Investment is fundamentally a behavioural exercise—"The time to buy is when there's blood in the streets, even if the blood is your own” Baron Rothschild.
Investors, however, seek to rationalise their decisions, relying on different tools to justify their choices.
At its core, all investment analysis boils down to answering one question: “Is today’s price low or high compared to X metric?” The answer then dictates execution.
This perspective is not widely embraced in the industry. Investing often requires countless data points and man-hours to construct models. Yet, all this effort ultimately serves a single purpose: comparing the current price to an expected price.
To model these expectations, practitioners have developed increasingly sophisticated approaches over the decades—ranging from quantitative, fundamental, technical, and macro-based models. Each framework operates with a specific time horizon, inputs and risk tolerance, shaping the investor's perspective.
For example, Macro-based models may operate on a 3–5 year time frame, accepting extended drawdowns in pursuit of long-term gains. Quantitative models might focus on short-term arbitrage, such as exploiting intraday ETF imbalances, with defined drawdown limits and upside targets.
Since different models emphasise different time horizons, risk-reward structures and market interpretations, they often lead practitioners to divergent expectations and execution strategies. Ultimately and irrespective of complexity, every approach aims to determine where today's price stands relative to expectations—reinforcing that investing is as much about psychology as it is about numbers.
In this paper, I will explore two approaches that form a part of our scorecard view formation – one fundamental and one technical aspect of framing the expectations.
Let’s take a step back and look at the information that feeds the market participants and two common hypotheses that are widely quoted: Random Walk and Efficient Market Hypothesis (EMH)
The Random Walk hypothesis ignores the most important component—the market’s memory or better put, actor’s memory. Actors remember the most recent prices. This affects their behaviour and expectations, resulting in market price movements from one day to the next – a phenomenon captured in statistical measure called conditional heteroskedasticity. Essentially the variance of price changes is not constant but depends on recent past price movements; this is a common characteristic observed in financial markets.
Fama’s EMH theorises that markets are inherently efficient and unpredictable. A fundamental assumption is that all relevant information is freely, widely and instantly shared among investors. According to this view, neither expert stock analysis nor market timing can reliably outperform the market in the long run.
However, practitioners know that markets are, at best semi-strong form of efficiency. The market participants adjust their ‘expectations’ of the future based on past data that has just been made available.
Now with that in mind, let us explore the price-to-earnings (P/E) ratio aspect of the fundamental toolkit, average PE multiples and how the ‘expectation’ framing works in a straightforward fundamental process.
The P/E ratio indicates how many years’ worth of current earnings a company would need to generate to match its current share price. Or what is Price as a multiple of historic earnings. The emphasis is on historic earnings, whilst the price encapsulates the expectation of the future total return. In essence, the fluctuation in the P/E ratio is solely due to the price component change, and, as and when earnings are announced we see a change in the price due to a change in expectations driven by the forward guidance.
The point to make here is: 1) the P/E ratio is just a derivative of price; 2) E component of P/E ratio reflects earnings for the last year that is reported around 3 months after the year end. So, if the market is efficient, then this information is old and shouldn’t affect the price at all; 3) it is the forward guidance in the Management Discussion & Analysis (M D&A) commentary and conference call that drives ‘expectations change’ leading to price change. I posit here that whilst earnings are an important metric, price changes are due to changes in expectations delivered by the new information – the forward guidance and expectations of the company’s management.
Why do I say that? Let’s look at a usual ‘framing expectations’ process, where an analyst takes the latest data made available by the 10-K and then extrapolates the company’s Profit & Loss (P/L), Balance Sheet (B/S) and Cashflow based on assumptions about the operating model and guidance provided by the recent press conference. To note – an analyst is extrapolating historical facts about the operating model with the expectation that future earnings will look similar, adjusted for M D&A commentary, and then discounting to come up with an answer to the question we asked in the second paragraph and frames the company as a ‘buy’ or ‘sell’.
For example, in the case of NVDA the perpetual growth after 5 years ranges from 6.25% to 7.25% and helps frame the fair value of the stock with an expected upside of 27%. To note, practitioners with different sets of ‘expectations’ will arrive at different answers.
