Search Results

< Back 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 https://akweix.shinyapps.io/trial_app/ Welcome to my R Shiny web app, the "Data Visualization of the Dynamic Efficiency of Oil and Gas Production in Ghana.” This web app leverages a myriad of data science techniques, including interactive visualizations, machine learning, sentiment analysis, natural language processing, data analytic tools and web scraping, to provide real-time, comprehensive analysis of Ghana’s oil and gas sector. The goal is to enhance information efficiency, market efficiency, and resource management efficiency, making it a valuable tool for practitioners, academics, and policymakers alike. The application is primarily centred on Ghana, especially regarding the visualizations. However, the data analytic tools developed can be applied to all markets and regions. Additionally, despite the application presenting key insights and tools that are applicable to both the Oil and Gas industry, greater emphasis was placed on Oil production due to its overall greater share of Ghana’s energy market and its more dynamic nature. anum_sean_data_science_final_report .pdf Download PDF • 2.66MB Previous Next

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

< Back 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. This study investigates the relationship between the box office sales of futurism-themed movies and the performance of the tech stock index. Utilizing regression analysis across three models, the research reveals significant insights: Tech Movies and Tech Stock Index: A strong positive correlation was found between the box office sales of tech movies and the tech stock index, with an Adjusted R-squared value of 0.8263, indicating a substantial predictive power of tech movie sales on tech stock prices. Top 10 Box Office and Tech Stock Index: A broader analysis including the top 10 box office sales showed a positive but less pronounced impact on the tech stock index, suggesting that the influence of tech movies is more specific and potent. Tech Movies and General Market Index: Tech movie sales also positively correlate with the general market index, though the relationship is weaker compared to the tech-specific index. In essence, the findings suggest that futurism movies, particularly those in the tech genre, have a noticeable impact on tech stock prices, offering valuable insights into the interplay between behavioral bias, entertainment and financial markets econometrics_project .pdf Download PDF • 542KB Previous Next

< Back Smartphone App for University Students An all-purpose app for Ashesi students. Project from 2017 Life at Ashesi University, like any university, can be overwhelming and disorganized. To streamline this experience, I suggest the development of a versatile mobile app that centralizes various essential services, thereby aiding in effective time management for students. The university offers a range of services including student support, counseling, and tutoring. However, accessing these services often proves to be a cumbersome and time-consuming process. In addition to these, many students are unaware of the contact details for on-campus emergency services and national emergency numbers in Ghana. In critical situations, this lack of information could lead to wastage of precious time. To tackle these issues, the proposed app would be a comprehensive solution. It would feature functionalities like accurate weather forecasts by integrating with the Accuweather website for Berekuso forecasts, a meal plan balance checker linked with the Ashesi meal plan webpage, and a digital menu for campus eateries like Akornor and Big Ben. Additionally, the app would include a directory of contact details for Ashesi’s various services and emergency services, with the added convenience of calling these contacts directly from the app. This integration would ensure that all necessary information and services are readily accessible to students, thereby enhancing their university experience and safety. Pseudocode 1. When the app is started the homepage is displayed. 2. The homepage displays titles “Meal Plan,” “Weather,” “Ashesi Services,” “Food” and “Emergency Services.” 3. If “Meal Plan” is selected, the webpage of the Ashesi Meal plan is displayed. 4. If “Weather” is selected, the webpage for accuweather (set for Berekuso) is displayed. 5. If “Food” is selected restaurants in Ashesi are displayed. 6. Select any restaurant and their menu shall be displayed. 7. If “Ashesi Services” is selected a list of Ashesi Services are displayed. 8. Select any service and their contact details is displayed for calling . 9. If “Emergency Services” is selected a list of Emergency Services are displayed. 10. Select any emergency service and their contact details is displayed for calling . Figure 1: Flowchart * Due to the senstivity of some information within the app, kindly request for access. Upon access being granted, the links below shall be temporarily activated. Download APK via Github: https://github.com/akweix/Ash-App Download Android App via Thunkabale: https://x.thunkable.com/copy/b63301e1a6082169dd0d9aa036ac119d Previous Next

< Back Initial margin requirement for Derivative Trading A simplified VaR-based approach to calculate the initial margin requirement for Derivative Trading Previous Next

< Back How much time do I have left? - Version 2 Visualizing and Quantifying our most valuable asset: "Time"; Version 2 Previous Next

< Back Alternative Data Regressor Framework: Flow Chart A framework for linear regression of alternative data against financial asset prices - the flow chart Previous Next

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

< Back How Much Time Do I have left? Visualizing and Quantifying our most valuable asset: "Time" Previous Next

