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FINANCE DISSERTATION TOPICS 2019-20

Reference Code Topic Readings/References

Pre-requisites

F1

Cash dividends and stock repurchases Payout policy is a well-develop strand of the corporate finance literature. Finance researchers in the area of payout policy mainly focus on factors explaining whether and to what extent corporations distribute cash through cash dividends and repurchase transactions. I would like to work with PG students who are interested in one or more of the following topics:

– Long-term trends in the frequency and magnitude of cash dividends and stock repurchases;

– The effects of executive equity-related incentives (e.g. holdings of executive stock options) on payout policy;

– The timing of repurchase transactions and its determinants.

A successful MSc dissertation should build on the existing academic literature and offer a significant contribution by, for instance, testing similar hypotheses in different contexts, formulating and testing variations to existing hypotheses, collecting and analysing new variables and unique datasets. Empirical projects in corporate finance require the use of statistical software, knowledge of financial databases and the ability to handle large datasets. References Cuny, C.J., Martin, G.S., Puthenpurackal, J.J., 2009. Stock options and total payout. Journal of Financial and Quantitative Analysis 44, 391-410. Banyi, M.L., Kahle, K.M., 2014. Declining propensity to pay? A re-examination of lifecycle theory. Journal of Corporate Finance 27, 345-366. De Cesari, A., Espenlaub, S., Khurshed, A., Simkovic, M., 2012. The effects of ownership and stock liquidity on the timing of repurchase transactions. Journal of Corporate Finance 18, 1023-1050.

Corporate Finance BMAN71152 Software: Excel and Stata Students will automatically be enrolled onto STATA BMAN73622.

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Dittmar, A.K., Field, L.C., 2014. Can managers time the market? Evidence using repurchase price data. Forthcoming in Journal of Financial Economics. Fama, E.F., French, K.R., 2001. Disappearing dividends: changing firm characteristics or lower propensity to pay? Journal of Financial Economics 60, 3-43. Kahle, K., 2002. When a buyback isn’t a buyback: open market repurchases and employee options. Journal of Financial Economics 63, 235-261.

F2

REAL OPTIONS Real option master’s theses apply recent models on investment evaluation, risk hedging and innovation to a real enterprise. Choose a company with suitable operating and investment alternatives, study an appropriate time series of inputs and/or outputs, and then illustrate the real value of the firm and best timing of management actions using Excel. As a background, there is a case in BMAN70192 for each topic. RISK MANAGEMENT The energy revolution due to natural gas fracking has enhanced climate control in the U.S. but resulted in a low gas prices and depressed market for frackers. What is best hedging policy, and how does this affect real investment decisions? Adkins, R. D. Paxson, P. Pereira and A. Rodrigues, “Investment Decisions with Finite-lived Collars”, Journal of Economic Dynamics & Control, 103 (2019), 185-204. CAPACITY & RESCALING Capacity is a strategic option, demotivating late starters, and enabling unconstrained volume, or, when rescaled, deferring abandonment. Theory is applied to U.S. gas pipelines and LPG/LNG facilities building export capacity to Asia and Britain/Europe. Adkins, R. and D. Paxson, “Rescaling-contraction with a Lower Cost Technology When Revenue Declines”, European Journal of Operational Research, 277 (2019):574-586. REAL OPTIONS & INNOVATION

Real Options BMAN70192 Excel Required

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Most innovation involves creating and exercising real option opportunities. The natural gas revolution in the US involves new extended lateral drilling innovations, and new global supply chains. What is the value of these options for Asian-Global energy? Paxson, D., “A Real Option View of Innovation & Productivity Growth” keynote address to the Real Options Conference Workshop on Innovation & Public Policy, Kings, London 26 June 2019.

F3

Performance of Credit Ratings in Structured Finance Asset-Backed Securities Securitisation is a major technique in Structured Finance to transform illiquid portfolios into marketable securities backed by the cash flows originating in the portfolios assets. The technique is in place for several decades now and has yielded mixed results, including evidence on episodes of crises such as the 2007 sub-prime crisis in the US. However, since then a number of regulatory and technical developments have reinforced the credibility of produced asset-backed securities, so that the European Central Bank has included such assets in its Asset Purchase Programs for monetary policy. A central role in the securitisation process belongs to Credit Rating Agencies (CRA). In this project we wish to assess the performance of major and younger CRAs in rating structured finance securities, after the US sub-prime crisis, in various asset classes such as residential and commercial mortgages, consumer loans, corporate loans, credit card receivables and non-performing loans across the globe. Effects on asset pricing and financial stability could also be addressed. Indicative References – Becker, B., and T. Milbourn. 2011. How did increased competition affect credit ratings? Journal of Financial Economics, Vol. 101 (3), pp.493–514. – Benmelech, E., and J. Dlugosz. 2009. The alchemy of CDO credit ratings, Journal of Monetary Economic, Vol. 56 (5), pp. 617–34. – Bolton P, X Freixas and J Shapiro, The Credit Ratings Game, Journal of Finance, Vol LXVII (1), pp. 85-111 – Coval, J.D., J. Jurek, and E. Stafford. 2009. The economics of structured finance. Journal of Economic Perspectives, Vol. 23 (1), pp. 3–25. – Griffin, J., and D.Y. Tang (2012), Did subjectivity play a role in CDO credit ratings? Journal of Finance, Vol. 67 (4), pp. 1293-1328

Matlab or Stata, Finance Theory, Credit Risk, Bloomberg Students will automatically be enrolled onto STATA BMAN73622.

