Which stocks best navigate economic and political risks?

Overview

On 11 July, The Wall Street Journal’s Senior Markets Columnist, James Mackintosh, asked: “how to invest when the global crises never stop?” Setting aside the irony (major stock indexes in Australia and the U.S. have recently scaled all-time highs), his question begs a more vital one: what are the key risks which investors face? The list is long, and its items and their order of importance are to significant extents subjective. However, and in light of recent events here and overseas, few Australian investors will reject, or even seriously question, two generalisations:

1. Political risks such as changes of governments’ policies (not necessarily as a result of elections), the threat or occurrence of geo-political and other conflicts, etc., particularly wars, can affect assets’ returns.
2. Economic risks such as changes of monetary policy can also influence investors’ results. In particular, recessions typically crush stocks and recoveries boost them.

Investors must thus navigate multiple – including political and economic – risks. This article details a pillar of Leithner & Company’s approach (for its logical foundation, see To lift your returns, swap these risks, 9 December 2024).

As a first step, I define and distinguish risk and uncertainty (and differentiate “Black Swans” from their “Grey” and “White” relatives). Almost everybody – including a few prominent people who really should but apparently don’t know better – regards risk and uncertainty as synonyms. Yet they differ fundamentally. Moreover, confusing them can encourage – and has produced – catastrophic misjudgments. Finally, most short-term market risk – including the severest variability – takes the form of random fluctuation whose cause is debatable and even unidentifiable.

Secondly, I detail two key risks, one political and the other economic, which over the past century owners of stocks have unavoidably, relatively frequently but not necessarily consciously encountered.

Thirdly, I confront these two risks with a century of valid and reliable data. Results from the first stage of my analysis replicate what’s well-known: a key political risk, “mid-term” election risk, has long impacted stocks’ returns. American equities typically sag during the first nine months of the second (“mid-term”) year of a president’s term; conversely, they usually rebound during its fourth quarter, and soar during its third year. Economic risk, too, is real: American stocks’ returns reliably fall sharply during recessions and dependably lift vigorously during recoveries.

The second stage of my analysis uncovers results which are comparatively unknown or have been largely ignored.

Australian stocks’ returns also typically dip during the first three quarters of American “mid-term” years; they also consistently rebound during these years’ final quarter, and soar during the president’s third year. Further, Australian stocks fall sharply during American recessions; they then recover during the subsequent recoveries.

Since the 1970s, if not before, these political and economic risks in the U.S. have reverberated to Australia.

So far this year, the All Ordinaries Index’s performance has broadly conformed to its norm during American mid-term years; the S&P 500’s, on the other hand, clearly hasn’t. Does the latter’s strong rise reflect an AI and data centre mania? Have American market participants thereby underestimated the possibility that adverse events might result from the upcoming mid-term elections? Are they ignoring or dismissing the chance that a “Grey Swan” might appear? (See also Trump will likely win – but won’t make America great again, 25 June 2024).

How to mitigate these political and economic risks? I also demonstrate that value stocks suffer much less than the S&P 500 and so-called “growth” stocks during the first nine months of mid-term years – yet they rise as strongly during its last quarter and the president’s third year. A key political risk – the “mid-term curse” – thus afflicts value stocks least and growth stocks most. Similarly, in response to recessions value stocks fall less than the Index, growth and small cap stocks; yet during recoveries they rebound just as strongly.

The implications are as obvious as they are profound.

Value stocks generally outperform the Index, growth and small cap stocks partly because they abate key economic and political risks much better than these alternatives (see also Why value investing usually outperforms, 29 April). “Growth,” in contrast, generally underperforms partly because it fails to attenuate – indeed, it exacerbates – these risks (see also Want to shrink your returns? Buy growth stocks! 23 March).

Fundamental questions unavoidably confront owners of equities. How to navigate political and economic risks? How to mitigate, and even profit from, their occurrence? My analysis demonstrates that the answer to both questions is clear: buy and hold value stocks. Conversely, if you want to magnify these risks’ effects – and thereby underperform over the long term – buy “growth” stocks.

Value’s “defence” (mitigation of the downside) is clearly superior; moreover, its “offence” (capture of the upside) is often better – and never worse – than the others. Value best mitigates major risks; additionally, it provides (to use Nassim Nicholas Taleb’s term) an “antifragile” bulwark against uncertainty.

A Crucial Distinction: Risk versus Uncertainty

It’s vital to appreciate: risk and uncertainty are NOT synonyms; indeed, they differ fundamentally. Although buyers and sellers in financial markets almost always use these two terms interchangeably, they differ in a crucial respect: whether you can (risk) or can’t (uncertainty) enumerate particular outcomes and estimate the probabilities that they occur. 

Risk, in short, isn’t just identifiable; it’s quantifiable and analysable. Uncertainty, on the other hand, is indeterminate. Known (or conceivable) possibilities underpin the calculation of risks; unknowable future states reflect uncertainty. Long series of historical data and tractable statistical models estimate risk; the lack of knowledge characterises uncertainty.

Risks, to use the insight of Donald Rumsfeld, are “known unknowns” (for details, see my description of the “Rumsfeld Matrix” in Tight(er) financial conditions will end the bull market, 24 March 2025).

Risk exists when you don’t know exactly what will happen, but you know all possible outcomes and the likelihoods that each will occur. Simple examples include tossing a coin or rolling a die. You don’t know what will result from specific toss or roll, but you do know what could occur; moreover, you know the probability of any particular outcome.

The S&P 500 Index’s returns illustrate risk – NOT uncertainty.

Over the past century, its CPI-adjusted 12-month total (including dividends) return has averaged 9.2%. Given the returns’ standard deviation (20.4%) and distribution (approximately standard normal albeit with slightly fat tails), over any randomly-selected 12-month interval – and, by inference, over any future 12-month interval – the probability (risk) of a negative return is 29%, of a loss of 10% or more is 17%, and of a loss of 20% or more is 8%.

Nobody can credibly state or reliably predict the Index’s return over the next year; accordingly, any attempt to do so is a fool’s errand (for details, see Stop kidding yourself: Nobody can “time the market,” 30 June 2025). We do, however, know the past probability of negative returns – and can plausibly infer it into the future.

In contrast, uncertainty arises when probabilities are impossible to estimate because the situation is unknowable in advance, completely novel (and thus lacking data of previous occurrences) or intractably complex. In recognition of economist Frank Knight and his landmark book Risk, Uncertainty and Profit (1921), uncertainty is often called “Knightian uncertainty.”

