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< 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 Forestry and Agricultural Summary of ECOWAS states Work in progress 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 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 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 Alternative Data Regressor Framework: Flow Chart A framework for linear regression of alternative data against financial asset prices - the flow chart Previous Next

< Back Web-Scrapper V1: Web Application Web Application for web-scrapping news articles Full code here on Python Anywhere 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 Sustainability Dimensions of Stocks on the SIX:Render 2 Quantitatively assessing Brundtland's Dimensions (1987). The case of the SIX Previous Next