< Back Scrapping Oil related articles Run on python via GoogleCollab # Install and set up necessary packages and dependencies !pip install selenium !apt-get update !apt install chromium-chromedriver import sys sys.path.insert(0,'/usr/lib/chromium-browser/chromedriver') from selenium import webdriver from selenium.webdriver.chrome.options import Options from bs4 import BeautifulSoup import pandas as pd # Set up Chrome options for Selenium chrome_options = Options() chrome_options.add_argument('--headless') chrome_options.add_argument('--no-sandbox') chrome_options.add_argument('--disable-dev-shm-usage') # Initialize the Chrome WebDriver with the specified options driver = webdriver.Chrome(options=chrome_options) # Fetch the Web Page url = 'https://news.google.com/search?q=oil%20prices' driver.get(url) # Get the page source and close the browser html = driver.page_source driver.quit() # Parse the Web Page using BeautifulSoup soup = BeautifulSoup(html, 'html.parser') articles = soup.find_all('article') # Extract the Necessary Information news_data = [] base_url = 'https://news.google.com' for article in articles: # Extracting the title and link title_link_element = article.find('a', class_='JtKRv', href=True) title = title_link_element.text.strip() if title_link_element else "No Title" link = base_url + title_link_element['href'][1:] if title_link_element else "No Link" # Extracting the date time_element = article.find('time') date = time_element['datetime'] if time_element and 'datetime' in time_element.attrs else time_element.text.strip() if time_element else "No Date" news_data.append([title, link, date]) # Store the Data in a DataFrame df = pd.DataFrame(news_data, columns=['Title', 'Link', 'Date']) csv_file = 'google_news_oil_prices.csv' df.to_csv(csv_file, index=False) # Download the file to your computer (only works in Google Colab) try: from google.colab import files files.download(csv_file) except ImportError: print("The files module is not available. This code is not running in Google Colab.") Future Projects: Relation of frequency of Oil related posts and sustainability risks Relation of frequency of Oil related posts and Stock Prices (General & Oil producing/intensive firms) Updated Code # Install and set up necessary packages and dependencies !pip install selenium !apt-get update !apt install chromium-chromedriver import sys sys.path.insert(0,'/usr/lib/chromium-browser/chromedriver') from selenium import webdriver from selenium.webdriver.chrome.options import Options from selenium.webdriver.common.by import By from selenium.webdriver.common.keys import Keys from bs4 import BeautifulSoup import pandas as pd import time from datetime import datetime, timedelta import re # Function to convert various date formats to a standardized format def convert_relative_date(text): current_datetime = datetime.now() current_year = current_datetime.year if 'hour' in text or 'hours' in text: return current_datetime.strftime('%Y-%m-%d') elif 'day' in text or 'days' in text: match = re.search(r'\d+', text) days_ago = int(match.group()) if match else 0 return (current_datetime - timedelta(days=days_ago)).strftime('%Y-%m-%d') elif 'minute' in text or 'minutes' in text: return current_datetime.strftime('%Y-%m-%d') elif 'yesterday' in text.lower(): return (current_datetime - timedelta(days=1)).strftime('%Y-%m-%d') else: try: parsed_date = datetime.strptime(text, '%b %d') return datetime(current_year, parsed_date.month, parsed_date.day).strftime('%Y-%m-%d') except ValueError: return text # Return the original text if parsing fails # Set up Chrome options for Selenium chrome_options = Options() chrome_options.add_argument('--headless') chrome_options.add_argument('--no-sandbox') chrome_options.add_argument('--disable-dev-shm-usage') # Initialize the Chrome WebDriver with the specified options driver = webdriver.Chrome(options=chrome_options) # Fetch the Web Page url = 'https://news.google.com/search?q=oil%20prices' driver.get(url) # Scroll the page to load more articles for _ in range(5): # Adjust the range for more or fewer scrolls driver.find_element(By.TAG_NAME, 'body').send_keys(Keys.END) time.sleep(2) # Wait for page to load # Get the page source and close the browser html = driver.page_source driver.quit() # Parse the Web Page using BeautifulSoup soup = BeautifulSoup(html, 'html.parser') articles = soup.find_all('article') # Extract the Necessary Information news_data = [] base_url = 'https://news.google.com' for article in articles: title_link_element = article.find('a', class_='JtKRv', href=True) title = title_link_element.text.strip() if title_link_element else "No Title" link = base_url + title_link_element['href'][1:] if title_link_element else "No Link" time_element = article.find('time') date = time_element.text.strip() if time_element else "No Date" news_data.append([title, link, date]) # Store the Data in a DataFrame df = pd.DataFrame(news_data, columns=['Title', 'Link', 'Date']) # Convert dates to a standardized format for i, row in df.iterrows(): df.at[i, 'Date'] = convert_relative_date(row['Date']) # Save the DataFrame to CSV csv_file = 'google_news_oil_prices.csv' df.to_csv(csv_file, index=False) # Download the file to your computer (only works in Google Colab) try: from google.colab import files files.download(csv_file) except ImportError: print("The files module is not available. This code is not running in Google Colab.") Previous Next

< Back Financial Performance of Ghana's political regimes from 1960 - 2000 Analysis of Economic Growth in Ghana, 1960 – 2000 – ARYEETEY & FOSU "Economic Growth in Ghana, 1960 - 2000" by Ernest Aryeetey and Augustin Kwasi Fosu examines the fluctuating economic growth in Ghana over four decades, marked by frequent policy changes and military coups. The study highlights the transition of Ghana's economy and its impact on the livelihoods of its citizens. Period Key Factors Impact on Growth Post-Independence (1960-1965) Kwame Nkrumah implemented his 7-year Plan, emphasizing high investments into public infrastructure and industry creation. The economy grew rapidly due to the investments, but the high government spending led to inflation and a decline in living standards. Busia and the Military (1966-1971) Busia and the military regime ushered pro-private capital policies, devaluing the Ghanaian Cedi and liberalizing the external sector. The economy initially grew due to the pro-business policies, but the devaluation of the cedi caused inflation and public anger, leading to another coup d'état. The Era of Five Regimes (1972-1983) This period was marked by economic decline due to high government intervention policies, low productivity, and external shocks such as drought and food shortages. The economy contracted significantly during this period, with GDP growth averaging only 2.2% per year. Inflation soared to 112% in 1983, and the government deficit reached 17% of GDP. After the Economic Reforms (1984-2000) The government launched the Economy Recovery Program (ERP) under the World Bank and the IMF, which liberalized the economy and led to increased growth. The ERP helped to stabilize the economy and promote growth, with GDP growth averaging 5.3% per year during this period. Inflation fell from 112% in 1983 to 10% in 1992, and the government deficit was reduced to 4% of GDP. 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.