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

< Back Sustainability Dimensions of Stocks on the SIX:Render 2 Quantitatively assessing Brundtland's Dimensions (1987). The case of the SIX Previous Next

< Back Game Theory: Prisoner's Dilemma Strategies Tools Recreating and Simulating Robert Axelrod's 1980 Computer Tournament. 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 Prisoner's Dilemma: Player 2 Allowing users to participate in Robert Axelrod's 1980 Computer Tournament. Previous Next

< Back Dynamic View of Ghana's Unemployment Investigating the trend and segmentation of employment in Ghana 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 Dynamic Forestry and Agricultural Summary of ECOWAS states Work in progress 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 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