Inefficiently Efficient: Decoding Market Madness Through Behavioural Finance

Date-20 Mar 2025

Since 1960s the Efficient Market Hypothesis (EMH) has dominated financial theory and provided a formal framework to understand how markets work. It states that asset prices fully reflect all available information. It can further be deduced that it is nearly impossible for investors to consistently achieve above-market returns. While EMH provides a strong theoretical foundation, real-world market behavior often diverges from this ideal. Repeated financial anomalies, speculative bubbles, and panic-driven crashes suggest that investors are not always rational, and markets are not always efficient—at least not in the ways classical finance predicts.

In 1979 Daniel Kahneman and Amos Twersky challenged this conventional view of markets and introduced the importance of cognitive biases and psychological factors into the investment decision-making process. They emphasized that markets are shaped not just by fundamentals, but by human emotion, sentiment, and heuristic-driven decision-making. The phenomenon of herding, overconfidence, loss aversion, confirmation bias, and framing effects all play a crucial role in shaping price movements. For example the emergence of meme stocks, retail-driven short squeezes, and hype-fueled investment trends in ESG and AI demonstrate how narratives and emotions often outweigh intrinsic value assessments.

This article explores multiple contemporary market episodes that illustrate the intersection of psychological biases and financial decision-making. We examine the rise of ESG investing where framing bias and social proof have influenced capital allocation sometimes at the expense of performance considerations. The GameStop mania and meme stock phenomenon highlight the power of retail herding and sentiment amplification through social media disrupting conventional price discovery mechanisms. The AI hype cycle mirrors historical speculative manias where representativeness heuristic and extrapolation bias drive valuations beyond sustainable levels. Finally, the sharp increase in SIP cancellations following the 2025 market correction underscores the role of myopic loss aversion as investors react disproportionately to short-term market declines. 

By analyzing these episodes through the lens of behavioral finance we challenge the simplistic binary view of ‘efficient’ versus ‘inefficient’ markets. Instead, we argue that markets are “inefficiently efficient”—they incorporate information over time, but not always in a rational, predictable manner. We need to understanding the key psychological forces at play because these insights would allows us as investors, analysts, and policymakers to navigate these inefficiencies more effectively. It helps us in distinguishing between temporary noise and fundamental value shifts. In an era where retail investors have unprecedented influence, social media algorithms amplify sentiment and accelerates trend formation, the insights from behavioural finance have never been more critical in making informed investment decisions.

Investors often view ESG as a moral imperative rather than a financial strategy, leading to the overvaluation of "green" assets and underweighting of potential risks.

Starting in the 1970s roughly 50 years ago ESG investing has surged in popularity, driven by the belief that sustainability aligns with superior financial performance. The proponents of the theme argue it reflects rational long-term risk management whereas behavioural biases like framing, social proof, and confirmation bias suggest otherwise.

Investors often view ESG as a moral imperative rather than a financial strategy which conveys to investors that you can do well while doing good, leading to the overvaluation of "green" assets and underweighting of potential risks which clearly shows that this is a case of framing bias. The clean energy boom resulted in capital pouring into renewable energy companies and projects which sometimes ignored fundamental valuation metrics. In addition the public endorsement of ESG as a investment theme by financial giants like BlackRock and Vanguard fueled mass adoption and gave social proof which resulted in investors following market consensus rather than conducting independent due diligence. This resulted in enabling companies to greenwash their actions and exploit investor sentiment without delivering any meaningful impact on ground. 

The evidence regarding the performance of ESG funds is mixed. While it is true that the ESG funds sometimes outperform, but it is also true that Investors often selectively interpret data to validate their belief in ESG's superiority, ignoring instances where risk-adjusted returns are no better than traditional portfolios. This confirmation bias leads to the assumption that any period of ESG outperformance validates the strategy, while underperformance is rationalized as short-term volatility. ESG investing is not inherently flawed but the thing to note here is that the capital allocation is often influenced by sentiment rather than fundamentals. Acknowledging behavioural biases can help investors separate genuine long-term value from market-driven hype, ensuring ESG strategies are both sustainable and financially sound. 

