Topics for master theses

Topics for master theses

The Department of Business and Management Science can offer the following Master Theses topics:

Investments, Insurance and Household Finance

  • Consumer behavior and insurance

    Consumer behavior and insurance

    Supervisor: Aysil Emirmahmutoglu.

  • Do we perceive risk differently under stress?

    Do we perceive risk differently under stress?

    Supervisor: Aysil Emirmahmutoglu.

  • Performance evaluation for mutual funds

    Performance evaluation for mutual funds

    Supervisor: Trond M. Døskeland.

  • Identifying financial behavioral biases for households

    Identifying financial behavioral biases for households

    Supervisor: Trond M. Døskeland.

  • Risk Management

    Risk Management

    Analyze a given company or a given line of business exposed to price risk from commodities/services sold or bought. May also be combined with currency risk. What is the level of risk, how do we measure it, which instruments are available to deal with this risk and how successful is the risk-reducing strategy, using these instruments. Hedging strategies are based on a combination of risk-reduction and expected values, so both components may be included in the analyses.

    Supervisor: Øystein Gjerde.

  • How did the US Tax Bill affect M&As?

    How did the US Tax Bill affect M&As?

    The 2017 TCJA may significantly change the pattern of corporate M&As. For instance, it has lifted the original use requirement for immediate depreciation of acquired assets. That makes it much more attractive to structure deals as asset purchases rather than stock acquisitions.

    Furthermore, net operating losses before and after January 1, 2018 are treated differently. An empirical analysis could use either Zephyr or SDC Platinum to explore early signs of these changes. 

    Supervisor: Maximilian Todtenhaupt.

  • Common ownership and tax avoidance

    Common ownership and tax avoidance

    Corporate tax planning is high on the agenda of both tax practitioners and policy makers. While some firms certainly try to avoid paying taxes, we know very little about how firms learn about different tax avoidance mechanisms. An important channel may be via common owners. Do firms engage in similar levels of tax avoidance if they are owned by the same investors? This project will investigate this question using balance sheet information and ownership details of large corporations.

    Supervisor: Maximilian Todtenhaupt.

  • Topics on Ship Finance

    Topics on Ship Finance

    Topic 1: Responsible investing in the shipping/offshore industry. How have (institutional) investors change their appetite in investing in the shipping/offshore industry, given public ESG concerns and initiatives from the industry?

    Topic 2: Green bond issuance in shipping company – criteria, impact on transition to zero emission, asset investment and investor appetite.

    Topic 3: Investment horizon in shipping. What determines the investment horizon? In the transition to zero emission, how shipping companies decide which asset class to invest? Short-term vs. long-term investment payback and performance analysis.

    Topic 4: Optimal capital structure for shipping companies – determinants: ownership profile, prices, leverage, cycles and other factors.

    Supervisor: Haiying Jia

  • Topics on Marine Insurance

    Topics on Marine Insurance

    Topic 1: Marine insurance risk aggregation. How to manage small-probability risk exposures? Geographically or event-linked exposures. Optimal risk transfer of low probability yet high impact events.

    Topic 2: Marine risk assessment – using Machine learning or other methods. Analysis of Marine traffic data, claim locations and loss prevention applications.

    Topic 3: Vessel utilization and its marine risks. Use innovative data sources to estimate vessel utilization, such as the emission data from MRV.

    Topic 4: Coinsurance efficiency improvement. How to improve efficiency by system/data sharing? Blockchain?

    Topic 5: INSURTECH Automated underwriting – system design view point, or technology viewpoint. How would a standard insurance portfolio perform is 100% can be automated.

    Topic 6: Ownership structural of marine insurance companies. What are the challenges connected to the business model? What ownership structure is optimal?

    Supervisor: Haiying Jia

  • Shipping & Finance: How do risk capital and risk limits affect the chartering policy of a ship operator?

    Shipping & Finance: How do risk capital and risk limits affect the chartering policy of a ship operator?

    Using: freight rates timeseries, optimal portfolio theory.

    Company: Western Bulk.

    Supervisor: Professor Roar Ådland

  • Shipping & Finance: Machine learning models for FFA trading

    Shipping & Finance: Machine learning models for FFA trading

    Using spatial AIS data for ship positions, open-source weather and macro data – can you develop a machine learning model to generate profitable trading signals? Requires knowledge of Python, implementation of machine learning models.

    Supervisor: Roar Ådland

  • Shipping & Finance: Inferring short-term market direction from intraday FFA Data

    Shipping & Finance: Inferring short-term market direction from intraday FFA Data

    Using intraday bid/offers spreads – is it possible to use pattern recognition or technical analysis to daytrade forward freight agreements? Knowledge of Python and machine learning is useful. 

    Supervisor: Professor Roar Ådland

  • Shipping & Finance: Quantifying fluctuations in FFA market liquidity

    Shipping & Finance: Quantifying fluctuations in FFA market liquidity

    Using high-frequency bid-offer quotes, analyze changes in the bid-offer spread over time and how it correlates with trading volume, seasonality and freight market level.
    Company: Zuma Labs/Braemar.

    Supervisor: Roar Ådland

  • Shipping & Finance: Improving freight option models.

    Shipping & Finance: Improving freight option models.

    Using real transaction data, estimate a volatility surface for freight options and assess whether it is different from other financial options markets w.r.t. implied volatility skew/smile.
    Company: EEX. 

    Supervisor: Professor Roar Ådland

  • Analysis of high-frequency supply data for oil tankers

    Analysis of high-frequency supply data for oil tankers

    Using unique daily spatial data for vessel employment, analyze how regional freight rate changes are driven by supply and demand, and whether the specifications and operator of a ship matters for its attractiveness in the market.

    Supervisor: Roar Ådland.

  • Finansiell økonomi (finansmarkeder/opsjoner/skatt): Er leterefusjonsordningen subsidiering?

    Finansiell økonomi (finansmarkeder/opsjoner/skatt): Er leterefusjonsordningen subsidiering?

    Den norske leterefusjonsordningen har i det siste vært under offentlig debatt. Ordningen går ut på at oljeselskaper som er utenfor skatteposisjon får skatterefusjon for leteutgifter. Ifølge myndighetene innebærer ordningen ikke subsidiering fordi den gir likebehandling mellom selskaper som er utenfor skatteposisjon og selskaper som er i skatteposisjon.

    Utfordring: Belyse denne problemstillingen ut fra økonomisk teori.

    Se: Kjell-Børge Freiberg og Siv Jensen: «Oljeleting gir millarder til statskassen». Innlegg i Dagens Næringsliv, 8. februar 2019.

    https://www.dn.no/innlegg/okonomi/energi/leterefusjonsordningen/oljeleting-gir-milliarder-til-statskassen/2-1-537307

    Supervisor: Petter Bjerksund.

  • Using Textual Analysis to identify whether there is a gender gap in financial white collar crime

    Using Textual Analysis to identify whether there is a gender gap in financial white collar crime

    There is very little systematic evidence on the gender gap in crime. In particular, it is difficult to quantify it for financial white collar crime, as they do not find their way into police statistics and as there is little female presence on the top levels of companies.

