But as we sit here on December 26, 2025, right in the middle of what should be this year’s rally, a simple question demands a rigorous answer: Is any of this actually real?

To find out, we analyzed 22 years of daily market data across four major indices - over 22,000 individual trading days. What we discovered challenges some assumptions, confirms others, and offers a sobering perspective on what this year’s struggling rally might mean for 2026.

The Legend and the Data

The Santa Claus Rally was first documented in 1972 by Yale Hirsch, founder of the Stock Trader’s Almanac. Hirsch noticed that the stock market displayed a peculiar tendency to rise during a specific seven-day window: the last five trading days of December and the first two trading days of January. He immortalized this observation with a warning that has echoed through trading floors for half a century:

“If Santa Claus should fail to call, bears may come to Broad and Wall.”

The reference to Broad and Wall - the intersection where the New York Stock Exchange stands - wasn’t merely poetic. Hirsch meant it as a genuine caution: when the rally fails, trouble often follows.

But folklore requires scrutiny. We pulled daily price data for the S&P 500 (SPY), Nasdaq 100 (QQQ), Russell 2000 (IWM), and Dow Jones (DIA) spanning from 2003 to 2025. The results are unambiguous: the Santa Claus Rally is not a myth. Over our 22-year dataset, the S&P 500 posted positive returns during this seven-day window 72.7% of the time, delivering an average gain of 0.59%. To appreciate what this means, consider that a typical seven-day period in the market averages just 0.2% with a 57% win rate. The Santa Rally delivers nearly three times the return with significantly higher reliability.

The pattern holds across different market environments. The rally succeeded during the bull markets of 2017 and 2019, during the recovery from the 2008 financial crisis (which produced the strongest rally in our dataset at +7.4%), and during the volatile post-pandemic years. When it fails, the losses are typically modest, the worst year in our data, 2014/2015, saw a decline of just 3%. The asymmetry is notable: the upside has historically outweighed the downside.

Yet not all indices participate equally in the holiday cheer. While the S&P 500 leads with its 72.7% win rate, the tech-heavy Nasdaq succeeds only 60% of the time with smaller average gains of 0.31%. Small-cap stocks, represented by the Russell 2000, fare even worse at 59.1%. This divergence suggests the rally is primarily a large-cap phenomenon, perhaps because the institutional dynamics that drive it (which we’ll explore shortly) affect blue-chip stocks more directly.

Why Santa Shows Up

Understanding why the rally occurs requires examining the unique market dynamics of late December. The most compelling explanation centers on tax-loss harvesting - the practice of selling losing positions before year-end to offset capital gains. This selling pressure, which peaks in early-to-mid December, artificially depresses stock prices. Once the December 31 deadline passes and this forced selling subsides, stocks naturally rebound as investors redeploy their capital.

Equally important is who’s actually trading during the holidays. Many institutional investors - hedge fund managers, pension fund traders, and other sophisticated market participants - take the week between Christmas and New Year’s off. This leaves the market disproportionately influenced by retail investors, who research consistently shows are more bullish and less likely to short stocks. With fewer skeptics actively trading, the path of least resistance tilts upward.

Our volume analysis confirms this dynamic. Holiday trading volume drops to approximately 70% of normal levels on average, with some years seeing even more dramatic declines - 2009’s holiday period saw volume plunge to just 30% of typical trading. In these thin markets, even modest buying pressure can move prices meaningfully. A few million dollars of year-end bonus money flowing into index funds has outsized impact when there’s no institutional selling to absorb it.

There’s also the phenomenon of window dressing. Fund managers, knowing their year-end portfolios will be published in annual reports, often buy strong performers in December to make their holdings look more impressive. This last-minute buying of winning stocks creates additional upward pressure on the very indices that comprise the Santa Claus Rally. Add in the general optimism that accompanies the holiday season - and the notable absence of potentially market-moving events like earnings releases or Fed meetings - and conditions are ripe for stocks to drift higher.