“Black Swans” exemplify uncertainty.

Nassim Nicholas Taleb popularised this concept (see in particular his book The Black Swan: The Impact of the Highly Improbable, Penguin, 2011). They’re events which are impossible to forecast, yet hindsight bias often makes them seem obvious in retrospect. Taleb regards some major scientific discoveries – and, by implication, technological advances – and certain historical events as “Black Swans.” As examples he cites the outbreak of the First World War, rise of the personal computer, stock market crash in October 1987, collapse of the Soviet Union, development of the Internet and attacks on 11 September 2001.

As a brief aside, Taleb has repeatedly denied that the COVID-19 pandemic qualified as a Black Swan; instead, he’s classified it as a “White Swan” (event whose massive impact isn’t merely precedented, but probable and thus widely anticipated). Before 2020, another global pandemic was expected at some point – not least by the World Health Organisation – because they’ve occurred repeatedly. Examples included the Spanish Flu (1918-1920), Asian Flu (1957-1958) and Hong Kong Flu (1968).

If the COVID-19 pandemic wasn’t a Black Swan, then governments’ panicked reactions – in the form of draconian, society-wide “lock-downs,” “mask mandates,” etc. – were.

The phrase “Black Swan” originates from the assumption, which prevailed until the late-17^th century, that all swans are white because no European had ever seen a non-white one (and thus tacitly assumed that such a creature didn’t exist). The Roman poet, Juvenal, used the phrase “black swan” to describe something that “everybody knows” is impossible. The discovery of such creatures by Dutch explorers of Western Australia by 1697 (and likely as early as 1636) falsified this universal and entrenched – at least among Europeans – belief.

Hence a “Black Swan” is an “impossibility” which nonetheless eventuates.

These events expose hidden vulnerabilities and create domino effects across interconnected systems such as financial markets. The best defence against them, reckons Taleb, is the erection of bulwarks rather than any futile attempt to foresee them (for details, see Antifragile: Things That Gain from Disorder, Random House, 2014).

The distinction between risk and uncertainty matters: confusing them produces poor decisions – and in extreme instances catastrophic misjudgments.

If, in effect, you treat risk as uncertainty by failing to apply valid and reliable data and plausible mathematical models to situations where probabilities are knowable, you ignore risk and thus become complacent. On the other hand, if you treat uncertainty as risk by attempting to apply mathematical models to situations where probabilities are unknowable, you also become overoptimistic – and fail to prepare for Black Swan events.

Overconfidence has widely been documented as a primary cause of poor decisions and consequent losses (see in particular Why you’re probably overconfident – and what you can do about it, 14 February 2022).

The (in)famous hedge fund, Long-Term Capital Management, provides a textbook example. Using a very short and grossly unrepresentative sample of data which told them what they wanted to hear, and disregarding its distribution’s fat tails, LTCM’s principals (which included eminent finance academics) concluded that its maximum possible annualised loss was ca. 20%.

Given that fundamental mistake, LTCM became grossly arrogant. Its principals emphatically denied that losses greater than 20% were possible – and on this basis constructed an absurdly fragile (because they borrowed so heavily) business model. Within a few years, the Russian and Asian debt crises of the late-1990s generated the very loss which LTCM smugly refused to consider because they insisted that it was virtually impossible. This gross error quickly bankrupted the firm (for details, see How Warren Buffett has trounced “the world’s greatest hedge fund manager,” 11 August 2025).

The consequences of LTCM’s collapse across global bond and stock markets were widespread. They necessitated a “bailout” (controlled liquidation) which the Federal Reserve orchestrated and into which it dragooned Wall Street banks. Arguably, the collapse widened, deepened and extended the debt crises.

Choosing Two Key Risks for Analysis

Market-Moving News or Unidentifiable Random Fluctuation?

Most short-term market risk – that is, volatility, including the severest variability – takes the form of random fluctuation whose cause is debatable and even unidentifiable. According to Jeremy Siegel (Stocks for the Long Run, 4^th ed., McGraw-Hill, 2008), “it might surprise investors that in the vast majority of cases, (the market’s biggest single-day) movements are not accompanied any news that explains why (it plunged or soared that day).” 

From its inception (on 26 May 1896) to 30 June 2026, the Dow Jones Industrial Average (DJIA) rose or fell at least 5% on 151 days. That’s 0.42% of the 35,628 trading days (that is, 33,939 weekdays, plus 2,939 Saturdays until 27 September 1952 (on which the AMEX and NYSE traded), minus ca. 1,250 public holidays) during this interval. Of the 126 ± 5% or more days to 2006, wrote Siegel, 59 were up and 67 were down, and “only 30 of these major moves can be identified with a specific … political or economic event, such as wars, political changes or government policy shifts.”

“That means,” Siegel emphasises, “that less than one in four major market moves can be clearly linked to a specific event.”

In particular, “the record 22.6% one-day fall … on 19 October 1987 (has not been)
associated with any one readily identifiable news event.” Surveys conducted by Robert Shiller immediately after the crash identified no external cause, just an internal feedback loop: people panicked and sold indiscriminately because everybody else was. The market plunged because each market participant didn’t know what was happening – but feared that everybody else knew something that he didn’t!

“Monetary policy,” Siegel continues, “is the biggest single driver of these massive market outbreaks of euphoria or fear. Out of the five largest moves in the stock market over the past century for which there is a clearly identified cause, (all exceeded 10% and occurred during the Great Depression) and four have been directly associated with changes in monetary policy.” Yet from 1940 to 2007 only four days of big moves have been attributed to a specific news event:

1. 27 October 1997 (plunge of 7.2%): attack on the Hong Kong dollar;
2. 17 September 2001 (drop of 7.1%): markets re-open after terrorist attacks;
3. 13 October 1989 (fall of 6.9%): collapse of leveraged buyout of United Airlines;
4. 26 September 1955 (descent of 6.5%): President Dwight Eisenhower suffers non-fatal heart attack.

Two of these “explanations,” it seems to me, are implausible; the other two also raise questions.

Why did an attack on the $HK cause American markets to plunge? Why would this attack cause American markets to plunge as much as the attacks on 11 September 2011? Why would the collapse of a leveraged buyout of an airline affect the entire market? As Siegel notes, the plunge on 13 October 1989, “though often attributed to the collapse of the leveraged buyout, can be questioned since the market was already down substantially on very little news before the collapse was announced.”

