MN52080 Econometrics and Data Analysis for Accounting and Finance Assessment Brief 2026 | UOB

MN52080 Assessment Brief

Empirical Project 1 – Individual Work

DUE DATE:   Wednesday 29 April 2026, 14:00 (GMT)

Weight of Project Report to Overall Assessment – 40%

Instructions

• The main text of the report should not exceed 1,500 (±10%) words in length, excluding the title page, tables, figures, references, and appendices. Please state the 9-digit student number only at the beginning of the report.
• You should submit three files: (1) a Word or PDF file of the final report with the name of your 9-digit student number (e.g., 123456789.pdf), (2) your code, which can be a .py or .ipynb file, with the name of your 9-digit student number followed by ‘_code’ (e.g., 123456789_code.ipynb), and (3) a completed School of Management coursework hand-in sheet, with the name of your 9-digit student number followed by ‘_declaration’ (e.g., 123456789_ docx).
• The project criteria detailed below will be used to mark your final report submission. Please keep the criteria in mind when preparing your report.
• Students may apply for a coursework extension. Detailed academic regulations can be found via the below link: https://www.bath.ac.uk/guides/coursework-extensions/#how-to-apply-for-acoursework-extension

Purpose Of This Empirical Project

One of the central questions in finance is how financial and economic variables explain the movement of stock market returns or the equity premium, as examined in highly influential studies such as Goyal and Welch (2008) and Goyal, Welch and Zafirov (2024). Researchers have investigated a wide range of predictive factors, including dividend yields, book-to-market ratio, interest rates, and macroeconomic indicators, to better understand and forecast stock market fluctuations (Campbell & Thompson, 2008; Cochrane, 2011; Fama & French, 1989). Understanding these relationships remains important for both academic research and practical investment decision-making, particularly in the context of asset pricing and portfolio management.

In recent years, climate change and environmental risks have increasingly attracted attention in financial economics, as researchers explore how climate-related factors may influence macroeconomic conditions and financial markets. Climate-related financial risks are commonly categorized into physical risks and transition risks (NGFS, 2019; Bolton et al., 2020). Physical risks arise from the direct impacts of climate change, such as extreme weather events, rising temperatures, and environmental degradation, which can disrupt economic activity and infrastructure. These risks are closely related to climate vulnerability, which reflects the degree to which a country is exposed and sensitive to climate hazards (Batten, Sowerbutts & Tanaka, 2020). In contrast, transition risks stem from the economic adjustments associated with the transition to a low-carbon economy. These risks may arise from climate-related policies, technological innovations, regulatory changes, and evolving market preferences that affect carbon-intensive sectors and investment decisions (Dietz et al., 2016; Battiston et al., 2017; Svartzman et al., 2021; Luo and Ma, 2025). As governments and financial institutions increasingly incorporate climate considerations into economic policy and investment strategies, countries’ institutional and economic capacities to manage this transition become increasingly important.

This project examines how climate readiness – measured by the Readiness Score of the Notre Dame

Global Adaptation Initiative (ND-GAIN) Index – or alternatively transition risk, proxied by (1 – Readiness Score), along with other traditional financial predictors, affects stock market returns in the US. In particular, the analysis investigates whether changes in the readiness score are associated with variations in excess stock market returns, and whether this relationship remains robust after controlling for established predictive variables commonly used in the asset pricing literature.

You are expected to apply the regression methods learned in the course to estimate a regression equation for the excess stock returns of the US market, addressing potential econometric issues such as multicollinearity, heteroskedasticity, and autocorrelation. Additionally, you should provide economic reasoning to support your findings and demonstrate proficiency in statistical analysis using Python (in Jupyter Notebook or Spyder).

