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< 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 Commentary: Ghana fixes new cocoa price to control smuggling An economic commentary on the Article, "Ghana fixes new cocoa price to control smuggling" Date the commentary was written: 0 6/ 12 /2015 Read the original article on Theafricareport.com : Ghana fixes new cocoa price to control smuggling | West Africa by Dasmani Laary - 05.10.2015 The article under consideration is about an increase in the fixed price of cocoa in Ghana in order to curb the smuggling of cocoa into Ivory Coast. Ivory Coast and Ghana share a boarder cutting through their respective cocoa plantations hence, smuggling easily occurs. The article is also about a subsidy granted to the cocoa farmers to raise their output. The Ghanaian government imposed a higher fixed price of cocoa, this can viewed as the government imposing a higher minimum price on cocoa as the fixed price is above the equilibrium price. A fixed price is a market price imposed by the government and producers are only allowed to sell at exactly that price. Cocoa Board is a government-controlled institution, fixes the buying price for cocoa in Ghana. Thus the cocoa market in Ghana is planned. The price-fixing is to protect cocoa farmers from volatile prices on the world market as the article says. From the article under consideration, the new fixed price of cocoa per ton is $1759 is an increase from the former $1444. This increase would prevent cocoa farmers from smuggling their cocoa to Ivory Coast to sell it at the once better price of $1718 per ton. Price elasticity of demand or supply refers to the responsiveness of quantity demanded or quantity supplied due to a change in price. The price elasticity of demand for cocoa is relatively elastic as Ivorian (and South American cocoa) are perfect substitutes. The supply of Ghanaian cocoa is also elastic because of the smuggling of cocoa between Ghana and Ivory Coast which depends on price hence in effect affects the supply of Ghanaian cocoa positively or negatively. So if the price is high in Ghana (higher than Ivory Coast Cocoa) smuggling from Ghana to Ivory Coast would be curbed and rather cocoa grown in Ivory Coast would be smuggled into Ghana hence increasing the supplied quantity of Ghanaian Cocoa. The effect of increasing the fixed price is shown on this diagram: Figure 1: Increasing the fixed price As a result of this increase in quantity supplied there would be an excess supply (QS1 to QS2) of Ghanaian cocoa. However cocoa can be stored for a long time without losing its quality, but the government would then need to spend more on storage facilities (as the article makes reference to warehouses being built). An increase in the fixed price would also make Ghanaian cocoa less competitive globally, as Ivorian cocoa (and South American cocoa) would be winning the price war. This may be prove costly and highly inefficient for the Ghanaian government as 15% of their GDP alone is from cocoa. However the aim of increasing the fixed price was to curb the smuggling of cocoa to Ivory Coast from Ghana and this action would achieve this aim. But not only would the policy do so, but it would also stimulate the smuggling of cocoa from Ivory Coast to Ghana, reversing the tables. Although I have discussed the decrease in international competitiveness of Ghanaian cocoa (as PED is relatively elastic) that does not necessarily mean there shall be a decrease in revenue. Firstly at the old and new fixed price are relatively higher than the equilibrium price, hence PED may be inelastic at those high prices. Also, due to multiple contracts and deals in place with cocoa processing firms, Ghanaian farmers would still be able to sell to their previous customers for example Nestle. Not only that, but as there would be a higher supply of cocoa from Ghana, Ghanaian farmers would be able to meet their contract obligations with those cocoa processing firms whereas due to the reduction in cocoa in Ivory Coast, Ivorian farmers may not meet their obligations hence their deals and contracts would be passed on to Ghanaian firms In a bid to further increase the supply of cocoa in Ghana (as the article refers to the targeted 900,000 tones output for the 2015/2016 which is an increase from the actual 700,000 output of the previous year) the government is giving cocoa farmers a subsidy. The bonus of 5 cedi per bag of 64 kilogrammes, is a subsidy per unit. A subsidy is financial aid given to producers by the government in order to decrease their cost of production and in effect increase their total output. The effect of a subsidy is shown in Figure 2; F igure 2: Effect of subsidy in the Ghanaian Cocoa Market As illustrated in Figure 2 producers (Ghanaian farmers) would have a higher revenue due the subsidy, however the Ghanaian government would have to pay a lot for this subsidy. Either way, this decreases the cost of production for farmers and in effect are able to produce more cocoa. The combination of the two policies discussed in this paper is simply going to lead to a very large increase in supply of cocoa in Ghana. However this may not be so beneficial for all stakeholders. Initially farmers may enjoy larger incomes, but may have to eventually sell off the cocoa in excess supply at a lower price due to expensive storage. Government expenditure would increase due to the subsidy and the stabilization fund discussed in the article. However if the demand for cocoa continues to increase in the global market, Ghanaian government and farmers would benefit greatly. Previous Next