And why did Ike’s non-fatal heart attack affect American markets more than twice as severely as John F. Kennedy’s assassination?

At 12.30 in Dallas (13.30 in New York) on 22 November 1963, a Friday, President Kennedy was shot. At that moment the Dow traded at 732. Rumours regarding an incident involving the President almost immediately began to circulate in trading pits (where most trade in that era occurred). At 14.33 New York time, official confirmation of his death reached markets. By then, on the news that he’d received last rites, trading had ceased (14.07); at its early close, the Dow fell 2.8% to 712.

On 25 November, a Monday, markets remained closed for the National Day of Mourning and JFK’s funeral. On Tuesday the 26^th they reopened – and the Dow soared 4.5% to 744, one of its best days in 25 years. Was the market signaling that Kennedy’s death was a net plus? (Similarly, on 14 September 1901, the day of President William McKinley’s assassination, the Dow fell more than 4%. But on the next day it zoomed almost 5%.)

“Explanations” of a market’s daily moves, including its extreme movements, can be inconsistent, nonsensical or unknowable. They’re frequently questionable because they’re contradictory: journalists often disagree about which event caused it to plunge or soar.

Siegel provides an example. On 15 November 1991 the DJIA shed nearly 4%. The front page of the next day’s Investor’s Business Daily blared the headline: “Dow Plunges 120 in Scary Selloff: Biotechs and Congress Get the Blame.” In contrast, the New York correspondent of London’s The Financial Times titled its lead front-page article “Wall Street Drops 120 Points on Concern at Russian Moves.”

“What is interesting,” he wrote, “is that such news, specifically that the Russian government had suspended oil licenses and (seized the country’s) gold supplies, was not mentioned even once in the Investor’s Business Daily article! That one major newspaper can highlight ‘reasons’ that another does not even report illustrates the difficulty in finding (plausible) explanations for the (daily fluctuations) of markets.”

When investigating the risks of major market movements, Siegel thus concludes: “it is sobering to realise that less than one in four can be linked to a news event of major political or economic import. This confirms the unpredictability of the market and the difficulty in forecasting market moves.”

I’ve computed the S&P 500 Index’s monthly return (not including dividends and ignoring CPI) since January 1871, counted the numbers of months whose returns is greater than ± 0%, greater than ± 1%, etc.; I then replicated this analysis for the All Ordinaries Index’s returns since June 1937; Figure 1 plots the results. These data are random-normal; accordingly, the bigger is the monthly return, the less likely is its occurrence.

Figure 1: Frequencies of Given Monthly Movements, All Ordinaries and S&P 500 Indexes

What’s the likelihood that these indexes’ monthly returns differ from zero? It’s a virtual certainty (probability of almost 100%). There’s a high probability (ca. 75%) that they fluctuate at least ± 1%, and a moderate one (ca. 43%) that they fluctuate at least ± 2.5%. And even bigger monthly movements aren’t unusual: on both the All Ords and S&P 500, movements of ± 5% or more have occurred ca. 15% of the time.

If, as Siegel has detailed, it’s seldom possible unambiguously to identify the triggers of most of Dow’s biggest single-day movements, it’s practically impossible credibly to specify the causes of its much more numerous and modest daily movements. Never mind the avuncular Alan Kohler on the 7pm news: typically, he’s merely pinning subjective “tails” on random donkeys. The same point applies to the All Ords’ and S&P 500’s one-month fluctuations.

I infer that the market’s daily and monthly movements, including large ones, is usually nothing more than random – and thus unpredictable before it occurs and unexplainable after it happens – fluctuation.

Did an attack on the Hong Kong dollar on 27 October 1997 really cause the DJIA to plunge 7.2%? I doubt it: these two events coincided, but I discern no reason to assume that the attack caused the plunge. The latter reflected extreme volatility whose cause was unidentifiable.

Markets’ short-term (day to day and month to month) fluctuations, regardless of their magnitudes, seldom have clear causes. This risk, in effect, takes the form of constant background noise.

That’s Why I’ve Selected Two Enduring, Significant, Systematic and Analysable Risks

Nobody can comprehensively identify, never mind reliably quantify, all of the micro-economic, macro-economic, liquidity, portfolio, political, valuation, etc., risks which investors inevitably but often unknowingly face. Still less can anybody dependably predict when they erupt. Moreover, and by definition, everybody confronts uncertainties but nobody can identify them in advance.

I’ve therefore selected two key risks for analysis. One is political (“midterm” elections) and the other is economic (recession).

I’ve selected them because, unlike one-day surges and plunges, they’re recurrent and frequent – and for at least a century their effects have been significant. I’ve also analysed them in the U.S. and establish their relevance to Australia because their analysis in those countries is most straightforward and relevant. In particular, the tenure of the Presidency and Congress is fixed – and thus the timing (as opposed to the results) of elections is perfectly predictable. In particular, “mid-term” elections occur every four years. Moreover, the National Bureau of Economic Research (NBER) is that country’s unofficial (and well-respected) arbiter of recessions’ starts and ends.

According to NBER, since the mid-19^th century the U.S. economy has been in recession approximately 15-20% of the time; since the Second World War, expansions have become much longer and recessions less frequent; as a result, the risk of recession has fallen to ca. 10%.

Political Risk: the “Mid-term Curse”

In the U.S., the president serves a fixed, four-year term, and members of the House of Representatives serve fixed two-year terms. Mid-term elections the House and approximately one-third of the Senate (whose members are elected to six-year terms; hence either 33 or 34, assuming no vacancies, are elected every two years) occur halfway through a president’s term.

The “mid-term curse” is the strong historical tendency at these elections for the president’s party to lose seats in Congress.

At the 25 mid-term elections over the past century, the president’s party has lost an average of 26 seats in the House and four in the Senate. In only three (12%) of these elections (i.e., in 1934, 1998 and 2002) has it gained seats in the House; in eight (32%) it’s gained seats in the Senate.

This year is a “mid-term” year, and these elections will occur on 3 November.

