Managing A Speculative Penny Stock Portfolio Judiciously
PEG Corporate Finance and Research view:
In Part 1, Part 2 and Part 3 of this penny stock series of articles, we have demonstrated a very simple method of creating a penny stock portfolio which are both reasonably sound in fundamentals (using analyst selections) and yet will give us enough traction (returns and time) to hold such a portfolio.
This involves the use of our big data-intensive and mathematical-intensive algorithm models. However, this is just one simple yet easy to understand method to create a reasonably safe portfolio of penny stocks.
That said, elite traders come in all shades and risk profiles. As an elite trader and investor, we are always greedy for more profits and pushing the boundaries within the efficient portfolio theory to reap the best returns at the most minimal risk.
In this final Part 4, we look at how quantitative analysis and algorithms can be used to create a “speculative” penny stock portfolio and yet be able to keep the risk to more manageable levels.
Have you ever blindly chase speculative fast-moving stocks and are filled with fear as soon as you buy them?
Now you don’t have to. With the returns and risks quantified in advance and in an easy to understand and track approach, you can still trade penny “speculative” stocks (or even all type of tradeable stocks) and yet not worry of getting your farm burned down.
That said, obviously the construction of a speculative penny stock portfolio carries a much higher risk than normal given the supernormal profits that we desire. Still, as elite traders, we do not avoid the risks but rather take calculated risk to pursue the potential supernormal profits of such a portfolio.
That’s where quantitative analysis and algorithms can contribute to the risk-management of such a portfolio.
If you simply add speculative penny stocks into a portfolio without understanding its volatility, you are likely trading blindly and throwing caution to the winds. Without understanding the volatility of your speculative penny stock portfolio, you will have no idea where you can manage the risks of such a portfolio.
How do you measure the volatility of your speculative penny stock portfolio?
As it is, speculative stocks by their nature tend to be high risk as such stocks typically are short of fundamental backings and move on rumour or unsubstantiated news or even involve insider plays. Even then, there is one critical fundamental indicator that you must at least ascertained before you can even consider a penny stock in your portfolio.
If the stock can surpass the above criteria, we can then use quantitative analysis to further analyse the stock potential profit target and its risk levels.
Through our algorithms, we can then derive each stock’s risk-reward ratio and choose the lowest hanging fruits to be included in our portfolio.
There are three key indicators that a speculative penny stock must fulfil before it is considered suitable to put into our portfolio. If you do not know these three indicators, you are then obviously trading haphazardly or blindly and exposing your capital to much more risk than you can bear.
Hence, many investors and traders have lost money badly by trying to trade speculative penny stocks without ascertaining in advance the the four indicators above.
Quantitative investing can be widely practised both as a stand-alone discipline and they also can be used in conjunction with traditional qualitative analysis for both return enhancement and risk mitigation.
However, from our experience, the best use of both qualitative and quantitative analysis is to match and blend them together.
This blend of both qualitative factors and algorithms are even more critical to create a speculative penny stock portfolio given the need to mitigate and control the high risks present in such a portfolio.
A blend here will see the best of both humans and machines in an unbeatable combination to create a supernormal profit penny stock portfolio with enough risk safeguards.
In fact, by using algorithms and mathematical models, not only will you be able to correctly ascertained the four indicators above as mentioned, you could easily track and monitor the risk and reward of your penny stock portfolio in real time, without any more efforts on your part.
Consider this. In a 2017 symposium in Harvard’s Institute for Applied Computational Science, R. Martin Chavez, the Deputy Chief Financial Officer of Goldman Sachs explained then that the company’s US cash equities trading division used to employ over 600 human traders back in 2000.
Today that number is down to just two human traders, with the rest of the jobs being taken over by automated trading platforms that are managed by around 200 computer engineers.
The rise in algorithm science however has also led to a proliferation of black box software and applications which are purchased off the shelf by naive traders and retailers hoping to benefit from automation and the power of mathematical applications.
However, by doing so without understanding the science behind the algorithms, the traders themselves became enslaved and have no idea why such applications such as robo-advisory platforms can still make them losses or sub-par performance.