    In this project, we can use textual analysis tools to collect data from the Securities and Exchange Commission (SEC) on insider trading and other frauds. We can match the name of the defendant to a gender, and quantify what is the difference between males and females.

    Then, we can correlate the gap, as well as the fraud itself, to past financial statements of involved companies in terms of gender representation in the board of the company and other indicators of company culture. Finally, we can compare the gap to other measures of female participation in the boardroom and determine whether white collar females seem more or less likely to commit crimes than white collar males. We can provide a partial answer to the question: Are companies going to become more responsible (do less criminal rule-breaking) if there are more females on the board?

    Methods: Textual analysis, web crawling, R

    Supervisor: Evelina Gavrilova-Zoutman.

  • Smuggling and money laundering

    Smuggling and money laundering

    It is well known that laundered money change their form from cash to other goods such as jewellery, antique objects or other collectible items. Do we observe abnormal flows of such good to/from tax havens? Do these flows shift with the signing of information exchange agreements between two countries?

    Methods: Regressions, machine learning.

    Supervisor: Evelina Gavrilova-Zoutman.

  • Dividend taxation, abusive stock swap and loan transactions

    Dividend taxation, abusive stock swap and loan transactions

    A tax strategy among investors is to recast a dividend payment as a swap payment in order to take advantage of favoured tax treatment given to swap agreements involving non-US persons in the US. US stock dividents paid to non-US persons are subject to the dividend tax, whereas “dividend equivalents” paid to non-US persons as part of a swap agreement are not subject to any US tax.

    Since a 2009 Senate hearing identified the tax evasion nature of these transactions, there has been little research on the topic. These transactions are likely the origin from which cum fraud schemes have arisen. Since 2009 the US has had reforms in their dividend tax and in the legislation surrounding the claiming of these dividend equivalents. What is the impact of these reforms on stock lending of US stocks?

    Supervisor: Floris Zoutman and Evelina Gavrilova-Zoutman

  • Dividend taxation, Cum-Cum Trading and Ex-Dividend Pricing

    Dividend taxation, Cum-Cum Trading and Ex-Dividend Pricing

    A common tax planning strategy among investors is to sell their stocks the day before dividends are due, and buy them back on the ex-dividend day. This strategy, known as cum-cum trading, allows investors to avoid paying dividend taxes. The Norwegian tax authorities are considering to implement policies that make cum-cum trading less attractive in order to generate more dividend tax revenue.

    Your task will be to see how cum-cum trading relates to taxation and other policy variables, using international stock market data. Questions that you could answer in this topic are: Do stock market experience excess trade around the dividend day? Does excess trade relate to the dividend tax rate? Do stock prices reflect the level of the dividend tax? Are policies aimed at combating cum-cum trading effective in other countries?

    Cum-cum trading strategies have been detected in Europe. Are they present in the Asian and South-American market? Are these trades a global problem that contributes to rising inequality?

    Supervisor: Floris Zoutman and Evelina Gavrilova-Zoutman

Business Taxation

  • Dividend taxation, Cum-Cum Trading and Ex-Dividend Pricing

    Dividend taxation, Cum-Cum Trading and Ex-Dividend Pricing

    A common tax planning strategy among investors is to sell their stocks the day before dividends are due, and buy them back on the ex-dividend day. This strategy, known as cum-cum trading, allows investors to avoid paying dividend taxes. The Norwegian tax authorities are considering to implement policies that make cum-cum trading less attractive in order to generate more dividend tax revenue.

    Your task will be to see how cum-cum trading relates to taxation and other policy variables, using international stock market data. Questions that you could answer in this topic are: Do stock market experience excess trade around the dividend day? Does excess trade relate to the dividend tax rate? Do stock prices reflect the level of the dividend tax? Are policies aimed at combating cum-cum trading effective in other countries?

    Cum-cum trading strategies have been detected in Europe. Are they present in the Asian and South-American market? Are these trades a global problem that contributes to rising inequality?

    Supervisor: Floris Zoutman and Evelina Gavrilova-Zoutman

  • Dividend taxation, abusive stock swap and loan transactions

    Dividend taxation, abusive stock swap and loan transactions

    A tax strategy among investors is to recast a dividend payment as a swap payment in order to take advantage of favoured tax treatment given to swap agreements involving non-US persons in the US. US stock dividents paid to non-US persons are subject to the dividend tax, whereas “dividend equivalents” paid to non-US persons as part of a swap agreement are not subject to any US tax.

    Since a 2009 Senate hearing identified the tax evasion nature of these transactions, there has been little research on the topic. These transactions are likely the origin from which cum fraud schemes have arisen. Since 2009 the US has had reforms in their dividend tax and in the legislation surrounding the claiming of these dividend equivalents. What is the impact of these reforms on stock lending of US stocks?

    Supervisor: Floris Zoutman and Evelina Gavrilova-Zoutman

  • How did the US Tax Bill affect M&As?

    How did the US Tax Bill affect M&As?

    The 2017 TCJA may significantly change the pattern of corporate M&As. For instance, it has lifted the original use requirement for immediate depreciation of acquired assets. That makes it much more attractive to structure deals as asset purchases rather than stock acquisitions.

    Furthermore, net operating losses before and after January 1, 2018 are treated differently. An empirical analysis could use either Zephyr or SDC Platinum to explore early signs of these changes. 

    Supervisor: Maximilian Todtenhaupt.

  • Common ownership and tax avoidance

    Common ownership and tax avoidance

    Corporate tax planning is high on the agenda of both tax practitioners and policy makers. While some firms certainly try to avoid paying taxes, we know very little about how firms learn about different tax avoidance mechanisms. An important channel may be via common owners. Do firms engage in similar levels of tax avoidance if they are owned by the same investors? This project will investigate this question using balance sheet information and ownership details of large corporations.

    Supervisor: Maximilian Todtenhaupt.

  • Inventors and tax havens

    Inventors and tax havens

    Inventors are an important source of innovation for any country. At the same time they are highly mobile and respond to tax incentives. Furthermore, the intellectual property they create (e.g. patents) can be used shift income to tax havens. How many inventors are involved in such tax avoidance behavior? This project will assess the importance of inventors in tax havens by combining data on international inventors with the Panama papers which have recently become available.

    Supervisor: Maximilian Todtenhaupt.

  • The Tax Haven Call

    The Tax Haven Call

    In political and institutional economy we think of countries as having extractive and inclusive institutions. Institutions are loosely defined as informal norms of behaviour. In an influential (but controversial) paper by Acemoglu et al. (2001) the type of institutions are shown to impact economic development. However, it is unclear whether what aspect of these informal norms have influenced countries like the Netherlands, Ireland and Bermuda to become tax havens?

    Dharmapala and Hines (2009) have found that governance is an important factor that separates tax havens from non-tax havens. Better-governed small countries are more likely to be successful tax havens than badly governed small countries. Governance and institutions are closely related, but the link is not explored in this article.

    In addition, the list of tax havens has expanded since 2009 and now we have continuous measures of secrecy and tax haven status, which can give better identification in re-examining the question: What makes a tax haven?

