FinPod

[00:00:00:00 - 00:01:18:08]
Have you ever made a financial decision that felt absolutely right at the time, but then looking back, maybe it didn't quite hold up. Oh, definitely. Like doubling down on a forecast that was clearly off or, you know, sticking with a plan that just wasn't working anymore. Or maybe overestimating how long your team could keep up some crazy growth push. Exactly. We've all been there. Right. And here's the thing I keep coming back to. It's often not really a math problem. It feels more like a psychology problem. It absolutely is. And that's really our mission for this deep dive, isn't it? To unpack how these cognitive biases, these emotional influences, really affect financial decision making, especially in the corporate world. Right. We're going to dive into some, well, some pretty major company stories where these biases kind of took center stage and hopefully give you a practical toolkit for challenging that flawed thinking in finance. That sounds like a really crucial conversation. So today we're drawing on some great sources like the psychology of financial decision making, bias in corporate finance, and also our notebook LM fact reference on behavioral finance examples. Yeah, good stuff there. It should give us a pretty comprehensive look at how, well, how our human psychology can steer even the sharpest financial models off course.

[00:01:20:00 - 00:02:39:16]
So where do we begin with this, this whole field? Well, let's start simple. What is behavioral finance? At its core, it's really the study of how those cognitive biases and, you know, emotional factors we just mentioned actually influence the financial choices we make. So it's about the human element behind the spreadsheets. Exactly. It means that even with like super sophisticated models and detailed dashboards, the human interpreting them can still be the weak link. Hmm. That's quite a thought. Kind of humbling, actually. It really is. But understanding that, understanding that we're not as rational as we think we are, that's actually a competitive advantage. Well, if you know the common pitfalls, especially in areas like, say, FP&A, corporate development, capital budgeting, understanding these traps helps you avoid them. It becomes a strategic asset for you and your company. Oh, okay. That makes sense. So let's talk about those pitfalls then. The usual suspects, these cognitive biases that show up in the workplace, particularly when money's involved. What's a common one? A really big one, and it can be quite subtle, is overconfidence bias. This is basically believing in your own abilities or your forecasts way more than the evidence warrants. Right. So you ignore the warning signs. Completely. You might see a manager, for instance, who truly believes their team can hit 25% growth, even though they've been flat for three years straight.

[00:02:40:22 - 00:05:04:23]
Yeah. This time it's different. Exactly. They filter out anything that contradicts that belief. It's tough to challenge because it feels so right to them internally. Okay. What's another one? Another really pervasive one is anchoring bias. This is where you rely way too heavily on the first piece of information you get the anchor. Right. Even if it's totally outdated or irrelevant later on. Precisely. Think about budgeting. So often next year's budget is heavily anchored to last year's performance. Yeah. Even if the market's completely changed, that force number just sticks. It becomes really hard to shake. Then there's loss aversion. This is a powerful one. It's our tendency to prefer avoiding losses much more strongly than acquiring equivalent gains. And that's linked to the sunk cost fallacy, isn't it? Absolutely. It's the main driver. So that's why finance teams might resist killing off an underperforming product because they've already spent so much time and money on it. Exactly. They've sunk the costs. So they feel this need to see it through, even if it's clearly a bad investment going forward. It's like throwing good money after bad just to avoid meeting the initial loss. Yeah. I've seen that happen. What else? We also see confirmation bias a lot. This is where you actively look for and interpret information in a way that confirms what you already believe. And you ignore anything that contradicts it. Conveniently, yes. You're basically building a case for what you want to be true rather than looking objectively at all the evidence. So you only seek out data supporting your assumptions. Maybe you dismiss external benchmarks or what competitors are doing because it doesn't fit your narrative. Right. It becomes this closed loop, just reinforcing your own blind spots. It's incredibly common. OK, one more. Let's talk about herding behavior. This is that tendency for people to follow the actions of a larger group, kind of like a herd. Even if it contradicts their own information or beliefs. Yeah, it's the everyone else is doing it mentality. You see companies jumping on a bandwagon, like investing heavily in AI or entering a new market maybe just because competitors are doing it. Not necessarily because their own analysis says it's the right move. Exactly. It's driven more by fear of missing out FOMO than by sound financial strategy. And you see these patterns everywhere from, you know, small startups to huge Fortune 500 companies. OK, so we've seen how these biases can kind of subtly nudge us off course.