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– Rösch D and H Scheule (2013), The path to impairment: do credit-rating agencies anticipate default events of structured finance transactions? European Journal of Finance, Vol. 19 (9), pp. 841–860 – Rösch D and H Scheule (2011), “Securitization rating performance and agency incentives”, BIS Paper No 58

F4

UNCERTAINTY AND THE FINANCIAL MARKET Students are expected to examine one of the following research questions:

1. The relations between the stock market variables (returns, valuation ratios, variance risk premium, etc.) and macroeconomic uncertainty measures.

2. The implications of macro/financial uncertainty on asset allocation. 3. The relation between stock market expected returns and stock market volatility at an

aggregate level, i.e., testing the risk-return trade-off. Students need to have taken a course in Time Series/Financial Econometrics and be competent in programming software packages such as EViews or MATLAB. References: Bansal, R., Khatchatrian, V., Yaron, A., (2005). Interpretable asset markets? European Economic Review 49, 531–560. Bansal, R., Yaron, A., (2004). Risks for the long run: A potential resolution of asset pricing puzzles. Journal of Finance 59, 1481–1509. Bansal, R., Kiku, D., Shaliastovich, I., Yaron, A., (2014). Volatility, the macroeconomy and asset prices. Journal of Finance forthcoming. Liu, H., Miao, J., (2015). Growth uncertainty, generalized disappointment aversion and production-based asset pricing. Journal of Monetary Economics, 69, 70-89. French, K. R., G. W. Schwert, and R. Stambaugh (1987). Expected stock returns and volatility. Journal of Financial Economics 19, 3-29. Glosten, L.R., R. Jagannathan, and D. E. Runkle (1993). On the relation between the expected value and the variance of the nominal excess return on stocks. Journal of Finance 48, 1779-1801. Ghysels, E., P., Santa-Clara, and R. Valkanov, (2005). There is a risk-return tradeoff after all. Journal of Financial Economics 76, 509–548.

Students need to have taken a course in Time Series/Financial Econometrics and be competent in programming software packages such as EViews or MATLAB.

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F5

THE ROLE OF INSTITUTIONAL INVESTORS IN CORPORATE GOVERNANCE Institutional Investors, especially those with large block holdings, are becoming significant factors in many aspects of corporate governance. Students can choose any topic around firm’s governance that they think institutional investors would be interested in, for example but not limited to executive remuneration, CEO turnover, earnings management, etc. Students will work on cross sectional panel data, in which case a good econometrics background, particularly knowledge of econometric panel data analysis is mandatory. You will be required to deal with fixed and random effects models, Hausman tests and simultaneous modelling using instrumental variables. USA and the UK are the preferred countries, but majority shareholder systems from other countries could also usefully be investigated. Indicative reading: Bebchuk, L.A. and Fried, J.M., (2004), Remuneration Without Performance: The Unfulfilled

Promise of Executive Compensation, Cambridge, Massachusetts: Harvard University Press.

Dahya, J., McConnell, J.J. and Travlos, N.G., (2002), ‘The Cadbury committee, corporate performance and top management turnover’, The Journal of Finance, Vol. 57, Issue 1 (February), pp.461-483.

Hartzell, J.C. and Starks, L.T., (2003), ‘Institutional investors and executive compensation’, The Journal of Finance, Vol. 58, Issue 6 (December), pp.2351-2374.

Goergen, M. and Renneboog, L., (2001), ‘Strong managers and passive institutional investors in the UK’, in: Barca, F. and Becht, M., (eds) The Control of Corporate Europe, pp.259-284, Oxford: OUP.

Kim, K.A. and Nofsinger, J.R., (2007), Corporate Governance (2nd Ed), New Jersey: Prentice Hall.

Mallin, C.A., (2004), Corporate Governance, Oxford: OUP. Shleifer, A. and Vishny, R.W., (1997), ‘A survey of corporate governance’, The Journal of

Finance, Vol. 52, Issue 2 (June), pp.737-783.

Required skills:

 Stata is a pre-requisite.

 Working knowledge on Cross-sectional regression analysis, and logistic regression analysis.

 Panel data analysis using PROBIT or LOGIT models.

 Dealing with fixed & random effects.

 Hausman tests; and

 Knowledge about relevant databases: Factiva, CRSP (on WRDS), Compustat (on WRDS), BoardEx, Thomson ONE, DataStream.

Students will be responsible for acquiring such skills and knowledge. Introductory database management courses offered by the library can be very helpful.

