Quantitative Analysis for Portfolio Management

Quantitative Analysis for Portfolio Management

Quantitative Analysis for Portfolio Management

Quantitative Analysis for Portfolio Management provides insight into historic performances of a specific stock. Portfolio managers commonly rely on a public company’s stock price data, dividends, free cash flow, & earnings to value the stock’s intrinsic value for entry and exit purposes. A consistent and well documented use of quantitative methods could greatly improve a portfolio manager’s trading decisions in different market conditions.

Quantifying intrinsic value is a common method for evaluating long-term investment because it provides the insight of the company’s current economic value as a going-concern entity. However, data from financial statements might be skewed by management judgment and manipulation to control stock value. Therefore, it is important to understand both the stock’s fundamentals such as its management team, industry competitiveness, and geographical risks etc.

Fundamental analysis requires market insights and industry knowledge and research analyst should recast and normalize data before performing valuation and analysis. One important is risk premium, which greatly affects the valuation because of the discounting effect. Complete due diligence is required for portfolio managers and analysts to review the data, reasoning, and methodologies used to arrive the cost of capital.

Some portfolio managers include technical analysis as an overlay process to generate market sentiment signals and trading rules for their stock holdings. Portfolio managers use moving average and reversion analysis to generate optimal entry and exit points for a stock.

Although the Mean-Variance Efficiency is frequently challenged (here is a good article from CFA Digest), we like to stress that historical data is use to improve decision making and not as a rule of thumb for complex decision-making process such portfolio management. To help challenge this static thinking, the use of variance minimization, return maximization and Sharpe maximization are also encouraged. You can apply all these regressions without optimization technology. We use multi-level vectorization programming to quickly analyze stock values and trading rules, if you are interested to integrate portfolio management with a quantitative analysis system, learn More about Portfolio Management Solutions