2020)) Abstract: We synthesize the field of machine learning with the canonical problem of empirical asset pricing: measuring asset risk premia. In the familiar empirical setting of cross section and time series stock return prediction, we perform a comparative analysis of methods in the machine learning repertoire, including generalized linear models, dimension reduction, boosted regression trees, random forests, and neural networks. At the broadest level, we find that machine learning offers an improved description of expected return behavior relative to traditional forecasting methods. We identify the best performing methods (trees and neural networks) and trace their predictive gains to allowance of nonlinear predictor interactions that are missed by other methods. Lastly, we find that all methods agree on the same small set of dominant predictive signals that includes variations on momentum, liquidity, and volatility. Improved risk premium measurement through machine learning can simplify the investigation into economic mechanisms of asset pricing and justifies its growing role in innovative financial technologies.
• 4. Engineering Lemons (main article: Vokata (2021)) Abstract: Recent complex financial products sold to households contradict the basic premise of canonical innovation theories: financial innovation benefits its adopters. In my 2006-2015 sample of over 28,000 yield enhancement products (YEP) the securities offer attractive yields but negative returns. The products lose money both ex ante and ex post due to their embedded fees: on average, YEPs charge 6-7% in annual fees and subsequently lose 6-7% relative to risk-adjusted benchmarks. Simple and cheap combinations of listed options often first-order dominate YEPs. Compe-tition, disclosure, or learning do not eliminate this inferior financial innovation over my sample period.

Literature
Besides the four papers listed above, there are typically several studies that the four papers build upon. A part of the work is to find these papers, for example by using google scholar. The four main articles can also be found via google scholar:
Anarkulova, Aizhan, Scott Cederburg, and Michael S O’Doherty, 2022, Stocks for the long run? Evidence from a broad sample of developed markets, Journal of Financial Economics 143, 409–433.
Gu, Shihao, Bryan Kelly, and Dacheng Xiu, 2020, Empirical asset pricing via machine learning, Review of Financial Studies 33, 2223–2273.
Jorda, Oscar, Katharina Knoll, Dmitry Kuvshinov, Moritz Schularick, and Alan M Taylor,2019, The rate of return on everything, 1870–2015, Quarterly Journal of Economics 134, 1225–1298.
Vokata, Petra, 2021, Engineering lemons, Journal of Financial Economics 142, 737–755.

Preliminary Schedule
Please let us know if one of the dates does not work for you.

Reaching out
There are neither fixed dates for nor a maximum number of meetings with the supervisors. If you have any questions, please feel free to arrange a meeting by emailing us at:

Julian Thimme (thimme ∂does-not-exist.kit edu)

Fanchen Meng (fanchen meng ∂does-not-exist.kit edu)

Content of the seminar
The seminar covers four academic studies from the field of financial economics that are of great societal relevance and were recently published in one of the leading journals in the field. The specific topics are described in more detail below. Participants are expected to carefully read the main papers and related research articles and discuss the central economic findings in light of the current societal situation and within the context of the existing literature. Particular attention should be paid to potentially controversial claims of the studies and to the key new insights they provide.

Deliverables
The seminar language is English. Participants can work in groups of two on a topic. Importantly, however, every participant has to hand in a separate seminar thesis of around 12 pages, written in English. In addition, each group is supposed to produce a podcast episode of around 20 minutes about the research article. Technical support and help concerning the structure and content of the podcast episode will be provided in the kick-off meeting.
After about half of the semester (see schedule below), there is a status update-meeting with the whole group, where participants are supposed to present their plans for their seminar theses and podcast episode and a rough roadmap for the remaining semester.
At the end of the semester, we will have a meeting where we will listen to and discuss about the produced podcast episodes.
The final grade will depend primarily on the thesis and the podcast, but also on the mid-term presentation and the participation in the discussions in the two meetings.