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F6

FORECASTING FINANCIAL MARKET RETURNS (e.g. stock/bond/real estate returns) or MACROECONOMIC VARIABLES (e.g., gdp growth/inflation/changes in exchange rates) using advanced time-series econometric methods Four or five lectures will be given to students during June and July to learn how to use econometric tools to forecast financial market returns or macroeconomic variables (US or any country of choice) and evaluate them. These lectures will closely follow the course ‘Forecasting Macroeconomic and Financial Variables’ by Dave Rapach. (http://sites.slu.edu/rapachde/home) Students will learn how to use MATLAB with examples in the lectures. Typically, students would be expected to review the literature on the chosen topic and update/extend a recently published paper on it. Readings/References

1. Essential readings 2.

Elliott, G. and A. Timmermann (2008), “Economic Forecasting,” Journal of Economic Literature 46:1, 3–56 Rapach, D.E. and G. Zhou (2013), “Forecasting Stock Returns,” in Handbook of Economic Forecasting, Vol. 2A, G. Elliott and A. Timmermann (Eds.), Amsterdam: Elsevier, pp. 328–383

2. Forecasting financial market returns 3.

Goyal, A. and I. Welch (2008), “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction,” Review of Financial Studies 21:4, 1455–1508 Ludvigson, S.C. and S. Ng (2009), “Macro Factors in Bond Risk Premia,” Review of Financial Studies 22:12, 5027–5067 Neely, C.J., D.E. Rapach, J. Tu, and G. Zhou (2014), “Forecasting the Equity Risk Premium: The Role of Technical Indicators,” Management Science 60:7, 1772–1791

3. Forecasting macroeconomic variables

STATA/Matlab are not a pre-requisite. Students are advised to enrol onto BMAN71122 ‘Time Series Econometrics’ (Eviews or/and Matlab will be taught in this class).

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Rapach, D.E. and J.K. Strauss (2008), “Forecasting U.S. Employment Growth Using Forecast Combining Methods,” Journal of Forecasting 27:1, 75–93 Stock, J.H. and M.W. Watson (2003), “Forecasting Output and Inflation: The Role of Asset Prices,” Journal of Economic Literature 41:3, 788–829 Stock, J.H. and M.W. Watson (2004), “Combination Forecasts of Output Growth in a Seven-Country Data Set,” Journal of Forecasting 23:6, 405–430

4. Forecasting evaluation 5.

Clark, T.E. and K.D. West (2007), “Approximately Normal Tests for Equal Predictive Accuracy in Nested Models,” Journal of Econometrics 138:1, 291–311 Diebold, F.X. and R.S. Mariano (1995), “Comparing Predictive Accuracy,” Journal of Business and Economic Statistics 13:3, 253–263 White, H. (2000), “A Reality Check for Data Mining,” Econometrica 68:5, 1097–1126

F7

Corporate Financial Decisions Information You will be expected to conduct an empirical study on corporate capital structure, debt maturity, or cash holdings. You may choose any topic in the broad area of capital structure and test established theories, such as the trade-off and pecking order theories, or more recent views, such as the market timing hypothesis etc. Alternatively, you may look at a new determinant of corporate leverage, debt maturity, or cash holdings. On the other hand, you may re-examine a conventional determinant (e.g., taxes) using new data and methods. You may also investigate the evolution of leverage, debt maturity, and cash holdings, as well as the impact of regulatory changes and macroeconomic conditions (including financial crises) on those financial decisions. In your project, you will need to collect company accounting data from standard databases (e.g., WRDS/Compustat, Datastream, Worldscope, and ThomsonOne). You will use panel data methodologies and standard econometric software packages (e.g., STATA). In order to develop your research topics, you should refer to several recent studies listed below. Reading

Stata is a prerequisite and students will automatically be enrolled onto STATA BMAN73622.

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Literature review Graham, J.R. and Leary, M.T., 2011. A Review of Empirical Capital Structure Research and Directions for the Future. Annual Review of Financial Economics, Vol. 3. Available at SSRN: http://ssrn.com/abstract=1729388. Recent research DeAngelo, H. and Roll, R. 2015. How Stable Are Corporate Capital Structures? Journal of Finance 70, 373–418. Graham, J.R., Leary, M.T., and Roberts, M.R., 2013. A Century of Capital Structure: The Leveraging of Corporate America. Journal of Financial Economics 118, 658–683. Graham, J.R. and Leary, M.T., 2018. The Evolution of Corporate Cash. Review of Financial Studies 31, 4288–4344. Harford, J., Klasa, S., and Maxwell, W.F., 2014. Refinancing Risk and Cash Holdings. Journal of Finance 69, 975–1012 Heider, F. and Ljungqvist, A., 2015. As Certain as Debt and Taxes: Estimating the Tax Sensitivity of Leverage from State Tax Changes. Journal of Financial Economics, 118, 684– 712. Klasa, S., Ortiz-Molina, H., Serfling, M., and Srinivasan, S., 2018. Protection of Trade Secrets and Capital Structure Decisions. Journal of Financial Economics, 128, 266−86.

