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< 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 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. Segmented Trading Hours of the SIX Swiss Exchange , as of 4th December 2023, as well as market holidays for 2024 have been incorporated in the basic web application below to display the current state of the SIX. Github: https://github.com/akweix/SIX_trading_hours Market Holidays Date Holiday Mon 01.01.2024 New Year's Day Tue 02.01.2024 Berchtholdstag Fri 29.03.2024 Good Friday Mon 01.04.2024 Easter Monday Wed 01.05.2024 Labour Day Thu 09.05.2024 Ascension Day Mon 20.05.2024 Whitmonday Thu 01.08.2024 National Day Tue 24.12.2024 Christmas Eve Wed 25.12.2024 Christmas Thu 26.12.2024 St. Stephen's Day Tue 31.12.2024 New Year's Eve The trading hours and segments of the SIX Swiss Exchange, as of 4th December 2023, are as follows: Start of Business Day : 06:00 CET Start of Clearing Day : 08:00 CET Opening of Various Segments : 08:30 CET: Bonds (CHF Swiss Confederation, CHF Swiss Pfandbriefe, Non-CHF) 09:00 CET: Blue Chip Shares, Mid-/Small-Cap Shares, Secondary Listing Shares, Sponsored Foreign Shares, Separate Trading Lines, Investment Funds, Exchange Traded Funds (ETFs), ETFs on Bonds of the Swiss Confederation, Exchange Traded Products (ETPs) 09:00 CET: Start of Trading for SwissAtMid, Swiss EBBO, Quote on Demand, ETF/ETP QOD Europe 09:15 CET: Sponsored Funds, Structured Products, Rights and Options 09:30 CET: Bonds (CHF) 15:00 CET: Sparks Shares, Global Depository Receipts End of Trading for Various Segments : 17:00 CET: Bonds (CHF Swiss Confederation, CHF Swiss Pfandbriefe, Non-CHF), ETFs on Bonds of the Swiss Confederation 17:15 CET: Structured Products, Rights and Options 17:20 CET: SwissAtMid, Swiss EBBO 17:30 CET: Quote on Demand, ETF/ETP QOD Europe Closing Auctions : 17:20 CET: Start for Blue Chip Shares, Mid-/Small-Cap Shares, Sparks Shares, Global Depository Receipts, Secondary Listing Shares, Sponsored Foreign Shares, Separate Trading Lines, Investment Funds 17:30 CET: Start for Sponsored Funds, ETFs, ETPs 17:30 CET: Run Auction and Close for Blue Chip Shares, Mid-/Small-Cap Shares, Sparks Shares, Global Depository Receipts, Secondary Listing Shares, Sponsored Foreign Shares, Separate Trading Lines, Investment Funds 17:35 CET: Run Auction and Close for Sponsored Funds, ETFs, ETPs Trading -At-Last : 17:30 CET: Start for Blue Chip Shares, Mid-/Small-Cap Shares, Sparks Shares, Global Depository Receipts, Investment Funds 17:40 CET: End for Blue Chip Shares, Mid-/Small-Cap Shares, Sparks Shares, Global Depository Receipts, Investment Funds End of Clearing Day : 18:15 CET End of Business Day : 22:00 CET Future Works: Identifying Market trends across market segments Identifying Market trends across trading days (seasonality) Previous Next

< Back Dynamic View of Ghana's Unemployment Investigating the trend and segmentation of employment in Ghana Previous Next

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

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

< Back 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) This Web-app is fully powered by GSE-API: Ghana Stock Exchange API found on http://dev.kwayisi.org/ . Github: https://github.com/akweix/GSE_price_finder Listed Companies and their Tickers on GSE Previous Next