    Starting point: Dharmapala, D. and Hines Jr, J.R., 2009. Which countries become tax havens?. Journal of Public Economics, 93(9-10), pp.1058-1068.

    Supervisor: Evelina Gavrilova-Zoutman.

  • Wealth Taxation in Norway

    Wealth Taxation in Norway

    The wealth tax is one of the most controversial aspects of the Norwegian tax system. Detractors believe that the wealth tax hurts economic growth by disincentivizing entrepreneurial activity and risk taking, and taking away a source of liquidity for business owners. Proponents argue that the wealth tax is a great instrument to reduce inequality.

    In this project you will use data on Norwegian tax payers to evaluate the arguments of the detractors. Specifically, the goal is to understand i.) whether the arguments are valid empirically, ii.) how large the concerns are quantitatively. Specific research questions could be i.) does the wealth tax discourage individuals/business owners from taking risk, ii.) does the wealth tax reduce liquidity for small businesses, iii.) does the wealth tax reduce innovation or iv). does the wealth tax discourage savings. The answer of each of these four questions is of great practical relevance to policy makers that have to make a trade-off between the efficiency cost and the equity gain associated with the wealth tax.

    Data: Individual tax return data

    Literature:

    • Berzins, Janis, Øyvind Bøhren and Bodan Stacesu (2019). Illiquid Shareholders and real firm effects: the personal wealth tax and financial constraints. Working Paper BI.
    • Akcigit, U., Grigsby, J., Nicholas, T., & Stantcheva, S. (2018). Taxation and Innovation in the 20th Century. NBER Working Paper.

    Supervisor: Floris Zoutman

  • Taxation and Innovation

    Taxation and Innovation

    In a recent working paper Akcigit et al (2018) use US data to show that higher income tax rates reduce innovation. The aim of this thesis is to find out if a similar relationship between innovation and taxation exists for Europe. Students will use the universe of patent applications to the European patent office to find out i.) if countries with lower tax rates file for more patents and ii.) whether changes in the tax rate cause changes in patent applications. The outcome is particularly relevant for policy makers, since innovation is an important driver of economic growth. If higher tax rates indeed reduce innovation, this provides a strong incentive for countries not to increase their tax rate, or even to reduce it.

    Data: Patent applications in Europe

    Literature: Akcigit, U., Grigsby, J., Nicholas, T., & Stantcheva, S. (2018). Taxation and Innovation in the 20th Century. NBER Working Paper.

    Supervisor: Floris Zoutman and Steffen Juranek

  • Smuggling and money laundering

    Smuggling and money laundering

    It is well known that laundered money change their form from cash to other goods such as jewellery, antique objects or other collectible items. Do we observe abnormal flows of such good to/from tax havens? Do these flows shift with the signing of information exchange agreements between two countries?

    Methods: Regressions, machine learning.

    Supervisor: Evelina Gavrilova-Zoutman.

  • Using Textual Analysis to identify whether there is a gender gap in financial white collar crime

    Using Textual Analysis to identify whether there is a gender gap in financial white collar crime

    There is very little systematic evidence on the gender gap in crime. In particular, it is difficult to quantify it for financial white collar crime, as they do not find their way into police statistics and as there is little female presence on the top levels of companies.

    In this project, we can use textual analysis tools to collect data from the Securities and Exchange Commission (SEC) on insider trading and other frauds. We can match the name of the defendant to a gender, and quantify what is the difference between males and females.

    Then, we can correlate the gap, as well as the fraud itself, to past financial statements of involved companies in terms of gender representation in the board of the company and other indicators of company culture. Finally, we can compare the gap to other measures of female participation in the boardroom and determine whether white collar females seem more or less likely to commit crimes than white collar males. We can provide a partial answer to the question: Are companies going to become more responsible (do less criminal rule-breaking) if there are more females on the board?

    Methods: Textual analysis, web crawling, R

    Supervisor: Evelina Gavrilova-Zoutman.

  • Who blows the whistle on cartels?

    Who blows the whistle on cartels?

    By using webcrawling and textual analysis, you can gather data from the EU commission website on who reports the presence of market cartels. What is the reward that these whistleblowers get? Did they participate in the cartel before they reported it? Are these companies characterized by better corporate governance? Why did they blow the whistle? Could it be because of big shifts in governance structure, new boards, etc? What are sentiments on social networks on the mentions of the cartels and on the activity of the whistleblower?

    Are cartels more likely to be reported in certain industries? By using the revealed data on these cartels, how can we tailor a detection strategy that would detect similar behaviour by other cartels? Can we flag potential offenders?

    Methods: Textual analysis, web crawling, sentiment analysis, R

    Supervisor: Evelina Gavrilova-Zoutman.

  • Detecting Corruption in the Oil-For-Food Program

    Detecting Corruption in the Oil-For-Food Program

    The Oil for Food Program (OFFP) was a relief effort orchestrated by the United Nations to help the people of Iraq after the Gulf War. It lasted from 1995 to 2003. Leaks from classified reports reveal that there has been rampant corruption, from the bank that handled the Iraq escrow account, to the trucking company that was supposed to handle the logistics of food transport. Even the then UN General Secretary Kofi Annan has been implicated in this corruption scandal. By looking at important events that influence the survival of the OFFP and stock prices of companies bidding for contracts, by virtue of insider trading, we can find an indirect proof for corruption.

    The methodology for this thesis is the same as in DellaVigna, S. and La Ferrara, E., 2010. Detecting illegal arms trade. American Economic Journal: Economic Policy, 2(4), pp.26-57.

    Supervisor: Evelina Gavrilova-Zoutman.

  • Detecting Illegal Arms Trade

    Detecting Illegal Arms Trade

    In an influential study from 2010 scholars have identified the likelihood that arms companies have been breaking embargo rules. The detection method hinges on insider trading as a result of war events that decrease or increase hostilities. The authors find a larger effect for companies from high corruption countries, where the cost of embargo violation is low. The authors also provide a methodology to detect individual culprits, who are likely to have sold arms in countries under an embargo.

    The data for this study spans 1990 to 2005. The question is: do these findings still hold in 2005 to 2020? Are the perpetrators different? For the whole data: Can we link perpetrators through their board composition? How connected are the boards of companies that violate embargoes?

    Additional questions: How does corporate governance influence illegal trading? Does the presence of an extensive network of offshore affiliate increase or decrease the stock price reaction to adverse events?

    Starting point: DellaVigna, S. and La Ferrara, E., 2010. Detecting illegal arms trade. American Economic Journal: Economic Policy, 2(4), pp.26-57.

    Supervisor: Evelina Gavrilova-Zoutman.

  • The Art of Avoiding Taxation

    The Art of Avoiding Taxation

    The meaning of modern art can be sometimes difficult to understand. What if there is no meaning at all but just a vehicle to avoid taxation? The scheme is simple: Put a splash on a painting, evaluate it at a high price and donate it to a museum. Get the tax cut due to charitable donations. How often is new art donated? Which companies benefit from these tax cuts?

    Methods: Textual analysis, web crawling, regressions.