F8

TOPIC 1 IS NOT OPEN TO MSc ACCOUNTING & FINANCE STUDENTS unless they have taken BMAN 71181 TOPIC 1 EXCHANGE RATE FORECASTING Rossi (2013) and Cheung et al., (2005) among others, document that the ability of macroeconomic fundamentals-based models to forecast exchange rates over short and medium term horizons have proved to be disappointing, conclusions which support the classic findings of Meese and Rogoff (1983). Recent reformulations of the links between

Students selecting this topic 1 will likely have taken International Finance option BMAN 71181 in semester 1, although this is not compulsory for MSc

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macroeconomic fundamentals and exchange rate, utilising Central Bank reaction functions based on Taylor rule formulations, appear to be more successful in exchange rate forecasting (Engel and West, 2005, 2006); Molodtsova et al., (2009, 2011)). This may indicate that current exchange rates may in fact be determined by expectations about the future path of macroeconomic fundamentals in the relevant economies, as informed by the policy regime. Models based on commodity prices have also enjoyed some recent success in forecasting. Of course, if investor expectations of equilibrium exchange rates reflect information concerning future macroeconomic fundamentals, then in turn, exchange rate changes may also be useful in providing forecasts of these future macroeconomic fundamentals (Engel and West (2005)). These are the empirical forecasting issues you will address in your dissertation. Using data from a carefully selected country or group of countries, you will specify and estimate an appropriate empirical model and examine the forecasting capabilities exhibited by your selected model specifications. Students selecting this topic need to have acquired good time series econometric skills during semester 2, and should be very comfortable with either MATLAB/EVIEWS/ STATA. Working on this topic will require manipulation of data derived from WRDS and Datastream, and a good understanding of regression analysis techniques useful for forecasting including an ability to conduct rolling regressions. Readings/References 1. Rossi, B. (2013) “Exchange Rate Predictability”, Journal of Economic Literature, 51,

1063-1119. 2. Molodtsova, T., et al., (2011) “Taylor Rules and the Euro, “Journal of Money, Credit and Banking, 43 (2-3), 535-552. 3. Molodtsova, T. and Papell, D. (2009) “Out-of-sample exchange rate predictability with Taylor Rule Fundamentals,” Journal of International Economics, 77 (2), 167-180. 4. Engel, C. and West, K.D. (2006), “Taylor Rules and the Deutschemark-Dollar Real Exchange Rate, “Journal of Money, Credit and Banking, 38 (5), 1175-1194. 5. Engel, C. and West, K.D. (2005), “Exchange Rates and Fundamentals”, Journal of Political Economy, v123, 485-517. 6. Cheung Y-W. et al. (2005), “Empirical Exchange Rate Models of the nineties: Are any fit to survive?” Journal of International Money and Finance, 24, 1150-1175. 7. Sarno, L. and Schmeling, M. (2014), “Which Fundamentals Drive Exchange Rates? A Cross-Sectional Perspective”, Journal of Money, Credit and Banking, 46, 267-292.

Finance students. You must have good time series/panel data econometric skills, and must be familiar with either MATLAB/EVIEWS/STATA. Working on this topic will require manipulation of data derived from WRDS and Datastream, and good understanding of regression analysis techniques useful for forecasting. STATA or EVIEWS is a pre- requisite and students will be automatically enrolled onto STATA BMAN 73622.

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TOPIC 2 REAL EXCHANGE RATE DETERMINATION This topic involves the use of an appropriately specified model to undertake an analysis of the determinants of fluctuations in the real exchange rate of a specific country of group of countries over time. This topic gives you great scope to utilise the techniques that you have studied in your research methods and econometrics modules. Readings/References 1. Taylor, M. (2006), “Real Exchange Rates and Purchasing Power Parity: mean-reversion in economic thought,” Applied Financial Economics, 16, 1-17. 2. Khan, M. and Choudris, E.U, (2004), “Real Exchange Rates and Developing Countries: Are Balassa-Samuelson Effects Present? IMF Working paper, no. 04/188, 2004. 3. Bleaney, M., and Tian, M., (2014), “Net Foreign Assets and Real Exchange Rates Revisited,” Oxford Economic Papers, 66, 1145-1158. 4. Bussiẻre, M.; Zorzi, M.; Chudik, A.; and Dieppe, A. (2010), ‘Methodological Advances in the Assessment of Equilibrium Exchange Rates’, European Central Bank Working Paper No. 1151 5. Christopoulos DK, Gente K, León-Ledesma MA. (2012), “Net foreign assets, productivity and real exchange rates in constrained economies”, European Economic Review, 2012, 56, 295- 316.

Students selecting this topic 2 should have taken International Finance option BMAN 71181 in semester 1. You must be familiar with either STATA. Working on this topic will require manipulation of data derived from WRDS and Datastream, and good understanding of regression analysis techniques useful for forecasting. STATA is a pre-requisite and students will automatically be enrolled onto STATA BMAN73622.

F9

OVER-ALLOCATION & STABILISATION OF INITIAL PUBLIC OFFERINGS (IPOs) Stabilisation involves underwriter’s intervention beyond the IPO date in an attempt to support the prices of IPOs. Unlike the US where underwriters are not compelled to publicly disclose their stabilizing activities, the rules in Malaysia, Hong Kong, India, Singapore and the United Kingdom require the following information to be disclosed by the underwriters within a specified period (depending on the country in question) following the expiry of the stabilization period: (i) whether the issue was stabilised; (ii) the expiry date of the stabilisation period; (iii) If there were more than one purchase for the purpose of stabilisation, the price range at which underwriters repurchased the shares; (iv) the extent to which the overallotment options were exercised; and (v) the date of the last purchase relating to stabilisation and the price at which it was made.

