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< 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

< Back Game Theory: Prisoner's Dilemma Strategies Tools Recreating and Simulating Robert Axelrod's 1980 Computer Tournament. Previous Next

< Back 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" Date the commentary was written: 21/ 09 /2016 Read the original article on Global Research : " Washington’s Decision to “Normalize” Relations with Cuba: Impede China’s Growing Influence in Latin America?" by Birsen Filip - 28.08.2016 The article under consideration is about the possible lifting of the Cuban embargo imposed by the American Government in 1936. The idea of removing this historic embargo has been introduced recently and is in the process of becoming a reality due to Barrack Obama. Barrack Obama, the present president of the United States according to the article, shocked the world by officially reestablishing diplomatic relations with Cuba and furthermore slowly lifting the historical embargo. However, this article explores the embargo lifting as a means of the USA to impede China’s International Market power growth in Latin America in light of the recent trade deal between China and Cuba. In this commentary, I shall be exploring the probable effects of lifting the embargo, with respect to the International Market and the Cuban economy. According to the article, it can be deduced that the USA is trying to prevent China from becoming a “monopoly” in the International Market. An embargo is a government order that restricts commerce or exchange with a specified country or the exchange of specific goods. An embargo is usually created as a result of unfavorable political or economic circumstances between nations. The restriction looks to isolate the country and create difficulties for its governing body, forcing it to act on the underlying issue. [1] In the case of the US embargo on Cuba, it is due to the relation Cuba was having with Communist powers. The Cuban embargo majorly affected the tourism in Cuba, sugar production, many other agricultural sectors and cigar firms. Figure 1: Current agricultural production in the Cuban economy As illustrated on the graph above, as the embargo technically prohibits Cuba from trading internationally (as the USA “penalizes” other countries that trade with Cuba) their agricultural goods although having an advantage of the lower price in comparison to the world price, Cuba cannot exploit that advantage. However, if the embargo is to be lifted Cuba would benefit greatly as they can produce many agricultural goods at a lower price than most countries and furthermore specialize in agricultural goods to even greatly increase their production. This would lead to an increase in jobs, increase in GDP and incomes in Cuba. Due to the embargo many goods and services have to be produced domestically as Cuba cannot benefit from international trade. Due to the production of a vast array of goods and services domestically Cuba cannot efficiently produce all goods and services, and the quality is quite low. For instance, it is not efficient for Cuba to produce heavy duty farming machines, whereas China having a comparative advantage in heavy duty machines can effectively produce them. A country has a comparative advantage in producing a product when it has the lowest opportunity cost for producing the product. Figure 2: Electronics and technological devices Market in Cuba currently As illustrated in the diagram above, currently Cuba’s technological industry and many other industries are producing at a higher price than the World Price. This mainly due to lack of specialization. The people of Cuba are subjected to some high priced goods and services which are very low in quality. However, if the embargo is to be lifted Cubans would have access to the lower priced, higher quality goods and services from the international market. Due to the large diversification in goods and services produced domestically, the Cuban economy has not specialized in particular products, hence does not hold any significant comparative advantage in any good or service production when compared to most countries. As Cuba would be able to trade much easier in the international market, hence would have access to cheaper raw resources from Africa and Americas, cheaper labor from Asia and greater capital from Europe and North America. Figure 3: Effect of lifting the Embargo in the Cuban Economy As shown on the diagram above, the lifting of the embargo would be highly beneficial for the Cuban economy. Aggregate demand and supply would increase. The total output of the economy increases from Y1 to Y2. The average price level of goods and services increases, but this increase is actually quite beneficial for Cuba as incomes would increase and producers make larger profits. The lack of specialization due to the embargo hinders the growth of the Cuban economy. However with the lifting of the embargo, this would increase economic activity and boost economic growth in Cuba. [1] http://www.investopedia.com/ Previous Next

< Back Beta of Fan Milk Ltd (FML): Ghana Stock Exchange (GSE) Finding the Beta of FML on the GSE using Python (Jupyter Notebook) FML Beta Results Timeframe Raw Beta Adjusted Beta 1-Month 0.563058 0.708706 3-Month 0.145659 0.430439 1-Year 0.408104 0.605403 2-Year 0.356980 0.571320 3-Year 0.366631 0.577754 5-Year 0.350336 0.566891 10-Year 0.372667 0.581778 View realtime data on FML via my GSE Stock data viewer: https://www.akweidata.com/projects-1/ghana-stock-exchange%3A-real-time-prices-web-app-v1 Data FML data retrieved from the Ghana Stock Exchange Website: https://gse.com.gh/trading-and-data/ GSE-CI data retrieved from Eikon Refinitiv Code import pandas as pd GSECI = pd.read_excel("GSECIdata") FML = pd.read_excel("FMLdata") GSECI.head() FML.head() GSECI.dtypes FML.dtypes # Convert the date columns to the same format # Assuming the date columns are named 'Date' in both dataframes GSECI['Date'] = pd.to_datetime(GSECI['Date'], format='%m/%d/%Y') FML['Date'] = pd.to_datetime(FML['Date'], format='%d/%m/%Y') # Now merge the dataframes on the 'Date' column combined_data = pd.merge(FML, GSECI, on='Date', suffixes=('_FML', '_GSECI')) # Display the first few rows of the combined dataframe to check the merge print(combined_data.head()) # Calculate daily returns for FML and GSE-CI combined_data['Return_FML'] = combined_data['Close_FML'].pct_change() combined_data['Return_GSECI'] = combined_data['Close_GSECI'].pct_change() # Drop the NaN values that result from pct_change() combined_data = combined_data.dropna() # Calculate covariance between FML's and GSE-CI's returns covariance_matrix = combined_data[['Return_FML', 'Return_GSECI']].cov() covariance = covariance_matrix.loc['Return_FML', 'Return_GSECI'] # Calculate the variance of GSE-CI's returns variance_gseci = combined_data['Return_GSECI'].var() # Calculate beta of FML beta_fml = covariance / variance_gseci print(f"The beta of FML is: {beta_fml}") import numpy as np import pandas as pd # Assuming 'combined_data' has already been defined and contains daily return data # Define a function to calculate raw and adjusted beta def calculate_beta(return_stock, return_market): covariance = return_stock.cov(return_market) variance = return_market.var() raw_beta = covariance / variance # Adjusted beta is calculated with the formula (2/3 * raw_beta + 1/3) adjusted_beta = (2/3 * raw_beta) + (1/3) return raw_beta, adjusted_beta # Define time frames in trading days time_frames = { '1-Month': 21, '3-Month': 63, '1-Year': 252, '2-Year': 504, '3-Year': 756, '5-Year': 1260, '10-Year': 2520 } # List to store beta values beta_values = [] # Calculate beta for each time frame for period, days in time_frames.items(): if days < len(combined_data): # Slice the last 'days' of trading data for the period period_data = combined_data.tail(days) raw_beta, adjusted_beta = calculate_beta(period_data['Return_FML'], period_data['Return_GSECI']) beta_values.append({'Timeframe': period, 'Raw Beta': raw_beta, 'Adjusted Beta': adjusted_beta}) # Convert the list of dictionaries to a DataFrame beta_df = pd.DataFrame(beta_values) # Print the beta values in tabular form print(beta_df.to_string(index=False)) 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 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 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

< Back Cocoa Production: Ghana and Ivory Coast - Historic Trend Work in progress Previous Next

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 Sustainability Dimensions of Stocks on the SIX:Render 3 Quantitatively assessing Brundtland's Dimensions (1987). The case of the SIX View Plot here: https://www.akweidata.com/sixsustainabilitydimensions Previous Next