A Related Myth Worth Debunking

While investigating the Santa Claus Rally, we tested another celebrated market anomaly: the January Effect. First documented by investment banker Sidney Wachtel in 1942, this theory holds that small-cap stocks dramatically outperform large caps in January, as beaten-down stocks rebound from year-end tax-loss selling. For decades, academic research confirmed the pattern was so reliable it posed a genuine puzzle for efficient market theorists.

Those days are over. In our 22-year dataset, the Russell 2000 outperformed the S&P 500 in January only 36.4% of the time - worse than a coin flip. On average, large caps actually beat small caps in January by 0.38 percentage points. The January Effect hasn’t just weakened; in modern markets, it has effectively reversed.

What killed the January Effect? Ironically, its own success. Once the pattern became widely known in academic and professional circles, institutional traders began front-running it - buying beaten-down small caps in late December rather than waiting for January. The arbitrage opportunity evaporated as everyone rushed to exploit it simultaneously. The proliferation of tax-advantaged retirement accounts (401(k)s, IRAs) further reduced the prevalence of tax-loss harvesting that created the opportunity in the first place. The January Effect serves as a cautionary tale: market anomalies, once publicized, often self-destruct.

What This Year Tells Us About Next

As of December 24, 2025, this year’s Santa Claus Rally is tracking slightly negative. This continues a troubling pattern: last year’s rally also failed, with the S&P 500 selling off every single trading day between Christmas and New Year’s - a historic first for the index. Despite this, 2024 still delivered a 23.3% annual gain, demonstrating that a failed rally isn’t necessarily a death sentence.

Yet history suggests we should pay attention. Research from LPL Financial shows meaningful divergence in outcomes based on rally results. When Santa delivers a positive seven-day window, the S&P 500 has historically averaged a 1.4% gain in January and a 10.4% return for the full year. When the rally fails, those figures drop to -0.1% for January and just 6.1% for the subsequent year. The difference - over four percentage points in annual returns - is economically significant.

Perhaps most notable is the rarity of our current situation. Since 1950, there have never been three consecutive years of negative Santa Claus Rally returns. We’re now facing the possibility of exactly that. Whether this represents mean-reversion opportunity or a genuine shift in market dynamics is impossible to know in advance. But the setup is historically unusual, and unusual setups demand attention.

The rally window remains open through January 5, 2026 - five more trading days for Santa to potentially reverse course. The market has already shown signs of recovery, with the S&P 500 hitting new all-time highs on Christmas Eve. Whether this represents the beginning of a late rally or merely a temporary bounce before further weakness will determine which side of Yale Hirsch’s famous ledger 2025 lands on.

What should investors make of all this?

The Santa Claus Rally is real - statistically validated across decades of data. But its something that has occurred historically, not a guarantee. It tells us something about typical market behavior during a specific window, not what will happen in any given year. The January Effect, by contrast, has been arbitraged into irrelevance - a reminder that even the most robust anomalies can disappear once they become common knowledge.

For this year specifically, the failing rally warrants caution but not panic. Markets don’t follow calendars; they follow fundamentals, policy, and sentiment. The Fed’s rate path, AI-driven earnings expectations, and Treasury yields will matter far more for 2026 than whether stocks managed to eke out gains in the final week of December. Use the Santa Claus Rally as one data point among many - a sentiment indicator that occasionally provides useful signal amid the noise. Just don’t mistake it for a crystal ball.

After all, even the original Santa requires a certain suspension of disbelief.

Note: This analysis was later quoted in an interview I gave to Market Watch. But that was only after it was published here. Subscribe here to be the first to know when I publish a new analysis

Methodology: Analysis based on daily OHLCV data for SPY, QQQ, IWM, and DIA from September 2003 through December 2025 (5,600+ trading days per index), sourced from Arx (my company’s) unique systems (arxhq.com). Santa Claus Rally defined as the last five trading days of December plus the first two trading days of January, per the Stock Trader’s Almanac convention. Returns calculated as close-to-close across the full seven-day window.

