< Back Value at Risk (VaR) for a portfolio Simple Tool using a historical simulation to find VaR Previous Next

< Back Web scrapping Box Office Sales A python code used to web scrape data from Box Office Mojo's Website. The objective at hand is to attain weekend box office sales in order to test a correlation of Sci -fi movies and Tech stock prices. Thus, a python code was developed to web scrape data from Box Office Mojo's Website in a manner that that shows the Top 10 box office sales for each weekend. Additionally, using OMDb API , the genre for each movie was found. Significant aspects of the code developed was based on a similair project by Jonathan Bown on Kaggle *My actual API key from OMDb API has been removed in the code below import requests from bs4 import BeautifulSoup import pandas as pd from google.colab import files import re def scrape_weekend_box_office(weekend_url): response = requests.get(weekend_url) soup = BeautifulSoup(response.text, 'html.parser') table = soup.find('table') if not table: return [] rows = table.findAll('tr')[1:] movies_data = [] for row in rows[:10]: cols = row.findAll('td') if cols and len(cols) > 3: movie_name = cols[2].get_text(strip=True) weekend_gross = cols[3].get_text(strip=True) movies_data.append((movie_name, weekend_gross)) return movies_data def check_genre(movie_name, api_key): params = {'t': movie_name, 'apikey': api_key} response = requests.get('http://www.omdbapi.com/', params=params) data = response.json() if 'Genre' in data: genres = [genre.strip().lower() for genre in data['Genre'].split(',')] return 1 if any(genre in genres for genre in ['sci-fi', 'fantasy', 'action']) else 0 return 0 def scrape_year_weekends(year, api_key): base_url = f'https://www.boxofficemojo.com/weekend/by-year/{year}/' response = requests.get(base_url) soup = BeautifulSoup(response.text, 'html.parser') links = soup.select('td.a-text-left a') all_data = [] seen_weekends = set() genre_cache = {} for link in links: weekend = link.get_text(strip=True) weekend_link = 'https://www.boxofficemojo.com' + link['href'].split('?')[0] match = re.search(r'(\d{4})W(\d+)', weekend_link) if match and weekend_link not in seen_weekends: seen_weekends.add(weekend_link) weekend_number = match.group(2) top_movies = scrape_weekend_box_office(weekend_link) for rank, (movie_name, weekend_gross) in enumerate(top_movies, start=1): if movie_name not in genre_cache: genre_indicator = check_genre(movie_name, api_key) genre_cache[movie_name] = genre_indicator else: genre_indicator = genre_cache[movie_name] all_data.append({ 'weekend_number': int(weekend_number), 'weekend': weekend, 'rank': rank, 'movie_name': movie_name, 'weekend_gross': weekend_gross, 'is_action_sci_fi_or_fantasy': genre_indicator }) return all_data omdb_api_key = ' ' # Replace with actual OMDb API key year = 2019 data = scrape_year_weekends(year, omdb_api_key) df = pd.DataFrame(data) df.drop_duplicates(subset=['weekend_number', 'rank'], inplace=True) df.sort_values(by=['weekend_number', 'rank'], inplace=True) csv_file = f'weekend_box_office_{year}.csv' df.to_csv(csv_file, index=False) files.download(csv_file) Previous Next

< Back The Solow Model and Human Capital in Developing Economies How can human capital enrichment lead to long-run economic growth? Human Capital and Economic Growth How enriched are the minds of the people in a society? How healthy, creative, and efficient are they? How can they bring about disruptive innovation (Robinson & Acemoglu) to lead an economy into virtuous cycles of prosperity? This is all answered by the workforce’s level of human capital. With various definitions at play, human capital can be basically defined as the set of skills used to efficiently create value in an economy. Thus, indicators of human capital are level of education, technical training, experiences, habits, and level of health. In today’s economy, natural resources nor population sizes are the major determinants of growth – human capital is. The paper by Dr. Armah titled, “Addressing Quality Issues In African Higher Education: A Focus On Ghana’s Emerging, Private, Graduate, Business Higher Education Sector,” focuses on one of Ghana’s key determinants and indicators of Human Capital – Higher education. Through the research key issues pertaining to the flaws in Ghana’s Higher Education system are put to light as limitations of human capital enrichment, thus, prohibitors of economic growth. With proposed solutions pertaining to more STEM focused teachings