In the House, Republicans hold the thinnest (218-212) majority in American history; there’s also one independent and four vacancies. In June, Cook Political Report projected that 181 seats are solidly Democratic; another 23 are likely Democratic or lean towards them. On the other hand, 186 are solidly Republican and 26 are likely or lean GOP. That leaves 18 “toss-ups;” of these, Democrats must win more than three-quarters (at least 14) in order to capture the House. In mid-July, according to betting markets, the likelihood that they’ll do so exceeded 80%.

In the Senate, however, it’s not easy to envisage a Democratic majority. The 35 seats being contested this year (including two to fill vacancies) are mostly in Southern, Midwestern and Rocky Mountain states where Democrats are weak. “The likelihood of Democrats grabbing the four seats they need to take control of the Senate,” says Karl Rove (“Who Has the Mid-term Upper Hand?” The Wall Street Journal, 15 July), “is slim” (see also “Democrats Are Not in Good Shape for the Mid-terms,” Zero Hedge, 14 May). Major prediction markets and bookmakers aren’t nearly so pessimistic: from them, I infer that the probability that Democrats will control the Senate after the mid-terms is 43-45%.

Why does the president’s party usually suffer at mid-terms?

Turnout is generally significantly lower (ca. 40%) at these elections than at presidential (ca. 60%) elections; in particular, at mid-terms the president’s supporters (and, more generally, followers of his party) are less likely to vote. Relatedly, voters – particularly those of the non-incumbent party – regard a mid-term as a referendum on the president’s performance. They’re thereby motivated to vote; as a result, the party in the White House is usually punished regardless of the president’s achievements or the country’s overall condition.

The mid-term curse often produces political deadlock: the opposition party gains control of one or both chambers of the Congress, and thus blocks, or at least blunts, the president’s agenda. The curse has long been so reliable that it’s practically a structural feature of American politics. It obliges a Chief Executive to change his strategy and policies – and in extreme cases to abandon them – during the final two years of his term.

Two specific risks to investors thus arise during the year or so before – and as a result of – American mid-term elections.

Firstly, gridlock between President and Congress can impair and even paralyse the federal government’s ability to address pressing issues. Secondly and more importantly (given that over the past half-century both President and Congress have ignored or denied a growing list of ever more important issues), policies regarding taxation, spending, regulation, etc., can veer in unexpected – and, for investors, undesirable – directions (see also The consensus is wrong: America’s economy is chronically ill, 13 January 2025).

It’s not just these risks: it’s their likely overestimation which causes stocks’ returns to stagnate and fall during the first three-quarters of mid-term election years. In contrast, in September and October the likely results of the mid-term election become clearer; and during the year or so thereafter, its consequences become evident. As a result, during these months markets tend to rebound strongly.

Data

I’ve analysed data collated by Robert Shiller (Standard & Poor’s 500 Index) and Richard French and his colleagues (value, growth and small cap stocks). For each month beginning in July 1926, French et al. rank-ordered each company listed on the AMEX, NASDAQ and NYSE according to the ratio of its shares’ price to its book value per share on the preceding 30 June.

They then assigned each company to one of three categories: (a) those ranked within the lowest three deciles (that is, the 30% of stocks whose price-to-book ratios are lowest) to the “low” category; (b) those in the four middle (40%-70%) deciles to the “middle” category; and (c) those ranked within the highest three deciles (i.e., the 30% of stocks whose price-to-book ratios are highest) to the “high” category. Finally, they computed each portfolio’s monthly total (that is, including dividends but excluding tax, brokerage and other costs) return.

Stocks whose ratios of price to book value are comparatively low are commonly regarded as value stocks; those with relatively high ratios are typically considered “growth” stocks.

I’ve corrected small caps’ returns for their initial mislabeling of micro-caps as small caps (for details, see Why intelligent investors avoid small caps, 13 April); as a result, their market capitalisation is at least $2 billion and less than $10 billion (both CPI-adjusted). In what follows, all returns are “real” (that is, CPI-adjusted) total (that is, including dividends) returns.

The “Mid-term Curse” and the S&P 500

For each month since January 1927 I calculated the S&P 500’s 12-month year-to-date (YTD) CPI-adjusted return: in other words, I computed its one-month return for January 1927, added February 1927’s return (creating a January-February 1927 cumulative return), added March’s return to February’s cumulative return, and so on to December 1927; I then repeated this process for each month and year to June 2026.

Next, I divided these monthly YTD returns into two segments: those which occurred during mid-term election years (that is, the second year of a presidential term) and those which occurred in the other (first, third and fourth) years. As a final step, for each series I computed the mean for all January returns, all February returns, …, and all December returns. Figure 2 plots the results.

Figure 2: Mean Year-to-Date Results, S&P 500 Index, January 1927-June 2026

They corroborate what others have found: the Index’s cumulative results in mid-term years are, statistically (for the sake of brevity I’ve omitted the results of tests of significance) and substantively, lower than in the other years.

In mid-term years, the mean cumulative return is 0.3%; in the other years, it’s 6.3%. In mid-term years, December’s mean YTD return is 2.4%; in the other years it’s 10.0%. In the other years the general trend over these 12 months is strongly linear. In mid-term years, however, it’s less strongly curvilinear: from January to September, and particularly from April to September, as the risks regarding the election’s outcome rise, the average cumulative return falls steadily and becomes negative.

Conversely, in October, as the election’s likely results become more apparent, the average return begins to increase; on the first Tuesday of November, the election occurs – and during the rest of that month and December, as the election’s implications become clearer, the average YTD return lifts sharply.

I’ve also computed the volatility (quantified as the standard deviation) of the cumulative returns summarised in Figure 2. Figure 3 plots the results. Are cumulative returns more volatile in mid-term years? The answer, in short, is “no.”

Figure 3: Volatility of Year-to-Date Results, S&P 500 Index, January 1927-June 2026

From January to June, the two series’ standard deviations are virtually identical. From July to December, results are more volatile in mid-term years; but neither substantively nor statistically are the differences years significant.

Given these means and standard deviations, plus the fact (for the sake of brevity I’ve omitted the details) that each month’s distribution of returns is approximately normal, it’s a simple matter to estimate the probability of a negative result during each month. Figure 4 plots them.

Figure 4: Probabilities of Negative Year-to-Date Returns, S&P 500 Index, January 1927-June 2026

The cumulative probability of a negative return in a mid-term year is, substantively and statistically, significantly higher than in other years.

In mid-term years, the probability of loss is 40% or more, and from May to October it exceeds 50%; as a result, the average is 48%. In the other years, the probability never surpasses 35% and falls as low as 23%; hence the average probability of loss is 29%.