Disclaimer: The information provided in this article, including any references to financial data, market performance, and trading strategies, is for informational purposes only and should not be considered as investment advice or a recommendation to buy or sell any type of securities. The author is not responsible for any investment decisions made based on the information provided in this article. Investors should always conduct their own research and consult with a professional financial advisor before making any investment decisions. Past performance is not indicative of future results, and no representation or warranty, express or implied, is made regarding future performance.

That revived an old debate: cost savings and long-term focus versus market transparency and investor confidence. The question is not whether companies would like less red tape. Many would. It is whether fewer reports would improve markets or simply blur the picture.

Why This Matters

Quarterly reporting has been baked into U.S. securities law since the 1970s. Exchange Act Rules 13a-13 and 15d-13 require domestic issuers to file Form 10-Q within 40 or 45 days after quarter-end. Earnings releases, typically furnished on Form 8-K, are not technically mandatory. Yet skipping them would rattle investors and widen spreads.

Changing that cadence would require formal SEC rule making. A proposal, public comment, and a final rule are needed, not just a presidential post. The Commission last examined reporting frequency in 2018 after Trump, then in office, asked whether quarterly filings and earnings releases were duplicative. Corporate commenters urged relief, particularly for small issuers swamped by compliance costs. Investor groups warned that longer gaps would leave markets flying blind.

What Other Markets Have Tried

Abroad, regulators have experimented rather than leapt. In the UK and across the EU, mandatory quarterlies were scrapped in 2014 following the Kay Review and changes to the Transparency Directive. Initially most companies kept reporting quarterly, but over time many shifted to semiannual statements. The notable outcome was not a surge in long-term investment. It was thinner analyst coverage and fewer forecasts, with little evidence of increased R&D.

Germany chose a middle lane. In the Deutsche Börse’s Prime Standard, companies still release quarterly statements. These are trimmed updates with key figures, cash flow, and short commentary instead of a full IFRS pack.

Japan has focused on eliminating duplication rather than interim disclosure itself. Starting in 2024 it abolished statutory Q1 and Q3 reports while keeping exchange-mandated “tanshin” updates.

Australia expects half-year and full-year results for most issuers. Resource explorers and cash-burning startups still file quarterly cash-flow reports. Canada retained quarterly reporting yet allows small “venture issuers” to use concise Quarterly Highlights in place of a full MD&A.

In Israel, certain early-stage R&D firms under size thresholds can file only twice a year. This eases burdens without abandoning timely updates. Even in the U.S., foreign private issuers already pair an annual 20-F with lighter interim 6-Ks. Hybrid models are not new to U.S. investors.

Who Wins, Who Loses

For cash-burning biotechs, cleantech ventures, and other pre-revenue innovators, producing four 10-Qs can feel like a tax on focus. Shifting to semiannual filings would free legal budgets and management bandwidth. It might also cool the penny-perfect EPS obsession.

But fewer official touchpoints widen information gaps and can raise volatility, especially for thinly covered names. The UK’s experience suggests that less frequent updates do not unlock bolder capital spending. They mainly reduce the information stream that supports valuations.

Frequent issuers such as SPAC veterans, serial secondaries, or shelf filers could also find fewer filings complicate registration and investor relations. Quarterly review work is also a significant source of revenue for audit firms, which would not welcome fewer cycles.

A Better Playbook

Rather than an all-or-nothing switch, the SEC could tailor the rhythm. Small reporting companies and R&D shops could be allowed full reports twice a year, with brief Q1 and Q3 updates focused on liquidity, burn, and milestones. Large-cap and index constituents could stay on full 10-Qs to preserve comparability.

Another path is a “10-Q lite.” This could include an unaudited balance sheet, cash-flow highlights, a short MD&A, and sector-specific metrics. It would resemble Germany’s quarterly statement. Whatever the form, Regulation FD and Form 8-K would continue to police selective disclosure.

Market discipline also matters. If asset managers or index providers demand quarterly detail, companies seeking capital or inclusion will comply whether rules require it or not.

What IR Teams Should Watch

IR leaders should map the tangible savings of semiannual reporting against potential hits to coverage, liquidity, and trust. For pre-revenue firms, a twice-yearly filing cadence paired with targeted KPI updates can produce a cleaner and more focused narrative than dense quarterlies.