within business, Armah, puts to light proposals that would adequately enrich Human Capital, thus, accelerate economic growth in Africa. Technology is the Rosetta stone of the economic growth literature . As such, emphasis on STEM education certainly is the best avenue for enriching human capital. Supported by the central argument in Easterly’s (2011) paper, technology is the largest determinant for vital long-run growth. Said technologies are only brough into existence by enriched workers with high levels of human capital. Sighting cases from the Singapore, Japan, Malaysia, China, and the Scandinavian countries, we come to see a positive relation with their prosperity and investments into their human capital. Specifically viewing Singapore, a nation with essentially no natural resources, and a small population, how is such a country able to attain such high sustainable growth? The answer lies in their human capital investments. With investments in their education, sanitation, and healthcare systems, Singapore is a testimony to the importance of human capital in creating sustainable long-run growth. Poverty and the Resource Curse i) According to the World Bank, poverty can be defined in simple absolute terms – those living on less than $1.9 per day. However, according to the UN, the concept of poverty entails much moe than income levels, it also refers to hunger, education availability, healthcare, discrimination and participation on decision making. UN broadly puts poverty into a deeper light by thoroughly seeing poverty as an extremity of poor living standards. Calculating poverty is quite tricky. But there are two key distinctions in measuring poverty: Absolute Poverty or Relative Poverty. Absolute poverty refers to a set standard or poverty line which can be used to compare and assess various countries at different times. However, relative poverty calculations are defined on the basis of environmental context. The measure varies from country to country and from time to time. Hence, absolute can be used for strong comparative analysis however relative calculations of poverty are able to thoroughly contextualize and deliver an accurate understanding of a region’s poverty. ii) The major macroeconomic determinants of poverty: 1. Unemployment Rate of unemployment is a clear macroeconomic indicator and determinant of poverty. Without sufficient work available, multiple households would be axed from their major source of income. 2. Inflation With high inflation rate, particularly that of food inflation, households purchasing power for staple goods would reduce. This essentially leads to household’s inability to make ends meet due to rising prices and falling income purchasing power. 3. Level of income Lastly, the most obvious determinant is income. With low incomes, households are pushed closer to the poverty line. iii) According to Esther Duflo’s paper, the poor population tend to live in large households 6 -12 members. The poor population live below $2.16 per day. They earn most of their money via temporary jobs, low skilled (low specialization) work and working in small scale ventures. They tend to be unbanked, have no form of insurance, and the only major asset they own is their land. iv) Countries in Africa such as Liberia, Congo, Zimbabwe and Ghana are typically poor despite their abundance of resources is due to the marriage of these four main concepts; 1. Resource Curse Also known as the paradox of plenty, resource abundant countries typically put themselves in a trap. They focus on industries related to their natural resources as their main source of generating wealth. As such, they fail to diversify the economies adequately. Thus, price shocks or market preference shifts pertaining to their main commodity (say Gold or Cocoa in Ghana or Oil in Gabon), leads to huge economic difficulties in said countries. As such, through their abundance of a few resources and focusing on just those resources, they leave their economies vulnerable, leading to poor economic performance. 2. The Dutch Disease The Dutch Disease, very similar to the resource curse, resonates the same story of a lack of economic diversification in resource abundant countries. However, in the case of the Dutch disease, said countries reduce investments in other sectors due to the discovery of a natural resource. The decrease investments in those sectors leads to unemployment and also reduces the economic diversity of the nation. Other unseen negative effects such as fall in total exports and a higher local currency. 