Students are expected to carry out an empirical analysis of the stabilisation activities undertaken by underwriters of a representative sample of IPOs from one of the above

STATA is a pre-requisite and students will automatically be enrolled onto STATA BMAN73622.

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countries. The empirical analysis may, for example, examine as to whether underwriters stabilise to protect uninformed (retail) or informed (institutional) investors; the price adjustment process of stabilised and non-stabilised IPOs; the relationship between the underwriting fees, the overallotment options and the profitability from stabilisation; and the distribution of the returns of the stabilised vs. non-stabilised IPOs. The latter research questions may be examined using transaction and quotation data. The topic can also be explored from a regulatory perspective. For example, the Indian regulator does not permit the underwriters to retain profits from stabilisation. Does this discourage the use of over-allotment options and stabilisation by underwriters?

Students can also choose any other topic relating to IPOs. The following can be used as a guide:

1. The role Anchor (e.g. cornerstone, strategic and sovereign wealth funds)

investors play in the pricing of the shares of IPO firms. 2. What effect (if any) does the expiry of the lock-up period of Anchor investors

have on stock prices? 3. Anchor investors and analysts following of the IPO firms 4. The impact of the claw-back provision on the pricing and the allocation of the

shares of IPO firms. 5. The compensation of the underwriters of IPO firms (gross spread only, gross

spread and warrants or gross spread and incentive fee). 6. Why underwriters invest in the IPO firms?

Reading Aggarwal, R. “Stabilization activities by underwriters after initial public offerings.” Journal of Finance 55 (2000), 1075-1104. Benveniste, L.M.; W.Y. Busaba; and W. J. Wilhelm. “Price stabilization as a bonding mechanism in new equity issues.” Journal of Financial Economics 42 (1996), 223-256. Benveniste, L.M: S.M. Erdal: and W.J. Wilhelm. “Who benefits from secondary market price stabilization of IPOs?” Journal of Banking and Finance 22 (1998), 741-767.

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Boehmer, E., and R. Fishe. “Underwriter short covering in the IPO aftermarket: a clinical study.” Journal of Corporate Finance 10 (2004), 575-594. Boreiko, D., and S. Lombardo “Stabilisation activity in Italian IPOs.” Working paper (2009), European Corporate Governance Institute. Chowdhry, B., and V. Nanda. “Stabilization, syndication, and pricing of IPOs.” Journal of Financial and Quantitative Analysis 31 (1996), 25-42. Chung, R., L. Kryzanowski, I., Rakita. “The relationship between overallotment options, underwriting fees and price stabilisation for Canadian IPOs.” Multinational Finance Journal 4 (2000), 5-34. Ellis, K.; R. Michaely; and M. O’hara. “When the underwriter is the market maker: An examination of trading in the IPO aftermarket.” Journal of Finance LV (2000), 1039-1074. Lewellen, K. “Risk, Reputation, and IPO price support.” Journal of Finance LXI (2006), 612- 653. Lombardo, S. “The stabilisation of the share of IPOs in the United States and the European Union.” European Business Organisation Law Review 8 (2007), 521-565. Ruud, J.S. “Underwriter price support and the IPO underpricing puzzle.” Journal of Financial Economics 34 (1993), 135-152. Schultz, P.H., and M.A. Zaman. “Aftermarket support and underpricing of initial public offerings.” Journal of Financial Economics, 35 (1994), 199-219. Zhang, D. “Why Do IPO Underwriters Allocate Extra Shares when They Expect to Buy Them Back?” Journal of Financial and Quantitative Analysis 39 (2004), 571-594. Mazouz, K., B. Saadouni, and S., Yin. “Stabilisation and underpricing of initial public offerings (IPOs): Evidence from Hong Kong”, Review of Quantitative Finance and Accounting, Volume 41 (2013),Issue 3, pp 417–439

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F10

PREDICTABILITY OF STOCK RETURNS AND DIVIDENDS Students would be expected to review the literature on stock return and dividend predictability and illustrate with empirical examples. Readings/References Cochrane, J.H. (1999) “New facts in finance”, available from: http://gsbwww.uchicago.edu/fac/john.cochrane/research/Papers/ep3Q99_3.pdf A Note on “Predicting Returns with Financial Ratios”, (Amit Goyal & Ivo Welch), 2003. http://www.bus.emory.edu/AGoyal/docs/predictingeqpdp_ms.pdf Ferson, W., Simin, T. and S. Sarkissian (2003) “Is Stock Return Predictability Spurious?” Journal of Investment Management available from http://www2.bc.edu/~fersonwa/ Lettau, M. and S. Ludvigson (2001) “Consumption, aggregate wealth and expected stock returns”, Journal of Finance, 56, 815-849.

None

F11

CSR and merger performance Corporate Social Responsibility (CSR) has increasingly become a topic about regular corporate operation. Apart from safeguarding shareholders’ interests, more and more companies take stakeholders’ interests into consideration when making important investment and financing decisions. In this study, you are asked to examine how the extent to which a firm emphasises CSR impacts its performance in mergers. You will measure the extent using companies’ actual conduct related to social responsibilities. You will potentially tackle this questions from both the acquirer’s and the target’s point of view. We expect you to formulate more specific hypotheses, building on the previous literature. You will also need to gather relevant data on CSR, mergers, and other required information, and use appropriate methodology to test these hypotheses.