< Back 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. https://akweix.shinyapps.io/HedonicValuationModel/ 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

About Portfolio by Sean Akwei Anum - a Master of Science in Finance Student at the University of Neuchatel

Quant Projects View Directory Notable Projects Data Visualization of the Dynamic Efficiency of Oil and Gas Production in Ghana Zurich Real Estate Hedonic Valuation Model Real-time: Ghana Stock Exchange Data Viewer The Solow Model and Human Capital in Developing Economies Sustainability Dimensions of Stocks on the SIX:Render 4 Google News Scrapper Proving the Butterfly Effect Alternative Data Regressor: V1

< Back Google News Scrapper Scrape Google News articles for a particulair keyword and date range You can use the google_news_scraper function by providing the keyword and date range as inputs. For example, google_news_scraper("oil prices", "2023-08-25", "2023-08-31") will fetch articles with the keyword "oil prices" published between August 25 and 31, 2023, and save it as a CSV file. # Install necessary packages !pip install selenium !apt-get update !apt install chromium-chromedriver import sys import pandas as pd from datetime import datetime, timedelta import re 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 time def convert_relative_date(text, current_datetime): 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 def google_news_scraper(keyword, start_date, end_date): # Convert start_date and end_date to datetime objects start_date = datetime.strptime(start_date, '%Y-%m-%d') end_date = datetime.strptime(end_date, '%Y-%m-%d') # 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') sys.path.insert(0,'/usr/lib/chromium-browser/chromedriver') # Initialize the Chrome WebDriver with the specified options driver = webdriver.Chrome(options=chrome_options) # Fetch the Web Page query = '+'.join(keyword.split()) url = f'https://news.google.com/search?q={query}' 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 current_datetime = datetime.now() for i, row in df.iterrows(): if row['Date']: df.at[i, 'Date'] = convert_relative_date(row['Date'], current_datetime) # Filter the DataFrame by the provided date range def is_valid_date(date_str): try: return start_date <= datetime.strptime(date_str, '%Y-%m-%d') <= end_date except (TypeError, ValueError): return False filtered_df = df[df['Date'].apply(is_valid_date)] # Save the filtered DataFrame to CSV csv_file = f'google_news_filtered_{query}.csv' filtered_df.to_csv(csv_file, index=False) print(f"Filtered articles saved to {csv_file}") # Check if running in an environment that supports file download try: from google.colab import files files.download(csv_file) except ImportError: print(f"Download not supported in this environment. Please manually retrieve the file: {csv_file}") # Prompt user for input keyword = input("Enter the search keyword: ") start_date = input("Enter the start date (YYYY-MM-DD): ") end_date = input("Enter the end date (YYYY-MM-DD): ") # Call the function with user input google_news_scraper(keyword, start_date, end_date) Project Github repository: https://github.com/seanxjohn/google_news_scrapper/tree/main Previous Next