3. Weak Institutions Despite having high amounts of resources, said countries cannot manage the production effectively. Till date, Nigeria does not know the exact amount of oil it drills each day! Weak institutions are instrumental for growth, as Acemoglu and Robinson repeatedly say. With weak institutions, corruption also prevails as in the case of Gabon in the Elf-Affair. Corrupt officials get in bed with multinational executives, thus, steal money the nation needs to develop as a whole. Hence, revenues from said countries resources are not shared with the society – the largest share goes to political cronies. 4. Poor Governance Relating to the earlier point, poor governance leads to a lack of transparency and accountability. This enables corruption to thrive. Solow Model From Wolphram Alpha: https://demonstrations.wolfram.com/SimpleSolowModel/ The graph below, created on Wolfram Alpha, shows steady state k*. K* is at a steady state when I = D, or better said where investments is equal to depreciation. Simple Solow Growth Model: Steady State Simple Solow Growth Model: Higher than k* In the long run, the economy always adjusts itself, thus, would always move towards the steady state. As such K would eventually shift from k1 towards the left (towards the initial K*). Logically speaking, if the economy is to operate with a capital stock higher than its steady state say at K1, we would come to find that depreciation is much higher than investment. If such is to occur in an economy, we would observe that the rate of capital entering the economic machine (investment) is less than that leaving economy (depreciation of capital goods). Thus, in the long run, or simply put as time goes on, if the rate at which capital is reducing is higher than capital coming inside. Thus, the amount of capital stock would gradually reduce from K1 up until it reaches K*. Thus, K moves towards the steady state k*. Previous Next

< Back Alternative Data Regressor Framework: Draft 1 A framework for linear regression of alternative data against financial asset prices What is Aternative Data? Alternative data is defined as non-traditional data that can provide an indication of future performance of a company outside of traditional sources, such as company filings, broker forecasts, and management guidance. This data can be used as part of the pre-trade investment analysis, as well as helping investors monitor the health of a company, industry, or economy. LSEG Examples of Alternative Data are: Social Media Sentiment, Web Traffic, Credit Card Transaction data, Satellite Imagery, Car Parking data, Mobile App usage and much more. What is an "Alternative Data Regressor"? Not a standard term but rather a phrase that I have essentially cooked up. My goal is to essentially use various tyes of alternative data (the regressor) to find a correlation with market values of financial assets (stocks and stock indices). Thus, the reason I have phrased it as the "Alternative Data Regressor." Conceptual Framework We need to develop a scientifc framework to test for correlation and possible causality. The objective is for the framework to be guide for a Python Algorithm that takes in datasets and tests for correlation Alternative Data Regressor Framework Collect Data : Gather weekly data on box office sales, interest rates (Prime and Federal Funds Rate), and a tech stock index. Clean and Organize Data : Prepare and organize the data for analysis Test for Stationarity : Apply stationarity tests Adjust Data for Non-Stationarity : If the data is not stationary, adjust it for seasonality, possibly using methods like the moving average or log transformations Re-test for Stationarity : After transforming the data, test for stationarity again. If the data is now stationary, proceed with the analysis. Significance Testing : Conduct appropriate statistical tests to check the significance of the relationships between the variables Develop Baseline Regression Model : Create a baseline regression model to analyze the relationship Refine the Model : Continuously adjust the model by experimenting with different forms of the control variables. Evaluate Models : Assess the various models and select the best one based on criteria like the R-squared or Adjusted R-squared value. Interpret the Regression Line : Use the chosen model to interpret the relationship between the Alternative data and financial market returns. Comparative