Below are points to set some context around intent and objective of article so that you can take a call if it is worth your time:
- The intent of this post is to provide understanding of multi-disciplinary areas of investing including fundamental analysis, technical analysis, quants investing and application of machine learning in investment journey.
- The intent of this blog is not to give any trading or investment strategy but to provide a line of thought to explore various lines of investment with ample reasoning and evidences. If I have made you curious by end of blog series, my job is done. We all need to travel our journey. As they say, “give a mana fish and you feed him for a day; teach a man to fish and you feed him for a lifetime.”. I am just sharing some of the things which have learnt over period of time and part of it can be value adding to some. In next set of articles, I will try to go in as much as detail possible and still try to keep it simple while uncovering some of the new topics which has been focus of my presentations.
- The scope of this post and blog covers multi-disciplinary topics highlighting what they are, pros, cons, value addition to overall investment process and how to approach it. Also, focus would be primarily on quants and machine learning and its application in fundamental and technical analysis. Further, it may not be possible to cover in one post. Hence, might need to stretch it over multiple posts.
- Also, I have received multiple questions, views etc. regarding some of these areas with respect to recent presentations and in general. Some of these questions were around why this is required, do we need this, why we need this, how can we do it, what technology, what approach, which book etc. This is an effort to address them comprehensively and not as a piece meal response. In case you have not seen the presentation, you can check here: https://www.youtube.com/watch?v=lyX0v0lH_h4&t=1375s
- There are multiple approaches to investing and no one method is right or wrong. It all depends on our own strength, weaknesses, interest, process alignment, behavioral alignment, adaptability and ability to embrace what suits us
- Every investor has his own journey driven by his learning, experience and interest. What suits one may not suit other
- One can make money by following a simple, complex, singular or hybrid style. Discussing one style does not mean that there is any disrespect for other styles. As said, everyone has their own journey and there could be multiple successful journeys through different paths
- One need to explore various paths but ultimately choose which is the he way he wants to go based on his own criteria. These decisions are more evolutionary than one-time event
Now, in case you are still reading and glued, there is a specific reason for choosing these set of words for this topic and let me be specific – why. Let us look at the key words. Investing refers to deployment of resources with an expectation to make profit. So, whether you are a growth investor, value investor, day trader, positional trader etc., if expectation is to achieve a profit, we are good. Circle refers to a group of people with a shared profession, interests, or acquaintances. Now, if one seems below image, there is a shared interest of these various practices towards investing and that is to have an edge in investing. So, if we are talking about circle of investing, it is about the shared network of people with common interest which inherently means shared network of their skills which provides an edge in investing. My humble effort is to introduce you to the circle of skills which makes various successful investment paths so that you choose your own creating any permutation-combination of these. These skills have cross impact relationship and though can work in isolation, they are capable of having a symbiotic impact, we will go in details later. So, the topic selected, the selection of word for the topic, the areas selected and the image below selected to depict, all have a very specific meaning.
Now, as we have set the context, let us explore each of the components of above image, one by one.
I consider the 1st two from top clockwise as domains of investing and remaining as application areas where one can leverage the two domains, apply these techniques and derive more than what the domain offers in isolation and that is the whole reason why we are discussing all these four topics together. Let us find out why and how.
Fundamental Analysis : Enough has been written about it and hence, I won’t describe it but as a laymen coming from data background, the way I would explain is – Any analysis towards understanding the inherent business of a company including financials, operations, management etc. which can help us to identify good opportunities to make money is financial analysis. There could be again various approaches to fundamental analysis. Some would like to go on ground and talk to vendors, suppliers, competitors etc. Some like to go through financials and do secondary research and make decisions and some do everything under the umbrella of fundamental analysis. Money making surely is possible but from an intellectual curiosity perspective, it leaves following open questions which may be possible with experience but there could be alternate ways to answer the same questions:
- Do we have enough evidences to prove our fundamental investing framework is working?
- Am I too late to realize mistakes in my process framework, is there a way to realize the mistakes before they occur with ample evidences?
- Can 1+1 be more than 2 when we analyze X companies in isolation vs Y companies in one sector vs Z companies in the universe?
- Do I have evidences to prove my qualitative hypothesis like what drives valuation, does factor X impacts valuation or not, if yes then how much?
- In case one is following a checklist, based fundamental investment, how good or bad is the checklist, are the parameters worth considering, are important parameters being left out, what about values of those parameters, how do I know if they are optimum?
- Have we felt like Abhimanyu in Chakravyuh who knew how to enter (buy side story) but did not know how to exit (Sell side story)? What about when – I purchased a good company but the realized only buying good company does not make money and in fact now I wonder what is a GOOD company?
Answering all these questions need lot of experience and wisdom. In part, they are all experiments, we fundamental investors do in our investment journey and get answers over a period of time. Can we do these experiments in an alternate universe to speed up our learning curve excluding our inner biases? We may not get the perfect solution because there is nothing like a perfect solution but idea is can, we be better than we who we are – better and faster, is it possible? We will come to seek answer later.
Technical Analysis: Flavor of the season, hmmm, now, let me punch some holes and bring your excitement down 😊. Now, the idea is not to appreciate or critique but as said, find the pros, cons, gaps which may not be in technique but in ourselves and how to overcome it.
So, first thing first, what is technical analysis all about. Again, without getting in much of detail, in layman terms, it refers to analyzing and predicting price movement by analyzing price charts and trade data statistics included in those charts. So, anyone doing technical analysis is already dealing with statistics. We go through N charts on social media platforms and have watched so many trading strategies. When I started learning technical analysis, there were few burning questions I had:
- If there is a particular chart pattern or trade strategy being advised, what are my odds to success? If 100 times the same pattern is made, how many times it is successful and how many times it fails? What is my risk reward here?
- Is trading strategy A better than B? What are situations when one works better than other?
- Is what written in books 50 years back for country A going to work same way in country B after 50 years?
- Why RSI as 14 , why not 15, will 15 give a better return than 14? What is the most optimum value?
- Are there patterns and statistical parameters which have not been discussed and can be rewarding?
As you see, I ended up having more questions than answers and no book, article, video could give enough answers backed by evidences which could quench my curiosity. Here, the intent is not to question the ability of fundamental analysis/fundamental analysts or technical analysis/technical analysts. Someone good at his profession has earned enough wisdom over the years to decide and seek answers of these questions with years of experience but someone like me, who is new but still has to go and fight with many such intelligent and experienced folks in the market to make my own share of profit , what is the option? What am I going to do? Wait till I get the experience, learn from mistakes? Probably yes but then also look for alternate ways to bell the cat and speed up the experience process.
So, basically whether we are dealing with fundamentals or technical, it is like looking a for a time machine where we can go back in past, live that life, learn from those successes and mistakes, get that experience. Evolve and do better. Can we get such a time machine who can at least partially gives that wisdom to us? Nothing is perfect but is there something which can help us to do this more intelligently, more efficiently and faster?
If these questions also create similar curiosity, makes more excited and yearn for not best but some possible answers, do read next part where we will be touching quants and machine learning in more detail with respect to central theme called investing. Let me leave you my last thoughts by introducing the rest two components in layman terms and will cover in much more detail in next article
Quants investing: Using this term loosely, it refers to ability create, back test and implement some rule-based investment style with an expected range of performance parameters from risk reward perspective.
Machine Learning and analytics: Machine learning is an area of study in computer science which in current context, helps to expand the quants toolkit by enabling us to learn from history and identify, experiment, build and optimize evidence based algorithms and thus, answer lot of hypothesis, question, doubts we have.