Source: Investing.com, 31 January 2025
Another framing aspect of P/E ratios is the comparison with average P/E. As a practitioner, we often find it problematic when P/E ratios are compared to their own or a relative ‘average’ of P/E ratios. The P component contracts/expands more often than E expansion (between two earning announcements) and comparing it to an average of X years may not be useful.
The long-term P/E average S&P 500 index is around 20x but notice how it has consistently remained above that mark for extended periods, especially after the Great Financial Crisis. One of the reasons for this is regime changes that have affected the price aspect of the PE – we have been in a QE and easing financial conditions for over a decade. This has led to an expansion of liquidity and new technologies to access complex financial instruments that have impacted the Price element of the P/E equation.
For a practitioner solely relying on fundamentals and price multiples this would mean underexposure to high multiple markets and stocks, with higher cash holding, leading to a difficult conversation defending relative performance against a broad benchmark.
Whilst 20X remains a reasonable estimate of long-term average, due to the price being non-stationary (as discussed earlier) and exogenous factors (such as liquidity and momentum-driven short-term trading), we are better served using a moving average to gauge if a market is relatively overbought or oversold.
Source: Bloomberg
The above chart highlights the limitations of using the Price-to-Earnings (P/E) ratio as a standalone metric for investment decisions. A strategy based on P/E ratios below their long-term average would have led to an overweight position in US equities from 2004 to around 2017 and an underweight position thereafter. A more effective approach considers the non-stationary nature of P/E ratios by analysing price movements relative to their 5-year moving average.
In our fundamental toolkit, we use the Cyclically Adjusted Price Earnings (CAPE) ratio, which adjusts for inflation’s impact on earnings. While CAPE shares the same limitations with traditional P/E ratios, such as reliance on historical data and inability to account for regime changes, it offers a more nuanced valuation perspective.
When conducting relative valuation analysis within our global asset framework, we acknowledge that markets can remain ‘overvalued’ or ‘undervalued’ for extended periods. For instance, the US market has stayed relatively expensive compared to other markets for over 30 years, particularly after the Global Financial Crisis (GFC) in major part due to quantitative easing (QE) policies.
This underscores the importance of considering broader economic and policy contexts when evaluating market valuations, rather than relying solely on traditional metrics.
Source: Barclays Indices, 31 January 2025
John Maynard Keynes famously remarked, “There is nothing so disastrous as a rational investment policy in an irrational world.”
The importance of technical analysis lies in its ability to address a critical aspect of market behaviour that fundamental analysis often overlooks: the psychological and emotional factors driving price movements. While fundamental analysis provides valuable insights into metrics such as price-to-earnings ratios and financial statements. It operates under the assumption that markets are rational and that all actors behave rationally and efficiently. However, as history has shown, markets are frequently influenced by human emotions such as fear, greed, and herd mentality. A recent example would be social media-driven activity that now makes roughly 60% of the S&P 500 index’s daily trading volume (via SPY and Futures).
It is human nature to see the market as we want to see it – biases and ‘expectations’ create an implicit guarantee of an outcome whilst minimising our ability to gauge risks objectively. Technical analysis helps bring objectivity to our view formation process and helps identify ‘hopium’. I often share a story with my team about how behaviour affects the markets:
Source: Japanese Candlestick Charting Techniques, 1991
In recent years, the rise of complex quantitative and technical strategies has been driven by three key factors: technological advancements, increased computational power, and the availability of vast amounts of data.
Technical analysis, while not without its drawbacks, offers a unique and often improved perspective on market behaviour, though it is not a cure-all for the limitations of fundamental metrics like the P/E ratio. Its primary goal is to determine whether a current price is high or low relative to a specific metric. However, markets can remain overbought for extended periods, continue to rise with minimal corrections or exhibit conflicting technical signals that may create confusion.
Despite these challenges, technical analysis derives its value from price movements, which reflect the transition from market ‘opinion’ to actual trading activity. This practice, known as tape reading or price reading, has been used probably since the American railroad boom and remains one of the readings in our toolkit.
Technical analysis, particularly the study of market structures, helps identify trends and potential directional moves by analysing patterns of market activity. Our proprietary indicators can provide additional insights, offering warnings or confirming market behaviour. For example, understanding market structure can help answer whether there is significant interest in a directional move, akin to identifying unusual trading activity in historical contexts like railroad stocks.
