Seminar "Predictability"

Preface

The seminar is open to Master students.

To successfully pass the seminar you need to write a paper and give a presentation. Papers can be written in either German or English and should have a length of 15-20 (team of two) or 20-25 pages (team of three). For hints on how to write a paper see our guidelines. You need to hand in a printed version and also a digital one (PDF). The seminar talks should be given in English.

The two main parts of your paper and presentation will be (i) explanation of the analytical methodology and (ii) replication of analyses from the key references. You should also provide an introduction, a short summary of the literature (which can be part of the introduction), and some concluding remarks. For most topics, you will need to use a software such as R or Matlab.

Please contact your supervisor to discuss the outline of your paper, your empirical part, and any questions that you may have. For organizational questions, please ask Nenad Ćurčić.

FAQ & Organisational matters

  • Do we get a grade? Yes. Your paper and your presentation will be graded and lead to one grade (equally weighted). Both the paper and presentation have to be passed.
  • What do we have to hand in? An outline of your paper to discuss the content of your paper and your final paper one week before the presentation.
  • Who is responsible? For content-related questions, please contact your supervisor. For organizational questions, please ask Nenad Ćurčić.

Time Table

  1. 29.01.2018 - 03.02.2018 Submission of your seminar preferences via online platform: http://econ.mathematik.uni-ulm.de:3838/semapps/stud_en/
  2. 04.02.2018 First round of seminar matching
  3. 11.02.2018 Second round of seminar matching
  4. 15.02.2018 Topic allocation (comlete the polls on Moodle until 22.02., link will be activated on 15.02.2018)
  5. 27.02.2018 General information about Seminar, introductory meeting, 16:30, room: He18, 1.20
  6. date tba Registration at the Higher Services Portal
  7. date tba Meet your supervisor to discuss the outline of the paper
  8. date tba Submission of the paper until 11:59 am, HeHo 18, room 1.00 (to Nenad)
  9. date tba Presentations, in Villa Eberhardt (exact schedule tba)

Topics

1.       OOS Tests

Explain the differences between in-sample and out-of-sample (OOS) tests and introduce different statistics used to evaluate OOS model performance. Then show how to assess the economic significance out-of-sample. Illustrate your entire explanations by updating the OOS analyses in Goyal and Welch (2007) for the variable dividend price ratio.

Key references:

  • Welch, I., & Goyal, A. (2008). A Comprehensive Look at the Empirical Performance of Equity Premium Prediction. Review of Financial Studies, 21, 1455-1508.
  • Campbell, J. Y., & Thompson, S. B. (2008). Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average? Review of Financial Studies, 21, 1509–1531.

supervisor: Nenad Ćurčić

students: tba on 26.02.2018.

 

2.     Predictability in Efficient Markets

Goyal and Welch (2007) showed that many predictors of the stock market fail after some time. Update Table 1 of Goyal and Welch (2007) for all years with data available. Up to which date does each parameter show a significant result? Discuss then possible causes why predictors fail after some time. In your discussion, you can focus on predictability patterns that can arise in a  market that is fully or largely efficient.

Key references:

  • Mclean, R.D., & Pontiff, J. (2016). Does Academic Research Destroy Stock Return Predictability?, The Journal of Finance, 71(1), pp. 5-32.
  • Welch, I., & Goyal, A. (2007). A comprehensive look at the empirical performance of equity premium prediction. The Review of Financial Studies, 21(4), 1455-1508.
  • Timmermann, A., & Granger, C. (2004). Efficient market hypothesis and forecasting. International Journal of Forecasting, 20, 15-27.

supervisor: Clara Franke

students: tba on 26.02.2018.

 

3.       Imposing restrictions

In order to increase prediction performance, some authors impose economically motivated restrictions. Your first task is to explain and compare restriction approaches with the focus on restrictions for stock return regression forecasts proposed by Campbell and Thompson (2008) as well as forecasts involving valuation ratios by Ferreira and Santa-Clara (2011). As a second task you should update the analysis done for the dividend-price ratio and reported in Table 1 in Campbell and Thompson (2008).

Key references:

  • Campbell, J. Y., & Thompson, S. B. (2008). Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average? Review of Financial Studies, 21, 1509–1531.
  • Ferreira, M.I., & Santa-Clara, P. (2011). Forecasting stock market returns: the sum of the parts is more than the whole. Journal of Financial Economics, 100, 514–537.

supervisor: Nenad Ćurčić

students: tba on 26.02.2018.

 

4.       Forecast combination

Many economic variables with in-sample predictive ability for the equity premium fail to deliver consistent out-of-sample forecasting gains relative to the historical average. However, forecasts combination is a relatively simple forecasting technique that seems to work well for equity premium prediction. You shall review the evidence and then update panel A from Table 1 in the study of Rapach et al. (2010) using data until this year focusing on the OOS R-squared for individual predictive regression models and the mean combination method. Moreover, you should also update the first graph from Figure 2. Data sources and an Excel template will be provided to you. Time permitting, you can update more.

Key reference:

  • Rapach, D., et al. (2010). Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy. Review of Financial Studies, 3, 821-62.

supervisor: Nenad Ćurčić

students: tba on 26.02.2018.