If the SEC offers flexibility, many larger names courting institutional capital may still opt into quarterlies voluntarily. Transparency remains a hallmark of good governance.

What Happens Next

Trump’s 2025 post put the topic back on the SEC’s radar. Yet the wheels turn slowly. Drafting, comment, analysis, and adoption typically take a year or more. Implementation often lags further to give issuers time to adapt.

Exchanges could also enter the fray. As Germany’s split between Prime and General standards shows, U.S. venues such as Nasdaq or NYSE could set higher disclosure bars for premium tiers even if federal rules loosen.

This article was originally published on investing.com

This content is intended solely for general informational purposes. It should not be construed as financial advice, an endorsement of any investment or strategy, or as professional expertise requiring licensure. Neither the author nor CapitaLynx Ltd and its representatives engage in licensed activities. We disclaim any intention or obligation to act as a financial advisor, legal advisor, accounting advisor or fiduciary. Recipients are advis

ed to seek independent professional advice from relevant licensed professionals before making financial decisions.

Historical Roots and the Eisenhower Connection

The phrase “Sell Rosh Hashanah, Buy Yom Kippur” has been part of trading folklore for decades. Its origins can be traced back to the belief that September, during which these holidays typically fall, is a tumultuous month for stocks. This belief is not unfounded. Historical data suggests that September has often been a challenging month for the stock market, with traders being advised to stay out of the market during this period to avoid potential losses.

The adage gained significant traction in 1955, a year that would forever be etched in trading history. On the eve of Rosh Hashanah, the Dow Jones Industrial Average closed at 483.66. A week later, it had risen by 0.78%. However, the following weekend brought shocking news: President Dwight Eisenhower suffered a heart attack. As Yom Kippur commenced, the markets reacted violently. The Dow plummeted by 6.54%, marking its second-largest single-day point drop at the time. Traders who heeded the advice to sell on Rosh Hashanah were spared from this drastic downturn.

The 2023 Perspective and Market Dynamics

In 2023, the “Sell Rosh Hashanah, Buy Yom Kippur” strategy made a dramatic return, with the commencement of the Jewish New Year coinciding with September’s quadruple witch expiration, a rare event that only occurs four times a year. This alignment added an extra layer of volatility to the market. Historical data from 2022 showed that adhering to this seasonal pattern would have resulted in significant rewards for investors.

Moreover, Yom Kippur, while not an official market holiday, is observed by many in the New York area, especially within the Jewish community. Their absence can dampen trading activity as positions are squared ahead of the holiday.

Empirical Evidence: A Deep Dive

While the saying has historical and religious significance, its relevance in today’s trading world is still a topic of discussion. Some traders swear by it, while others see it as a relic of the past. However, with the days of the week in 2023 aligning similarly to 1955, and with events like a triple-witch, UAW strike, and an FOMC meeting occurring around the same time, the adage might still hold some weight.

Recent studies have delved into the empirical evidence behind this phenomenon. One such study from NYU Stern examined the S&P 500 and FTSE 100 daily rates of return during Jewish holidays and found that the average returns for the holiday values compared to the controls never differed by more than around two-thirds of a percent. Interestingly, Yom Kippur showed a significant rise in daily return variance compared to both the S&P 500 and the FTSE, indicating higher volatility on this day.

Additionally, the study underscored that trading volumes were notably reduced on both Rosh Hashanah and Yom Kippur compared to regular trading days, pointing to the diminished activity during these sacred observances. Given the introspective nature of these holidays, many traders likely choose to distance themselves from the market’s frenetic pace.

Similarly, according to 2017 data, in the previous 21 year, the day before Yom Kippur has often been weak for the market. The DJIA, for instance, advanced only 38.1% of the time, recording an average loss of 0.27%. Similarly, NASDAQ and S&P 500 have shown bearish tendencies around this time, further emphasizing the potential influence of this religious observance on market dynamics.