Analysis : Compare the effects of using other variables (typically more traditional variables) on the predicting power. Example of Alternative Data Regressor Framework: Testing the Relation of Sci-Fi Movies Box Office Sales & the Prices of Tech Stocks in the US (Using R) Collect Data : Gather weekly data on box office sales, interest rates (Prime and Federal Funds Rate), and a tech stock index. Clean and Organize Data : Prepare and organize the data for analysis ==> remove incomplete enteries; remove outliers Test for Stationarity : Apply stationarity tests ==> Augmented Dickey-Fuller test Adjust Data for Non-Stationarity : If the data is not stationary, adjust it for seasonality, possibly using methods like the moving average or log transformations ==> Moving Average # Apply moving average to adjust for seasonality and create lagged variables (1-week lag) project_data_clean <- project_data %>% mutate( ma_TOP_10 = rollmean(TOP_10, window_size, align = "right", fill = NA), ma_tech_movie = rollmean(tech_movie, window_size, align = "right", fill = NA), ma_Tech_index = rollmean(Tech_Index, window_size, align = "right", fill = NA), ma_Market_index = rollmean(Market_index, window_size, align = "right", fill = NA), ma_TOP_10_lag = lag(ma_TOP_10, 1), ma_tech_movie_lag = lag(ma_tech_movie, 1) ) Re-test for Stationarity : After transforming the data, test for stationarity again. If the data is now stationary, proceed with the analysis. Significance Testing : Conduct appropriate statistical tests to check the significance of the relationships between the variables ==> Spearman Correlation Test # Spearman Correlation Test for Moving Average Adjusted and Lagged Tech Movie and Tech Index spearman_test_ma_tech_movies <- cor.test(project_data_clean$ma_tech_movie_lag, project_data_clean$ma_Tech_index, method = "spearman") spearman_test_ma_tech_movies # Spearman Correlation Test for Moving Average Adjusted and Lagged Total Top 10 Box Office and Market Index spearman_test_ma_top_10 <- cor.test(project_data_clean$ma_TOP_10_lag, project_data_clean$ma_Market_index, method = "spearman") spearman_test_ma_top_10 Develop Baseline Regression Model : Create a baseline regression model to analyze the relationship between box office sales (lagged by a week) and stock market performance, including the control variables (interest rates). Refine the Model : Continuously adjust the model by experimenting with different forms of the control variables. #1 : Classic model1 <- lm(ma_Market_index ~ ma_TOP_10_lag, data = project_data_clean) summary(model1) #2 : Including Economic Indicators (control variables) model2 <- lm(ma_Market_index ~ ma_TOP_10_lag + PRIME + FED, data = project_data_clean) summary(model2) #Model 2 is the most accurate! #3 : Including proxy for market premium model3 <- lm(ma_Market_index ~ ma_TOP_10_lag + PRIME + FED + `PRIME - FED`, data = project_data_clean) summary(model3) Evaluate Models : Assess the various models and select the best one based on criteria like the R-squared or Adjusted R-squared value ==> Adjusted R-squared Interpret the Regression Line : Use the chosen model to interpret the relationship between box office sales and stock market returns. a. Focus : Apply the model specifically to sci-fi movies to examine their impact on the tech stock index. Comparative Analysis : Compare the effects of using total box office sales versus sci-fi box office sales in predicting tech stock index changes. #Model 1: Target - Tech Movies Box Office Sales to Tech Stock Index model_target <- lm(ma_Tech_index ~ ma_tech_movie_lag + PRIME + FED, data = project_data_clean) summary(model_target) #Has the highest accuracy! Sci-fi movies are the best predictor for tech stock prices! #Model 2: Proxy - Top 10 Box Office to Tech Stock Index model_proxy <- lm(ma_Tech_index ~ ma_TOP_10_lag + PRIME + FED, data = project_data_clean) summary(model_proxy) #Model 3: Indirect - Tech Movies Box Office to Market model_indirect <- lm(ma_Market_index ~ ma_tech_movie_lag + PRIME + FED, data = project_data_clean) summary(model_indirect) Future Works: Develop a program with Python that takes in datasets as an input and the output is the possible correlations (based on the process outlined above) Previous Next

