Mudassir's current topics of research at Harvard include the following (all models, research papers and analysis can be provided on demand; please contact the author directly in case of interest):

1)
Project summary:
Using beta and logistical regressions to answer the simple question: "should retail investors invest in IPOs?"

Goal:
Use historical data to aid investment decisions made by retail investors, in the highly debated (and partly misunderstood) concept of IPOs. Is getting in as early as one can actually the best decision?

Approach:
The statistical anaysis includes data of IPOs from 1998-2019, and takes into account the post hoc performance of companies after they went public, historical performance of the market (S&P500), performance of alternative investments, performance of US Treasuries as a benchmark, and incorporates external factors such as the dotcom bubble early 2000s and the financial crisis of 2008. Post hoc effects of COVID 19 were NOT taken into consideration.

• Data used was a combination of publicly available data and data from Harvard Business School Publishing.

Outcome:
The model used 75% of the data as a training sample, and tested accuracy on the rest 25% test sample. Acoording to the results of random test samples, the model has a 78% accuracy of predicting IPO performances over the following 5 years, specifically for the tech and manufacturing industries.

2)
Project Summary:
Using the programming language R and associated libraries to develop a pricing model for Uber (or any similar ride sharing company)

Goal:
Help new ride sharing services develop a suitable pricing model, one of the toughest business decisions in this field, due to the dynamic nature of all dependant variables and inputs.

Approach:
The model studied the current situation of Uber, based on predicted Uber rides depending on time of day, weather, traffic, no. of regular customers active, and even accounting for inflation. The model automatically adjusts ride price, with the goal of maximising profit for the company (such as increase price in typically rainy months, where people tend to prefer to use ride sharing, however the increase should not be so much as to dissuade new customers). The model used (and is accurate in predicting) ride sharing servies only for Munich, Frankfurt and London.

• Data used was a combination of publicly available data and data from Harvard Business School Publishing.

Outcome:
Based on a training and test sample, pricing models developed using this model would have helped increase Uber profits by 3-5%, and would be of paramount importance to any new ride sharing services operating in metropilation cities.

3)
Project Summary:
Developed an open economy macro model, to compare effects of different monetary or fiscal policy on the output of a country, in times of a crisis (such as COVID19).

Goal:
Help understand the implications of monetary and fiscal decisions taken by the U.S. government and Federal Reserve in response to a crisis. Understanding the quantitative and qualitative implications of these decisions can help public policy decision makers, as well as investors who are directly effected by aforementioned decisions.

Approach:
The model incorprates changes in interest rate set by the central bank, changes in consumer spending and government spending as a result of the crisis, and effects of exchange rate on the imports and exports as well as on the ouput as a whole. Short run and long term equilibria were differentiated, on the basis of a typical IS-LM-FX Model.

• Data used was publicly available from https://data.worldbank.org/. The analysis and tools were aided by the Department of Economics, Harvard University.

Outcome:
The model undertook a detailed analyis of monetary and fiscal decisions taken in past crises, and can be used to help understand the implications of similar situations in the future.

4)
Project Summary:
Developed an open economy macro model, to answer the simple question: "if all country borders were to be completely opened, which countries would tend to gain the most, and why?"

Goal:
Help understand the advantages and disadvantes of financial globalization, a hotly discussed topic within economic and public policy circles.

Approach:
The model uses the Cobb-Douglas aggregate production function, and uses data from 1970-2018 to asses effects of productivity, marginal product of capital, real output per worker, elastcity and physical capital per worker, to understand why some countries have stronger outputs and higher productivity, and who stands to gain the most from financial globalization.

• Data used was publicly available from https://data.worldbank.org/. The analysis and tools were aided by the Department of Economics, Harvard University.

Outcome:
The model displayed which countries have performed the best in terms of increased output due to capital inflow from 1970-2018, and explained which factors played the major role in said performance. This benchmark can be used to understand which countries need what conditions, in order to benefit from financial globalization.

5)
Project Summary:
An in depth comparison of the operations of McDonald's vs Burger King

Goal:
To understand the differences in the production process between two companies that have seemingly similar products, but largely different histroical performance. What does McDonald's do better, how do they pay such attention to minute details, and what can Burger King change moving into the future?

Approach:
A Burger King and McDonald's branch in close proximity of each other were studied, with data from the same time period. Detailed process maps, production cycle times at each step, throughput times, and performance during busy periods were compared and analyzed. The analysis went forward to understand why the differences exist in the first place. Financial reports of both the branches from the same period were used as a benchmark to understand performance.

• All the data, detailed accounts of managers, workers and stakeholders were provided by Harvard Business School cases.

Outcome:
The analysis provided an in depth understanding of how small differences in production process steps and decisions taken by upper management can impact the performance of a business.

6)
Project Summary:
Assessed the organizational problems of a Fortune 100 company, ranging from a lack of diversity to internal resistance to change, to problems within the company culture.

Goal:
Help a specific company facing general organizational behaviour problems, analyse the As-Is situation and provide a To-Be process.

Approach:
After careful study of the problems, the analysis went ahead to provide 2 solutions to company, in close collabartion with company C-Suite, and laid an implementation plan. The solutions comprised of a change in organizational structure, to engage more cross functional communication and dissuade the current silo mentality, and an implementation top-down long term plan for changing company culture, including how to battle specific forms of resistance to change.

• All data and information provided was confidential, delivered directly by the company

Outcome:
The company was provided the results, with predicted positive effects and time scope till they are observable, and they chose to go forward with the implementation plan.

7)
Project Summary:
Performed a Strategic Analysis on The University of Oxford (aided by visting Oxford fellows at the Harvard Kennedy School), including recommendations to maintain and create a competitive advantage in academia.

Goal:
Provide concrete recommendations on the basis of due dilligence to The University of Oxford, to help achieve their 2030 Strategic Objective, including ammendments in their Strategy Statement.

Approach:
The deep dive and due dilligence involved an external analysis (primarily done using Harvard Business School Professor Michael Porter's famous 5 forces analysis) and an Internal Analysis (SWOT, Core Competencies, Business Model, Synergy analysis and Disruptive Forces Analysis). Using quantitative and qualitative analysis on available data, a concrete action plan was devised, with 3 actionable To-Dos and a detailed timeline. The objective of the recommendations was not to 'instruct' the University, but rather provide a general guidance, allude to possible risks and point out key factors regarding enrollment rates and tuition, that would be more relevant in the future.

• All data and information provided was confidential, provided by the University of Oxford.

Outcome:
After several updates and customer feedback, The University of Oxford was provided and presented the results of the detailed analysis (to good effect and acclamation); whether they act upon the recommendations is upto their own discretion.