    Supervisor: Evelina Gavrilova-Zoutman

  • The disintegration of institutions and the smuggling of antiquities

    The disintegration of institutions and the smuggling of antiquities

    Countries that enter a period of internal strife such as a civil war experience a disintegration of institutions. When factions are battling for control, who would protect the property rights of victims? This creates an excellent climate to export antique objects with little oversight. Can we link export flows to civil war status? Can we flip this, and use flows of antiquities to flag countries where rule of law is disintegrating?

    Supervisor: Evelina Gavrilova-Zoutman

  • COVID-19 and lockdown measures: understanding the mechanisms

    COVID-19 and lockdown measures: understanding the mechanisms

    During the COVID-19 pandemic most countries imposed Non-Pharmaceutical Interventions (NPIs) or lockdown measures in an effort to halt the spread of the disease. In a case study focusing on the Scandinavian countries we show that in Scandinavia the NPIs introduced by Norway and Denmark were extraordinarily effective in reducing the pressure on the health care system and mortality (Juranek and Zoutman 2020). In this thesis we want you to pick apart the mechanisms. Which NPIs are most effective in reducing the spread of the disease? When should countries introduce NPIs (traditional epidemiology suggests that early NPIs will be much more effective than the same NPIs introduced at later stages)? What is the relationship between mobility on the one hand, and the spread of COVID-19 on the other hand? All of these questions remain mostly unanswered and are of extreme importance to policy makers trying to stop a second (or third) COVID-19 wave, or trying to fight a new infectious disease in the future.

    Data: the EU has created a database which contains an overview of all NPIs passed in EEA countries. The database also contains records on hospitalizations, which for many reasons is the most valuable measure in tracking the spread of the disease. Google has publicly available data on mobility. For Scandinavian countries we can also track data at the regional level.

    Methods: the methods depend on the background and training of the student undertaking the thesis. Many meaningful relationship can be estimated through linear regression. However, it is also possible to use more advanced methods such as machine learning or epidemiological models.

    Reference: Juranek, Steffen and Floris Zoutman (2020). “The Effect of Non-Pharmaceutical Interventions on the Demand for Health Care and Mortality: Evidence on COVID-19 in Scandinavia” SSRN Working Paper.

    Supervisor: Floris Zoutman

  • COVID-19 and Non-Pharmaceutical Interventions: A cost-benefit analysis

    COVID-19 and Non-Pharmaceutical Interventions: A cost-benefit analysis

    All countries in developed countries have introduced Non-Pharmaceutical Interventions (NPIs) such as social distancing, handwashing, mask wearing and school closures, in an effort to stop the spread of COVID-19. The charge has up to now been lead almost exclusively by epidemiologists. In most countries economists are not included in the team of experts that advice the government on these decisions. That’s a pity, because economics, and especially, old-fashioned cost-benefit analysis has a lot to offer under these conditions. Roughly speaking NPIs all have their economic costs and benefits (i.e. their ability to halt the spread of COVID-19). From a cost-benefit standpoint it is possible to sort NPIs from “cheap” (high benefits, low costs) to “expensive” (low costs, high benefits). In case of a pandemic the objective of the government should be to keep the pandemic suppressed at the lowest possible cost. This means that we should pass NPIs in order from cheap to expensive. In particular, the most expensive measures should only be introduced in case cheaper methods do not suffice.

    To give a practical example, handwashing is cheap in the sense that it has very little economic costs and is most likely quite effective at stopping the spread of COVID-19. Social distancing is relatively more costly as it comes with high economic costs (think for instance about spacing people out in classrooms, public transport etc.), and is probably not much more effective than handwashing. This does not imply that we should not practice social distancing, but it does imply that handwashing comes “first”.

    In this thesis, you will do two things. First, you explain in more detail the principles of cost-benefit analysis applied to the COVID-19 pandemic. Second, you provide your best guestimates of the costs and benefits of NPIs passed in Norway using, for instance, academic literature, media sources and interviews with experts. Third, you apply your cost-benefit analysis with the guestimates to provide clear policy advice to Norwegian policy makers on which measures should come first.

    Methods: cost-benefit analysis, literature review, interviews with experts

  • COVID-19 and the Labor Market: Understanding the Mechanisms

    COVID-19 and the Labor Market: Understanding the Mechanisms

    Juranek et al (2020) study the labor market in the Nordic countries during the COVID-19 pandemic. We find that the pandemic has had disastrous consequences in terms of both unemployed and furloughed workers in all four countries. Sweden, comparatively does the best, and Norway is at the bottom (at least in the short run). Part of the difference in labor market outcomes is driven by differences in lockdown measures, which Sweden (in)famously abstained from, but there are also differences in labor market policies. Moreover, the severity of the COVID-19 pandemic itself may have a direct effect on labor markets. In this project, you will try to unpack the mechanisms focusing on data from the Nordic countries. As a first pass, you will extend the data of Juranek et al (2020) and replicate their analysis over a longer time frame. Afterwards, you will build a statistical model that disentangles the mechanisms between COVID-19 and the labor market using causal diagrams, regression analysis and/or machine learning tools. The results will be helpful in understanding how the pandemic affected labor markets, and guide policy makers in passing measures that fight the pandemic but minimize the damage to the labor market. Data: Labor market data from the Nordic countries available from the various statistical agencies, data on mobility available from Google and data on the spread of the pandemic, available from health institutes. Methods: Linear Regression, causal diagrams, machine learnings (the choice of methods can be adjusted based on the student’s background). References: Juranek, Steffen, Jörg Paetzold, Hannes Winner and Floris Zoutman (2020) “Labor Market Effects of COVID-19 in Sweden and its Neighbors: Evidence from Novel Administrative Data” SSRN Working Paper

Data Science and Analytics

  • Statistical analysis of hourly consumption in an interventional pilot study

    Statistical analysis of hourly consumption in an interventional pilot study

    Study: Elvia AS is planning to implement an interventional pilot study called AktiveHjem. In this pilot, Elvia will study different implementations of new power tariffs (2 tariffs will be tested) in the net tariff of end users. Control groups where the end users don’t receive any change of their net tariffs will be planned.

    Data: The data consists of timeseries of the active consumption of end users of Elvia. Only end users that have been included in the study will be analysed (including control groups). The timeseries have an hourly resolution and range from one to two years before the start of the intervention to the end of the intervention. We will therefore have both control group(s) to compare the intervention to, and historical data of the same end users before intervention. Some relevant metadata about end users (and user groups) will also be provided. Analysis: The analysis consists in implementing a group analysis (GLM) to test if an intervention has had an effect in the groups that received it. The variables that influence the hourly consumption are already well defined and the model is, to some details, set up. The core of the job is to implement and test the model in order to robustly conclude about the effect of the interventions. Fagbakgrunn som kreves for oppgaven: Statistikk, økonometri, samfunnsøkonomi, IT

    Om dette høres spennende ut ta kontakt med:
    Silje Elise Harsem - Prosjektleder for Aktive Hjem og leder for selskapsstrategi i Elvia silje.harsem@hafslund.no.