The “Mid-term Curse” and Growth, Small Cap and Value Stocks

For each month since January 1927, using the data which Robert French and his colleagues have compiled, I’ve replicated the analysis in the previous section for growth, small cap and value stocks (momentum speculators’ results are so abysmal that I’ve omitted them; for details, see Why value investing crushes momentum speculation, 9 February). For these stocks, Figure 5 is the counterpart of the Index’s first series in Figure 2.

During mid-term election years, value’s cumulative return averages 2.3%, small caps’ 0.7%, the Index’s 0.3% and growth’s -1.1%. During these years, value outperforms, small caps are silver medallists, the Index gets bronze and growth stocks the wooden spoon. At the end of these years, small caps’ YTD returns (5.2%) pip value stocks’ (5.0%), and the Index and growth (2.4%) lag.

Figure 5: Mean Year-to-Date Results, Mid-term Election Years, January 1927-June 2026

We already know that value stocks generally beat growth stocks, etc. (for details, see (Want to shrink your returns? Buy growth stocks! 23 March and Why value investing usually outperforms, 29 April). Figure 6, which plots cumulative results outside of mid-term election years and is the counterpart of the Index’s second series in Figure 2, elaborates this fundamental result.

Figure 6: Mean Year-to-Date Results, Presidential Years 1, 3 and 4, January 1927-June 2026

Without exception, in each month value’s cumulative return exceeds the others’ returns. The mean of value’s monthly YTD returns is 8.8%; small caps’ is 6.6%, the Index’s 6.3% and growth’s 5.5%.

Given these means and standard deviations, plus the fact (for the sake of brevity I’ve again omitted details) that in each month each portfolio’s distribution of returns is approximately normal, it’s a simple matter to estimate their probabilities of cumulatively negative results. Figure 7 plots them.

Figure 7: Probabilities of Negative Year-to-Date Returns, Mid-term Election Years, January 1927-June 2026

In the first eleven months of mid-term years, value stocks’ probability of a negative return is lowest – and growth stocks’ probability is highest.

During the mid-term year, the average probability of a negative result is 53% (growth stocks), 48% (Index), 47% (small caps) and 42% (value stocks). By December, these probabilities are, respectively, 46%, 45%, 41% and 43%. Statistically, in January-September value’s probability of loss is significantly lower than growth’s, but isn’t significantly lower than small caps’.

The Post-Mid-Term Relief Rally

American stocks have mostly outperformed during the third year of a president’s term. It’s become a reliable phenomenon: without exception since 1962 and regardless of which party won the mid-term, stocks have risen in Year 3. Outperformance occurs after mid-terms because they abate or remove a key risk: whether or not a divided government occurs, mid-terms create a known balance of power.

Furthermore, in order to win a second term or to assist his successor, during his third year a president often introduces or intensifies stimulative (from a Keynesian point of view) economic policies ahead of the next presidential election. Finally, mean regression also plays a role: as we’ve seen, significant short-term pullbacks usually occur during mid-term election years; as a result, the following year usually experiences a strong rebound.

Figure 8 plots the Index’s year-to-date returns during the second and third years of presidential terms since July 1927. In mid-term years, the mean cumulative return is 0.3%; in Year 3, it’s 9.7%. In Year 2, December’s mean cumulative return is 2.4%; in Year 3 it’s 13.4%. Finally, in Year 3 the general trend is strongly linear. In mid-term years, however, and as we’ve already seen (Figure 2), it’s less strongly curvilinear.

Figure 8: Mean Year-to-Date Results, S&P 500 Index, January 1927-June 2026

I’ve replicated the analysis for growth, small cap and value stocks. For these stocks, Figure 9 is the counterpart of the Index’s second (Year 3) series in Figure 8. During Year 3, value’s cumulative return averages 10.0%, small caps’ 10.9%, the Index’s 9.7% and growth’s 10.6%. Value neither outperforms nor underperforms; indeed, nobody significantly outperforms anybody else.

Figure 9: Mean Year-to-Date Results, Presidential Year 3, January 1927-June 2026

In December of Year 3, growth stocks’ YTD return (16.7%) exceeds value stocks’ (14.6%) and the Index’s (13.4%); and thanks to a huge (5.07%) one-month spurt in December, small caps’ cumulative returns (17.9%) exceed the others’. These differences are neither statistically nor substantively significant.

During post-mid-term relief rallies over the past century, value stocks haven’t outperformed. On the other hand, neither have they significantly underperformed. During Year 2 of a president’s term, value’s defence is clearly best; during Year 3, its offence is no better or worse than the others.

On the whole, therefore, considering downside as well as downdraught as well as upswing, value has mitigated this political (“mid-term curse”) risk better than growth, small caps and the Index.

The Risk of Recession

Stocks’ downdraughts and upswings are often related to recessions and recoveries. Founded in 1920, the National Bureau of Economic Research (NBER) is an American private nonprofit research organisation “committed to undertaking and disseminating unbiased economic research among public policymakers, business professionals, and the academic community.”

NBER is probably best known as the provider (and since the 1960s has been the quasi-official arbiter) of the start and end dates of economic cycles – and thus recessions – in the U.S.

My definition of recession is simple: a recession is what NBER says it is; moreover, a given recession starts and ends when NBER says so. A recession, according to NBER, “is the period between a peak of economic activity and its subsequent trough, or lowest point. Between trough and peak, the economy is in an expansion. Expansion is the normal state of the economy; most recessions are brief. However, the time that it takes for the economy to return to its previous peak level of activity or its previous trend path may be quite extended.”

Before the fact, a recession’s start and end dates are practically impossible to predict; after the fact, they’re hardly easy to ascertain. A recession “involves a significant decline in economic activity that is spread across the economy and lasts more than a few months. In our (NBER’s) … definition, we treat the three criteria – depth, diffusion, and duration – as somewhat interchangeable. That is, while each criterion needs to be met individually to some degree, extreme conditions revealed by one criterion may partially offset weaker indications from another.”

If that’s not clear-cut, it’s because the real world is innately messy. What’s evident is that recession isn’t merely – or even primarily – two consecutive negative quarters of Gross Domestic Product.

