Term 1 - The PGPMAX Journey Begins
13 years into my career as a software engineer, I was feeling a bit of a rut. I was getting bored of solving the same kind of problems. I realized that I was coasting and not really feeling invested in my work. It was time to shake things up and move to something that excited me. After some deliberation, I decided apply to the Indian School of Business to the PGPMAX program. Essays written, forms filled, application in, and now, a few months later.. Here I am at the Indian School of Business!
Years after I last entered a formal classroom of any sort, I am back to student life. It is familiar and altogther different at the same time. After term 1, I am excited and terrified in equal measure. Will I be able to keep up with the super-sharp cohort of peers some of whom seem like they should be teaching the classes rather than attending them? I decided to write this journal to document my journey and keep me grounded in the process. I hope to be able to look back at this journal and see how far I have come and what I have learned.
I have spent most of my career being and being around software engineers. We like certainty. We write requirements and define interfaces in terms of input and specific output. We try to make our systems behave predictably — giving the same result no matter how many times a program is run. No more, no less.
Yet time and again, given the same starting data and constraints, I have seen people arrive at completely different decisions. And at times, each of them will insist their way is the right one, despite evidence to the contrary.
I have always looked at numbers in black and white — they are what they are and there isn't much room for other interpretation. And boy, was I wrong?
Accounting Analysis and Information for Executives
This was one of the major things that struck and stayed with me during Accounting Analysis and Information for Executives in term 1 of my ISB PGPMAX course. I had always thought of financial statements as a collection of boring numberical facts. Revenue is revenue. Cash is cash. Understanding how revenue gets recognized, yet cash may not be affected, was a revelation. How to match expenses to revenue, how to account for depreciation, how to balance the famous accounting equation of Assets = Liabilities + Equity, made me see it in a new light. It is elegant and complex and critical to business. The numbers may change from one year to the next, but surely the numbers don’t mean anything else. I began to realize that this was only partly true.
The numbers may be one-dimensional. The story that emerges from those numbers is anything but.
A financial statement gives us a set of observations from which we try to understand what is happening inside the business—and, more importantly, why. So, before seeing the big picture, I needed to learn the composition of that picture. I needed to get better at reading and understanding the balance sheet, the income statement and the cash flow statements. I cannot profess to have reached the level of seasoned accountants but I am not running for the hills if I see the annual report of a company. The real fun, I discovered, begins when we stop looking at these statements individually and start asking what they are telling us about the business. Let’s try it out on a real business.
Real-World Financial Insights for a Company
Decision Making Under Uncertainty
Now that I had some knowledge on how to look at the statements, there were decisions to be made. There is always some information asymmetry even if you have inhaled all the reports there are about a company. We have to blend our own knowledge with the evidence and risk preferences to arrive at a decision.
This was where Decision Making Under Uncertainty gave me a new framework to view decision-making. I always thought, like an algorithm, given some variables, everyone should arrive at the same result – but DMUU dropped those beliefs like a hot potato and made me think from scratch. DMUU is like a vast ocean with hidden and unexplored depth. The course was and eye-opener and a mind-bender (partly becaused of the Math involved). It really a starting point and one can pick any one branch and as deep or as wide as one dares. I have barely managed to scratch the surface of this and I am sure I will have to revisit these in some form or fashion.
As I prepare for Term 2, I am realising that many DMMU concepts are central to any course in business. I have to befriend probability and statistics. The utility of goods/money depends upon preferences and it might mean different things to different people. What might risk mean to the rich and famous versus an average working-class person? How does it affect business managers? DMUU asked and answered many questions, at times held up a mirror that was uncomfortable to look into, but it was necessary too.
To see how these abstract concepts of survival, reputation, and loss aversion play out in your own mind, let’s look at a behavioral experiment that reveals exactly where you sit on the Risk-o-Meter.
Risk-o-Meter
Through a series of similar gambles in class, we learnt that expectation value maximization, while it has its uses, falls flat in the face of human psychology. Human beings are irrational, contradictory, and driven by factors that cannot be captured by simplistic models. I learnt about utility theory and prospect theory and how they can be used to model human behavior. I also learnt the importance of framing and how it can influence our decisions.
I came across the concept of Thinking Fast and Slow by Daniel Kahneman. Fast thinking is our automatic, emotional gut reaction that handles daily life instantly, while slow thinking is our deliberate, logical effort used to solve complex problems. And because we cannot be deliberate all the time, we need to build a muscle of thinking fast through recognizing patterns through practical experience at work. Easier said than done!
And what about bias in decision-making? How do we know if I are being rational or driven by some emotion or instinct? Do I exhibit biases? Does everyone? Am I aware of it? Am I more risk-averse or more risk-seeking? Do I change my beliefs given new information?
Know Your Biases
The key thing about biases is not to try to correct them, but to be aware that they exist and how we can try to mitigate their effects. For instance, having more diverse members in a team can bring diverse perspectives. I learnt that the while the decision process may be strong or correct, the outcome may not always match the desired. I understood the 4 scenarios of deserved success (good decision process+ good outcome), dumb luck (bad process + good outcome), unjust failure (good process + bad outcome) and poetic justice(bad process + bad outcome). And what does a company do in a scenario like that depends completely on culture of the organization. Culture is built bit by bit over time and this discussion can quickly turn philosophical. Suffice to say, if ever we are in a position to make decisions that affect others, we need to be extra-careful.
The Two Lenses: Numbers and Uncertainty
As the term drew to a close, a startling picture began to emerge. Accounting Analysis and Information for Executives and Decision Making Under Uncertainty — two seemingly unrelated courses — actually do have a connection. Together, they offer two lenses on the same reality: one reads the numbers, the other reads the uncertainty behind them. The financial analysis of a company's statements can hint at management's risk preferences, and the Management Discussion and Analysis sections can expose their biases.
Financial reports reveal far more than a company's health if read closely. Spending patterns can act as an early warning system for risk: rising R&D or capex signals a bet on the future, while SG&A growing faster than revenue often points to bloat or eroding pricing power. Revenue details, meanwhile, offer a window into consumer behavior — a shift in revenue mix shows where demand is genuinely moving, and a rising Days Sales Outstanding can hint that customers are under financial strain even as headline sales look fine. Beyond the numbers, footnotes and management's discussion often carry the most honest signal: vague language or the quiet disappearance of a once-touted metric can say more about a company's trajectory than the figures themselves.
The numbers also carry the fingerprints of human bias. Optimistic estimates — on bad debts, asset life, or warranty costs — tend to flatter profits, and a striking number of companies report earnings that just barely beat analyst targets, a pattern too consistent to be coincidence. Management also tends to credit good results to skill while blaming bad ones on "macro headwinds," a subtle self-serving spin worth watching for. None of these signals prove wrongdoing on their own, but tracked over time and compared against peers, they show where a company's reporting drifts from reality towards the story it wants told.
Read this way, a financial statement stops being a scorecard and becomes a story — one written partly in numbers, and partly in the choices behind them.