Conclusion

The stock market’s movements are shaped by both analytical data and the intricacies of human behavior. The saying “Sell Rosh Hashanah, Buy Yom Kippur” exemplifies this interplay, weaving together historical trends with deep-rooted cultural and emotional influences. Regardless of whether investors adhere to this maxim, it underscores the market’s inherent volatility and the diverse forces that propel its direction.

Disclaimer: The information provided in this article, including any references to financial data, market performance, and trading strategies, is for informational purposes only and should not be considered as investment advice or a recommendation to buy or sell any type of securities. The author is not responsible for any investment decisions made based on the information provided in this article. Investors should always conduct their own research and consult with a professional financial advisor before making any investment decisions. Past performance is not indicative of future results, and no representation or warranty, express or implied, is made regarding future performance.

No university campus is complete without a fierce rivalry between STEM and humanities students — and it's fair to say that the scientists have been winning the competition for a long time now. Artists and thinkers may have dominated during the Renaissance, but the Industrial Revolution has been the era of the tech worker. Apple's market cap is bigger than 96% of world economies, and digitally transformed enterprises now make up almost half of global GDP.

But as technology achieves more milestones and reaches a certain critical mass, I think that humanities are about to make a long-awaited comeback. Technological innovation — especially artificial intelligence — is crying out for us to ponder critical questions about human nature. Let's look at some of the biggest debates and how disciplines like philosophy, history, law and politics can help us answer them.

The rise of sentient AI (?)

The potentially ominous or destructive consequences of artificial intelligence have been the subject of countless books, films and TV shows. For a while, that might have seemed like nothing more than fear-mongering speculation — but as technology continues to advance, ethical debates are starting to seem more relevant.

As AI becomes capable of replacing an increasing number of professions and many people are left jobless, it raises all kinds of moral dilemmas. Is it the role of the government to offer a universal basic income and completely restructure our society, or do we let people fend for themselves and call it survival of the fittest?

Then there's the question of how ethical it is to use AI to enhance human performance and avoid human failure in the first place. Where do we draw the line between a "human" and a "machine?" And if the lines become blurred, do robots need the same rights as humans? The decisions we make will ultimately determine the future of the human race and could make us stronger or weaker (or see us eliminated completely).

Humans or machines?

One of the AI advances raising eyebrows is Google's Language Model for Dialogue Applications (LaMDA). The system was first introduced as a way of connecting different Google services together, but ended up striking debate about whether the LaMDA was, in fact, sentient — as Google engineer Blake Lemoine claimed after witnessing how realistic its conversations were.

Ultimately, the general consensus was that Lemoine's arguments were nonsensical. The LaMDA was only using predictive statistical techniques to hold a conversation — just because its algorithms are sophisticated enough to seemingly have a dialogue, it doesn't mean the LaMDA is sentient. However, it does raise an important question about where things would stand if a theoretical AI system was able to do everything a human can, including having original thoughts and feelings. Would it deserve the same rights humans have?

The Turing test

The debate over what exactly we should see as human is nothing new. Back in 1950, Alan Turing created the Turing test to determine whether a machine can be sufficiently intelligent and similar enough to humans that we can submit that machines have some level of "consciousness."

However, not everyone agrees. The philosopher John Searle came up with a thought experiment called "Searle's Chinese room," which says that the program of a machine that only speaks in Chinese could be given to a person that doesn't speak Chinese in the form of cards. That person would then be able to follow the instructions on the cards to fool someone outside of the room into thinking they could speak Chinese if they communicated through a slot in the door; but clearly, this isn't the case.

According to Lemoine, Google isn't willing to allow a Turing test to be performed on the LaMDA, so it seems Searle isn't alone in his reservations. But who is going to settle these issues?

An area for the humanities

As more of our lives become enriched by AI, more of these questions will arise. 80,000 Hours, a nonprofit run by Oxford academics that focuses on how individuals can have the greatest impact in their careers, has highlighted positively shaping the development of artificial intelligence as one of the most prominent issues the world faces right now.

And although some of the work is likely to focus on research into technical solutions for how to program AI in a way that works for humans, policy and ethical research are also set to play a huge role. We need people who can tackle debates, such as which tasks humans have fundamental value in performing and which should be replaced by machines, or how humans and machines can work together as human-machine teams (HMTs).

