Availability of credible primary data is the dominant limitation, and even when data is available, much effort is needed to transform it for efficient use. Data is needed both across time and breadth. If long period data is available for a narrow set of stocks, it isn't enough. If data is available for a large amount of stocks but for a short period, it isn't enough as well. With both availability and quality in place, data tends to be very large in size. Analysing it needs the latest technology, both hardware and software. With all these facilities in place, the final ingredient is time. There are no shortcuts.

Financial data scientists at NJ have been developing the datasets necessary for meaningful and efficient analysis over the past three years. We, at NJ have the expertise to navigate the entire development and implementation cycle without any external dependency.

Factor investing relies on the identification and measurement of time tested metrics that contribute positively to stock price performance.

Factor metrics like Quality, Value, Momentum and Low Volatility have proved to be the most effective and efficient. Size has worked in developed markets but Liquidity in developing markets isn't supportive. Parameters to measure each of these factors are devised and validated. Rules based on these parameters are tested to determine their efficacy across time, breadth and geography. Viable rules are then tested with each other to determine compatibility and cohesiveness. Harmonious rules are combined to form a protocol that governs investments. This protocol is followed without any exceptions and discretion.