It is essential you possess the skills for sophisticated data handling and data analysis (e.g. using STATA, SAS). Good knowledge of econometrics, especially on cross-sectional data analysis, is also essential. Students will automatically be enrolled onto STATA BMAN73622.

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It is essential you possess the skills for sophisticated data handling and data analysis (e.g. using STATA, SAS). Good knowledge of econometrics, especially on cross-sectional data analysis, is also essential. Preliminary reading: Barber, Brad M., and John D. Lyon, 1997a, Detecting long-run abnormal stock returns: The empirical power and specification of test statistics, Journal of Financial Economics 43, 341-372. Deng, Xin, Jun-Koo Kang, Buen Sin Low, 2013. Corporate social responsibility and stakeholder value maximization: evidence from mergers. Journal of Financial Economics 110, 87–109. Gao, Ning, 2011. The adverse selection effect of corporate cash reserve: Evidence from acquisitions solely finance by stock. Journal of Corporate Finance, 17, 789–808. Healy, Paul M., Krishna G. Palepu, and Richard S Ruback, 1992. Does corporate performance improve after mergers? Journal of Financial Economics 31, 135–175

F12

INITIAL PUBLIC OFFERINGS (IPOs): DEBT IPOs, ADVISER SWITCHING, CONTRACTING, GOVERNANCE, PERFORMANCE, AND VENTURE CAPITAL BACKING An Initial Public Offering (IPO) is the process by which a private firm sells its shares to new investors for the very first time. An IPO involves a listing on a stock exchange. Usually an IPO is a once in a life-time event for a private firm and requires a number of parties such as the issuing firm, investment banks, venture capitalists (VCs) and other advisors, to work together. This presents an interesting opportunity to study financial contracting, the role of VCs and the performance of newly listed firms in the aftermarket. Readings/References Doidge, C., Karolyi G. and R. Stulz (2011) The U.S. left behind: the rise of IPO activity around the world, NBER working paper 16916. Khurshed, A. “The mechanics and performance of initial public offerings”, Harriman House (Finance Essentials) 2019.

Stata is a prerequisite and students will automatically be enrolled onto STATA BMAN73622.

F13

INSIDER TRADING, DARK TRADING AND MARKET QUALITY

Students must have a strong background in

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In this project, you will be expected to conduct an empirical study on insider trading characteristics and its effects on market quality and dark trading. Data can be sourced from standard databases (such as WRDS, Datastream, Thomson Reuters Tick History) as well as external data sources. Students are required to have a general understanding of financial markets and market quality (you can refer to Angel et al (2011) for an overview on market quality). One set of dissertation topics will focus on insider trading and its effects on market quality in lit markets. A second set of dissertation topics will analyse insider trading and the effects on dark trading. Other possible research topics can include (but are not limited to) the profitability of insider trading, different types and characteristics of insider traders, and predictability for news announcements. Within these broad research topics, I expect student to formulate a specific research question that makes a clear contribution to the existing literature. Prerequisites Students are assumed to have some background in quantitative research methods and be able employ an econometric software package (preferably SAS) to perform the estimations. Recommended Reading:

 Angel, J. J., L. E. Harris, and C. S. Spatt. 2011. Equity trading in the 21st century. Quarterly Journal of Finance 1(1), 1–53.

 Cao, C., Field, L. C., & Hanka, G. (2004). Does Insider Trading Impair Market Liquidity? Evidence from IPO Lockup Expirations. Journal of Financial and Quantitative Analysis, 39(1), 25–46.

 Cohen, L., Malloy, C. and Pomorski, L. (2012), Decoding Inside Information. Journal of Finance, 67(3), 1009-1043.

 Comerton-Forde, C. and Putniņš, T.J., 2015. Dark trading and price discovery. Journal of Financial Economics, 118(1),70-92.

 Degryse, H., De Jong, F. and Kervel, V.V., 2015. The impact of dark trading and visible fragmentation on market quality. Review of Finance, 19(4), 1587-1622.

 Gresse, C. (2017). Effects of lit and dark market fragmentation on liquidity. Journal of Financial Markets, 35, 1–20.

 Nimalendran, M. and Ray, S., 2014. Informational linkages between dark and lit trading venues. Journal of Financial Markets, 17,230-261.

econometrics (e.g. from the topics in the course Time Series Econometrics BMAN 71122) and strong programming skills. STATA (or another programming language such as SAS) is a pre- requisite and students will automatically be enrolled onto STATA BMAN73622.