Then there are all the legal and political implications of a society filled with AI. For instance, if an AI engine running an autonomous car makes a mistake, who is responsible? There are cases to argue for the fault being with the company that designed the model, the human the model learned its driving from or the AI itself.

For questions such as the last one, lawyers and policymakers are needed to analyze the issues at hand and advise governments on how to react. Their efforts would also be complemented by historians and philosophers who can look back and see where we've come short, what has kept us going as a human race and how AI can fit into this. Anthropologists will also have plenty to offer based on their studies of human societies and civilizations over time.

An exponential rise in AI requires revitalizing the humanities

The rise of AI may happen faster than anyone could anticipate. Metcalfe's Law says that every additional person added to a network doubles the potential value of that network — meaning a network becomes exponentially more powerful with each addition. We've seen this happen with the spread of social networks, but the law is a potentially terrifying prospect when we talk about the fast ascent of AI. And to make sense of the issues outlined in this article, we need thinkers from all disciplines. Yet the number of students who obtained humanities degrees in 2020 in the U.S. decreased by 25% since 2012.

As AI becomes a greater part of our daily lives and technology continues to advance, nobody in their right mind would deny that we need brilliant algorithm developers, AI researchers and engineers. But we'll also need philosophers, policymakers, anthropologists and other thinkers to guide AI, set limits and help in situations of human failure. This requires people with a deep understanding of the world.

At a time when the humanities are largely viewed as "pointless degrees," and young people are discouraged from studying them, I would contend that there's a unique opportunity to revitalize them as disciplines that are more relevant than ever — but this requires collaborations between technical and non-technical disciplines that that are complex to build. Either way, these functions will inevitably be performed, but how well will depend on our ability to prepare future professionals in these areas who have both multidisciplinary and interdisciplinary views of the humanities.

Published on Entrepreneur.com

Between 2009 and 2021, the stock market enjoyed its longest bull market ever, with returns of more than 400%. But what goes up must come down. Although the economy managed to bounce back quickly from the immediate crash brought on by Covid-19, it seems that we're now well and truly experiencing a downturn — meaning an entire generation is about to experience a bear market for the first time.

This will likely come as a shock to many at-home traders, but the impact will be particularly brutal for venture capital investors. After years of successfully "picking winners" that went on to yield impressive returns (especially in the tech sector), these investors are about to discover how good they really are. Were they doing their due diligence about market fundamentals, or simply following the trends and getting away with it? The next few months and years are about to be a serious reality check.

Is the tech bubble bursting?

For the past few years, any discussion about "high-growth stocks" or "industries to watch" has largely centered around tech in its many forms — fintech, Software-as-a-service (SaaS), cleantech. Yet now, these sectors are experiencing the most painful drop.

The stock market is down around 18% overall (give or take a few percentage points depending on the day), and tech stocks have seen a drop closer to 30%. Given these tech companies made up around 25% of the S&P 500 in 2021, they're not just falling faster than anything else — they're a significant factor in why the stock market overall is declining. The Tiger Global Fund, for example, which focuses on the internet, software and fintech sectors, lost 52% just in the past year.

So, what's going on? When the pandemic first hit, software solutions suddenly became a crucial part of everyday life and workplaces, with individuals and businesses forced to well and truly go digital. Share prices skyrocketed for companies like Zoom and Netflix, and 2490 firms achieved so-called unicorn status in the first six months of 2021 alone (meaning valuations of $1 billion or above). Moreover, this was all happening on the backdrop of 10+ years of low interest rates.

But now, with inflation up and a correction occurring, it seems the market may have gotten overexcited, with many tech firms overvalued due to pure speculation. For instance, Zoom went from a share price of $73 in January 2020 to $349 a year later — and now it's back down to $109.

Some have compared the current situation to the so-called dot-com bubble of the early 2000s, which saw many investors pour their money into just about any internet-related company in hopes they'd become "the next big thing," despite some of those firms lacking a genuine value proposition. Pets.com is the classic example.