    Informasjonsmøte: 25. mai
    Påmeldingsfrist til informasjonsmøte: 15. Mai
    Søknadsfrist
    7. juni

    Kontak hos NHH: Jonas Andersson

  • Text analysis and patents

    Text analysis and patents

    Patents are legal documents that are used by innovators to protect their innovations. The aim of the thesis is to use text analysis of different kinds to use the vast amount of information contained in the documents. This information can be potentially used to identify patenting trends, to understand the innovation process better, to improve our understanding of the importance of IPR etc. The information in the text can also be combined with Machine Learning techniques to predict, for example, the success of an application, the likelihood of an opposition or the likelihood of an infringement.

    Supervisor: Steffen Juranek.

  • Sports Analytics

    Sports Analytics

    Due to its massive popularity and often large availability of data, sports present great opportunities for the application of analytics techniques. I have dedicated a great share of my research to topics related to sports analytics, including tournament scheduling, referee assignment, fairness, and ranking design. These are only examples from the broad range of topics in the agenda of sports analytics nowadays. There is a lot of literature about it. I would be open to discuss your specific interests and to provide you with references that could set the basis for a potentially fun and relevant master thesis.

    Supervisor: Mario Guajardo.

  • Using Textual Analysis to identify whether there is a gender gap in financial white collar crime

    Using Textual Analysis to identify whether there is a gender gap in financial white collar crime

    There is very little systematic evidence on the gender gap in crime. In particular, it is difficult to quantify it for financial white collar crime, as they do not find their way into police statistics and as there is little female presence on the top levels of companies.

    In this project, we can use textual analysis tools to collect data from the Securities and Exchange Commission (SEC) on insider trading and other frauds. We can match the name of the defendant to a gender, and quantify what is the difference between males and females.

    Then, we can correlate the gap, as well as the fraud itself, to past financial statements of involved companies in terms of gender representation in the board of the company and other indicators of company culture. Finally, we can compare the gap to other measures of female participation in the boardroom and determine whether white collar females seem more or less likely to commit crimes than white collar males. We can provide a partial answer to the question: Are companies going to become more responsible (do less criminal rule-breaking) if there are more females on the board?

    Methods: Textual analysis, web crawling, R

    Supervisor: Evelina Gavrilova-Zoutman.

  • Smuggling and money laundering

    Smuggling and money laundering

    It is well known that laundered money change their form from cash to other goods such as jewellery, antique objects or other collectible items. Do we observe abnormal flows of such good to/from tax havens? Do these flows shift with the signing of information exchange agreements between two countries?

    Methods: Regressions, machine learning.

    Supervisor: Evelina Gavrilova-Zoutman.

  • Who blows the whistle on cartels?

    Who blows the whistle on cartels?

    By using webcrawling and textual analysis, you can gather data from the EU commission website on who reports the presence of market cartels. What is the reward that these whistleblowers get? Did they participate in the cartel before they reported it? Are these companies characterized by better corporate governance? Why did they blow the whistle? Could it be because of big shifts in governance structure, new boards, etc? What are sentiments on social networks on the mentions of the cartels and on the activity of the whistleblower?

    Are cartels more likely to be reported in certain industries? By using the revealed data on these cartels, how can we tailor a detection strategy that would detect similar behaviour by other cartels? Can we flag potential offenders?

    Methods: Textual analysis, web crawling, sentiment analysis, R

    Supervisor: Evelina Gavrilova-Zoutman.

  • Correcting witness reports through Machine Learning

    Correcting witness reports through Machine Learning

    It is well known that witness accounts on crime are often unreliable. The effects of stress or poor light on the victim can create a skewed perception of how the perpetrator looked. This, coupled with an unconscious discriminatory bias, leads to the victim reporting that the perpetrator was unknown or from the black racial minority. Often the witness report is based on an estimate – e.g. “ the perpetrator looked black, around 25 years”.

    Comparing actual arrests to reports, we can try to put a number on the amount of witness error. By training a machine learning model on a subsample, we can try to predict the gender, race and age of unobserved criminals. This can impact the computation of statistics on black/white, male/female and young/old crime gaps. What if blacks are actually responsible for a very small fraction of crimes? What if females are responsible for more crimes? This could lead us to rethink current racial profiling strategies in crime detection.

    Data: National Incident Based Reporting System, US

    Starting point: Imbens, G.W. and Lemieux, T., 2008. Regression discontinuity designs: A guide to practice. Journal of econometrics, 142(2), pp.615-635.
    Fryer Jr, R.G., 2016. An empirical analysis of racial differences in police use of force (No. w22399). National Bureau of Economic Research

    Supervisor: Evelina Gavrilova-Zoutman.

  • When the police is cheating

    When the police is cheating

    Police agencies get funding based on the amount of crime in their jurisdiction. This gives them the incentive to manipulate crime statistics. This can mean that for e.g. aggravated assaults are downgraded to simple assaults, or simple crimes are elevated to felonies. The police can add drug charges, in order to appear as being successful against organized crime. All this behaviour is hidden behind crime statistics.

    Through a combination of machine learning and local linear estimates we can try to determine how police agencies cheat. With the use of election outcomes as instrumental variables, we can try to find a causal effect. The findings of this project could have a strong impact on how crime figures are viewed. With corrected crime figures we can replicate previous analyses and determine whether well known policies actually impact crime or not.

    A separate question would be how do policing incentives influence police cheating in reporting statistics? E.g. federal grants that increase the police labor force vs. militarization donations that increase police capital. Both of these types of policies expect a crime decrease as a result and this is what we find in the literature, but is this finding warranted? Do we observe more crime downgrading after the policies than before?

    Data: National Incident Based Reporting System, US

    Supervisor: Evelina Gavrilova-Zoutman.

  • Nonlinear econometrics

    Nonlinear econometrics

    A large portion of empirical research within economics and finance is based on linear models, of which the linear regression is by far the most prominent. Is this because we live in a linear world, or at least an approximately linear world, or is it the case that we implicitly close our eyes to important features i our data by not considering nonlinear methods on equal footing as traditional ones?

    Questions like this may take your master project in several directions, such as (listed from least to most statistical/mathematical maturity recommended to complete the project, all of them benefit from programming skills):

    1. To what degree is linear regression the main vehicle for measuring marginal effects of explanatory variables X to a response variable Y within economic research (within a certain field/ in Norway/ at NHH or otherwise suitably limited)? Why do researchers choose this method (convenience/interpretability of coefficients/easy presentation/theoretical foundations/...)?  Then, figure out to which degree such concerns can be addressed by a corresponding nonlinear model. A nice touch would be to re-do a recent linear study nonlinearly and see if there indeed are effects that were missed.

    2. A bit more technical version of the point above is to write a thesis that revolves around the systematical development of a tool in your programming language of choice (such as R or Python) that implements as many needs as possible of the linearly oriented researcher in a nonlinear framework, with pre-work consisting of providing a theoretical foundation, and as post-work perhaps testing your «product» by trying to «sell it» to an experienced researcher.