Projects Alternative Data Regressor Framework: Draft 1 A framework for linear regression of alternative data against financial asset prices View Alternative Data Regressor Framework: Flow Chart A framework for linear regression of alternative data against financial asset prices - the flow chart View Alternative Data Regressor: V1 A Python Program to attain a linear regression of some alternative data against financial asset prices . A CSV file is the input. The output is the regression results. View Beta of Fan Milk Ltd (FML): Ghana Stock Exchange (GSE) Finding the Beta of FML on the GSE using Python (Jupyter Notebook) View Cocoa Production: Ghana and Ivory Coast - 2022 Summary of Cocoa Production in Ghana and Ivory Coast in 2022. View Cocoa Production: Ghana and Ivory Coast - Historic Trend Work in progress View Cocoa Production: West Africa - 2022 Work in progress View Commentary: Brexit could lead to recession, says Bank of England An economic commentary on the article "Brexit could lead to recession, says Bank of England" View Commentary: Ghana fixes new cocoa price to control smuggling An economic commentary on the Article, "Ghana fixes new cocoa price to control smuggling" View Commentary: Washington’s Decision to “Normalize” Relations with Cuba..." An economic commentary on the article "Washington’s Decision to “Normalize” Relations with Cuba: Impede China’s Growing Influence in Latin America" View Convering Excel to CSV: Web Application A basic Web application written in HTML and Javascript to convert excel files to CSV. View Data Visualization of the Dynamic Efficiency of Oil and Gas Production in Ghana A comprehensive tool for understanding the Real-time Efficiency of Oil and Gas production in Ghana View Do Sustainable Funds in Switzerland outperform the Market? What is the performance of "sustainable" funds in relation to the market? Let's explore the case of the SIX Swiss Exchange View Dynamic Forestry and Agricultural Summary of ECOWAS states Work in progress View Dynamic View of Ghana's Unemployment Investigating the trend and segmentation of employment in Ghana View Dynamic View of Trading Hours: SIX Swiss Exchange V1 Dynamic View of the opening and closing hours of the SIX Swiss Stock exchange for 2024. Additionally, current summary of the market's activity is stated. View Dynamic view of Ghana's Forestry Work in progress View Dynamic view of Ghana's Insurance Industry Work in progress View ESG Strategies: Passive and Active Management Do the laws of passive and active strategies also affect sustainability investing? View Electricity Consumption as a proxy of production: Draft 1 Using publicly available data on Swiss Power Consumption, this exploration seeks to identify an association with power consumption and select firms output View Expected Loss Calculator A simple tool to calculate the Expected Loss for a credit portfolio. View Financial Performance of Ghana's political regimes from 1960 - 2000 Analysis of Economic Growth in Ghana, 1960 – 2000 – ARYEETEY & FOSU View Fixed Deposits Offers in Ghana A simple directory that shows Fixed Deposit offers in Ghana View Game Theory: Prisoner's Dilemma Strategies Tools Recreating and Simulating Robert Axelrod's 1980 Computer Tournament. View Ghana Stock Exchange: Real-Time Prices Web App V1 A basic web-application to find real time summaries of stocks on Ghana's Stock Exchange (GSE) View Google News Scrapper Scrape Google News articles for a particulair keyword and date range View Hedonic Valuation Model: Real Estate in Zurich With significant portions of banks portfolios consisting of mortgage loans, it is paramount to develop a strong model for valuating real estate. View Hollywood Boulevard to Wall Street: Futurism in Movies and Tech-Stock Prices This study investigates the relationship between the box office sales of futurism-themed movies and the performance of the tech stock index. View How Much Time Do I have left? Visualizing and Quantifying our most valuable asset: "Time" View How much time do I have left? - Version 2 Visualizing and Quantifying our most valuable asset: "Time"; Version 2 View Initial margin requirement for Derivative Trading A simplified VaR-based approach to calculate the initial margin requirement for Derivative Trading View Is ignorance truly bliss? Investigating the link between the lack of general information and the conception of the economy in Ghana. Project from 2018 View Manipulating File Paths: Backward to Foward Slashes A program made to convert backward slashes in file path names to foward slashes. Targeted for Windows users when copying paths to R or Pthon. View Paradox of Choice and Utility Maximization: Music Traditional Asset Pricing models are conceptually based on