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on Financial Literacy and Education |
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Issue of 2019‒08‒12
three papers chosen by |
| By: | Noemi Oggero (Collegio Carlo Alberto); Mariacristina Rossi (University of Turin and CeRP-Collegio Carlo Alberto); Elisa Ughetto (Politecnico, Turin) |
| Abstract: | We investigate the attitudes to entrepreneurship of Italian households, focusing on the importance of digital skills and financial literacy as potentially relevant factors shaping entrepreneurial entry. We put the gender focus to our analysis to detect whether, and to what extent, women and men differ in their propensity to run a business. We carry out our research by using a sample of the Bank of Italy SHIW dataset for the year 2008 and 2010. Our findings suggest a strong heterogeneity, between men and women, of the importance of digital skills and financial literacy as entrepreneurial drivers. Results show that the impact of financial literacy on the probability of being an entrepreneur is significant, but only for men. Digital skills increase the probability of being entrepreneur with a bigger effect for men than for women. |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:crp:wpaper:187&r=all |
| By: | Franklin Allen; Elena Carletti; Robert Cull; Jun QJ Qian; Lemma Senbet; Patricio Valenzuela |
| Abstract: | We explore the relationship between bank branch expansion, financial inclusion and profitability for Equity Bank. Unlike traditional banks in Kenya, Equity Bank pursues branching strategies that target underserved territories and less privileged households. Its presence has a positive and significant impact on households’ access to bank accounts and credit. It increased financial inclusion by 31 percent of the adult population between 2006 and 2015. Access is especially improved for Kenyans who are less educated, do not own their own home and live in lessdeveloped areas. Equity Bank’s business model proves to be profitable both at bank and branch level. |
| Keywords: | Equity Bank, bank penetration, bank account, microfinance. |
| JEL: | G2 O1 R2 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:baf:cbafwp:cbafwp18104&r=all |
| By: | Joseph Attia |
| Abstract: | How effective are the most common trading models? The answer may help investors realize upsides to using each model, act as a segue for investors into more complex financial analysis and machine learning, and to increase financial literacy amongst students. Creating original versions of popular models, like linear regression, K-Nearest Neighbor, and moving average crossovers, we can test how each model performs on the most popular stocks and largest indexes. With the results for each, we can compare the models, and understand which model reliably increases performance. The trials showed that while all three models reduced losses on stocks with strong overall downward trends, the two machine learning models did not work as well to increase profits. Moving averages crossovers outperformed a continuous investment every time, although did result in a more volatile investment as well. Furthermore, once finished creating the program that implements moving average crossover, what are the optimal periods to use? A massive test consisting of 169,880 trials, showed the best periods to use to increase investment performance (5,10) and to decrease volatility (33,44). In addition, the data showed numerous trends such as a smaller short SMA period is accompanied by higher performance. Plotting volatility against performance shows that the high risk, high reward saying holds true and shows that for investments, as the volatility increases so does its performance. |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1907.10407&r=all |