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F14

The examples below are ideas for research projects where there appears to be a suitable research gap and where it looks like there may be some interesting results. In practice, my students often think of their own, related ideas, starting with my suggestions but then developing them in their own directions. TOPIC 1: BEHAVIOURAL FINANCE THE DISPOSITION EFFECT The Disposition Effect (Kahneman and Tversky 1979; Shefrin and Statman 1985) predicts that investors subject to this bias will tend to sell assets on which they have an unrealised gain (Winners) too early, and conversely, hold on to assets on which they suffer an unrealised loss (Losers). The Disposition Effect makes testable predictions about prices and volumes, and so has been one of the most popular aspects of Behavioural Finance to feature in the literature. However, most studies on this have tested its predictions on datasets of discount brokerages, where individual investors are identifiable (Odean 1998b; Barber and Odean 2000; Goetzmann and Massa 2008; Dhar and Ning Zhu 2006). Comparatively few have examined its effects using marketwide data, and some of the most innovative studies use price and volume data from the whole market: for instance, Statman et al (2006) conduct VAR on a wide sample of US stocks, and show that elevated prior return leads to elevated contemporaneous excess turnover. Another research gap is that virtually all studies have been conducted on equities, and within that, only on US equities. Research ideas here include: a) Ferris et al (1988) execute a simple but elegant test of the Disposition Effect using prior prices and turnover, but only conduct it using publicly-available data for 30 small US stocks. Likewise, Kaustia (2004) shows that Disposition Effect applies to US stocks’ trading after their IPOs. Dissertation students last year used both of these approaches to show the disposition effect amongst Chinese stocks, and one also extensively explored other local seasonalities. However, all other markets remain untested, and the Ferris et al (1988) analysis has yet to be run on the US general equity market. Does the Ferris et al (1988) analysis work for US / UK / European stocks / Asian stocks, for which there has been virtually no Disposition Effect research? Do other countries’ equity markets show the Kaustia (2004) Disposition Effects after IPOs?

STATA (or R or Matlab) is a pre-requisite and students will automatically be enrolled onto STATA BMAN73622.

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b) The Disposition Effect, and the rival explanation of investor overconfidence (Odean 1998a), have been almost entirely conducted on equities. Do the Ferris et al (1988) or Statman et al (2006) methodologies reveal the Disposition Effect when applied to currencies or commodities? Potential difficulties here include accounting for the interaction between the cash and futures markets. Data can be extracted from Datastream or from WRDS through the web interface. This may well involve the use of large datasets and a good deal of spreadsheet modelling or programming. TOPIC 2: ASSET PRICING / PORTFOLIO INVESTMENT DOWNSIDE RISK / DOWNSIDE BETAS Behavioural Finance tells us that investors are loss-averse rather than risk-averse, and that investors ought to be more sensitive to downside movements in the market; the Sortino ratio (Sortino and Price 1994) and the Harlow –Rao (1989) / Bawa – Lindenberg (1977) downside betas. In unpublished research for the UK, the Frazzini and Pedersen (2014) Betting Against Beta “anomaly” localises to down-market betas, and the up-market Betting Against Beta factor is not significant. This research could be continued in various directions: 1) The downside beta version of the Frazzini and Pedersen (2014) Betting Against Beta factor could be tested in other markets, or with other parameters; is it more profitable when calculated using daily or weekly data, or using a short or a long estimation period? Are downside betas stable over time for each stock? 2) The econometrics of the down-market Betting Against Beta factor needs to be investigated: to what macroeconomic variables is it related? Is it correlated with the Minimum Volatility factor? 3) Indexes of companies selected for high Environmental, Social and Governance (ESG) scores tend to have lower downside risk than the non-ESG indices from which their constituents are drawn (Giese et al. 2019), but not much research has been done on individual stocks, asking

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what part of ESG ratings reduce downside risk; is it good Environmental, or good Social, or good Governance ratings? The best study so far has been Oikonomou et al (2012), but this study only uses the basic KLD rating system, data for 1991 to 2008, and only uses the Harlow –Rao (1989) and Bawa – Lindenberg (1977) downside betas; without investigating downside measures of volatility. One option here would be to test downside risk using Bloomberg’s ESG ratings or the Asset4 ratings available on Datastream. Data can be extracted from Datastream or from WRDS through the web interface. This may well involve the use of large datasets and a good deal of spreadsheet modelling or programming. FACTOR PREDICTION As Passive Management engages increasingly with Smart Beta strategies, it will become more important for asset managers to be able to predict forecast the performance of factors, given historical values of those factors and historical macroeconomic variables. Various theories have been proposed for the macroeconomic factors which HML and SMB proxy for; are there any models which have out-of-sample predictive validity for predicting the major factor premia (Size, Value, Momentum, Quality, Low Vol) now used in passive equity management? This could be analysed in a VAR framework, with in-sample and out-of-sample tests applied. All these topics will require detailed knowledge of econometrics and regression techniques. Students are responsible for teaching themselves the necessary database and programming skills, though pointers to resources can be provided. Data preparation can be done in Excel, but you will need to be comfortable using Matlab / Stata / R for the final stage econometric tests. References: Barber, Brad M., and Terrance Odean. 2000. ‘Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors’. Journal of Finance 55 (2): 773–806. Bawa, Vijay S., and Eric B. Lindenberg. 1977. ‘Capital Market Equilibrium in a Mean- Lower Partial Moment Framework’. Journal of Financial Economics 5 (2): 189–200. https://doi.org/10.1016/0304-405X(77)90017-4.