Most of the recessions identified by NBER conform to this criterion. Crucially, however, the commencement of a slump can precede or follow GDP’s first negative quarter; similarly, its end can precede or follow the final quarter of negative growth. As a result, (a) a recession’s start and end dates and (b) periods of negative GDP growth often don’t coincide.

Unavoidably, to a significant extent NBER’s determinations are subjective. “The determination of the months of peaks and troughs,” it says, “is based on a range of monthly measures of aggregate real economic activity published by federal statistical agencies … There is no fixed rule about what measures contribute information to the process or how they are weighted in our decisions. In recent decades, the two measures we have put the most weight on are real personal income less transfers and nonfarm payroll employment.”

Two Key Junctures When Value Generally Outperforms

In Misbehaving: The Making of Behavioral Economics (WW Norton, 2016), Richard Thaler (recipient in 2017 of the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel, which is mislabeled as “the Nobel Prize in Economics”) concluded that value investing works because it reflects “a simple regression to the mean.”

Mean regression is a statistical phenomenon. If initial measurements of a variable, such stocks’ total returns calculated as CPI-adjusted compound annual growth rates (CAGRs), become extreme (very high or low), then subsequent returns will trend closer (“regress”) to their overall average (mean).

A simple analysis, which I introduced in Why value investing usually outperforms (29 April), has profound consequences.

For each month since July 1926 I calculated (as CAGRs) rolling five-year (60-month), CPI-adjusted total returns of the S&P 500 and the value, growth, momentum and small cap portfolios (I corrected the latter for its initial mislabelling of micro-caps as small caps; for details, see Why Intelligent Investors Avoid Small Caps, 13 April).

Table 1: Mean Five-Year CPI-Adjusted CAGRs, by Quintile of S&P 500’s Previous Five-Year CAGR, Four Portfolios, July 1926-February 2026

For each month, and for each portfolio’s as well as the Index’s CAGRs, I then matched this past five-year CAGR to its CAGR over the subsequent years. I then sorted these series by the Index’s past five-year CAGRs; separated the data into five equal (by numbers of observations) segments; and for each quintile, computed mean CAGRs for the previous and next five years. Table 1 summarises the results.

It demonstrates that

1. The Index, as well as value, growth and small cap stocks, regress to their medium-term means: the higher was the CAGR during the preceding five years, the lower it will fall during the next. Equally, the lower was the CAGR during the previous five years falls, the higher it will rise during the next.
2. The value portfolio slightly outperforms the Index on the upswing (the mean CAGRs in Quintile #5 of 20.1% and 19.7% respectively); it also considerably outperforms on the downswing (mean CAGRs in Quintile #1 of -0.8% and -4.2% respectively.
3. During both busts (Quintile #1) and booms (Quintile #5), on average value outperforms growth and small caps.

In short, value stocks haven’t merely outperformed generally (that is, over short-term, medium-term and long-term periods) over the past century; they’ve also excelled during medium-term extremes.

On average they’ve outperformed (fallen less than growth stocks, etc.) in Quintile #1 – which contains all bear markets, corrections, crises and market downturns. They’ve also tended to excel (generate higher returns than momentum, etc.) in Quintile #5, which contains subsequent recoveries, upswings and bull markets.

Value, Growth and Small Caps during Recessions and Recoveries

I’ve assigned each month since July 1926 to two categories: according to the NBER, (1) it fell within a recession or (2) outside of a recession. For each month, I then computed the Index’s and growth’s, small caps’ and value stocks’ total, CPI-adjusted returns (expressed as CAGRs) over the previous 12 months, 60 months (five years) and 120 months (ten years), and over the next 12 months, five years and ten years.

I then computed mean CAGRs for short-term, medium-term and long-term intervals whose final month was in recession (“previous”), and for intervals over the next 12, 60 and 120 months whose first month was in recession (“next”). Table 2 summarises the results.

Table 2: Mean CPI-Adjusted CAGRs, Four Portfolios, Months Ending and Beginning in Recession, July 1926-June 2026

It demonstrates that, over short-term, medium-term and long-term intervals, value stocks outperform both on the downside (recession) and subsequent upside (recovery).

During 12-month intervals which culminate in recession, as ascertained by NBER, value stocks’ returns averaged -9.6%. This loss is less than the Index’s, growth stocks’ and especially small caps’.

In the short-term, recessions harm small caps most and value stocks least. The same applies over medium and long terms.

During five-year intervals ending in recession, value stocks’ return averages 6.3% per year; that’s more than small caps (4.3%), the Index (4.2%) and growth stocks (3.5%). Finally, value stocks outperform over ten-year periods ending in recession.

What about the intervals starting in recession? Value also outperforms.

Over the next 12 months, value stocks’ return averages 20.1%; that gain exceeds small caps (18.6%), growth stocks (13.5%) and the Index (12.4%). Over the next five and ten years, value stocks’ returns average 11.8% and 10.3% per year respectively; these CAGRs exceeds the Index’s growth stocks’ and small caps’.

What about Australia?

To what extent do the foregoing results apply to Australia? As a rough rule, events which affect American markets also impact Australian markets. Moreover, the old adage “when the U.S. economy sneezes, the Australian economy catches a cold” has historically been accurate. (Although America certainly remains a – and likely the – major driver of global financial sentiment, Australia’s trade relationships with Asia, particularly China, underpin its economic health.)

Do recessions in the U.S. systematically impact stocks in Australia? They do (see in particular Recessions usually crush shares – but investors can always reduce their ravages, 31 October 2022).

As drivers of global financial sentiment, it’s realistic to assume that the results of American elections affect the returns of Australian stocks. To ascertain mid-terms’ influence, I computed the All Ordinaries Index’s monthly, CPI-adjusted YTD total returns since January 1974. I then labelled each month according to its position (Year 1, 2, 3 or 4) in an American president’s term of office, excluded returns in Year 1 and Year 4, and calculated mean YTD returns within each month of Year 2 (the mid-term year) and Year 3.

Figure 10 plots the results – and corroborates the assumption that the results of American mid-term elections systematically affect the returns of Australian stocks. The mid-term curse, in short, extends to the All Ordinaries Index.

Figure 10: Mean Year-to-Date Results, All Ordinaries and S&P 500 Indexes

During mid-term years, the Ords’ YTD results average -1.3%. That’s even worse than the S&P 500 (0.3%). In December of mid-term years, the mean YTD results are, respectively, -2.5% and 2.4%. On the other hand, in Year 3 the Ords outperforms the S&P 500: during these years, the Ords’ YTD results average 11.4%. That’s better than the S&P 500’s mean (9.7%). In December of Year 3, the mean YTD results are, respectively, 15.6% and 13.4%.