Individual topics

• 1. The Rate of Return on Everything, 1870-2015 (main article: Jorda, Knoll, Kuvshinov, Schularick, and Taylor (2019) Abstract: What is the aggregate real rate of return in the economy? Is it higher than the growth rate of the economy and, if so, by how much? Is there a tendency for returns to fall in the long-run? Which particular assets have the highest long-run returns? We answer these questions on the basis of a new and comprehensive dataset for all major asset classes, including housing. The annual data on total returns for equity, housing, bonds, and bills cover 16 advanced economies from 1870 to 2015, and our new evidence reveals many new findings and puzzles.
• 2. Stocks for the Long Run? Evidence from a Broad Sample of Developed Markets (main article: Anarkulova, Cederburg, and O’Doherty (2022)) Abstract: We characterize the distribution of long-term equity returns based on the historical record of stock market performance in a broad cross section of 39 developed countries over the period from 1841 to 2019. Our comprehensive sample mitigates concerns over survivor and easy data biases that plague other work in this area. A bootstrap simulation analysis implies substantial uncertainty about long-horizon stock market outcomes, and we estimate a 12% chance that a diversified investor with a 30-year investment horizon will lose relative to in inflation. The results contradict the conventional advice that stocks are safe investments over long holding periods.
• 3. Empirical Asset Pricing via Machine Learning (main article: Gu, Kelly, and Xiu (2020)) Abstract: We synthesize the field of machine learning with the canonical problem of empirical asset pricing: measuring asset risk premia. In the familiar empirical setting of cross section and time series stock return prediction, we perform a comparative analysis of methods in the machine learning repertoire, including generalized linear models, dimension reduction, boosted regression trees, random forests, and neural networks. At the broadest level, we find that machine learning offers an improved description of expected return behavior relative to traditional forecasting methods. We identify the best performing methods (trees and neural networks) and trace their predictive gains to allowance of nonlinear predictor interactions that are missed by other methods. Lastly, we find that all methods agree on the same small set of dominant predictive signals that includes variations on momentum, liquidity, and volatility. Improved risk premium measurement through machine learning can simplify the investigation into economic mechanisms of asset pricing and justifies its growing role in innovative financial technologies.
• 4. Engineering Lemons (main article: Vokata (2021)) Abstract: Recent complex financial products sold to households contradict the basic premise of canonical innovation theories: financial innovation benefits its adopters. In my 2006-2015 sample of over 28,000 yield enhancement products (YEP) the securities offer attractive yields but negative returns. The products lose money both ex ante and ex post due to their embedded fees: on average, YEPs charge 6-7% in annual fees and subsequently lose 6-7% relative to risk-adjusted benchmarks. Simple and cheap combinations of listed options often first-order dominate YEPs. Compe-tition, disclosure, or learning do not eliminate this inferior financial innovation over my sample period.

Literature
Besides the four papers listed above, there are typically several studies that the four papers build upon. A part of the work is to find these papers, for example by using google scholar. The four main articles can also be found via google scholar:
Anarkulova, Aizhan, Scott Cederburg, and Michael S O’Doherty, 2022, Stocks for the long run? Evidence from a broad sample of developed markets, Journal of Financial Economics 143, 409–433.
Gu, Shihao, Bryan Kelly, and Dacheng Xiu, 2020, Empirical asset pricing via machine learning, Review of Financial Studies 33, 2223–2273.
Jorda, Oscar, Katharina Knoll, Dmitry Kuvshinov, Moritz Schularick, and Alan M Taylor,2019, The rate of return on everything, 1870–2015, Quarterly Journal of Economics 134, 1225–1298.
Vokata, Petra, 2021, Engineering lemons, Journal of Financial Economics 142, 737–755.

Preliminary Schedule
Please let us know if one of the dates does not work for you.

Reaching out
There are neither fixed dates for nor a maximum number of meetings with the supervisors. If you have any questions, please feel free to arrange a meeting by emailing us at:

Julian Thimme (thimme ∂does-not-exist.kit edu)

Fanchen Meng (fanchen meng ∂does-not-exist.kit edu)

Content of the seminar:

Behavioral biases exert a profound impact on individuals' investment decisions, often swaying choices away from rational and optimal paths. Emotional responses, such as fear or greed, can lead to impulsive actions, causing investors to buy or sell based on short-term sentiments rather than a thorough analysis of market fundamentals. A large variety of these biases have been shown in experiments and in portfolio data of individual investors. They are known to prevent individuals from making timely adjustments to their portfolios, leading to an overall underperformance. From a broader perspective, suboptimal investment decisions may drive prices of financial assets away from their rational counterparts. Recognizing and mitigating these behavioral biases is essential for fostering a more objective and strategic approach to investment and to fairer and more informative prices. In this seminar, we want to review and discuss some of the most prominent behavioral biases and discuss their impact.

Deliverables:

The seminar language is English. Every participant has to hand in a seminar thesis of 12-15 pages, written in English. The main goal is to thoroughly describe the behavioral bias, referring to the pertinent academic literature. Taking this as a starting point, students can either a) shed light on the consequences of the bias in financial decision making for financial markets as a whole (again using existing studies in the literature) or b) run their own experiment (small scale, with fellow students as participants).  After about half of the semester (see schedule below), there is a status update-meeting with the whole group, where participants are supposed to share their plans for their seminar theses and presentation and a rough roadmap for the remaining semester. At the end of the semester, all participants present their main results (in English!) and we discuss their findings with the group. The final grade will depend on the thesis, the presentation, and the participation in the discussions in the two meetings (status update and presentations).

