Know Your Investment Appetite: A Lesson on Stock Beta

A good investor/trader knows his personality inside out. He also knows his targeted returns and the risks that he is willing to take in order to achieve them. That means doing a lot of homework even better sinking money into an random stock that may look good on pure 'technical analysis'.

One big homework question to ask ourselves, apart from the most rudimentary risk/reward policy but similar, is the expected Beta correlation between the stock daily returns and that of the STI. Remember that buying into a stock entails accepting its market and idiosyncratic risk. 

Idiosyncratic Risk. This risk lies solely on the actions of the company and its operating risks. In other words, there is nothing to do with market sentiments, just stock-based sentiments. To understand this in another light, consider a downbeat market where a stock can still be increasing in price because it just launched a new product. There is an Alpha measure for this risk, as the % improvement in prices whenever something happens could much more than another company's. That is the inherent idiosyncratic risk or volatility we have to stomach. 

Market Risk. The former is the one in discussion today, measured by the stock's Beta value. In a market rally, we expect stocks to be buoyed in prices because market sentiments are improving. That means, we are seeking to profit the most out of this market rally and therefore seeking to harness stocks with high Beta values. That will give our money the highest possible 'virtual leverage'.

Below is a chart provided by SGX MyGateway entailing all the Beta values for the STI component stocks and their YTD total returns. Take note that the prices are correct only until 21st May from year start.

Source: SGX MyGateway

In short, ceteris paribas (assuming all idiosyncratic risks are equal; when in fact they are definitely not, so some stocks with high beta may have a lower total risk/volatility than another that may have a very high idiosyncratic risk)

1. In a market rally, trade highest Beta
2. In a downbeat market, trade the lowest Beta (defensive stocks)
3. For portfolio asset allocation, mix stocks with generally high beta (to outbeat the market) while averaging out their Alpha (idiosyncratic risks)

For the interested, I have included some details on how you can also calculate these Alpha and Beta values yourself using MS Excel, Yahoo Finance (for data)

1. Download stock data as required (daily/weekly/monthly and time period according to your analysis needs) from Yahoo Finance Quotes.
2. Download STI data as required (daily/weekly/monthly and time period according to your analysis needs) from Yahoo Finance Quotes.
3. Tally both closing prices side by side. Remember, we are trying to compare the % returns for the stock in question and the STI across each trading day so the data must match.
4. Weed out days that do not tally. Yahoo Finance usually has data that are missing so there may be a need to match the trading days manually. In this light, it is inadvisable to be dealing with daily prices over a huge range. Even if there is that need, pay attention that a stock beta and alpha may vary over time periods and market psychology so my advice is to keep it to last 2-3 years maximum.
5. Calculate returns in % (in this case, for illustration, I have calculated and compared daily returns)                                                         
6. Insert scatter plot for both data, with the row for STI % returns on the x-axis (considered, in linear regression analysis, as the independent variable since we are seeking to find out how Capitaland moves relative to the STI) and stock in question % returns on the y-axis.
7. Right click any data point on the scatter plot and select add trendline. Check the boxes (lower portion of the new window that opens) to include the equation and regression coefficient. 
8. Ta-da! The graph as it is ready for interpretation.                                                                                                                                               ..
9. For the illustration above, Beta = 1.446, Alpha = 0.004. This means that (over the period of analysis) when the STI moves 1%, Capitaland will move 1.44%, outperforming the market in good times. This should come as no surprise given the weightage of Capitaland in the STI. Let's not forget the inverse is true too! A downbeat market will see Capitaland retreat by 1.44x STI's amount. In this case, the alpha is pretty low but it does not suggest an absence of idiosyncratic risk (this topic is getting more technical) but an absence of it RELATIVE to the STI. To understand this, you must first appreciate the circular argument logic that encompasses this analysis with Capitaland. As it is a big component of the STI, its idiosyncratic risk has also found its way into the STI portion so there is little meaning in finding out that risk value while comparing Capitaland with the STI. A better approach would be to compare Capitaland with Keppeland in order to find out the relative idiosyncratic values and the argument continues here. 
10. A quick check with the recent rally that started on the 4 June for the STI, Capitaland's prices increased 18.7% (4 June at $2.46 til today 4pm, price of $2.88) whereas the STI saw a 7.1% increase in points (4 June at 2700 til today 4pm, of 2962), clearly validating the high market beta that Capitaland enjoys.
11. Rsquared measures the fit of the straight line to the data set where a value close to 1 indicates a very good straight line relationship fit and hence more confidence to work with the straight line equation as an approximation to the data. In this case, the Rsquared value of 0.64 gives some confidence that the straight line equation can solve our risk analysis problem. 

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