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To date, there are many myths surrounding security selection and fundamental analysis due to the notion that all available information is perfectly priced in security. However, fundamental analysis is still very useful in determining the financial health of security, which is much needed for long-term investing. The aim of this study was to perform a valuation analysis of international stock markets so as to recommend buy or sell signals suitable for investment decisions. This study made use of the coefficients of a multiple regression analysis and value drivers for the S&P 500, Nasdaq, JSE, DAX, Nikkei 225, BIST 100, FTSE, and SSE as of January 23, 2023. The findings revealed that the JSE, DAX, Nikkei 225, and FTSE are undervalued, which translates to a buy signal, while the S&P 500, Nasdaq, BIST 100, and SSE are overvalued. These findings imply that investing in the JSE, DAX, Nikkei 225, and FTSE will produce decent capital gains on a risk-adjusted basis.

In the dynamic world of financial markets, the prices of assets can exhibit dramatic fluctuations, sometimes soaring to dizzying heights or plummeting to alarming lows. However, amidst the chaos, a fascinating phenomenon emerges: a tendency for prices to revert back to their long-term average or mean level. This concept known as mean reversion has intrigued traders, investors, and researchers for decades. Understanding mean reversion provides valuable insights into market dynamics, investor behavior, and the potential for profitable trading strategies. The aim of this study was to empirically investigate mean reversion in financial markets. This study employed a Hurst model for a sample of five financial markets from June 1, 2018 to June 1, 2023. The findings revealed that four out of the five sampled financial markets exhibit mean reversion, which challenges the efficient market hypothesis concept. Therefore, portfolio managers and active market participants can utilize long-term memory to optimize asset allocation decisions by considering the persistent effects of past returns and adjusting portfolio weights to take advantage of potential return predictability and manage risk.

Mutual fund is one of the investment instruments widely used as an investment option. There are two essential things to be considered by the Investment Manager as a mutual fund manager: the stock selection and the market timing ability. The Covid-19 pandemic has caused many investors to experience potential investment losses. Understanding stock selection and market timing will be needed for determining which Investment Managers perform well in managing equity mutual fund, especially while dealing with the Covid-19 pandemic. The objective of this study is to analyze the ability of Investment Managers to perform stock selection and market timing, the performance of equity mutual fund, and the effect of stock selection and market timing ability on the performance of equity mutual fund in Indonesia. This study applies the Fama net selectivity methods to assess stock selection ability and Treynor-Mazuy and Henriksson-Merton models to analyze market timing ability. Sharpe, Treynor, and Jensen's alpha methods are also used for measuring mutual fund performance. The data is processed per mutual fund to recognize each mutual fund's stock selection and market timing ability. Furthermore, the research spans two time periods: before and following the Covid-19 pandemic. A dataset of 104 equity mutual funds from 2016-2021 has been analyzed. The findings show that only 9 and 12 Investment Managers have stock selection ability prior to and following the Covid-19 pandemic, respectively. According to Treynor Mazuy model, there are only two Investment Managers with market timing ability prior to the Covid-19 pandemic, and only twelve Investment Managers have market timing ability during the Covid-19 pandemic, according to the Henriksson-Merton model. Every mutual fund with stock selection ability outperformed the benchmark. However, not all equity mutual fund with market timing ability outperformed the benchmark.

The dynamics of investing in stock markets have changed considerably over the past twenty years. The value and growth investing concept has drawn much attention and continues to dominate portfolio management. Factor investing is an approach that aims at quantifying particular characteristics through matrixes of quality, value, momentum, and growth to improve the returns of a portfolio. Although the three-factor style of investing has gained widespread acceptance, the five-factor continues to be a source of disagreement among academics and industry practitioners. This study aimed to review the merits of five-factor investing critically. A critical literature review was used to review prior literature. The narratives from the literature reveal that the five-factor investing style will result in multiple sources of expected return. Hence, using five factors in portfolio formation has merits and will significantly improve the returns and most likely outperform the market portfolio. Factor premiums have proven to be drivers of asset return. Therefore, market participants and investment managers can intentionally assume additional exposures to deliver superior risk-adjusted returns compared to the market with the five-factor investing style. According to the author's knowledge, this is the first study to objectively analyze the literature on five-factor investing and propose that active managers can earn positive alphas on a risk-adjusted basis through factor exposures.

The debate on whether stock prices follow a random walk continues, becoming the foundation of modern portfolio theory. Supporters of the random walk theory still believe that modeling stock prices is a wasteful exercise. However, evidence from momentum pricing strategy and price reversals suggests otherwise. This study aimed to empirically explore the random walk theory in five international stock markets before and during the Covid-19 pandemic from June 30, 2017, to June 30, 2019, and January 1, 2020, to December 31, 2021. A multivariate runs test and a generalised distribution function was applied to investigate the theory. The results revealed no significant difference between the observed S-statistics runs and the expected ones, concluding that it is not common to observe random walks in financial markets consistently. Hence price changes in financial markets are significantly affected not only based on new information and investors’ expectations but also by irrationalities that exist among market participants. These irrationalities can be modeled using a generalised distribution function to produce price patterns.