Individual topics:

• 1. Overconfidence
• 2. Prospect theory
• 3. Disappointment aversion
• 4. Ambiguity aversion
• 5. Mental accounting
• 6. Framing

Schedule:

• Feb.04, 2024, 23:59 Application deadline first round
• Mar.17, 2024, 23:59 Application deadline second round
• Apr.15, 2024, 16:00 Kick-off meeting and assignment of topics
• May.27, 2024, 16:00 Status update-meeting
• Jul.08, 2024, 23:59 Deadline for handing in the seminar theses
• Jul.15, 2024, 14:00 Presentations

 Reaching out:

There are neither fixed dates for nor a maximum number of meetings with the supervisors. If you have any questions, please feel free to arrange a meeting by e-mailing us at

• Julian Thimme (thimme∂kit.edu)
• Fanchen Meng (fanchen.meng∂kit.edu)

Content of the seminar:

The equity premium puzzle is a longstanding and perplexing phenomenon in the field of finance. It revolves around the apparent disconnect between the expected returns on stocks and the risk-free interest rate. Asset pricing theories imply that investors demand an equity risk premium, that means, a higher return for taking on the additional risk associated with investing in stocks compared to the risk-free rate offered by assets like government bonds. The puzzle arises from the magnitude of this premium. Empirical evidence over many decades has shown that stocks have historically provided significantly higher returns than could be justified by standard financial models. These models predict that the equity risk premium should be relatively small, because stock market returns are only weakly correlated with macroeconomic fundamentals, especially household consumption. The key question is: Why are stock returns so high, given that they barely affect the overall financial well-being of investors? Researchers and economists have proposed various explanations for the equity premium puzzle. Some argue that investors may be excessively risk-averse, while others suggest that the true risks are underestimated by financial economists. The goal of this seminar is to shed light on a variety of potential explanations and discuss their main economic intuitions. Resolving the equity premium puzzle is not only a theoretical challenge but also has significant implications for investment strategies, portfolio management, and understanding the dynamics of financial markets. Researchers continue to explore this intriguing phenomenon in their quest for a comprehensive explanation that can bridge the gap between theory and empirical observations in asset pricing. In that sense, the seminar provides insights into cutting-edge research in financial economics.

Deliverables:

The seminar language is English. Every participant has to hand in a seminar thesis of 12-15 pages, written in English. The main goal is to thoroughly describe the economic rationale behind the chosen article. Taking this as a starting point, the thesis can either a) outline criticisms of the presented model (using the follow-up literature) or b) shed light on the model’s (or some model extensions’) ability to explain other phenomena in asset pricing. After about half of the semester (see schedule below), there is a status update-meeting with the whole group, where participants are supposed to share their plans for their seminar theses and presentation and a rough roadmap for the remaining semester. At the end of the semester, all participants present their main results (in English!) and we discuss similarities and differences across the alternative economic models. The final grade will depend on the thesis, the presentation, and the participation in the discussions in the two meetings (status update and presentations).

Individual topics:

• 1. Long-run risk (main article: Bansal and Yaron (2004)) The long-run risks model posits that the economy faces persistent and unpredictable shocks that affect future consumption growth, representing a source of long-run risk. Investors are assumed to be concerned about these economic risks and demand a risk premium in compensation for bearing them. The model’s key innovation is its focus on fluctuations in long-term risk, rather than the local correlation between stock returns and household consumption.
• 2. Habit formation (main article: Campbell and Cochrane (1999)) The main idea of this framework is that the preferences of investors incorporate so called habit formation. This means that investors derive utility not only from their current consumption but also from the deviation of consumption from a habit or target level. When consumers are accustomed to a certain standard of living, they experience disutility if their consumption falls below this habit level. This effectively makes them excessively risk averse, explaining the high risk premia on stocks.
• 3. Rare disasters (main article: Barro (2006)) The model posits that there is a small but nonzero probability of catastrophic events occurring, which can lead to massive economic losses and wealth destruction. Investors are assumed to be highly concerned about these rare disasters. They demand a substantial risk premium for holding assets that are vulnerable to such events. It is argued that the feared disaster has not occurred (at least in the USA), so that asset returns seem surprisingly high while fundamentals are surprisingly smooth.
• 4. Intermediary frictions (main article: He and Krishnamurthy (2013)) The intermediary asset pricing model emphasizes the role of financial intermediaries, such as banks and shadow banks, in the economy. It suggests that asset prices are influenced not only by the behavior of households but also by these institutions. When they are under stress and face regulatory constraints, they reduce their exposures to risky assets, leading to higher stock returns.