Consider the clean energy boom of the early 2020s. During this time many ESG-focused funds heavily allocated capital to renewable energy companies based on the belief that a green transition was inevitable. While the long-term structural shift toward sustainability is undeniable, short-term valuations were often inflated beyond reasonable projections. A relevant example from the Indian market point of view is Adani Green which went from a modest price of 220, in Jan 2020, to an all-time high price of around 2900 by April 2022 which is a staggering price rise of 1318%. The financial performance was not in sync with the stock price’s performance clearly indicating the valuation of the company at a point were unjustifiable even by the most optimistic projections which shows that multiple psychological biases were at play and investors were being irrational. Similar biases could be seen at play in the case of Suzlon Energy’s rise resulting in deviation of valuation from the fundamentals of the business. 

In early 2021, GameStop (GME) became the centerpiece of an unprecedented retail-driven market frenzy. What started as a niche and curiosity driven discussion on Reddit’s r/WallStreetBets escalated into a full-blown short squeeze with GME’s stock price soaring from somewhere around $20 to an intraday high of $483, defying all the traditional valuation metrics. The episode exposed critical flaws in market efficiency and showcased herding behavior, FOMO (fear of missing out), and social reinforcement as dominant drivers of asset prices.

At its core, the GameStop saga was driven by sentiments and reflection on prices, where price movements influence investor beliefs, further fueling price changes

During COVID-19 lockdown the market saw record rise of commission free trading apps like Robinhood which enabled retail investors to trade from their homes and without considerable fees. Using the social media they shared information at the speed which was never possible before and this resulted in a mass mobilization against institutional investors. Due to ease of information sharing and confinement in homes the online communities turned into places where similar voices were being amplified at a mass scale. This resulted in amplified risk-taking leading to coordinated, sentiment-driven price action rather than fundamental valuation adjustments. This phenomenon mirrors past speculative bubbles but at an unprecedented scale due to the democratization of trading and real-time groupthink dynamics.

At its core, the GameStop saga was driven by sentiments and reflection on the prices where price movements influenced investor beliefs, further fueling price changes. Over 100% of GameStop’s float was shorted, meaning hedge funds were forced to cover their positions at ever-rising prices, creating a feedback loop. This self-reinforcing cycle resulted in Melvin Capital lose over 50% of its portfolio highlighting how coordinated retail action can disrupt traditional market structures. Even after the short squeeze the investors justified GME’s high prices using evolving narratives which shifted focus from it being a  “short squeeze play” to a belief in GameStop’s digital transformation. Confirmation and recency bias drove investors to ignore fundamental red flags, sustaining inflated valuations for a long time. The similar price movement was observed in other Meme stocks like AMC, Bed Bath & Beyond, and BlackBerry which proved the power of collective belief over traditional financial analysis.

The GameStop mania wasn’t just an anomaly rather it signaled a shift in market dynamics. Due to high speed and scale of information-sharing tools like social media retail investors now have structural influence because they can align their actions. In present markets, the sentiment-driven rallies can override fundamentals. While EMH suggests stock prices reflect intrinsic value, the meme stock phenomenon underscores that in modern markets, at least in short term the perception often trumps reality.

Now coming to the AI-driven stock rally starting in 2020, this rally mirrors the classic speculative booms, where investors extrapolate early success into long-term dominance or growth. Companies like NVIDIA, Microsoft, and Alphabet saw their stock prices surge due to rapid advancements in generative AI which resulted in investors pricing in exponential growth and above market earnings growth. The stock price of Nvidia jumped by over 300% in 2023. This rally was primarily driven by the fact that chips made by Nvidia dominate the AI industry. On the similar lines, AI-linked ETFs saw record inflows, with the Global X Robotics & AI ETF (BOTZ) gaining over 50% in a single year. However, historical market cycles suggest that such rallies often overestimate near-term revenue realization while underpricing competitive and regulatory risks.

Investors are often assuming that AI’s growth trajectory will mirror the past technological revolutions (e.g., the internet boom) leading to a representativeness bias. It is true that in the long term the market potential of AI is massive but the projections in the short term might be over-optimistic. A McKinsey report (2023) projected AI to add $13 trillion to global GDP by 2030, yet short-term monetization and revenue generating use cases remains unclear. Unlike SaaS models, where software firms scaled rapidly, AI’s high infrastructure costs and uncertain regulatory landscape surrounding training bias, ethical concerns and other legal issues pose unique challenges which are specific to AI industry.

