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< 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 Cocoa Production: West Africa - 2022 Work in progress Previous Next

< Back Sustainability Dimensions of Stocks on the SIX:Render 1 Quantitatively assessing Brundtland's Dimensions (1987). The case of the SIX Previous Next

< Back Prisoner's Dilemma: Player 2 Allowing users to participate in Robert Axelrod's 1980 Computer Tournament. Previous Next

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

< Back Game Theory: Prisoner's Dilemma Strategies Tools Recreating and Simulating Robert Axelrod's 1980 Computer Tournament. 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 Hollywood Boulevard to Wall Street: Futurism in Movies and Tech-Stock Prices This study investigates the relationship between the box office sales of futurism-themed movies and the performance of the tech stock index. This study investigates the relationship between the box office sales of futurism-themed movies and the performance of the tech stock index. Utilizing regression analysis across three models, the research reveals significant insights: Tech Movies and Tech Stock Index: A strong positive correlation was found between the box office sales of tech movies and the tech stock index, with an Adjusted R-squared value of 0.8263, indicating a substantial predictive power of tech movie sales on tech stock prices. Top 10 Box Office and Tech Stock Index: A broader analysis including the top 10 box office sales showed a positive but less pronounced impact on the tech stock index, suggesting that the influence of tech movies is more specific and potent. Tech Movies and General Market Index: Tech movie sales also positively correlate with the general market index, though the relationship is weaker compared to the tech-specific index. In essence, the findings suggest that futurism movies, particularly those in the tech genre, have a noticeable impact on tech stock prices, offering valuable insights into the interplay between behavioral bias, entertainment and financial markets econometrics_project .pdf Download PDF • 542KB Previous Next