    3. Financial time series are typically nonlinear in the sense that the correlation coefficient does not, in general, give good descriptions of dependencies across time and space. This has naturally lead to the development of nonlinear methods to model financial processes. For example, the classical theory for portfolio allocation that Harry Markowitz introduced in the 1950s balances expected return (as measured by means) and risk (as measured by standard deviations and correlations) in order to provide the optimal distribution of wealth across different assets.

    The Markowitz portfolio theory is very simple and easy to implement. But, it explicitly assumes that dependence between assets is linear, so the decades following its introduction have seen many attempts to improve the Markowitz method by modelling dependencies nonlinearly. Many authors note, however, that it is actually quite hard to attain higher returns using modern methods compared to the classical approach. This project may contain a survey of modern portfolio selection methods (which will require the ability to read fairly technical research papers), and a discussion part where we try to answer the question whether beating the classical approach indeed is «hard», and if so, why?

    Supervisor: Håkon Otneim.

  • Shipping & Finance: Inferring short-term market direction from intraday FFA Data

    Shipping & Finance: Inferring short-term market direction from intraday FFA Data

    Using intraday bid/offers spreads – is it possible to use pattern recognition or technical analysis to daytrade forward freight agreements? Knowledge of Python and machine learning is useful. 

    Supervisor: Professor Roar Ådland

  • Shipping: Vessel speed analysis using AIS data

    Shipping: Vessel speed analysis using AIS data

    Using AIS data on ship positions, investigating how vessel speeds react to changes in fuel prices and spot freight rates in the short- and long run.
    Requires familiarity with co-integration tests and econometrics.

    Supervisor: Professor Roar Ådland

  • Shipping: Economic optimization of underwater hull-cleaning intervals

    Shipping: Economic optimization of underwater hull-cleaning intervals

    Using fuel consumption and cost data from individual ships: How often should the company clean the hull of a ship to reduce fuel costs. Advanced – requires some knowledge of optimization, optimal stopping problems, semi-parametric regressions.

    Company: Golden Ocean or SKS Tankers.

    Supervisor: Professor Roar Ådland

Energy Markets, Resource Management and Sustainability

Incentives, Contracts and Firm Behaviour

  • Economics of organization and management control

    Economics of organization and management control

    Including the use of bonus pay (case studies or across companies), how to measure performance, balanced scorecard (or alternative ways to use key performance indicators for management control purposes), drivers of profitability in an industry or a company, beyond budgeting, transfer pricing, and organizational boundaries.

    Supervisor: Iver Bragelien.

  • How did the US Tax Bill affect M&As?

    How did the US Tax Bill affect M&As?

    The 2017 TCJA may significantly change the pattern of corporate M&As. For instance, it has lifted the original use requirement for immediate depreciation of acquired assets. That makes it much more attractive to structure deals as asset purchases rather than stock acquisitions.

    Furthermore, net operating losses before and after January 1, 2018 are treated differently. An empirical analysis could use either Zephyr or SDC Platinum to explore early signs of these changes. 

    Supervisor: Maximilian Todtenhaupt.

  • Common ownership and tax avoidance

    Common ownership and tax avoidance

    Corporate tax planning is high on the agenda of both tax practitioners and policy makers. While some firms certainly try to avoid paying taxes, we know very little about how firms learn about different tax avoidance mechanisms. An important channel may be via common owners. Do firms engage in similar levels of tax avoidance if they are owned by the same investors? This project will investigate this question using balance sheet information and ownership details of large corporations.

    Supervisor: Maximilian Todtenhaupt.

  • Women and innovation

    Women and innovation

    It can be empirically observed that women are underrepresented in science and research. This is likely to have negative consequences for firm productivity and economic growth because talent is not used efficiently. 
    There are several ways by which a Master Thesis can contribute in identifying potential causes and remedies. Examples are the analysis of the examination procedure or an analysis of geographical patterns.

    Supervisor: Steffen Juranek.

  • Text analysis and patents

    Text analysis and patents

    Patents are legal documents that are used by innovators to protect their innovations. The aim of the thesis is to use text analysis of different kinds to use the vast amount of information contained in the documents. This information can be potentially used to identify patenting trends, to understand the innovation process better, to improve our understanding of the importance of IPR etc. The information in the text can also be combined with Machine Learning techniques to predict, for example, the success of an application, the likelihood of an opposition or the likelihood of an infringement.

    Supervisor: Steffen Juranek.

  • Taxation and Innovation

    Taxation and Innovation

    In a recent working paper Akcigit et al (2018) use US data to show that higher income tax rates reduce innovation. The aim of this thesis is to find out if a similar relationship between innovation and taxation exists for Europe. Students will use the universe of patent applications to the European patent office to find out i.) if countries with lower tax rates file for more patents and ii.) whether changes in the tax rate cause changes in patent applications. The outcome is particularly relevant for policy makers, since innovation is an important driver of economic growth. If higher tax rates indeed reduce innovation, this provides a strong incentive for countries not to increase their tax rate, or even to reduce it.

    Data: Patent applications in Europe

    Literature: Akcigit, U., Grigsby, J., Nicholas, T., & Stantcheva, S. (2018). Taxation and Innovation in the 20th Century. NBER Working Paper.

    Supervisor: Floris Zoutman and Steffen Juranek

  • AAThe publicity effect of patent lawsuits

    AAThe publicity effect of patent lawsuits

    Citations to patents matter because they indicate knowledge spillovers and indicate the importance of a technology. It is well-known that patents with more citations are more likely to be taken to court. However, it is less clear whether there is a publicity effect of lawsuits, i.e., whether lawsuits lead to more citations - potentially indicating that lawsuits enhance knowledge dispersion. The thesis should empricially analyze the latter effect.

    Supervisor: Steffen Juranek.

  • Who blows the whistle on cartels?

    Who blows the whistle on cartels?

    By using webcrawling and textual analysis, you can gather data from the EU commission website on who reports the presence of market cartels. What is the reward that these whistleblowers get? Did they participate in the cartel before they reported it? Are these companies characterized by better corporate governance? Why did they blow the whistle? Could it be because of big shifts in governance structure, new boards, etc? What are sentiments on social networks on the mentions of the cartels and on the activity of the whistleblower?

    Are cartels more likely to be reported in certain industries? By using the revealed data on these cartels, how can we tailor a detection strategy that would detect similar behaviour by other cartels? Can we flag potential offenders?

    Methods: Textual analysis, web crawling, sentiment analysis, R

    Supervisor: Evelina Gavrilova-Zoutman.

  • Sivilrettslig inndragning av utbytte fra kriminelle handlinger

    Sivilrettslig inndragning av utbytte fra kriminelle handlinger

    JD nå jobber med forslag til lov om sivilrettslig inndragning (etter en utredning av prof. Jon Petter Rui). Tidligere er det blitt foreslått en nasjonal enhet som skal sikre fratagelse av verdier gjennom både sivilrettslige og strafferettslige verktøy. Disse to tiltakene ble utredet parallelt. Regjeringen sa nylig at dette hadde noen praktiske hindre, uten å gå nærmere inn på hva disse er. Er sivilrettslig inndragning et effektivt virkemiddel? Kan det bøte på det ofte rapporterte problemet om manglende kapasitet og/eller kompetanse i politidistriktet? Hva er de praktiske hindringene regjeringen refererer til? Hva er erfaringene i andre land som har brukt dette, hva er potensielle fallgruver og hva har hatt best effekt? Hvor stor er den preventive effekten av å ramme utbyttet/profitten fra den kriminelle virksomheten? Samarbeidsforum kan bidra som diskusjonspartner.