[00:05:06:06 - 00:08:20:12]
But what happens when they really take the wheel and, well, drive a multi billion dollar company off a cliff? Yeah, let's shift gears and look at some of those dramatic real world failures. Cases where the numbers might have been clear, but the human element, the biases blinded the decision makers. And these weren't just like spreadsheet errors. Not at all. These were fundamental failures of the decision making frameworks. OK, let's start with the classic Kodak. They famously resisted going digital to protect their film business. What were the financial biases at play there? Well, primarily you saw loss aversion wanting to protect the existing film revenue and status quo bias, just clinging to what they knew. So the finance teams, they underestimated the digital threat. Massively, they underestimated the revenue drop from digital disruption and as a result, kept over investing capital in the old thing technology. The deeper insight there, I think, is how powerful the inertia of a successful legacy business can be. It made it almost impossible, even for smart finance people, to really grasp the existential threat, not just to revenue, but to their whole business model. That's a sobering thought. Past success blinding you to the future. What about Hewlett Packard, HP, their acquisition of autonomy in 2011 for what, $11 billion? Yeah. And then they had to write down $8.8 billion of it just a few years later. Huge overvaluation. What drove that? Which biases? That looked like a pretty clear case of overconfidence, especially from leadership and confirmation bias. So they ignored warning signs. Apparently, yeah. Reports suggest there were red flags during due diligence, but leadership was so convinced of the strategic fit they pushed it through. They ignored internal models, market skepticism. So one person's strong conviction, fueled by bias, can override everything. It seems so. And it led to massive value destruction. It shows how dangerous that combination can be. OK, and then there's WeWork. The rapid expansion, the mounting losses, the unclear path to profit. That always felt like hype over fundamentals. You're right. WeWork is fascinating because you see a mix of biases. There was anchoring bias. They kept anchoring to those huge prior evaluations from funding rounds. Right. There was hurting behavior. All the investor FOMO, everyone wanting in. And definitely massive overconfidence in the business model itself. So the finance team's forecasts, they weren't based on reality. They seem to be based purely on growth optimism, this narrative of changing the world, not on sustainable cash flows. It shows how a compelling story amplified by FOMO and overconfidence can totally decouple financial planning from economic reality. Making dissent look irrational, even when it's spot on. Exactly. And the classic example of just not seeing the threat coming. Blockbuster failing to respond adequately to Netflix. Oh, yeah. That was clearly driven by status quo bias, clinging to their physical stores and just a profound underestimation of the disruption from streaming. Even when the signs were there. Yeah, even with early indicators, they kept pouring capital into physical locations and DVDs instead of adapting. It shows how deeply ingrained your mental model of what we do can be. Blinds you to change, even when it's staring you in the face. Even when your customers are leaving in droves for the competitor. Incredible. What about General Electric?