< Back Is ignorance truly bliss? Investigating the link between the lack of general information and the conception of the economy in Ghana. Project from 2018 I am an avid lover of George Orwell’s book 1984. Within the book, a major concept that plays on is that ignorance is bliss. Hence, people living in poverty (the proles) with limited availability to information on the economy somehow live happily. So, I started to ponder upon how such a concept plays within my continent. Is ignorance truly bliss? With poverty in Africa being so high, is the absence of information about the economy to the general members of the society the cause of social stability in spite of economic turmoil (Asogwa, Eze, & Ezema, 2017)? When the economy is in shambles and the political sphere is in disarray are we still under the misconception that all is well? Does happiness exist in poverty?(Graham, 2011) Research Topic: Is ignorance truly bliss? Investigating the link between the lack of general information and the conception of the economy in Ghana. Is the conception of the present economic condition in a country by members of the public associated with how often they use the internet as a source of news? Data & methods This sample is taking from the Afro – barometer. It depicts 2400 respondents from Ghana answering questions pertaining to their perception of the economy and their use of the internet as a source of news. In relation to the research topic, investigating the use of the internet as a source of news is what is being presented as “general information.” Other sources such as the radio (Fombad, Madeleine, & Jiyane, 2016) or newspapers (Omolewa, 2008) are not suitable sources of information for this project. However, the nature of the internet makes it the best source of estimating ones general knowledge (Stilwell, 2018) The observations from this sample were achieved via surveys. Thus, using the two categorical variables of “Conception of the economy” and “frequency of the use of the internet as a source of news.” According to the Chi – squared test, there is an association between these two variables Results: Univariate Analysis From the graph above, we come to find that 85% of Ghanaians have a negative conception of the economy whereas for positive conception it is only 15%. As depicted above, we come to find that within the rural setting there is a greater positive conception of the economy as compared to the urban setting. Results: Bivariate Analysis Key Findings From the univariate analysis key proportions are brought to light. From the sample, most Ghanaians have a negative conception of the economy. The Urban rural relationship simply sheds light to the relative differences –this graph is a bonus feature. However, we come to find that per or Bivariate Analysis there is an association between “Perception of the economy” and “Use of the internet as a source of Information” It must be said, the association is not extremely strong – although there is still an association Moreover, as the use of the internet increases, the more likely one perceives the Ghanaian economy in a positive manner As the use of the internet decreases, the more likely one perceives the Ghanaian economy in a negative manner Conclusion The overarching question from my research question being Is ignorance bliss appears to be less likely the case in Ghana –using the internet as a source of awareness. I have come to find out that from my sample, the lower one’s usage of the internet as a source of news, the more likely they are to rate the economy negatively. Thus, ignorance is not bliss, but awareness is rather. Why is this the case, contrary to other research works and the 1984 book? Two huge lurking variables are the availability of information and technology in low income households as well as and ones competency with the internet due to their education level (Ajakaiye, Olu, & Kimenyi, 2011). Hence, with a low income and inability to use the internet most likely due to a low education level, such a person is more likely to be living in undesirable conditions, thus, they have a negative conception of the economy. However, this claim arising from the lurking variables can be refuted with the simple logic that this paradox of “Poverty and happiness/bliss” has been proven (Graham, 2011) So why are the findings as such? Why is it so that the higher one’s usage of the internet as a source of news, the more likely they are to have a positive conception of the Ghanaian economy? This is so because the Ghanaian economy is relatively perfoming well. Though there is much poverty and low infrastructure capacity, there is still a significant amount of development. With a strong economic growth and increasing standards of living, the state o the Ghanaian economy is not as bad as many would claim – especially in relation to other low income countries. Hence, the higher one’s use of the internet, the more likely they are to know about the economy, especially in relation to other low income countries. They come to understand that all is not as bad as it seems, hence, tend to have a higher perception of the Ghanaian economy. Thus, the answer to the overarching question Is ignorance bliss? Yes – but only if things are going bad. In the case of Ghana’s economy, things are actually going well, hence, ignorance is not bliss in this case. So, fellow Ghanaians, put the pitchforks down, for no revolts are required here. The Ghanaian economy is doing well and those with access to the best source of information – the internet –are most likely to know this, hence are more likely to have a more positive conception of the economy. This research has introduced another aspect to the age old question of, Is ignorance bliss? By depicting that such is most likely to be the case if and only if the surroundings are in a tumultuous state. References Ajakaiye, Olu, and Mwangi S. Kimenyi. “Higher Education and Economic Development in Africa: Introduction and Overview.” Journal of African Economies 20, no. suppl_3 (August 1, 2011): iii3–13. https://doi.org/10.1093/jae/ejr027 . Asogwa, Brendan Eze, and Ifeanyi Jonas Ezema. “Freedom of Access to Government Information in Africa: Trends, Status and Challenges.” Records Management Journal 27, no. 3 (August 18, 2017): 318–38. https://doi.org/10.1108/RMJ-08-2015-0029 . Fombad, Madeleine C., and Glenrose Veli Jiyane. “The Role of Community Radios in Information Dissemination to Rural Women in South Africa.” Journal of Librarianship and Information Science, September 22, 2016, 0961000616668960. https://doi.org/10.1177/0961000616668960 . Graham, Carol. “Adaptation amidst Prosperity and Adversity: Insights from Happiness Studies from around the World.” The World Bank Research Observer 26, no. 1 (February 1, 2011): 105–37. https://doi.org/10.1093/wbro/lkq004 . OMOLEWA, MICHAEL. “ADULT LITERACY IN AFRICA: THE PUSH AND PULL FACTORS.” International Review of Education / Internationale Zeitschrift Für Erziehungswissenschaft / Revue Internationale de l’Education 54, no. 5/6 (2008): 697–711. Stilwell, Christine. “Information as Currency, Democracy, and Public Libraries.” Library Management 39, no. 5 (April 18, 2018): 295–306. https://doi.org/10.1108/LM-08-2017-0078 . Previous Next

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

< Back Sustainability Dimensions of Stocks on the SIX:Render 2 Quantitatively assessing Brundtland's Dimensions (1987). The case of the SIX 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 Convering Excel to CSV: Web Application A basic Web application written in HTML and Javascript to convert excel files to CSV. Github: https://github.com/akweix/excel_to_csv Previous Next

< Back Cocoa Production: Ghana and Ivory Coast - 2022 Summary of Cocoa Production in Ghana and Ivory Coast in 2022. Previous Next