    In cooperation with Skatteetaten.

    Supervisor: Evelina Gavrilova-Zoutman.

  • Streaming markets for music and books

    Streaming markets for music and books

    Supervisor: Øystein Foros.

  • Interplay between content providers and distributors in digital platforms

    Interplay between content providers and distributors in digital platforms

    Supervisor: Øystein Foros.

  • The Appstore battle with music and app providers (the case of Fortnite and Spotify)

    The Appstore battle with music and app providers (the case of Fortnite and Spotify)

    Supervisor: Øystein Foros.

  • The war among consoles 2020: Sony (PlayStation 5-) vs Microsoft (Xbox Series)

    The war among consoles 2020: Sony (PlayStation 5-) vs Microsoft (Xbox Series)

    Supervisor: Øystein Foros.

  • Konkurransen i mobilmarkedet

    Konkurransen i mobilmarkedet

    Sammenligne f.eks. Norge og Finland som har ulik markedsstruktur. Mye deskriptiv empiri for å bedre forstå markedet og forskjellen mellom landene.

    Supervisor: Øystein Foros.

  • Bokavtalen

    Bokavtalen

    Kulturminister Trine Skei Grande har nylig foreslått å utvide bokavtalen. Det vil gi en lengre fastprisperiode og høyere priser på opptil 65 prosent av bøkene. Den norske bokavtalen, som gir forleggerne rett til å sette en fast pris på nye bøker, er havnet i søkelyset til Efta-landenes overvåkningsorgan Esa. Nylig besvarte Nærings- og fiskeridepartementet (NFD), på vegne av den norske regjeringen, en rekke spørsmål fra Esa om forholdet mellom bokavtalen og EØS-avtalens eksplisitte forbud mot prissamarbeid i artikkel 53. Her kunne det være interessant å sammenligne med kommisjonens sak mot Amazon ift most-favored nation (MFN) klausuler.

    Supervisor: Øystein Foros.

  • Personalisert prising

    Personalisert prising

    Med utgangspunkt i prosjektet Moving towards the market of one? Competition with personalized pricing and endogenous mismatch costs, jobbe med applikasjoner? Kan også knyttet opp mot AI.

    Supervisor: Øystein Foros.

Shipping, Logistics and Operations Management

  • Topics on Ship Finance

    Topics on Ship Finance

    Topic 1: Responsible investing in the shipping/offshore industry. How have (institutional) investors change their appetite in investing in the shipping/offshore industry, given public ESG concerns and initiatives from the industry?

    Topic 2: Green bond issuance in shipping company – criteria, impact on transition to zero emission, asset investment and investor appetite.

    Topic 3: Investment horizon in shipping. What determines the investment horizon? In the transition to zero emission, how shipping companies decide which asset class to invest? Short-term vs. long-term investment payback and performance analysis.

    Topic 4: Optimal capital structure for shipping companies – determinants: ownership profile, prices, leverage, cycles and other factors.

    Supervisor: Haiying Jia

  • Topics on Marine Insurance

    Topics on Marine Insurance

    Topic 1: Marine insurance risk aggregation. How to manage small-probability risk exposures? Geographically or event-linked exposures. Optimal risk transfer of low probability yet high impact events.

    Topic 2: Marine risk assessment – using Machine learning or other methods. Analysis of Marine traffic data, claim locations and loss prevention applications.

    Topic 3: Vessel utilization and its marine risks. Use innovative data sources to estimate vessel utilization, such as the emission data from MRV.

    Topic 4: Coinsurance efficiency improvement. How to improve efficiency by system/data sharing? Blockchain?

    Topic 5: INSURTECH Automated underwriting – system design view point, or technology viewpoint. How would a standard insurance portfolio perform is 100% can be automated.

    Topic 6: Ownership structural of marine insurance companies. What are the challenges connected to the business model? What ownership structure is optimal?

    Supervisor: Haiying Jia

  • Shipping: How can CO2 emissions be priced in chartering contracts?

    Shipping: How can CO2 emissions be priced in chartering contracts?

    Cargo owners and charterers are the key to reducing emissions in global seaborne transportation. How can we revise contracts such that all stakeholders have an incentive to perform efficiently?

    Supervisor: Professor Roar Ådland

  • Shipping: Using weather forecasts to improve ship earnings

    Shipping: Using weather forecasts to improve ship earnings

    How can weather statistics and medium-term weather forecasts be used to improve pre-calculations of the voyage results for drybulk vessels? How accurate are the “rules-of-thumb” weather margins currently used in comparison?

    Company: Golden Ocean.

    Supervisor: Professor Roar Ådland

  • Shipping: Economic optimization of underwater hull-cleaning intervals

    Shipping: Economic optimization of underwater hull-cleaning intervals

    Using fuel consumption and cost data from individual ships: How often should the company clean the hull of a ship to reduce fuel costs. Advanced – requires some knowledge of optimization, optimal stopping problems, semi-parametric regressions.

    Company: Golden Ocean or SKS Tankers.

    Supervisor: Professor Roar Ådland

  • Shipping: How can vessel performance data be used to optimize commercial operation?

    Shipping: How can vessel performance data be used to optimize commercial operation?

    Using real transaction data, estimate a volatility surface for freight options and assess whether it is different from other financial options markets w.r.t. implied volatility skew/smile.

    Company: EEX.

    Supervisor: Professor Roar Ådland

  • Shipping: The economics of IMO 2020

    Shipping: The economics of IMO 2020

    Using updated data on timecharter and/or voyage charter rates, investigate whether freight rates reacted to the new regulations implemented on January 1, 2020.

    Supervisor: Roar Ådland

  • Shipping: Vessel speed analysis using AIS data

    Shipping: Vessel speed analysis using AIS data

    Using AIS data on ship positions, investigating how vessel speeds react to changes in fuel prices and spot freight rates in the short- and long run.
    Requires familiarity with co-integration tests and econometrics.

    Supervisor: Professor Roar Ådland

  • Shipping: Network analysis in the international oil market

    Shipping: Network analysis in the international oil market

    Using micro-data on the buyer and seller of crude oil cargoes, to investigate the structure of commercial relationships in the global crude oil market and how it changed with the drop in the oil price in 2014. Part of this is related to risk management – do agents have a portfolio of counterparts or do they rely on one source?

    Supervisor: Professor Roar Ådland

  • Shipping & Finance: Machine learning models for FFA trading

    Shipping & Finance: Machine learning models for FFA trading

    Using spatial AIS data for ship positions, open-source weather and macro data – can you develop a machine learning model to generate profitable trading signals? Requires knowledge of Python, implementation of machine learning models.

    Supervisor: Roar Ådland

  • Shipping & Finance: How do risk capital and risk limits affect the chartering policy of a ship operator?