[00:08:21:13 - 00:14:06:10]
GE in the 2010s, they kept doubling down on earnings targets, long term projections, even as industrial trends were obviously shifting. Yeah, that demonstrates anchoring bias, anchoring to their legacy performance and confirmation bias during their long range planning. So their internal models just didn't keep up. Basically, yeah, they failed to properly incorporate the changing external market conditions, which led to misguidance. Big asset write downs. The lesson there. I think the lesson from GE is how institutional inertia combined with this strong internal story of dominance can stop even a giant company from objectively seeing the shifting landscape and adjusting its financial models. OK. And Nokia, another giant that stumbled with the shift of smartphones. Right. They delayed their pivot, even though early data clearly showed iOS and Android were taking over. What was that? Overconfidence? That and escalation of commitment. They were overconfident in their existing products, the Symbian OS, and they'd committed so much to it. Their financial decisions just didn't reflect the incredibly rapid change in consumer demand. So their past success became an anchor. A huge emotional anchor. It made it incredibly hard to pivot away from what used to work, even when this massive shift was happening. It really is hard to let go, isn't it? OK. OK. Finally, let's touch on the Boeing 737, and Max crisis, the cost pressures, the competition with Airbus leading to rushed production and tragic safety issues. Yeah, that's a powerful and sobering example. You saw overconfidence and definite signs of groupthink in both their financial projections and the product development process. So finance and operations, they downplayed risks. Reports indicate they downplayed risk signals coming from engineering, things about delays in order to meet earnings expectations and production schedules. The insight from Boeing, I think, is that when financial targets become absolutely paramount and internal dissent gets suppressed by groupthink, even a company known for safety can let cost cutting and market pressure compromise its core values with devastating consequences. Truly tragic, devastating consequences. Those were incredibly powerful examples. So it seems like if you're working in, say, F.P. and A. Budgeting, strategic planning, you're particularly vulnerable here. Absolutely, because so many of your decisions involve forecasting, trying to predict the future under uncertainty. That's fertile ground for bias. OK, so how does bias specifically show up in those finance roles? What does it look like day to day? Well, for one, those top down targets from leadership or the board, they can quickly become untouchable assumptions. So forecasts end up reflecting the target, not reality. Exactly. Teams anchor to those expectations instead of reflecting actual trends they might be seeing. OK, what else? Scenario planning often focuses almost exclusively on the upside. The realistic case or worse, the downside case gets ignored or downplayed. As if talking about failure might make it happen. Sort of, yeah. And under pressure, especially in big review meetings, the narrative often trumps the data. You paint the picture you think leadership wants to see. And the consequences of all this. Well, connecting it back, the consequences are pretty stark. You get misallocated capital. Companies pivot too late and lose market share. Profitability suffers. And your credibility takes a hit, right? If your forecasts keep missing. Definitely. It erodes trust in the entire analytical process, not just the numbers themselves. All right. This sounds a bit discouraging, but there must be things we can do, right? Let's shift to solutions. Actionable strategies, hopefully science backed to make your financial decisions. Where do we start? A fantastic starting point and surprisingly effective is to use pre-mortems. Pre-mortems? What's that? Before you even start a big initiative, get the team together and ask this question. OK, let's imagine it's 12 months from now and this initiative has completely failed. What went wrong? Oh, interesting. So you force yourselves to think about failure before it happens. Exactly. It completely shifts the frame. It helps surface blind spots and potential risks before you commit serious capital. That sounds simple, but potentially really powerful, given how much we naturally focus on success. It's incredibly powerful because it directly counters that optimism bias and confirmation bias. OK, what's next? Building on that idea of challenging assumptions, encourage dissent in forecasting reviews, actively create a safe space for people to challenge the numbers, even if it feels uncomfortable. This is not just nodding along. Right. You could even rotate who plays the role of devil's advocate for each forecast review. That directly tackles group thinking confirmation bias. But how do you keep it constructive? You frame it as a collective search for stronger, more robust numbers, not a personal criticism. It's about stress testing the assumptions. OK, strategy number three. Apply base rates. Don't just rely on your internal assumptions and feelings about this project. What do you mean by base rates? Look at historical data. Compare your projections to industry benchmarks or data from similar initiatives your company or even other companies have done in the past. So get an outside view like a reality check. Exactly. It provides objective data points to counter anchoring bias and that feeling of, oh, but our situation is totally unique. Usually it isn't. Good point. OK, what else? Fourth, when you do scenario planning, don't just model the best case. You absolutely have to model the downside and the realistic cases, too. It seems obvious, but you're saying it doesn't happen enough. Not nearly thoroughly enough. In my experience, doing this properly prepares you for a much wider range of outcomes and directly counters that overconfidence bias. It makes plans far more robust. Makes sense. One more. Finally, and this might feel counterintuitive sometimes.

[00:14:07:10 - 00:15:13:18]
Slow down high stakes decisions. Slow down in today's fast paced world. For the really big decisions, yes, implement cooling off periods before the final sign off on major spending or investments. Don't let urgency override rational thought. So build in a pause for reflection. Exactly. It allows emotion or pressure to subside and more rational thinking to take over. It reduces impulsive decisions. OK, so bring this all together. Finance pros were trained to trust the numbers. But the message here is it's just as important to interrogate the narrative we build around those numbers. Precisely. If you really want to level up your decision making, you have to start by understanding where bias creeps in. Become aware of it. And then build systems to protect yourself. Yes. Build frameworks. Use these kinds of strategies to protect your models and your decisions from your own inherent mindset traps. It's about making your decision making process resilient to human nature. That's a great way to frame it. Protect your models from your mindset. Lots to think about there. It really is constant vigilance. Until next time, stay rational, stay skeptical and stay sharp.