Our research showed that search volume on Google serves or GSV as an intuitive proxy for overall stock market recognition. We proposed a predictive parsimonious model TFARM (two factor auto-regressive methodology) on stock market liquidity measures (bid - ask spread, market efficiency coefficient, trading probability, turnover ratio (TR), and total volume (TV)) and employed public and free information such as GSV (Google search volume) on a dataset from NSE (National Stock Exchange) for period between 2004-2016 divided into pre, during, and post subprime crisis of 2007-2008. We found that an increase in Google search queries was linked to a rise in stock liquidity and trading activity. We characterized the improved liquidity to a decrease in asymmetric information costs and thus, concluded that GSV mainly measured attention from uninformed investors. Moreover, we found evidence that an increase in search volume was associated with temporarily higher future returns, which reinforced the previous findings. Impact of GSV on both TV and TR in terms of direction was similar in nature and consistent with the findings of Preis, Moat, and Stanley (2013).