The rationality hypothesis has been a very popular topic among the academics. Being a widely accepted hypothesis as part of the traditional finance theories, an investor is deemed a rational agent and makes rational decisions by exhausting all available alternatives. However, recently, new behavioural finance theories have been gaining ground as many empirical findings, which have been left unanswered by the traditional theories, can be explained by these behavioural-approach based theories. This research examined the impact of psychological factors on risk-taking behaviour in investment decisions. In particular, this research considered the possible effects of psychological factors, namely herding, heuristics, prospect, market, self-attribution bias, and familiarity bias, in making investment decisions. The findings in this paper declared that risk-taking behaviour in investment is affected by herding factors, heuristics factors, prospect factors, market factors and self-attribution bias factors. The familiarity bias factors do not significantly affect risk-taking behaviour in financial investment. Keywords: Behavioural finance, Herding, Heuristics, Prospect, Market, Self-attribution bias, Familiarity bias

The aim of this study is to choose and estimate the effect of foreign investments in engine for economic growth and hence poverty reduction in the developing countries. The study concluded that there is a weak effect of non-moral of foreign investments on decreasing unemployment in the developing countries, regardless of the existence or absence of development. In addition, the study concluded with a negative effect for the total local production on unemployment and poverty. Finally, we concluded that this may be a result of profit repatriation of foreign firms, crowding out of domestic investment because of FDI or low level of human capital in the country.Consequently, the study recommended to despite how desirable the inflow of FDI is to developing countries, care should be taken when attracting foreign investments and they should be directed to the productive sectors of the economy. Also government should create a competitive environment so as to maximize the benefits of FDI because by exposing foreign investors to an even playing field with indigenous investors.

The revolution in neural networks is a significant technological shift. It has an impact on not only all aspects of production and life, but also economic research. Neural networks have not only been a significant tool for economic study in recent years, but have also become an important topic of economics research, resulting in a large body of literature. The stock market is an important part of the country’s economic development, as well as our daily lives. Large dimensions and multiple collinearity characterize the stock index data. To minimize the number of dimensions in the data, multiple collinearity should be removed, and the stock price can then be forecast. To begin, a deep autoencoder based on the Restricted Boltzmann machine is built to encode high-dimensional input into low-dimensional space. Then, using a BP neural network, a regression model is created between low-dimensional coding sequence and stock price. The deep autoencoder’s capacity to extract this feature is superior to that of principal component analysis and factor analysis, according to the findings of the experiments. Utilizing the coded data, the proposed model can lower the computational cost and achieve higher prediction accuracy than using the original high-dimensional data.