utility maximization. However, what about the role of the quantity of choices in utility maximization? View Photography Tool: Black & White Conversion A basic photo editor to convert PNG pictures from color to Black and White View Plain Vanilla Bond Price Calculator A web application that takes the arguments of FV, Coupon Rate, YTM and Periods to price a Plain Vanilla Bond View Prisoner's Dilemma: Player 2 Allowing users to participate in Robert Axelrod's 1980 Computer Tournament. View Proving the Butterfly Effect Within the context of metreology and physics, we can explore the butterfly effect View Scrapping Data using Python A Python application designed to generate a histogram depicting the frequency of articles published on Google News in 2022 concerning '@celebjets'. View Scrapping Oil related articles Run on python via GoogleCollab View Smartphone App for University Students An all-purpose app for Ashesi students. Project from 2017 View Snapshot Macroeconomic Summary of ECOWAS States: 2022 As at the end of 2022, this is was the macroeconomic status of each ECOWAS state View Sustainability Dimensions of Stocks on the SIX:Render 1 Quantitatively assessing Brundtland's Dimensions (1987). The case of the SIX View Sustainability Dimensions of Stocks on the SIX:Render 2 Quantitatively assessing Brundtland's Dimensions (1987). The case of the SIX View Sustainability Dimensions of Stocks on the SIX:Render 3 Quantitatively assessing Brundtland's Dimensions (1987). The case of the SIX View The Solow Model and Human Capital in Developing Economies How can human capital enrichment lead to long-run economic growth? View Value at Risk (VaR) for a portfolio Simple Tool using a historical simulation to find VaR View Web scrapping Box Office Sales A python code used to web scrape data from Box Office Mojo's Website. View Web-Scrapper V1: Web Application Web Application for web-scrapping news articles View frankenstein.io - Draft 1 Restructuring & Simplifying "Frankenstein codes" View

< Back Dynamic view of Ghana's Forestry Work in progress Previous Next

Profit is the only Green : Visualization of Swiss Stocks & SRI portfolios Sustainability BY SEAN AKWEI ANUM Abstract Socially Responsible Investing (SRI), which is increasingly popular, emphasizes social and environmental factors in investment decisions to promote sustainability. In theory, SRI outperforms, especially in the long run. While practitioners typically remain skeptical, this unique return-based sustainability assessment of Swiss Stocks demonstrates SRI’s over performance and ability to promote sustainability. Research Question Is SRI a significant means of promoting Sustainability? Sustainability, per the 1987 United Nations Brundtland Commission, means meeting present needs without compromising future generations, involving social, economic, and environmental aspects. In investments, it translates to SRI, blending social and environmental factors into investment decisions. This project seeks to quantitatively depict these sustainability dimensions for stocks and SRI portfolios on the SIX (Swiss Stock Exchange). Methodology Data Proxies The data required deals with the three sustainability parameters for each stock on the Swiss Exchange: Environmental, Social and Economic. Company Name Environmental Score Social Score Economic Score The Quantitative proxies are as follows: 1. Environmental: An ESG rating with a numeric individual score (pillar) for a firm’s environmental impact; 2. Social: An ESG rating with a numeric individual score for a firm’s Social impact; 3. Economic: a risk-adjusted measure of the firm’s expected return: Capital Asset Pricing Model (CAPM) Visualizing Three Parameters A 3D plot was chosen to visualize three quantitative parameters, effectively showing their relationship and intersections. Stocks with high environmental, social, and economic scores are classified as “Sustainable,” while those with low scores are deemed “At Risk.” Stocks with scores between these extremes are categorized as “Acceptable.” The final visualization seeks to visualize the relative distribution of individual stocks & SRI portfolios regarding the three parameters. Hence, a standardized score for each parameter was used. The logic was to ensure that all parameters could be drawn down to a somewhat “equal” scale, thus ensuring an informative visual effect. Constructing SRI Portfolios Using the collected individual stock data, four SRI funds were created: Negative Screening: