The study sought to examine the relevance of firm fundamentals in explaining stock returns of non-financial firms listed at the Nairobi Securities Exchange. The study employed a descriptive research design. A census targeting the 44 non-financial firms listed between the years 2004 and 2013 was conducted. The study used secondary data obtained from Nairobi Securities Exchange. The relationship between stock returns and three fundamentals was measured using the Karl Pearson moment correlation coefficient while regression analysis was used to determine the effect of change in total assets, change in revenue and change in financial leverage on stock returns. The overall significance of the model was tested using F test while the significance of the individual independent variable was tested using t-test. The study found a weak positive correlation between stock returns and change in total assets, while change in revenue and change in financial leverage exhibited a negative relationship with stock returns. However, the relationship between stock returns, change in total assets, change in revenue and change in financial leverage was found not to be significant. The study concluded that change in total assets, change in revenue and change in financial leverage cannot be used to meaningfully estimate stock returns for non-financial firms listed at the Nairobi Securities Exchange. Further studies may explore the fundamental factors that significantly influences stock returns at the Nairobi Securities Exchange by further analyzing the information reported in financial statements.

This study sought to establish the relationship between liquidity risk and failure of commercial banks in Kenya in the years 2013 to 2016. Additionally, the study endeavoured to establish the effect of capital adequacy, asset quality, management quality, earnings, sensitivity to market and size on the failure of banks in Kenya. To achieve this goal, secondary data was collected from the websites of operational banks while data for failed banks was collected from reports published by the central bank of Kenya, corroborated with publications in past years newspapers. Panel logit regression was used to analyze the data using Eviews 9.5 student version. The results of the regression revealed that there was a positive and significant relationship between liquidity risk and bank failure, implying that liquidity increased the likelihood of failure. The study also found a positive and significant relationship between bank failure and asset quality and earnings indicating that they increased the likelihood of failure. The study found a negative and significant relationship between bank failure and management quality and sensitivity to market implying that they decreased the likelihood of bank failure. Capital adequacy and bank size were found to have insignificant relationship with the failure of commercial banks in Kenya. These findings are valuable to managers in understanding how the variables of the study increase or decrease the likelihood of failure so that they may come up with appropriate strategies for managing the various risks facing their banks

The study sought to establish the relationship between debt knowledge and indebtedness in Kenya. Positivism paradigm was used in this study. The study adopted a cross sectional and correlational descriptive research design. The study targeted about 2.4 million employees in the formal sector. Three stage sampling was done, first, cluster sampling and then, stratified sampling and finally random sampling. The study used primary data collected by use of self-administered questionnaires. A pilot test of the questionnaire was conducted on 40 respondents to check its validity and reliability. 1000 questionnaires were circulated. Of the returned, 581 questionnaires were considered usable. Cronbach's alpha for likert type items was found reliable (over 0.7). Data analysis used IBM SPSS statistics 21 for descriptive and correlation analysis. Further, OLS Multiple regression models were used to examine the relationships between the independent variable and the dependent variable. The findings reveal that debt experience has a significant effect on indebtedness. Results also found that aggregated debt literacy only explain a mere 9.8 % of respondent's indebtedness. The study will help to buttress economic theories of borrowing. Further the government, policy makers, employers and scholars will benefit from the findings of the study. Future research should explore the effect of dimensions like debt attitude and financial socialization on indebtedness. Further, debt literacy for individuals in the informal sector need to be related to their indebtedness while the lenders' perspective need to be sought.

This study sought to establish the determinants of bear market performance by taking a survey of investors at the Nairobi Securities Exchange. To achieve this, a quantitative and qualitative research design was adopted and the study involved administering questionnaires to 500 retail investors participating at the Nairobi Securities Exchange through five purposively selected stock brokerage firms based in Mombasa Town. Convenient sampling technique was used to administer questionnaires to respondents. Data was analyzed by the use of descriptive statistics and correlation analysis was carried out to determine the relationship between the variables. A multiple regression model was employed to analyze the independent variables and their effect on bear market performance. The ANOVA result at a p-level of .05 showed that all the four variables; transaction cost, mobilization of resources by retail investors, financial literacy and cultural values had an influence on bear market performance. The Pearson Moment correlation analysis showed that bear market performance was weakly associated with transaction costs and financial literacy while the relationship between bear market performance and mobilization of resources by retail investors as well as cultural values was largely insignificant. The study recommends that further research should be carried out on the economic cycle and its influence on bear market performance.

Effect of Behavioural Biases on Tactical Asset Allocation in Insurance Companies in Mombasa Town, Kenya 1. Introduction Investor psychology and emotions are key determinants of investments in financial markets and the value of financial securities as is widely recognized (Nyamute, Lishenga & Oloko, 2015). An investor is said to be behaviourally biased when the investor makes decisions that are not rational, that is, faulty decisions (Pompian, 2012). Biased investors are subject to given beliefs or attitudes as they make decisions which are said to be irrational (Shefrin, 2007). Singh (2010) stated that investors may make cognitive errors because of faulty reasoning caused by emotions and investor psychology. Tactical asset allocation is an allocation within a portfolio that takes advantage of short-term opportunities which result in an extra return, based on preset asset mix. Tactical asset allocation is considered as a practice that helps to improve returns from the initial long-term asset mix set by the enterprise (Dziwok, 2014).Insurance companies can be said to be practicing tactical asset allocation when they adjust the asset mix in line with forecasts of movements of investment returns in the short term. In tactical asset allocation, the investor considers changes to the initial asset targets for the overall portfolio and within asset classes (Usman, 2018). Insurance companies modify their portfolios by reallocating funds to various asset classes in the midst of changes in the business environment. An example of such change is interest rate capping in Kenya which had implications for portfolio mix of insurance companies which usually buy bank bonds. The insurance companies are critical in raising funds and risk management, to facilitate financial and economic development (Li, 2019). Interest rate capping in Kenya resulted in generally low interest rates offered by banks. The advent of low interest rates affected the financial connection between banking and insurance sectors as it changes the patterns of funding banks and the strategies of investment in insurance company business (Niedrig, 2015). Efficiently carrying out tactical allocation of funds in insurance companies are key in enhancing the competitiveness of insurance business (Li, 2019). Behavioral biases are wrongs and potential damaging behaviors caused by an erroneous decision. Psychologists have noted that people are usually overconfident; they usually overrate their abilities to achieve investment targets, that is, performance of investments. One can rarely find a person rating his ability as below average (Byrne& Utkus, 2013). Practically, people usually view the world positively as regards their endeavours and plans. Despite the fact that such world views can help one to forget disappointing experiences, it can result in biased fund allocation decisions. This is because the investor will be tempted to exaggerate their ability to engage in successful investment ventures and have a narrow view of the real factors surrounding the investment decis...