Data

The ND-GAIN Country Index provides data on Readiness Scores, which measure a country’s capacity to effectively deploy investments and support adaptation to climate change. As such, the readiness score can be viewed as a proxy for a country’s capacity to manage climate transition risks. The data are obtained from the official ND-GAIN website^[1] and are provided in the file readiness.csv, which contains annual observations for multiple countries. The ND-GAIN Readiness Score ranges from 0 (low readiness) to 1 (high readiness) and reflects the institutional, economic, and social conditions that enable effective responses to climate-related challenges. For example, a score of 0.22 indicates relatively low readiness, suggesting limited institutional capacity and fewer resources available to support climate adaptation investments, whereas a score of 0.78 denotes relatively high readiness, indicating strong governance structures, well-developed infrastructure, and substantial economic capacity to support climate adaptation and transition efforts. You may also consider constructing a proxy for transition risk defined as one minus the ND-GAIN Readiness Score. Higher values of this variable represent lower institutional and economic preparedness to support climate-related investments and therefore reflect greater exposure to climate transition risks.

For this project, you should extract the United States data only, as the analysis focuses on the US market. If your analysis uses quarterly or monthly data, you are expected to extend the annual NDGAIN series accordingly. For instance, if the annual ND-GAIN Readiness Score for 20XX is 0.49, you should assign the same value (0.49) to all months or quarters within 20XX. Other data are available in Goyal_Welch_data.xlsx. Definitions of these variables, along with their abbreviations as they appear in Goyal_Welch_data.xlsx, are listed below. You will need to refer to the study of Welch and Goyal (2008) and use the provided data to properly create additional variables used in your regression model, such as the Dividend Price Ratio (d/p), Dividend Yield (d/y), Dividend Payout Ratio (d/e), Default Yield Spread (dfy), Term Spread (tms), and Default Return Spread (dfr).

• Price (Index) is the S&P 500 index price.
• Dividends (D12) are 12-month moving sums of dividends paid on the S&P 500 index. The data are from Robert Shiller’s website from 1871 to 1987. Dividends from 1988 and after are from the S&P Corporation.
• Earnings (E12) are 12-month moving sums of earnings on the S&P 500 index.
• Book-to-Market Ratio (b/m) is the ratio of book value to market value for the Dow Jones Industrial Average.
• Treasury Bills (tbl) rates from 1920 to 1933 are the S. Yields On Short-Term United States Securities, Three-Six Month Treasury Notes and Certificates, Three Month Treasury series in the NBER Macrohistory data base. Treasury-bill rates from 1934 onwards are the 3-Month Treasury Bill: Secondary Market Rate from the economic research data base at the Federal Reserve Bank at St. Louis (FRED).
• Corporate Bond Yields on AAA and BAA-rated bonds (AAA and BAA): They are collected from Federal Reserve Economic Data (FRED).
• Long Term Yield (lty): Long-term government bond yields for the period 1919 to 1925 is the S. Yield on Long-Term United States Bonds series from NBER’s Macrohistory database. Yields from 1926 and after are from Ibbotson’s Stocks, Bonds, Bills and Inflation Yearbook.
• Net Equity Expansion (ntis) is the ratio of twelve-month moving sums of net issues by NYSE listed stocks divided by the total market capitalization of NYSE stocks.
• Risk-free Rate (Rfree): The risk-free rate is the Treasury-bill rate from 1920 onwards. Because there was no risk-free short-term debt prior to the 1920s, they were estimated based on the Commercial paper rates.
• Inflation (infl): Inflation is the Consumer Price Index (All Urban Consumers) from the Bureau of Labor Statistics.
• Long Term Rate of Return (ltr): Long-term government bond returns are from Ibbotson’s Stocks, Bonds, Bills and Inflation Yearbook
• Long-term corporate bond (corpr): Long-term corporate bond returns are from Ibbotson’s Stocks, Bonds, Bills and Inflation Yearbook.
• Stock Returns: The total rate of return on the US stock market is the S&P 500 index returns from the Center for Research in Security Press (CRSP). Stock returns are the continuously compounded returns on the S&P 500 index, including dividends. In the provided data file, both the returns on the S&P 500 index, including dividends (CRSP_SPvw) and excluding dividends (CRSP_SPvwx) are provided.