Literature:

 Besides the four papers mentioned above, there are several follow-up papers with refinements of the original economic ideas. A part of the work is to find these follow-up papers, for example by using google scholar.

The paper by Mehra and Prescott (1985) provided the first thorough description of the equity premium puzzle.

The main articles are :

• Bansal, R., and A. Yaron, 2004, Risks for the long-run: A potential resolution of asset pricing puzzles, Journal of Finance 59, 1481–1509.
• Barro, Robert J, 2006, Rare disasters and asset markets in the twentieth century, Quarterly Journal of Economics 121, 823–866.
• Campbell, John Y, and John H Cochrane, 1999, By force of habit: A consumption-based explanation of aggregate stock market behavior, Journal of Political Economy 107, 205–251.
• He, Zhiguo, and Arvind Krishnamurthy, 2013, Intermediary asset pricing, American Economic Review 103, 732–770.
• Mehra, R., and E. Prescott, 1985, The equity premium: A puzzle, Journal of Monetary Economics 15, 145–161.

Schedule:

• Oct 08, 2023, 23:59 Application deadline
• Oct 23, 2023, 16:00 Kick-off meeting and assignment of topics
• Dec 11, 2023, 16:00 Status update-meeting
• Jan 29, 2024, 23:59 Deadline for handing in the seminar theses
• Feb 05, 2024, 14:00 Presentations

 Reaching out:

There are neither fixed dates for nor a maximum number of meetings with the supervisors. If you have any questions, please feel free to arrange a meeting by e-mailing us at

• Julian Thimme (thimme ∂does-not-exist.kit edu)
• Viktoria Klaus (viktoria klaus ∂does-not-exist.kit edu)
• Fanchen Meng (fanchen.meng∂kit.edu)

"Economists have recently been increasingly focusing on the study of financial crises - and for good reason. As the global financial crisis unfolded in 2008, the profession, as well as the whole world, was reminded of the significance of these events in terms of the historical tendency of financial crises to recur over time, their ability to affect both rich and poor countries, and the deep and lasting damage they can inflict on economies, societies, and states" (cited from Sufi and Taylor (2021): Financial Crises: A Survey, Working Paper). In this seminar, you will examine various financial crises of the past 100 years, exploring their causes, developments, and the effectiveness of the measures taken.

Topics:

• Great Depression 1929
• Mexican Peso Crisis 1994
• Asian Financial Crisis 1997
• Dot Com Crisis 2001
• Global Financial Crisis 2008
• EURO Debt Crisis 2010
• COVID Crisis 2020

"The intersection between finance and technology, known as fintech, has led to a dramatic growth of innovations and changed the landscape of financial markets. While fintech plays a crucial role in democratizing access to credit for non-bank customers and consumers with thin credit histories worldwide, consumers who are currently well served by traditional banking services are also turning to fintech companies for faster services and greater transparency. Fintech, particularly blockchain, has the potential to be disruptive to financial systems and intermediation" (cited from Allen/Gu/Jagtiani (2020), A Survey of Fintech Research and Policy Discussion, Working Paper). This seminar deals with the major developments in modern financial markets, including cryptocurrencies, crowdinvesting, initial coin offerings, security tokenization, social trading, and robo-advisors.

Topics:

• Traditional Money Creation vs. Cryptocurrencies
• Bank Lending vs. Crowdinvesting
• IPOs vs. ICOs
• Centralized Trading vs. Security Tokenization
• (International) Payment Processing by Banks vs. Peer-to-Peer
• Investment Advisory vs. Social Trading and Robo-Advisors

Active investors try to identify stocks that should be bought or sold at specific times in order to achieve the highest possible portfolio return. In recent years, researchers and investors have identified numerous stock-specific signals and characteristics that can be helpful when picking stocks. In this seminar, we will focus on signals based on past stock performance. A well-known example is the "momentum" of a stock, i.e. the return over the past twelve months. We will implement different strategies to get an own impression of the profitability of the strategies and possible economic explanations.

Topics:

• Volatility (total volatility and idiosyncratic volatility)
• Momentum (momentum and calendar momentum)
• Reversal (short-term and long-term reversal)
• Market risk (Beta and coskewness)
• Return patterns (lottery characteristics and streaks)