The stock market index is one of the main tools used by investors and financial managers to describe the market and compare the returns on specific investments. Common approaches to index calculation rely on a company's market value generating a weighted average as the index. This work presents new methods of computing adaptive stock market indices based on dynamical properties of the underlying index constituents, and introduces measures to evaluate their performance. The premise behind this work is that the influence of each stock on other stocks should be a major factor in determining the weight given to each stock in the index composition. The methodologies presented here provide the means to construct a dynamic adaptive index, which can be used as a benchmark for the underlying dynamics of the market. We investigate the components of the S&P500 index, and the components of the TA25 index, representing a large (NYSE) and a small (TASE) developed market, respectively. We focus our study on periods before and during the 2008 Sub-prime mortgage crisis. Our results provide evidence that the adaptive-indices provide an effective tool for policy and decision makers to monitor the stability and dynamics of the markets, and identify bubble formation and their ensuing collapse.