Requirements

• You are required to develop a multiple regression model to explain the excess returns of the US market. Define your research question or the objectives of your study and explain the relationships between variables and their expected signs using sound economic or financial reasoning. You must include at least three independent variables, one of which should be the ND-GAIN Readiness Score or alternatively a transition risk proxy constructed as (1 − NDGAIN Readiness Score). The remaining variables should be selected or constructed based on insights from the relevant literature. Present your econometric model clearly and justify your variable choices. Please note, we do not require out-of-sample regression in this project.
• You should prepare a suitably large monthly or quarterly dataset for your empirical study. For example, a dataset with 300 observations spanning 25 years of monthly data from January 1996 to December 2020 would be appropriate. However, you are allowed to choose a different time period. If quarterly data are used, the dataset should be constructed by extracting the end-of-quarter observations from the monthly data in xlsx. Additionally, you need to properly integrate the ND-GAIN Readiness Score with the rest of your dataset. When selecting variables, check for missing values and clearly explain how you address any data gaps. You should also consider how to handle potential extreme or outlier values to ensure the robustness of your results^[2] . Define the type of data you are using (e.g., pooled crosssectional data), and discuss any limitations associated with using economic or financial data. You do not need to provide detailed definitions for the variables given in the provided Excel files as they are already explained above but make sure to refer to variable names correctly and clearly explain any new variables you construct.

Regression Analysis and Diagnostic Tests

• Use the appropriate test to identify the presence of multicollinearity within the data. Interpret the results and try to resolve it if problematic multicollinearity is found.
• Perform a multiple regression analysis to determine the values for the parameters. Explain the significance of the parameters in the context of ‘ceteris paribus’ and the ‘partial effect’. Interpret the test statistics for parameters and explain their implications based on your results. Discuss the potential consequences of omitting a key explanatory variable.
• Explain the meaning of 𝑅𝑅² and adjusted 𝑅𝑅² concerning your sample of data.
• Interpret the F-statistic of your regression, explaining the suitability of your model.
• Determine whether the error terms are normally distributed.
• Determine whether heteroskedasticity is present in the errors using the appropriate tests. Interpret the results of each test, discuss the test advantages and limitations, and suggest actions to take if heteroskedasticity is detected.
• Determine the presence of autocorrelation in the errors by conducting appropriate tests. Interpret your results of each test, discuss the advantages and limitations of the test, and suggest actions to take if autocorrelation is detected.
• The economic conditions also play an important role in explaining excess market returns. Create a dummy variable to indicate whether it is a recession period and run your regression with a proper interpretation. You do not need to re-run the previous diagnostic tests when introducing this dummy variable. A list of US recessions can be found on The Wikipedia (https://en.wikipedia.org/wiki/List_of_recessions_in_the_United_States). Additionally, consider whether a structure break may exist and provide evidence to support your conclusion.

Formatting Requirements And Use Of Generative Ai

Your final report must be prepared using A4 page size, and all pages must be numbered. Only your 9digit student number should appear on the title page; names must not be included. The report must be written in Times New Roman, 12-point font, with standard margins of 2.5 cm on all sides. Line spacing must be set to 1.5, with an additional line space between paragraphs and headings. All sections and subsections must be clearly numbered and titled. Footnotes should be used rather than endnotes. Tables and figures must be placed at appropriate points within the main text rather than collected at the end of the document (although they may appear on separate pages where necessary). All references must be properly cited and presented in the final reference list in accordance with the Harvard referencing style. The title page must include the unit code and unit name, the report title, and the student number. You must complete the separate School of Management coursework handin sheet and submit it together with your report. Please read the Generative AI policy carefully (available on Moodle) and properly cite if generative AI has been used in your work, For example:

ChatGPT, 2023. Why is citing and referencing your sources important? [Online]. San Francisco, Calif.: OpenAI. Available from: https://chatgpt.com/share/782cb099-a0dc-45b1-8da40e99713f2d45 [Accessed 4 September 2023].

MN52080 Assessment Marking Scheme

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