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Dhar, Ravi, and Ning Zhu. 2006. ‘Up Close and Personal: Investor Sophistication and the Disposition Effect’. Management Science 52 (5): 726–40. https://doi.org/10.1287/mnsc.1040.0473. Ferris, Stephen P., Robert A. Haugen, and Anil K. Makhija. 1988. ‘Predicting Contemporary Volume with Historic Volume at Differential Price Levels: Evidence Supporting the Disposition Effect’. Journal of Finance 43 (3): 677–97. https://doi.org/10.2307/2328191. Frazzini, Andrea, and Lasse Heje Pedersen. 2014. ‘Betting against Beta’. Journal of Financial Economics 111 (1): 1–25. https://doi.org/10.1016/j.jfineco.2013.10.005. Giese, Guido, Linda-Eling Lee, Dimitris Melas, Zoltán Nagy, and Laura Nishikawa. 2019. ‘Performance and Risk Analysis of Index-Based ESG Portfolios’. The Journal of Index Investing 9 (4): 46–57. https://doi.org/10.3905/jii.2019.9.4.046. Goetzmann, William N., and Massimo Massa. 2008. ‘Disposition Matters: Volume, Volatility, and Price Impact of a Behavioral Bias’. Journal of Portfolio Management 34 (2): 103–25. Harlow, W. V., and Ramesh K. S. Rao. 1989. ‘Asset Pricing in a Generalized Mean-Lower Partial Moment Framework: Theory and Evidence’. Journal of Financial and Quantitative Analysis 24 (3): 285–311. https://doi.org/10.2307/2330813. Kahneman, Daniel, and Amos Tversky. 1979. ‘Prospect Theory: An Analysis of Decision under Risk’. Econometrica 47 (2): 263–91. https://doi.org/10.2307/1914185. Kaustia, Markku. 2004. ‘Market-Wide Impact of the Disposition Effect: Evidence from IPO Trading Volume’. Journal of Financial Markets 7 (2): 207–35. https://doi.org/10.1016/j.finmar.2003.11.002. Odean, Terrance. 1998a. ‘Volume, Volatility, Price, and Profit When All Traders Are above Average’. The Journal of Finance 53 (6): 1887–1934. ———. 1998b. ‘Are Investors Reluctant to Realize Their Losses?’ Journal of Finance 53 (5): 1775–98. https://doi.org/10.1111/0022-1082.00072. Oikonomou, Ioannis, Chris Brooks, and Stephen Pavelin. 2012. ‘The Impact of Corporate Social Performance on Financial Risk and Utility: A Longitudinal Analysis’. Financial Management 41 (2): 483–515. https://doi.org/10.1111/j.1755-053X.2012.01190.x. Shefrin, Hersh, and Meir Statman. 1985. ‘The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence’. Journal of Finance 40 (3): 777–90. https://doi.org/10.2307/2327802. Sortino, Frank A., and Lee N. Price. 1994. ‘Performance Measurement in a Downside Risk Framework’. The Journal of Investing 3 (3): 59–64.

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Statman, Meir, Steven Thorley, and Keith Vorkink. 2006. ‘Investor Overconfidence and Trading Volume’. Review of Financial Studies 19 (4): 1531–65.

F15

The effect of buyback blackout periods on stock returns Academic evidence suggests that stock buybacks can boost share prices by lowering the number of shares outstanding—driving up earnings and dividends per share. However, companies often do not repurchase shares in the weeks before reporting earnings to avoid accusations of insider trading. This has the potential to remove a key source of demand for shares that has lifted major indexes in recent years. However, assessing the impact of the buyback-blackout period is challenging because there is not a federally mandated blackout period. Companies are also still able to repurchase shares through a SEC rule allowing trading on a pre-set schedule. That means repurchases can still occur during a normal blackout period, creating uncertainty among investors about the potential impact of blackout periods on stock returns. The objective of this thesis is to examine the unconditional and conditional impact of stock buyback blackout periods on stock returns, trading volume, and stock return volatility and whether it may lead to predictability. A requirement of the thesis is to come up with measures that allow differentiating which firms’ stocks are more or less likely to be affected by buyback blackout periods. This thesis will allow students to get used to handling big datasets, learn about the regulatory underpinning of stock buybacks in the US, and get an up-to-date understanding of the impact of buybacks on the stock market. Introductory Readings/References Bettis, J.C., Coles, J.L. and Lemmon, M.L., 2000. Corporate policies restricting trading by insiders. Journal of financial economics, 57(2), 191-220. Farrell, K., Unlu, E. and Yu, J., 2014. Stock repurchases as an earnings management mechanism: The impact of financing constraints. Journal of Corporate Finance, 25, 1-15. Fu, F. and Huang, S., 2015. The persistence of long-run abnormal returns following stock repurchases and offerings. Management Science, 62(4), pp.964-984. Miller, J.M. and McConnell, J.J., 1995. Open-market share repurchase programs and bid- ask spreads on the NYSE: Implications for corporate payout policy. Journal of Financial and Quantitative analysis, 30(3), 365-382

Requires working knowledge of Stata. Data are available via WRDS (Compustat, CRSP, Thomson), SDC and CapitalIQ. Students will automatically be enrolled onto STATA BMAN73622.