    Shipping & Finance: How do risk capital and risk limits affect the chartering policy of a ship operator?

    Using: freight rates timeseries, optimal portfolio theory.

    Company: Western Bulk.

    Supervisor: Professor Roar Ådland

  • Shipping & Finance: Inferring short-term market direction from intraday FFA Data

    Shipping & Finance: Inferring short-term market direction from intraday FFA Data

    Using intraday bid/offers spreads – is it possible to use pattern recognition or technical analysis to daytrade forward freight agreements? Knowledge of Python and machine learning is useful. 

    Supervisor: Professor Roar Ådland

  • Shipping & Finance: Quantifying fluctuations in FFA market liquidity

    Shipping & Finance: Quantifying fluctuations in FFA market liquidity

    Using high-frequency bid-offer quotes, analyze changes in the bid-offer spread over time and how it correlates with trading volume, seasonality and freight market level.
    Company: Zuma Labs/Braemar.

    Supervisor: Roar Ådland

  • Analysis of high-frequency supply data for oil tankers

    Analysis of high-frequency supply data for oil tankers

    Using unique daily spatial data for vessel employment, analyze how regional freight rate changes are driven by supply and demand, and whether the specifications and operator of a ship matters for its attractiveness in the market.

    Supervisor: Roar Ådland.

  • Small city logistics

    Small city logistics

    Urban population growth is driving an increase in the amount of freight that goes into and out of cities. That growth poses an increasing challenge to freight transportation in smaller compact cities with difficult topology, which is typical for most Norwegian cities and numerous cities abroad. This transportation challenge is exacerbated by phenomena such as an increase in internet trade, the demand for fast delivery, and a reduction in the ownership of private cars in the city centre which could be used for shopping. The result is an increase in the total volume of freight, and more critically, in the total number of deliveries, normally managed by a large variety of transportation companies. Unless planned for and regulated, a consequence might be increased traffic, with enhanced energy consumption, that competes for available space and may affect living conditions for a growing urban population.

    This project will study small city logistics, with a focus on Bergen, to find the options available for the authorities, as well as business models for a better city logistics setup. Will be done in cooperation with the City of Bergen and Bergen Chamber of Commerce. The project can be qualitative as well as quantitative.

    Supervisor: Stein W. Wallace.

  • How will autonomous vessels change the operations in the shipping industry (including deep-sea shipping, short-sea shipping and local waterway transport)?

    How will autonomous vessels change the operations in the shipping industry (including deep-sea shipping, short-sea shipping and local waterway transport)?

    Background

    If you are a hunter from the Stone Age and one day you are facing an offer to replace you wooden stick with a brand new shotgun, will you still use your new weapon just as a harder stick made of steel to kill your prey, or use it in a better way? Similar challenges are now faced by the shipping companies due to the forthcoming technological evolution, namely the autonomous ship. Obviously, an autonomous ship with no crews on board can significantly reduce a shipping company’s crew cost.

    However, just like the increased hardness of a shotgun in the hunter example, the reduction of crew cost might just be a tiny benefit of the autonomy of our ships. Besides the lower crew cost, what are the fundamental advantages of an autonomous vessel comparing to the conventional manned ship?

    Greater potentials are expected by better utilizing these advantages with innovative ideas in the daily operation of the vessels, such as higher frequency of ferry in the night time, flexible hub location for waterway taxi and multi-functioned vessels with different remote control teams. The world’s first commercial autonomous vessel (Yara Birkeland) will be soon launched in Norway in the end of 2018. And it is a great opportunity for the students here to also take the leading position in the research of the autonomous vessel.

    Potential Reference

    Toth, P., Vigo, D. (2002). The Vehicle Routing Problem. Philadelphia: SIAM Publications Stopford, M. (2009). Maritime Economics. London: Routledge.

    Contact: Yewen Gu

  • Using autonomous vehicles to improve our emergency services

    Using autonomous vehicles to improve our emergency services

    The aim of emergency medical services (EMS) is to provide timely assistance to emergencies in order to save lives. Within this service, quality and capacity have sometimes deteriorated because staffing is not satisfactory and because the organization and directives are not clear. My interest is to work on the use of autonomous vessels to help ameliorate the burden that EMS staffing represents in the case of boat ambulances, and to improve the logistics planning of the system.

    The aim is to analyze the use of autonomous vessels to improve response times and coverage. For example, by combining autonomous vessels with geographic information systems, one may use real time information of potential patients to improve the deployment of the resources. In particular my interest is to explore the following key research topic: designing algorithms with predictive capabilities that can be included in real time systems and capable of managing a continuous feed of data points coming from users’ cell phones and other sources.

    Supervisor: Julio C. Goez.

  • The Operational and Economic Impact of Autonomous Ship Application, Comparing to the Traditional Manned Vessels. Wartsila, Fjellstrand, NYK

    The Operational and Economic Impact of Autonomous Ship Application, Comparing to the Traditional Manned Vessels. Wartsila, Fjellstrand, NYK

    Supervisor: Yewen Gu.

  • Repositioning of Empty Vessels in the Dry Bulk Shipping Market

    Repositioning of Empty Vessels in the Dry Bulk Shipping Market

    Aim: find key drivers for decision-making process of repositioning empty vessels - current market conditions, sentiment - repeating patterns, etc.

    Supervisor: Vít Procházka.

  • Emission Abatement Technology for a Shipping Company - is the Uncertainty of Fuel Prices Important?

    Emission Abatement Technology for a Shipping Company - is the Uncertainty of Fuel Prices Important?

    Is the uncertainty of fuel prices important to be considered when a shipping company selects its emission abatement technology for the compliance of ECA regulation.

    Supervisor: Yewen Gu.

  • Operations Research Applications in Tine

    Operations Research Applications in Tine

    Dairy farmers with combined milk and beef production face complex decisions regarding optimum milk yield, slaughter age for bulls, calving age for heifers, disposal of farm land etc. The aim for this topic is to explore how operations research may help farmers improve their decision making process to increase their profitability. In this project the students will interact with TINE, Norway's largest producer, distributor and exporter of dairy products with 11,400 members (owners) and 9,000 cooperative farms.

    Supervisors: Mario Guajardo and Julio C. Goez .

  • Logistics/sharing economy: Analytics for car-sharing models

    Logistics/sharing economy: Analytics for car-sharing models

    Car-sharing provides short-term vehicle access to a group of user members who share the use of a vehicle fleet owned by a car-sharing organization that maintains, manages, and insures the vehicles. An example of this model in Bergen is bildeleringer. Managing the fleet involves decisions such as the size of the fleet, how to position and reposition the vehicles, maintenance schedules, and pricing approaches. Strong background on analytics required

    Supervisors: Julio C. Goez and Mario Guajadaro.

  • Optimization of requirements of cloud computing resources

    Optimization of requirements of cloud computing resources

    The providers of online applications usually need to find the deployment of minimum cost for running it in the cloud. For the deployment, the planner on the application side must consider renting resources from cloud providers. However, there is a service level constraint that must be satisfied to ensure the quality of the service.

    Supervisor: Julio C. Goez.