The answer is simple in that human minds cannot be entirely replaced by machines and even the best algorithms sometimes suffer from data defects and surprisingly logic comprehension. You have probably heard of indiscriminate buying and selling of stocks wildly by automated programs.
While advances will continuously be made in the fields of data analytics, artificial intelligence, computing power, etc., traders and investors who rely blindly on such applications will eventually suffer from “double blindness” firstly by trading blindly on their own and then following blindly the machine trades.
If you don’t create and code the algorithm strategies yourself, you would likely to have no clue when such a strategy breaks down or when abnormal tail-end events occur such as a black swan event.
And if your applications are used by thousands other traders out in the same market, you are likely to find yourself crowded out as a trader with no trading edge eventually. Have you ever heard the large investment banks sell their trading programs in the market?
But let’s leave that to other time of discussion in the future and come back to our speculative penny stocks portfolio construction. Using our own coded proprietary algorithms, we can now create a more optimum speculative penny stock portfolio with safer risk safeguards and high reward probabilities (see table below).
The above table shows that there is probably only around 20 optimum stocks out of 100 speculative penny stocks priced below 20 sen and represents a snapshot for the day the filtering is done only.
Entry into the portfolio could have been triggered earlier by the algorithm and exit triggered would be adjusted into the portfolio.
The portfolio is dynamic and adaptive to all market conditions and entries and exits triggered will be used to rebalance the portfolio.
A selection of stocks from the portfolio universe would then be used to create one’s own preferred portfolio size of 3 or 5 or 10 stocks.
Please note that for speculative penny stocks, you should only entry at cycle lows to reduce your risk at all times and these could easily be identified and tracked by the algorithm.
In fact, cycle theory is a superb methodology of entering and exiting speculative penny stocks to mitigate your risk.
For such a speculative portfolio, we had constrained the stock selections to only non-consensus stocks, which typically means the stocks in the portfolio may not have strong fundamentals and would have high volatility but also high risk and reward outcomes.
As mentioned earlier, we do not avoid the risks but rather want to take calculated risks to pursue the potential supernormal profits of such a portfolio.
The best part of it is that our speculative penny stocks portfolio is dynamic and adaptive and we can continuously rebalance the portfolio easily with ease of tracking and monitoring.
Such a speculative portfolio of penny stocks is definitely not for everybody but with a blend of both qualitative and algorithm approaches, the risks can be minimised while pursuing the potential supernormal profits such a portfolio offer.
The reality is that our algorithm models could be easily used to create all sorts of optimum portfolio from the ones catering to highly conservative traders and investors to highly speculative ones like the one highlighted in this article series.
We believe that whether you are a global institutional fund or a stand-alone retail investor, quantitative methodologies and algorithms could easily help you create optimum portfolios that are suitable for your own risk-reward profile and appetite in investment and trading.
They also can be used in conjunction with traditional qualitative analysis for both return enhancement and risk mitigation.
We believe a blend will see the best of both humans and machines in an unbeatable combination – sort of like a perfect match.
Important Disclaimer and Terms and Conditions
Certain information has been redacted and the full content is restricted to PEG Holdings clients, shareholders, fund management entities, and any affiliates or business partners approved by PEG Holdings only. You can register your interest and personal log-in here.
The content in the Research and Market section are only for general market information and should not be construed as an investment advice or constitute a recommendation to buy, hold or sell any instruments or markets. The information represents our personal view only and is for educational and general purposes only.
You should consult with your licensed investment adviser before attempting any real-life trading or investments in the marketplace. All information provided can change without notice and is not guaranteed for accuracy and completeness and is not for use by the general public. Please view the full disclaimer and terms and conditions notice here.
Please note that all algorithms calculated are dynamic and will change as prices, volume and other proprietary factors etc. move in real time. All information pertaining to any technical or algorithm content are the proprietary assets of our affiliate partner, Bulls and Bears Research.
For more information on the algorithm, please contact our Corporate Finance and Research unit at:
Head of Strategy
Head of Corporate Finance