Just like before, investors have continued to put vast sums into business models and sectors they that they always truly understand (Web 3.0, anyone?), and the economy may finally be catching up. Moreover, recent transactions have taken "forward-looking" valuations to a whole new level, causing investors to be hyper-focused on specific startup categories (such as SAAS) that tend to trade a high revenue multiples, thus creating a plethora of overvalued startups that haven't always validated their product-market fit or have a path to a solid business model — not to mention profitability or a decent runway.

What it means for investors

Venture capital investors have one primary aim: to identify young companies with high growth potential and invest with the hopes of yielding big profits later on. In recent years, "big profits" have been mostly associated with the tech industry, especially following the digital acceleration brought on by the pandemic. Suddenly, it didn't take much for tech companies to secure good valuations, regardless of whether they truly boasted good market fundamentals — so it didn't exactly take a genius investment strategy to achieve impressive returns.

As this trend continued over time, the confidence of investors grew, and some became more careless. They may have forgotten there was a possibility of all the buzz being a bubble, or maybe they simply didn't care. Because as long as the valuations kept going up in subsequent funding rounds, all was well in the world of non-liquid assets.

In 2021, VC exit values reached $774 billion in the U.S., and venture capital outperformed all other asset classes. But now that the boom time may finally be coming to an end, dumb luck and riding the wave aren't going to cut it anymore.

Over the next few years, the investors who win out will be those who did their due diligence — those who, instead of simply following in the crowd, looked into crucial market fundamentals such as a company's product, market validation, management, business model, etc. Not to mention those who were fiscally responsible enough to put money away for tougher times and preserve a decent runway.

What does the future hold?

The current downward trends of the market aren't just pure speculation. Inflation is running rampant at the moment (8.3% between April 2021 and April 2022), which reduces the spending power of consumers and businesses alike and makes it tougher to make sales. It also makes it harder for businesses to budget and plan for the future.

With interest rates rising to combat inflation, borrowing is more expensive than before, which will naturally lead to lower valuations and a tougher environment, at least for the foreseeable future. Interest rates were one of the main factors driving growth in the first place, so they'll be a crucial factor in changing the environment.

Yet there is also much to be optimistic about — the downward trend is likely a temporary blip, rather than a bubble bursting. The role of the internet is now cemented, technology is not going anywhere, and thus, there will always be valuable tech stocks and companies that will continue to provide fundamental value.

While some companies (and maybe even funds) will go down, this is actually a healthy correction for the market overall. Solid companies with a good product that are solving an important problem still have a place. Moreover, companies with strong management that know how to adapt to the markets and optimize for the long run, will likely do well long-term, getting the unique opportunity to increase their market share as their competitors drop down. After all, although plenty of companies fared poorly during the dot-com crash, there were also quite a few that survived and went on to thrive, such as Amazon, eBay and PayPal.

Today's young venture capital investors had better hope they're one step ahead of their peers. Those who did their homework, those who conducted due diligence, developed an investment thesis and didn't completely ignore fundamentals may fare well, while others could be in hot water. Either way, for both VCs' startups, this is likely to separate the men from the boys, resulting in a stronger and healthier economy.

Published on Entrepreneur.com

The classic human error is assuming we’re doing a good job, because we have nothing better to compare ourselves to.

Humans operate with small data sets and execute flawed analytical reports. Machines operate with far larger ones and are capable of carrying out advanced analysis, achieving far more accurate results.

Thus, for example, far too often, we’ll see someone go to a certain college, get a certain degree, and then have a successful career and assume it will work for us as well. But in practice, our conclusion may be flawed, due to a number of reasons. Namely, humans can only contain and analyze a limited amount of data. Moreover, we aren’t always able to execute advanced analysis and remove bias, often leading us to skewed results or a failure to separate between correlation and causation.

In the context of AI transformations, employees often don’t see the urgent need for improving their workflow, because they are only seeing the data-context they have in front of them, not all the data they are missing, or, the larger data-context.

In other words, the exponential creation rate of data, along with its natural vastness, mean that we can be so far behind, we aren’t even capable of recognizing what we are missing.

