Categories: quant

ViniyogIndia📈Value Portfolio

ViniyogIndia Value portfolio is based on a multifactor strategy that uses Value as its primary factor. Suitable for aggressive investors.

ViniyogIndia offers model portfolios based on Quantitative Factor Investing strategy. Factors are quantitative attributes that can be used to explain asset returns.

Mathematically, if we try to model Asset Pricing behavior as a liner multivariate function, then factors represent the independent or explanatory variables of the function.

Factor strategies have been extensively researched globally as well as in India. The below chart for example, summarizes the risk-return characteristics of single-factor portfolios in India between October 2005 and June 2017. Over the period, all major single-factor portfolios outperformed the S&P LargeMidCap.

ViniyogIndia’s factor portfolios use a combination of factors that are proven to work well in the Indian markets.

Source: S&P Dow Jones Indices LLC. Data from October 2005 to June 2017. Index performance based on total return in INR. Past performance is no guarantee of future results

Portfolio Design Rules

Traditional value measures such as Price to Earnings Ratio (PER) or Price to Book Value (PBV) no longer have any predictive value, therefore, conventional value index such as the N500 Value 50 has mostly underperformed the market since its inception in 2005.

Further, a study conducted by S&P Dow Jones using data from Sep. 30, 2005, to April 30, 2016, concluded that typical value portfolio constructed using classic valuation ratios had higher risk than the market and the factor premium was not realized for the period under consideration.

We therefore use a combination of alternative and unconventional value measures that still retain predictive ability to construct our value portfolio.

Portfolio of approximately 20 stocks picked from the NSE universe.
Composite multifactor specification used to measure Value. Stocks are ranked against the individual factors after which they are ranked using their average normalized factor scores.
Further combined with secondary factors to ensure factor diversification and improve risk adjusted returns.
Illiquidity filter to remove low volume| turnover stocks.
Rebalanced half-yearly to reduce portfolio churn.

Risk Management Rules

Limits on exposure to any single stock or sector

Suitability

Suitable for aggressive investors ONLY.

Performance measurement & attribution

To interpret the sources of return for our strategy we perform a regression analysis using Carhart 4 Factor Model. The results are shown in the table below:

ALPHA	MKT	SMB	HML	WML
2.23	0.87	0.59	0.27	0.14
~0.0	~0.0	~0.0	~0.0	0.01

The monthly alpha or excess return for the strategy is 2.23%. This is generated using a combination of secondary factors that tries to enhance portfolio returns while reducing risks.

Additionally, exposure to standard factors such as market beta, size, value and momentum also contribute to the overall portfolio returns.

Sector Allocation

Historical sector allocation of the strategy shows adequate diversification. Analysis of historical sector allocation shows greater allocation towards sectors, such as Chemicals, IT Services, Power Generation, Gas Distribution & Textiles.

Subscribe to this portfolio @ Rs 3599/ 6m

Investments in securities markets are subject to market risks. Read all the related documents carefully before investing. Registration granted by SEBI, membership of BASL and certification from NISM in no way guarantee performance of the intermediary or provide any assurance of returns to investors.