Given the means and standard deviations of the data plotted in Figure 10, plus the fact that each month’s distribution of YTD returns is approximately normal, it’s a simple matter to estimate the probability of a negative result during each month. Figure 11 plots them.

Figure 11: Probabilities of Negative Year-to-Date Returns, Two Countries and Two Presidential Years

As in the U.S., so too in Australia: the cumulative probability of a negative return in a mid-term year is, substantively and statistically, significantly higher than in Year 3.

In mid-term years in the U.S., the probability of loss is always at least 40%; in Australia, it often approaches 60%; as a result, the average probability in the U.S. is 48% and in Australia is 52%. In Year 3 in the U.S., the average probability of a negative return is 9.7%; in Australia, it’s 11.4%. Statistically and substantively, in Australia as well as the U.S., the differences between Year 2 and Year 3 are significant.

There are no data – there certainly aren’t any publicly-available data – in Australia whose validity and reliability compare to Kenneth French’s. As a result, we can’t ascertain the performance of Australian value stocks vis-à-vis Australian growth and small cap stocks in light of the economic and political risks I’ve detailed.

Given the results in Figure 10 and Figure 11, however, it’s reasonable to suppose that the results of Figure 1 to Figure 9, and of Table 1 and Table 2, also apply, more or less, to Australia. So far this year, the All Ordinaries Index’s performance has conformed to its norm during American mid-term years; the S&P 500, on the other hand, clearly hasn’t.

Is this because the latter’s levitation reflects an AI and data centre mania? Have American market participants thereby ignored or dismissed the risks stemming from the upcoming mid-term elections?

Is DSA a “Grey Swan”?

Risks and uncertainties aren’t disjoint. The boundary between them, in other words, is a permeable spectrum rather than a solid wall; hence the intermediate category “Grey Swans.” They’re not unpredictable: they’re low-probability but empirically high-impact events. Their occurrence isn’t unprecedented, but it’s so infrequent that available data are insufficient reliably to gauge the timing of their next occurrence. Examples include 

1. a massively destructive earthquake in Tokyo or along the San Andreas Fault;
2. surprise results of elections or referenda (such as “Brexit” and the U.S. Presidential election in 2016);
3. geo-political escalations and critical chokepoint closures in regions like the Taiwan Strait (which sever semiconductor supply chains) or the Strait of Hormuz (which spark a dramatic spike in crude oil prices);
4. cyberattacks which target vulnerabilities in a nation’s electricity grid, telecommunications or banking backbone, etc. causing an extended infrastructure blackout;
5. real estate crashes, sovereign debt crises and another global financial crisis.

It’s possible that Americans investors aren’t just under-estimating mid-term election risk; they could be ignoring the possibility of a political earthquake.

“Surely the most under-reported story in Western politics today,” writes Greg Sheridan (“Radical socialists hijack the Democrats,” The Australian, 28 July), “is the rise of the Democratic Socialists of America (DSA) and their incredible success in (primary elections), getting extremist candidates up at the expense of mainstream Democrats.”

Laughably, the mainstream media routinely smears One Nation in Australia and Reform in Britain “far right” parties. They blithely ignore or willfully deny the truth: the Democratic Party in the U.S. may be becoming a left-wing extremist party.

DSA doesn’t just demand a “future without capitalism;” it also – explicitly – intends to undermine and destroy the U.S. Constitution. The left – not just its extremists – has long despised the Electoral College because it thwarts the ability of populous states to oppress small ones, and thereby to dominate elections. In its latest manifesto, “Workers Deserve More,” DSA goes much further: it intends to “replace the President and Supreme Court with an executive and judiciary chosen by and subordinate to Congress.” For good measure, it also wants to “abolish the Senate.”

“It’s important,” The Wall Street Journal editorialised on 26 July, “to understand how (extreme) this is, essentially blowing up the Constitution’s separation of powers.” DSA would also “end the independent judiciary, which is a check against legislative and executive violation of individual rights. Third World dictators seek to make the judiciary subservient to the ruling party for this reason …”

The implication is obvious: DSA seeks to transform America into a Third Word dictatorship. If your reaction is “you’re crazy! That’s never going to happen,” my response echoes Rosa Luxemburg and Leon Trotsky: before it occurs, everybody “knows” that such a thing is extremely unlikely; once it occurs, everybody agrees that it was inevitable.

DSA’s candidates have won state and local victories in Colorado, Georgia, Kentucky and New York, and are on the ballot in Michigan, Missouri and Wisconsin. That’s hardly an imminent red tide. Yet two of the Democrats’ most popular and influential figures, New York Congresswoman Alexandria Ocasio-Cortez and New York City’s Mayor, Zohran Mamdani, are DSA members, and no one will be surprised if Ocasio-Cortez runs for president in 2028.

Moreover, and as Jason Riley observes, “the mainstreaming of socialism also appears in polling, which shows that people furthest to the left tend to be younger. A Cato Institute survey published last year found that 62% of adults under 30 hold a ‘favourable’ view of socialism.”

“Nor,” he elaborates, “is the trend limited to Democrats. The polling firm Echelon reported last month that, while ‘older Democrats are split on’ socialism and younger ones are ‘very much in favour,’ it’s ‘also worth noting that social democracy and socialism elicit less unfavourable views among younger Republicans compared to their older fellow partisans’” (see “Socialists May Prove Surprisingly Strong in November,” The Wall Street Journal, 4 August, and “Socialism Is Here – and It’s Serious,” The Wall Street Journal, 6 August).

What explains DSA’s rise? According to Daniel Lipinski, a Democrat who represented Illinois’s Third District from 2005 to 2021, “it is best to understand what is happening inside the Democratic Party today not as a widespread endorsement of the DSA platform, but more broadly as a protest by those fearful about the future and feeling betrayed by the perceived fecklessness of party leaders. Such emotions make fertile ground for demagogues.”

“It is possible,” Lipinski concludes, “that (at some point) the DSA could not only capture the Democratic Party but also ride it to victory. That isn’t a prediction. It is a warning” (see “Democratic Socialism Is No Tea Party,” The Wall Street Journal, 4 August).

