Equity portfolio risk (volatility) estimation using market information and sentiment

Previously, I have presented different findings on how news sentiment can be used in alpha generation in both short-term trading and longer-term investing. The focus of this posting will be on how news sentiment can be used, supplementing option implied volatility, in constructing forward-looking co-variance matrices that updates quickly as market conditions change. The adjusted estimate of the co-variance matrix can be applied for instance in multifactor models, which are often used to describe equity portfolio risk.

Here are the key findings of a joint study between Northfield Information Services and Mitra et.al. from CARISMA at Brunel University:

• News sentiment improves reaction times. A news sentiment adjusted co-variance matrix reacts more quickly to changes in volatility as compared to a pure option-implied volatility adjusted co-variance matrix.
• News sentiment is available when option-implied volatility is not. A news sentiment adjusted co-variance matrix can be constructed in cases where an option-implied volatility adjustment is impossible due to the lack of exchange traded options.
  

According to Mitra et.al. option-implied volatility can be used as a measure of the extent to which market participants believe current conditions that affect volatility are different from their typical state. This since traders are likely to respond quickly to new information that impacts expectations of future volatility because option prices are directly dependent on such volatility expectations. As a result, including option-implied volatility in the construction of the co-variance matrix should help provide more sensible estimates of future volatility.
An alternative and untapped source for picking up changes in market conditions that are manifested as time varying volatility is through the use of quantified news. Mitra et.al. provide the following example.

“If on a typical trading day there are ten to fifteen news wire service stories about Firm X, and today there are two hundred news wire service stories about Firm X, we can assert that there is a significantly greater than usual amount of information being imparted to investors about this firm. As such, more substantial share price movements may result than would be typical. We might even be able to analyze whether the content of the news stories would be considered broadly negative or positive with respect to the operations or valuation of Firm X. In essence, the volume and nature of textual news can be used like option-implied volatility to very rapidly adjust our expectations of future volatility for a particular firm or an entire market.”

In order to estimate the co-variance matrix, Mitra et.al. use a statistical factor model applying principal component analysis to extract orthogonal factors. This model is referred to as the “basic model”. The model is further updated applying a scaling factor based on the relative change in option-implied volatility as compared to the relative change in volatility using the basic model. A similar update is made using a news sentiment scaling factor. It should be noticed that the construction of a news sentiment scaling factor is possible for a larger set of companies than is covered by the options exchange.

For experimentation purposes, Mitra et.al. considers two different portfolios: the Eurostoxx 50 and Dow 30 constituents.

The first example focuses on the period 17 to 23 January 2008, where sentiment worsened and option implied volatility surged following several significant events including the announcement of a stimulus plan for the economy by President Bush, and a Fed interest rate cut by 75 basis points, the largest cut since October 1984. In Europe, Societe Generale was hit by the fraud scandal involving Jerome Kerviel. Looking at this period, Mitra et.al. find a clear indication that a sentiment-based model picks up on increased volatility at an earlier date than the model only using option implied volatility, See Table 1. Both models react quicker than the “basic model”. It should be noted that on 21 January 2008 there was a sharp decline in non-US stock markets (the US market was closed); hence Mitra et.al. argues that it is reasonable to assume that stock volatility rose on this date.

In the second example, Mitra et.al. focus on the period 18 to 24 September 2008 where several significant events took place including Lehman's filling for bankruptcy, Bank of America's announcement of its intention to purchase Merrill Lynch, the Fed's announcement of the AIG rescue, Lloyds takeover of HBOS and on 19 September restrictions were imposed on short selling of financial stocks. Table 2 shows the volatility for a portfolio of three financial stocks with equal weights on each stock: Bank of America, CitiGroup and J.P. Morgan Chase. Similarly, Table 3 shows the figures for a portfolio of three non-financial stocks: Johnson and Johnson, Kraft Foods and Coca Cola.

Mitra et.al. argue that in most cases there is higher volatility for the financial portfolio when the volatility estimate is updated using option implied data and likewise are found to increase when the news sentiment data is processed. On comparing the estimates for the financial and non-financial companies they find that the financial stocks volatility has risen significantly more than the non-financial stocks. This seems a sensible result for this period, given the market conditions and the different news announcements.

The results of the above study indicate that news sentiment could add value in the construction of forward-looking co-variance matrices, which could be a useful input to multifactor models. I find interesting that such adjustments can be made for a large set of stocks where exchange traded options are not available.