Many AI stocks trade at extreme forward price-to-earnings (P/E) ratios, reminiscent of past speculative cycles.

Financial news and social media platforms played a crucial role in fueling speculative buying, reinforcing confirmation bias among investors. Headlines declaring AI the "next industrial revolution" drove massive retail participation in AI stocks which resembled the frenzy of dot-com bubble of the late 1990s. The retail flows into AI-heavy ETFs increasing 6x from 2022 to 2024 (Bloomberg, 2024). However history suggests that such frenzies often result in mispricing which are evident in Zoom and Peloton’s pandemic-era surges and subsequent crashes.

Many AI stocks trade at extreme forward price-to-earnings (P/E) ratios, reminiscent of past speculative cycles. At one time NVIDIA’s P/E touched 80+ in mid-2024, significantly above its historical average, while AI software firms with limited revenue saw their valuations soar. It is true that AI will undoubtedly reshape industries, the stock market’s reaction reflects more than just fundamentals—it embodies extrapolation bias, media-driven hype (confirmation & recency bias), and momentum trading. Whether this rally leads to sustained long-term gains or a speculative bust remains uncertain, but investors must differentiate genuine value creation from trend-driven euphoria. This is yet another classic case where sentiments and group-thinking is driving the price action in the short term.

Finally, we discuss the curious case of Indian stock markets, where a market correction that started in October 2024 has led retail investors to close their SIPs at a record pace by February of 2025. The SIP closure ratio hit 122% in the month of February.  During this period, the headline index Nifty 50 corrected by roughly 15 % and midcap and smallcap indexes corrected by roughly 20% and 30% respectively. 

The correction's recency overshadowed the historical resilience and recovery potential of equity markets, causing a shift towards more conservative investment approaches.​

This clearly shows that investment decisions of retail investors are largely driven by sentiments. According to AMFI in February 2025, equity mutual fund inflows declined to a ten-month low of approximately ₹293 billion, a 26% decrease from the previous month, as investors sought safety amid market volatility. Most importantly, the number of active SIP accounts fell to 82.6 million from 83.4 million in January, indicating a trend of investors discontinuing their systematic investments. ​

This irrational behaviour, when they should be buying more instead of stopping/selling, can be attributed to myopic loss aversion, where investors exhibit a heightened sensitivity to short-term losses over long-term gains. The sharp market correction compelled many investors to stop their SIP contributions and instead priortize immediate capital preservation despite abundant proofs confirming the long-term benefits of consistent investing through market cycles.​ Additionally the influence of recency bias led investors to overweight recent market performance in their decision-making processes. The correction's recency overshadowed the historical resilience and recovery potential of equity markets, causing a shift towards more conservative investment approaches.​

For financial advisors and policymakers understanding these key behavioral factors is essential in order to develop strategies that encourage sustained investment discipline, emphasizing the importance of long-term vision over reactive decisions influenced by short-term market fluctuations.

The Market is Inefficiently Efficient : It incorporates all available information but in a manner which is highly influenced by investor psychology

Financial markets have long been viewed through the lens of the Efficient Market Hypothesis (EMH), yet the episodes explored in this article reinforce a more nuanced reality—markets process information over time, but not always in a rational or predictable manner. Behavioral biases, social influence, and psychological heuristics frequently distort price discovery, leading to cycles of euphoria, panic, and mispricing.

These episodes challenge the rigid dichotomy of "efficient" versus "inefficient" markets. Instead, looking at the analysis we propose that markets are "inefficiently efficient"—they eventually incorporate all available information, but in a manner heavily influenced by investor psychology. Recognizing these inefficiencies is not just an academic exercise; it has real implications for investors, policymakers, and market participants. Understanding behavioral finance and its key insights allows us to differentiate between structural shifts and temporary bubbles/ narratives, filter out noise from fundamental value and design investment strategies that are resilient to cognitive biases.

As financial markets evolve in an era of retail dominance, algorithmic trading, and sentiment-driven cycles, the importance of behavioral insights has never been greater. A rational investor is not one who ignores emotions, but one who understands them and invests accordingly protecting his decision-making from emotional swings.

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