< Back 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? When studying utility maximization in finance, we typically look at an investor's efficiency/utility frontier. But what of the dynamic of choices? With financial instritutions offerring a wide host of investment solutions, the Paradox of Choice should certianly play a key role in the frontiers. Paradox of Choice Theory: The Paradox of Choice, a concept popularized by psychologist Barry Schwartz, suggests that while some degree of choice is necessary and beneficial, there comes a point where an excess of choices can lead to decreased utility. Increased Complexity: More choices can increase the complexity of the decision-making process. This can lead to anxiety, stress, and indecision, which can reduce overall utility or satisfaction. Regret and Opportunity Costs: With more options, individuals may experience regret or concern about missing out on unchosen alternatives. The awareness of opportunity costs can diminish the satisfaction derived from the chosen option. Expectation of Perfection: A multitude of choices might lead individuals to expect a perfect decision. When this expectation is not met, it can result in lower satisfaction. A simple illustration of the Paradox of Choice: Music Selection A modern issue at hand is the selection of the right song for a particulair activity. with apps such as Spotify offerring over 20 million songs, one can quickly feel overwhelemed. Thus, resolving to currated playlists, appears to be a utility maximization route - essentially removing choices Paradox of Choice in Music Selection Overwhelming Options: With streaming services offering millions of songs, listeners are faced with an almost infinite array of choices. This abundance can make the decision-making process overwhelming. Decision Fatigue: The effort required to sift through numerous options can lead to decision fatigue, where the listener becomes too tired to make a choice, or defaults to familiar options, thereby missing out on potentially enjoyable new music. Regret and Second-Guessing: Even after choosing a song, listeners might experience regret or second-guess their choice, wondering if there might be a better song they haven't discovered yet. This can diminish the satisfaction derived from the chosen song. High Expectations: With so many options, listeners might develop unrealistically high expectations for each song they choose. If a song doesn't immediately meet these expectations, they might skip it, perpetuating the cycle of searching without finding the "right" song. ... this leads to a decrease in utility To further conceptualize and understand the paradox of choice to test utility, at least within the field of music, I developed a minmalistic music app. It simply allows one to replay a song or change the song - to a random song they have no control over! The app is very basic and uses music from Youtube. A preselected playlist was built into the app. The objective of the app is to simplify the musical experience by reducing choices and observing the effect on the users utility. Try it out here. App was built for Android: https://github.com/akweix/Music_Minimalism 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 frankenstein.io - Draft 1 Restructuring & Simplifying "Frankenstein codes" With the emergence of LLMs alongside the traditional sources of community shared codes (Stackoverflow, Github, etc), most coding projects are nothing more than Frankenstein codes - code generated by picking up sections from various sources and combining them together into one project - notoriously common for unskilled programmers or coders wishing to maximize productivity. "Frankenstein.io" is an AI-powered web application designed for coders. It takes full code submissions and, using guided objectives, logically restructures the code to simplify it while ensuring the output remains exactly the same as the original. Additionally, the application includes a citation tool that tracks and cites open-source contributions and other sources from which the code is derived. Logical Breakdown: Code Simplification Frankenstein.io allows users to input full code submissions, which are then analyzed and restructured using advanced AI algorithms. The primary goal is to ensure that the simplified code produces the same results as the original. Additionally, the platform provides a detailed narrative explaining the changes made during the simplification process. Citation Tool To enhance transparency and accountability, Frankenstein.io tracks the sources of code snippets used in the simplification process. It automatically generates citations for open-source contributions and displays these citations alongside the simplified code. Technology Stack Frontend The frontend of Frankenstein.io is built using HTML, CSS, and JavaScript, with frameworks like React.js or Vue.js. For a more robust structure, Next.js or Nuxt.js frameworks are employed. Backend The backend is developed using Node.js with Express.js or Python with Flask/Django. AI and ML models, such as TensorFlow or PyTorch, are used for code analysis and restructuring. The database management relies on MongoDB or PostgreSQL. AI/ML Natural Language Processing (NLP) is utilized for understanding code objectives, while machine learning models identify and apply code simplifications. Version Control & Deployment For version control, GitHub or GitLab is used. The application is containerized using Docker and orchestrated with Kubernetes. Deployment is managed on platforms like AWS, Google Cloud, or Azure. Development Plan Research & Planning Initial steps include conducting market research to understand the target audience's needs and defining the specific objectives and requirements for the AI algorithms. Planning the user interface and user experience is also crucial. Design & Prototyping Wireframes and mockups for the web application are created, followed by the design of the UI/UX to ensure a seamless user experience. AI Algorithm Development This phase involves developing and training AI models to analyze and simplify code, as well as testing these models on various code samples to ensure accuracy. Backend Development Setting up the server environment and database is essential, along with developing API endpoints for code submission, analysis, and retrieval. Frontend Development The user interface is developed based on the designs, implementing features for code submission, viewing simplified code, and displaying citations. Integration & Testing AI models are integrated with the backend, followed by thorough testing to ensure the application works as expected. User testing is also conducted to gather feedback. Deployment & Maintenance The application is deployed to a production environment, with continuous monitoring for any issues and regular maintenance and updates. User Flow Code Submission Users can submit their code through a user-friendly interface, allowing them to provide guided objectives for the AI to focus on. Objective Setting Users specify what they want the AI to focus on during the code analysis. Code Analysis The AI analyzes the code, restructures it, and ensures the output remains the same. Simplified Code Display Users can view the simplified code along with a narrative explaining the changes. Citation Display Citations for code sources are displayed alongside the simplified code. Potential Challenges Ensuring the AI can handle various coding languages and styles is a significant challenge, along with maintaining the accuracy and reliability of the AI's code simplification. Properly attributing sources and managing citations to avoid plagiarism is also crucial. Previous Next