This SRI fund excludes investments in companies or sectors that do not meet specific ethical, environmental, or social criteria. Best in class: This fund selects companies that outperform their peers in environmental, social, and governance (ESG) criteria within each sector. Thematic Approach: This fund focuses on specific sustainability themes or sectors, such as renewable energy or social justice. ESG integration: This fund incorporates ESG factors into traditional financial analysis to identify risks and opportunities not captured by conventional methods. Data Sources Data was collected for each of the three parameters. Data was attained via the Thompson Reuters financial market portal Refinitiv Eikon. Environmental Pillar Score (ESG rating) Measures a company’s impact on living and non-living natural systems, including the air, land and water, as well as complete ecosystems. Social Pillar Score (ESG Rating) Measures a company’s capacity to generate trust and loyalty with its workforce, customers and society through its use of best management practices. Economic Pillar Score ( Beta) A measure of how much the stock moves for a given move in the market. Note, the Economic score was further computed with the Capital Asset Pricing Model (CAPM), which is CAPM = Risk-free rate+Beta*(Risk Premium), where risk-free rate and risk premium in Switzerland is 1.135% Source: World Government Bonds and 5.5% Source: NYU respectively. Data was collected based on completeness. As such, despite the SIX listing 250 stocks, the project at hand uses 187. One stock, IGEA Pharma NV, was excluded as it was an extremely negative outlier that terribly affected the scale of the entire visualization. Constructing “Sustainable” and “At Risk Criteria” The Sustainability Criterion was defined as Environmental Score ≥ 70 (out of 100), Social ≥ 70 (out of 100); and Economic score ≥ 6.64% (Average Market Return). Consequently, the standardized scores were 1.05, 0.83 and 0, respectively. At Risk Criterion was defined as : Environmental Score ≤ 30 (out of 100); Social ≤ 30; and Economic score ≤ 3.34% (one standard deviation below Market Average Return). Consequently, the standardized scores were -0.30, -0.68 and -1 respectively. Conditions are based on core financial theories. Data for SRI Portfolios Regarding the Negative Screening and Best in Class Approach, using the ESG data collected, I easily constructed said portfolios. However, for the Thematic Approach and ESG integration, I replicated existing funds employing these strategies. They are the “Ethos Swiss Governance Index Large” and the “ETHOS II - Ethos Swiss Sustainable Equities -A” respectively. Final Visualization The graph is interactive. Average-sized points represent a stock on the Swiss Exchange. The bigger Orange points represent SRI portfolios, and the Big Black point represents the Market Average. Results and Conclusion Market’s Performance The sustainability cuboid includes 11% of stocks and three-quarters of SRI strategies, whereas the at-risk quadrant contains 6% of stocks. The general market performance is deemed acceptable, with many stocks nearing the Sustainability cuboid. Despite needing substantial progress, these findings indicate a promising trend towards sustainability in the Swiss Stock Market. SRI Performances To answer the Research Question, SRI funds appear to promote sustainability. This is supported by the visualization showing 3 out of 4 strategies as sustainable. Contrary to expectations, “ESG Integration” is the only strategy classified as non-sustainable. In theory, the best strategy should be “ESG integration”, whereas the other three are seen as simplistic and lacking a nuanced ESG assessment concerning market returns. My paradoxical result likely arises because, unlike simpler strategies, “ESG Integration” involves more subjective and active management, leading to significant performance variations among different managers. Testing this hypothesis with another fund using “ESG Integration” yielded a “Sustainable result”, highlighting the classic debate between active and passive management but now within SRI. Concluding Remarks Ironically, firms with controversial reputations like Nestle, UBS, and Credit Suisse have good non-economic scores, while Cantonal banks unexpectedly show low scores. This raises questions about how these public entities might be causing more social and environmental harm and calls for a deeper examination of the legitimacy of ESG scores.