“The common denominator in all of these DSA proposals,” WSJ’s editorial warned, “is eliminating barriers to rapid, radical change. They want a single-branch legislature that can sweep into power in a single election, dominate the President, and neuter the judiciary as a check on that power. With those checks gone, they can impose their agenda that will be nearly impossible to undo.”

What’s the likelihood that DSA will eventually impose its iron fist upon the USA?

In The Weekend Australian (8-9 August), Greg Sheridan wrote: “the rise of extremism, left and right, … means the threat of (another) American civil war is not entirely far-fetched.” On the other hand, America’s reasonable majority emphatically rejects extremism. And as Kimberley Strassel (“Can Democrats Resist Socialism?” The Wall Street Journal, 6 August) observes: “most of the Democratic world knows this socialist swerve is the dumbest political move ever, including some candidates themselves … What they all understand is that socialism doesn’t sell in America – at least beyond a contingent of disillusioned young people.”

Even more reassuring are the latest polls. “Socialism” is deeply unpopular (just 39% approve), support for “capitalism” is middling (54%) but for “free enterprise” is strong (81%). (See “How Americans View Capitalism, Socialism and Free Enterprise,” Gallup News, 6 August).

Yet the list of countries which have been destroyed by a small minority of violent extremists is long. In the U.S., this risk is probably very low; equally, as a Grey Swan nobody can say with any reliable degree of accuracy. What will be the consequences if left-wing extremism captures the U.S.? That’s easy to foresee. It never creates plentiful and affordable goods and services which people want; instead, it always generates economic and financial disasters.

Above all, socialism usually creates huge numbers of dead bodies: it thus bodes extremely poorly for “progressive” obsessions like “social cohesion” and inequality! If DSA wins, a miniscule elite will gain massively – and everybody else, including most investors, will lose immeasurably.

Conclusions and Implications

In recent articles, I’ve established two crucial conclusions:

1. Generally, that is, over, short-term, medium-term and long-term intervals since 1926, value stocks have outperformed growth stocks (see in particular Want to shrink your returns? Buy growth stocks! 23 March).
2. Specifically, value stocks outperform over key intervals: they outperform on the upside (that is, when a benchmark such as the S&P 500 Index booms) and on the downside (when it busts).

In this article, I’ve shown that value stocks outperform the Index, growth and small cap stocks partly because they moderate key economic and political risks (see also Why value investing usually outperforms, 29 April). “Growth,” in contrast, generally underperforms partly because it fails to mitigate – indeed, it magnifies – these risks.

According to Nassim Nicholas Taleb, antifragility is the property of processes and objects which grow stronger when exposed to stressors and volatility. Fragile processes and objects break under pressure; robust ones resist it; and antifragile ones improve or strengthen in response to it. Muscles are an example: if you lift heavy weights, the stress causes microscopic damage but over time your body rebuilds them stronger than before.

Value’s “defence” (mitigation of the downside) is clearly best; moreover, its “offence” (capture of the upside) is often better – and never worse – than the others. Value mitigates major risks and provides an “antifragile” bulwark against risk and uncertainty.

“Uncertainty is the only certainty there is,” wrote John Allen Paulos in A Mathematician Plays the Stock Market (Basic Books, 2003), “and knowing how to live with insecurity is the only security.” Fighting or fleeing the unknown only creates more anxiety; contentment and peace come from building internal resilience – Taleb dubbed it “anti-fragility” – rather than attempting to control unknowable and unpredictable external outcomes.

“Mid-term elections and politics as a whole,” one “analyst” asserted earlier this year, generate a lot of … uncertainty.” “We’re told markets despise uncertainty,” one funds manager recently added. “If that’s true, (mid-term election) years offer a maximum dose.” Yet another funds manager “describes today’s investment environment as one shaped by uncertainty.” Utterly oblivious to this statement’s meaning, the latter resorted to gibberish: “the most important thing to see is the elephant in the room, which is that we’re in a highly uncertain risk environment.”

These glib utterances, and plenty of others like them, aren’t merely nonsensical: they reflect a pervasive – and harmful – confusion of risk and uncertainty.

Those who babble about “uncertainty” tend thereby to ignore significant risks. I’ve considered and quantified a key political risk which investors (many of them seemingly unwittingly) face. How best to mitigate it? The answer is clear: buy and hold value stocks (see also How we’ve prepared for the next bust, 28 November 2022).

The same applies to recessions. At best it’s very difficult, and in practice it’s virtually impossible, to predict them. Some of them end before economists or policymakers realise that they’ve even started! Arbiters such as NBER can identify them only in hindsight, and often months or even a year after they’ve begun.

That’s not merely because crucial economic data such as GDP, etc., are released months after the intervals of time they describe; subsequently they’re often revised significantly. In Newsweek magazine on 19 September 1966, Paul Samuelson (he and Milton Friedman were probably the best-known American economists of that era) famously quipped one consequence: “the stock market has predicted nine of the last five recessions.”

According to Taleb, a Black Swan is an uncertain event which (because it’s unprecedented) comes as a total surprise, whose impact is massive – and which is rationalised in hindsight not merely as predictable but as inevitable. He’s repeatedly cited “Black Monday” (19 October 1987), when the Dow Jones Industrial average plunged 22.6%, as a prime example of the phenomenon. Indeed, it inspired his framework on unprecedented and high-impact uncertainty.

What form will the next Black Swan take? When will it occur? How severe and long-lived will it be? What will be its consequences? By definition, nobody knows – or can possibly know.

Faced with the risk of recession and the uncertainty of Black and Grey Swans, what can an investor do? You can’t reliably foresee risks such as recessions; you can, however, be ready when they occur. Even more importantly, you can never anticipate Black Swans; you can, however, erect “antifragile” structures which will withstand them.

In The Wall Street Journal (11 July), James Mackintosh asked: “how to invest when the global crises never stop?” My answer, buttressed with results from the analysis of a century of data, is simple: buy and hold value stocks. How best to profit from the relatively low stock prices which the mid-term curse and recessions tend to produce? Buy and hold value stocks (see also How we prepare for – and profit from – recessions, 18 August 2023). More generally, how best to mitigate crucial and unavoidable short- and medium-term political and economic risks? Buy and hold value stocks.

Most importantly, how to withstand Black and Grey Swans? How best to generate reasonable long-term results? For Australian as well as American investors, the answer to these questions includes buying and holding value stocks.

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