Investors often anchor to specific price levels, which technical analysis can identify, revealing trends and herd behaviour. Predictive models, though inconsistent in their success, use concepts like support/resistance levels and golden ratios to forecast price movements. However, the effectiveness of these models, like fundamental analysis, depends on market participants' beliefs and participation in them.
The influence of technical analysis has grown significantly in recent years, particularly with the rise of social media platforms like X (formerly Twitter) and Reddit. These platforms have created a new category of market participants—0DTE traders—who now account for roughly 60% of the daily trading volume in the S&P 500 Index.
Unlike traditional investors, these traders often disregard fundamental analysis, relying instead on technical signals and subscription-based forums that provide technical insights. Their trading is fuelled by ample liquidity, accessible technology, and advanced computational tools. When a critical mass of traders aligns behind a price move, it generates momentum, triggering algorithmic trading systems that exploit or hedge against these movements. This interplay between retail traders, technical analysis, and algorithms has reshaped market dynamics, emphasising the growing importance of technical analysis in today’s markets.
Source: Tier1Alpha.com, 5 February 2025
It’s a fascinating new dimension of behavioural investing. Recently, there has been significant interest in 0DTEs, or 0-Day-to-Expiry Options—options that settle daily at market close. These have become a popular choice for day traders. For option writers, 0DTEs offer a streamlined way to collect premiums due to the rapid time decay of these short-duration options. Option buyers, provide cheap, leveraged exposure to speculative bets, with an expectation of a gain, like dreaming of that new Lamborghini. With the liquidity accumulated in recent years, buyers can purchase 0DTE options with a modest amount of capital.
However, the mechanics behind 0DTEs create a ripple effect in the market. When an option writer sells these options, they must hedge their position, typically using futures. The more options sold, the more futures contracts are needed for hedging, particularly on ETFs like SPY (which tracks the S&P 500 Index). This increased demand for futures translates into higher demand for the underlying ETF. For instance, in the case of call options, the SPY operator must buy more units of the basket, driving prices upward. When this process occurs on a large scale, it creates a self-reinforcing cycle: buyers enjoy limited downside, sellers can write unlimited options, and the hedging activity in futures amplifies price movements.
This dynamic is further exacerbated by momentum ignition algorithms, which detect and exploit these price trends. The result is a self-perpetuating system driven not by fundamentals but by ‘liquidity’ and the ‘expectations’ shaped by technical analysis. Technical indicators capture the combined impact of these flows, feeding into momentum strategies that amplify price action. In essence, 0DTEs and their associated hedging mechanisms have created a feedback loop where technical analysis, liquidity, and speculative behaviour converge, significantly influencing market volatility and direction in ways that are increasingly detached from traditional fundamental drivers.
The key point from the above is Liquidity and a different understanding of risk combined with the expectation of outsized return has led to this phenomenon.
Since the 2008 financial crisis, global debt, particularly in the United States, has ballooned to unprecedented levels. Despite ongoing debates about how to control and reduce this debt, leaders across the globe continue to expand it. Governments have employed various tactics to artificially manage debt figures whilst issuing more debt, running fiscal deficits and adding to liquidity in the system. One way this is done is via debasement of the currency. Debasement refers to the process of increasing the money supply or liquidity, effectively ‘printing more money.’ This is particularly feasible with a fiat currency like the U.S. dollar, especially when that currency serves as the global reserve.
Debasement effects the ‘price’ of real assets as we have noted in our discussion earlier and distorts traditional valuation metrics like the price-to-earnings (P/E) ratio. As the value of money decreases, asset prices rise, making stocks and markets appear more expensive than they might be in real terms. This effect is particularly pronounced in indices with a high concentration of technology stocks or other rare assets.
At ABP, our view formation and expectations of the future take all of the above into account and more. We spend considerable time and resources understanding how Macro, Fundamentals, Technical and Geopolitical risks may impact our expectations and frame our views accordingly. This is a departure from usual investment processes but has helped us deliver year-on-year (YOY) outperformance against industry benchmarks since our inception.
We pride ourselves on being a modern global multi-asset manager with a strong track record of delivering results. Understanding the interplay of market dynamics across asset classes is central to our investment process. By staying attuned to these complexities, we aim to navigate the challenges and opportunities presented by debt, inflation and debasement effectively.