Investors typically build portfolios for retirement. Investment portfolios are typically based on four asset classes that are commonly managed by large investment firms. The research presented in this article involves the development of an artificial neural network-based methodology that investors can use to support decisions related to determining how assets are allocated within an investment portfolio. The machine learning-based methodology was applied during a time period that included the stock market crash of 2008. Even though this time period was highly volatile, the methodology produced desirable results. Methodologies such as the one presented in this article should be considered by investors because they have produced promising results, especially within unstable markets.
On January 1, 2020, most large and mid-sized U.S. banks adopted Current Expected Credit Losses (CECL), a new accounting standard for estimating allowances. Allowance for credit losses is an estimate of the amount that a bank is unlikely to recover from a financial asset.
This study analyzes the relationship between gender and risk aversion and overconfidence in making financial asset investment decisions for investors in Surabaya. The sample in this study amounted to 179 investors. Data were collected using a questionnaire. Methods of data analysis were performed using cross-tabulations and chi-square. This analysis shows that gender has a significant relationship with risk aversion and overconfidence in making financial asset investment decisions.