 

5.       Predicting stock market returns with the least-angle regression (LARS) approach

When using an OLS approach for prediction, it might be tempting to use all sort of available data as explanatory variables. This approach is often referred to as the “kitchen sink” approach. A major drawback of this approach is that it is likely to suffer from overfitting and thus leads to poor predictive performances. Shrinkage approaches like the ones used by Li & Tsiakas can tackle this issue and improve the predictive performance.

A different but surprisingly very similar way of addressing the issue is a least-angle regression (LARS).

Your task is to introduce the concepts of LARS on the one hand and shrinkage approaches on the other. The focus should be on a detailed explanation of LARS. Then use LARS to predict returns for the same data and time period as in section 3 of the 2017 paper from Li and Tsiakas. Compare your results to those from Li and Tsiakas.

The following R package might be useful: lars

Key references:

  • Li, J., & Tsiakas, I. (2017): Equity premium prediction: The role of economic and statistical constraints. Journal of Financial Markets, 36, pp. 56-75.
  • Efron, B. et al. (2004): Least angle regression. The Annals of Statistics 32(2), pp. 407-499

supervisor: Carsten Schäfer-Siebert

students: tba on 26.02.2018.

 

6.       Tower building and stock market returns

Summarize the empirical findings on the relationship between skyscraper building on the one hand, and economic activity and stock market returns on the other hand. Then update Table 1, Figure II, Figure III, Table 2 and variation 6 from Table 5 of Löffler (2013). Time permitting you can update more. If you have trouble implementing the Hodrick estimator you can use Newey/West. For stock market data, use the data sources used in the paper. Tower building data will be provided to you.

Key reference:

  • Löffler, G. (2013). Tower building and stock market returns. Journal of Financial Research, 36(3), 413-434.

supervisor: Prof. Dr. Gunter Löffler

students: tba on 26.02.2018.

 

7.       Predicting stock returns with idiosyncratic volatility

Idiosyncratic volatility belongs to the class of implicit data. This means that idiosyncratic volatility is not directly observable but must be estimated based on a model, which will be a key part of your analysis.
Since one can theoretically get rid of idiosyncratic volatility using diversification, it is not obvious that it should be priced. Yet, there are quite a few papers which link idiosyncratic volatility to the cross‑section of returns.

Your task is to replicate parts of the key reference paper: Investigate the predictive power of idiosyncratic volatility for the stocks listed in the STOXX Europe 600 index in January 2015. Use the STOXX Europe 600 as your regional reference and to calculate the Fama-French factors, as indicated in the template that will be handed to you. Use daily data from 2015 up to now.
You shall also give a brief overview about the meaning of idiosyncratic volatility for asset pricing and discuss why idiosyncratic volatility might have predictive power for stock returns.

Key reference: 

  • Ang, A. et al. (2009): High idiosyncratic volatility and low returns: International and further U.S. evidence. Journal of Financial Economics 91(1), pp. 1-23

available at www.sciencedirect.com/science/article/pii/S0304405X08001542

supervisor: Carsten Schäfer-Siebert

students: tba on 26.02.2018.

 

8.       Predicting index volatility with GARCH models

When prediction is performed through parametric models, one has to choose how exactly the model should look like. For example, there are a lot of different GARCH models available, which all could be used to predict the volatility of a stock index.

Have a look at the study of Awartani and Corradi. Use daily DAX closing prices to replicate parts of their study: Compare the predictive performances of the introduced GARCH specifications over different periods, forecast horizons and for different test statistics. You shall introduce RiskMetrics and at least 5 different further GARCH specifications, may restrict yourself to 3 different choices to model the conditional mean and may assume p = q = 1 for all considered GARCH models. You don’t need to implement White’s reality check.

The following R packages might be useful: rugarch, forecast

Key reference:

supervisor: Carsten Schäfer-Siebert

students: tba on 26.02.2018.

 

9.       Seasonality of returns

First, identify returns in months t-1, …, t-144 to predict future monthly returns in month t of DAX 30 stocks for the years 1989-2017. Use cross-sectional Fama-MacBeth regressions and follow Keloharju et al. (2016). Then evaluate the performance of a trading strategy build on these results. Further, discuss similarities and differences between your results and Keloharju (2016).

Key reference:

  • Keloharju, M., et al. (2016). Return Seasonalities. The Journal of Finance, 71, 1557-1590.

supervisor: Clara Franke

students: tba on 26.02.2018.

 

10.   Momentum and reversal strategy

Test a momentum strategy and a reversal strategy for the 100 stocks contained in the S&P 100 in 2015. Do it for different time horizons (daily, monthly) from 2015 until today. A list of the constituents of the S&P 100 in 2015 will be provided to you.  You can get the return indexs data from Datastream. What explanations does behavioral finance offer for reversal and momentum strategies?

Key references:

  • Barroso, P. & Santa-Clara, P. (2015). Momentum has its moments. Journal of Financial Economics, 116(1), 111-120.
  • Cheng, Si, et al. (2017). Short-Term Reversals: The Effects of Past Returns and Institutional Exits. Journal of Financial and Quantitative Analysis, 52(1), 143-173.

supervisor: Clara Franke

students: tba on 26.02.2018.

News

29.01.2018 - 03.02.2018 Submission of your seminar preferences via online platform:  http://econ.mathematik.uni-ulm.de:3838/semapps/stud_en/

Dates and Room

Please note the detailed timetable.

Module description

This seminar is open for Master students.

Module description