In StocksCafe, all beta shown is computed with linear regression using data from the last 3 years against STI.
Beta can be used to measure how a stock or portfolio is likely to respond to changes in STI.
For example, stocks with beta equal to 1 would have their prices move in tandem with STI.
That is, if STI goes up by 3%, they are also likely to go up by about 3%.
If STI goes down by 5%, they are likely to go down by about 5% too.
Conversely, stocks have negative beta would have their prices move opposite of STI.
For example, if a stock has a beta of -1, when STI goes up by 3%, it is likely to go down by around 3%, and
when STI goes down by 5%, it is likely to go up by around 5%.
As for stocks with beta close to 0, changes in STI are less likely to affect them.
That is, whatever changes there are to STI has no effect on their prices.
How about stocks with beta greater 1? If a stock has beta of 5, whenever a STI goes up by 3%, it is likely to go up by around 15%.
And when STI goes down by 5%, it is likely to down by 25%!
If you are able to follow up till this point, I believe you would be able to deduce what the likely scenarios
for stocks with beta lesser than -1 (e.g. -5) would be.
Of course, the stock market is more unpredictable than the scenarios that we have discussed above.
While the magnitude is not that predictable, the direction of movement is generally correct, especially if you are looking at a longer period of time and against high liquidity stocks.
Generally, beta close to 0 is preferred because it means the portfolio is less affected by market movements (i.e. STI).
In StocksCafe, we use RSI to measure the flow of investment in and out of the stock.
RSI is a value between 0 and 100 and when RSI is high (above 70), it is generally seen as a time to sell
and when RSI is low (below 30), it is generally seen as a buy signal by people who believe in technical analysis.
In StocksCafe, trend is computed by a combination of short-term momentum of the last 30 trading days and
long-term momentum of the last 200 trading days.
We believe that a buy signal would be having a positive long-term momentum and a negative short-term momentum.
That is, the stock price has been moving in an upward trend over the past 200 trading days, but falling slightly over the past 30 trading days.
This is because we believe that positive long-term momentum is a reflection of increasingly stronger fundamentals
and a short-term fall could simply be some recent bad news that does no damage to the company's profitability in the long run.
Of course, this argument would not always hold and therefore, it is important to perform your due diligence in researching the reason behind the recent fall
(as well as the reason for the long-term increase).
Liquidity is computed using the last 60 trading days of a stock.
Naturally, the more liquid a stock, the better it is deemed to be since it will be easier to find a buyer
when you want to sell, and a seller when you want to buy, which leads to a smaller spread.
In StocksCafe, we are using the last 60 trading days of data to compute the daily volatility of stocks.
The less volatile a stock is, the better its ratings/score would be.
Score is computed using our proprietary formulae with the above 4 (flow, trend, liquidity and volatility) signals.
It will be a value between 0 and 100. The higher the score, the better we deem it to be.
Dividends Yield is computed based on the total dividends for that year and weighted average close for the day before ExDate.
Note that the formulae used differs from current yield.
Current Yield is computed based on the total dividends of previous year and latest close for the stock.
Note that we deliberately chose this formulae to enable comparison of latest close against previous year's dividends.
Value At Risk (VaR)
Here, we computed the 99% monthly value at risk using the variance-covariance method based on the last 3 years data.
Basically, it meant that based on historical data, it is 99% confident that you will NOT lose more than VaR % of your portfolio in a month.
Of course, the lower the VaR, the better it is because it would mean lower risk.
Expected Shortfall (e-Shortfall or ES)
complements value at risk as value at risk measures
"How bad can things get in normal situations (i.e. 99% of the case)?" and expected shortfall measures
"In stressed situations (i.e. the 1% case), what is the expected loss?".
Recent average is the volume-weighted average traded price for the last 22 trading days (or about 1 calendar month).
Mathematically, Recent Average = (total value traded in the last 22 trading days) / (total volume traded in the same period).
Recent Average Indicator (RAI)
RAI tells us how expensive (or cheap) is the last close price with respect to Recent Average.
Mathematically, RAI = [(Last close) / (Recent Average)] - 1. It is converted into percentage (i.e. multiplied by 100) for simplicity.
Example: If a stock latest close is SGD 11 and recent average is SGD 10, its RAI would be 10%. In other words, the latest close is 10% more expensive than its recent average.
So, a negative RAI indicates that the stock is currently trading below its recent average.
Price over Earnings (PE)
PE is the latest price over trailing earnings.
PE-20 is the 20 days traded value weighted moving average of PE.
PE-200 is the 200 days traded value weighted moving average of PE.
Price-Earnings Recent Average Indicator (PEI)
PEI tells us how expensive (or cheap) the latest P/E is with respect to the recent average P/E (200 days).
Mathematically, PEI = [(Latest P/E) / (Recent Average P/E)] - 1.
It is converted into percentage (i.e. multiplied by 100) for simplicity.
Example: If a stock's latest P/E is 11 and the recent average P/E is 10, its PEI would be 10%.
In other words, the latest P/E is 10% more than its recent average P/E.
Therefore, a negative PEI indicates that the stock is currently trading below its recent average.
It is usually better to buy a stock when its P/E is lower than its recent average (i.e. PEI is negative).
This is because, often, it just means that investors are over-pessimistic.
NAV Per Share
NAV Per Share = (Total Assets - Total Liabilities) / Shares Outstanding