6.1 Characteristics of OSE
6.1.4 Market sentiment
Market sentiment influences stock prices. The VIX index in Chicago is an index of market sentiment. Index-options at the money, close to the exercise date, is a better indicator of market sentiment than an individual stock. The analysts’ estimates can also be an indicator of market sentiment notwithstanding that analysts are biased towards buy suggestions. This bias is due to broker houses getting easier stock issuing orders from companies. Another indicator of sentiment is P/E ratios. They are higher at good times and lower at bad times even if the fundamentals of a company haven’t changed. This is called multiples contraction. Long term estimates (3 or 6 months ahead) are exposed to changes in risk premium over time, so that the analysts’ estimates don’t come true. Underreaction is likely for liquid shares. Overreaction is possible for shares that are not liquid since trading occurs less frequently (Chopra,
Lakonishok and Ritter 1992).
83 6.1.5 The role of analysts
A type of analysis frequently used is called consensus analysis. Investors in OSE are
interested in consensus estimates, i.e. the mean or median of estimates. They are also attentive to the highest and lowest estimates so much so that they contact the respective analysts for background information. Investors focus mainly on operating cash flow (OCF), net cash flow (NCF), earnings before interest, tax, depreciation and amortization (EBITDA), earnings per share (EPS) and enterprise value (EV). Models of multiples based on ratios like price/earnings (P/E),, earnings before interest, tax, depreciation and amortization/enterprise value
(EBITDA/EV) and book to market (B/M) are frequently used to come up with a fair value.
These models are calibrated for the business sector a company operates in. An analyst compares these figures with the prevailing yardstick and investigates potential underlying factors to significant deviations from the norm for the line of business at question. For the real estate or the shipping sector are market prices most relevant for estimating a company’s assets. For companies producing commodities like aluminum, fertilizers are costs of capacity replacement a relevant factor. The same applies for sea-farming.
The submission of accounting reports leads to examination of reported results against their corresponding estimates. Deviations between these two sets produce changes in stock prices at the announcement day. Analysts are solicitous over the influence of accounting choices on the reporting figures. Accounting practices can have a significant impact on the accounting statements. Managers are known to exert efforts to sway reported figures towards a desired direction (Healy 1985). Bonus contracts can provide a motive to this behavior. The analysts’
job is to eliminate these effects by making suitable adjustments.
84 6.2 Testing sentiment risk
6.2.1 Hypotheses
With the theory of heterogeneity based on the models by Xiouros (2009) and Iori (2002) as a starting point we formulated a set of hypotheses. Because of non-stationarity issues which are discussed in another section in our thesis, we had to use the differencing of the dispersion of beliefs. This can fit nicely in our tests since it is related in this setting to the sentiment risk.
We tested the following hypotheses for data on Oslo Stock Exchange:
H - 1: The absolute values of stock returns are proportional to the sentiment risk expressed as the absolute value of differencing of analysts’ stock price targets.
H - 2: The absolute values of stock returns are proportional to the sentiment risk expressed as the absolute value of differencing of analysts’ EBITDA estimates.
H - 3: The absolute values of stock returns are proportional to the changes of sentiment risk expressed as the absolute value of the second differencing of analysts’ EPS estimates.
H - 4: Stock returns’ volatility is proportional to the sentiment risk expressed as the absolute value of differencing of analysts’ stock price targets.
H - 5: Trade volume of stock is proportional to the sentiment risk expressed as the absolute value of differencing of analysts’ stock price targets.
H - 6: Trade volume of stock is proportional to stock returns’ volatility.
85 Model specification
Using data from the financial databases Datastream and Factset (see appendix A - xvii) we run in EViews least square regressions of the following equations:
H - 1
The hypothesis was tested in two ways, using data for market value from the financial database Datastream and data for enterprise value from the financial database Factset:
|∑ [(
∑ ) ( )]
| | ∑ [(
∑ ) ( )]
| ̂ M - 1
|∑ [(
∑ ) ( )]
| | ∑ [(
∑ ) ( )]
| ̂ M - 2 where denotes the first differencing.
H - 2
|∑ [(
∑ ) ( )]
| | ∑ [(
∑ ) ( )]
| ̂ M - 3
H - 3
|∑ [(
∑ ) ( )]
| | ∑ [(
∑ ) ( )]
| ̂ M - 4 where denotes the second differencing.
86 H - 4
The hypothesis was tested using √( ) as measure of volatility. We tested the hypothesis in two ways, using data for market value from the financial database Datastream and data for enterprise value from the financial database Factset:
∑ [(
∑ ) √( ) ]
| ∑ [(
∑ ) ( )]
| ̂ M - 5
∑ [(
∑ ) √( ) ]
| ∑ [(
∑ ) ( )]
| ̂ M - 6
H - 5
∑ [(
∑ ) ( )]
| ∑ [(
∑ ) ( )]
| ̂ M - 7
H - 6
The hypothesis was tested using √( ) as measure of volatility:
∑ [(
∑ ) ( )]
∑ [(
∑ ) √( ) ]
̂ M - 8
The symbols in the above equations are as follows:
stands for an indicator function which takes the value of 0 if an observation is not available and 1 otherwise.
̂ stands for the residual, where stands for market value of stock at time
stands for netto return of stock at time
( ) stands for standard deviation of analysts’ price target of stock at time
87
stands for the trade volume measured in thousands of stocks for company at time . stands for enterprise value of stock at time .
stands for earnings before interests and taxes, depreciation and amortization and ( ) stands for standard deviation of analysts’ estimates of stock at time .
stands for earnings per stock and ( ) stands for standard deviation of analysts’
estimates of stock at time .
6.2.2 Methodology