Lingering Negative Data-Habits:

Analysts are inherently forced to filter out large amounts of data based on generic axioms because the alternative requires the analyst to work with an impossible amount of data. While the advantage of having a small data set is that the analyst can perform higher quality manual analysis, he often loses out on lots of information from the data he excludes.

But the damage is greater than just that. Other than just missing out on specific data-points of value, the analyst may become unaware of full segments of data that have value, due to false assumptions. This creates an endless loop where in each subsequent tasks, the analyst continues to falsely pre-determine his segments based on past (unreliable) experiences and results, thus creating a fatal downwards spiral.

This often nurtures a false sense of certainty as to the validity of the results with no substantial way of knowing what is falling between the cracks. Organizations end up with an over-developed sense of confidence in their data and draw conclusions which are only true for the small data-context they have in front of them.

Similarly, as humans, analysts tend to be biased towards implementing filtration systems that output the data-set with the highest probability to contain value. This makes sense when there is no alternative to human-centric tasks. However, when we introduce machines into our workflow, it is critical to shake this underlying bias.

If a machine is doing the work, purely from a business standpoint, the rate of non-valuable to valuable data-points is far less important. The only important metric becomes the net amount of valuable data alongside the cost of time required by any humans alongside the machine.

In sum, Making machine-centric tasks more “humanly possible” in nature, is detrimental to our goals.

Adjusting our Mindset for the age of Big Data:

A human analyst may be able to segment data with 100% accuracy, while a machine may only be able to perform the same analysis with 85% accuracy after training. Despite that, what we, as humans, tend to ignore, is that 85% of 100X data which the machine is able to analyze may be more valuable and in fact, more accurate. Our initial response, as humans, tends to be opposing the use of the algorithm, thinking that only accurate results are of value.

This is yet another human bias that needs to be removed once having introduced machines into a workflow.

Obviously, the quality of analysis is important, but when determining how to build a process, we need to remind ourselves that quantity actually creates higher quality.

In other words, our instinctive definition of accuracy is skewed due to the small context in which we are seeing it.

Creating a Symbiotic relationship between Humans and Machines:

While the age where machines can do it all is not yet upon us, the age of human-machine teams has already arrived. There are many approaches to this, but to my mind, the most important success factor is building these systems in a manor that ensures that the value of each component’s work (human and machine) contributes to the other, symbiotically.

For example, in a case where we require high quality data segmentation and analysis, a machine alone will likely fail us. However, having a machine segment data, with human in-depth review as a quality-control can solve this, allow for reviewing of more data, and in many cases, be more cost-effective than using a human analyst alone. Alternatively, even if we don’t trust the machine to do any of the data analysis, we can allow it to guide us in selecting the subset for human segmentation. Ironically, today, our first and most dangerous filtration is done by humans, usually not based on conclusive evidence.

The key here is create systems that allow for the various elements of every task to be performed by the team component that is best suited for it.

Leading Human Machine Teams:

As managers and leaders, we have to train ourselves to look at the larger data context, and constantly suspect we are missing something. A failure to criticize, in this case, can be detrimental to an operation.

None of this is to say that there is no place for human analysis, but rather, that human ability is far more effective when enriched with machine capabilities. We need to create human-machine analysis teams that work in conjunction with one another, not opposing one another.

Looking ahead, it’s critical for managers to be able to separate between the role of the machine and the role of the human. Doing this well will allow humans and machines to work together, as “human-machine teams”, resulting in an outcome greater than it’s individual components.

If in the past, one of the manager’s key responsibilities was to assign the most appropriate team member to every task, today, that role has expanded to include evaluating which part of each task should be human-centric, and which should be machine-centric.

Those able to bridge the gap between the humanities-oriented task of putting these technologies into a human and business context with the technological know-how to guide their implementation, will be best positioned to lead a succesfull transformation.

We must always attempt to “know the unknowable”, even within our data. But the key to achieving that is first recognizing that our we all suffer from data-tunnel-vision and in doing so, we can attempt to achieve human-machine symbiosis that will transform an operation.

Originally published on Hackernoon and GeekCulture