If you have been following the business news, Tech investing, a darling in the 2020 pandemic era, is no longer producing the same blistering returns as before. Recent actions taken by Chinese regulators have created a severe dampener to Chinese markets as major fund houses decide to withdraw capital for fear of greater reprisals from the regulator.
I have a modest position in iShares Hang Seng Tech ETF (Ticker: 3067), and it has thus far returned all my capital gains to the stock market. Although I don’t intend to do anything to my holdings, the question as to whether this is a sustained downturn is unlikely to be answered by financial analysis.
An investor will need to be able to read the minds of the Chinese Communist Party. If the intent was limited to curtailing the tuition agencies in China and make it cheaper to have children, markets could be poised for recovery within a few months. If the intent was to address the inequality between the common folk and folks in the tech sector, then expect a giant hammer to follow in the financial markets.
Chinese markets already have high valuations and political risks. Even if you reduce these risks and move into the Singapore markets, Tech firms are hard to screen.
As part of the next batch of ERM, I prepared a factor model for the Technology sector. This article contains details of my efforts.
How to build a factor model?
We built the factor model with the following:
- We pick a universe of fifteen stocks with recent analyst attention
We picked fifteen Singapore Technology stocks for our technology universe that have done relatively well of late and attracted a significant share of investment research among local stockbrokers. The universe selected would not be defined by some index, so readers can adjust and amend the universe in any way that they wish.
This subjective exercise resulted in the following counters:
- CSE Global
- UMS
- Riverstone Holdings
- AEM Holdings
- Innotek
- Valuetronics
- Frencken Group
- Fu Yu
- iFast
- ISDN
- Singapore Technologies Engineering
- Venture
- Aztech Global
- Nanofilm
- Grand Venture Technology
Do note that discerning readers may disagree that counters like iFast and CSE Global should even be part of the mix. I chalk this up to the subjective nature of investing in real life.
- Create a baseline performance of an equally weighted performance
The next step would be to create a baseline performance of the 15 stocks. Imagine how your portfolio would have performed for the past 1,3,5, or 10 years had you held it in equal shares. E.g. If you had $15,000, you invested $1,000 into each stock.
The backtest will record the annualised return, the semi-deviation or downside risk, and the Sortino ratio that measures the following:
| (1 year) Return | (1 year) Semi-Deviation | (1 year) Sortino Ratio | (3 years) Return | (3 years) Semi-Deviation | (3 years) Sortino Ratio | (5 years) Return | (5 years) Semi-Deviation | (5 years) Sortino Ratio | (10 years) Return | (10 years) Semi-Deviation | (10 years) Sortino Ratio | Average Sortino |
| 39.80 | 16.50 | 2.23 | 34.90 | 14.50 | 2.20 | 38.70 | 13.40 | 2.66 | 32.00 | 26.80 | 1.08 | 2.04 |
Backtesting software can include Bloomberg or Pyinvesting.com.
Sortino ratios of this strategy are relatively high as numbers above one are already quite good. Our baseline scored 2.04.
Readers can build a portfolio of these 15 stocks.
- Sub-set eight stocks with superior factors and backtest them to see if they can beat the baseline
The real challenge is to cherrypick within this universe of 15 stocks. To do this, we subset eight stocks with superior factors.
For example, suppose we wish to test whether a value strategy would work in this universe. In that case, we will build portfolios containing eight stocks with the lowest Price Earnings ratio and compare performances against the baseline.
If we wanted to test for a growth strategy, we would pick eight stocks with the highest revenue growth over the past three years. An explanation of each factor would take too long. Instead, I will produce the factor test data here:
Superior strategies will be rated based on average Sortino ratios. Disappointedly, the only two factors that resulted in superior risk-adjusted returns are marked in light green.
Now we know that:
- Tech stocks with a low six-month volume tend to outperform
- Tech stocks with a high 180-day relative strength indicator tend to outperform
In this universe, which is already very highly optimised, stocks ignored in the past and had newfound momentum tend to outperform the rest.
This result puts most fundamental investors in a bind because they cannot rely on growth or value investing approaches to obtain superior performance.
- Combine superior factors to create a final strategy
Software like Pyinvesting allows the user to combine two factors into a scoring system. An explanation will take up more than one article, but we can test the performance of a five stock portfolio that has the highest composite score of its low six-month volume and momentum measure.
Fortunately for us, these five stocks brought drastic improvements to the Sortino ratio.
- Screen using this two-factor strategy
After going through the lengthy process of determining a superior strategy, we have the following insights on the Singapore Tech Sector: Picking five stocks with a low 6-month volume and high momentum led to outstanding performance for the 1,3,5 and 10 year periods in the past.
The final question is which stocks are these in the markets right now?
Running a daily screen on Pyinvesting, we obtain the following counters on 1st August 2021.
As it turns out, iFast, Innotek, Frencken, Valuetronics and CSE Global made the final cut for the portfolio. As this factor model does not employ fundamental analysis, ERM students will qualitatively review each counter by aggressively reading analyst reports and blogs to determine which final 2-3 counters to buy with their limited capital.
Qualitative review is more important when going through Tech firms as the risk of data mining can be much higher, but this process has reduced the readings down to a manageable level.
The ERM program can quickly form teams of three student-analysts to attack one stock each.
Conclusions – 2 reasons why tech investing is so hard
The nature of Tech investing in Singapore is challenging. There are no rules to firmly place a strategy within the well-established growth or value domains. We have no evidence that sifting stocks based on value or growth screens would lead to superior performance.
To describe Tech investing in Singapore, we need to find stocks that were ignored in the past but have found fresh momentum since. Beyond this, we may have no choice but to resort to understanding the narratives driving each tech counter.
It is interesting to note that iFast scored exceptionally well using this factor model and is first amongst equals in this stock screen. Students have to balance this finding with the recent spate of negative analyst reports of late.
As I have performed this exercise without peeking through the final screen, I do not adopt the same bearish stance many analysts have towards iFast. I have decided to hold onto my current investments, perhaps accumulating even more when it drops further.
Time will tell whether I am right or wrong.
