How to Research the Stock Market

If you want to research the stock market, stop guessing, start analysing, and actually make money — because the market does not care about your feelings, your horoscope, or the guy at work who “always knows a hot tip.”

Let me introduce myself. I’m a trader. I’ve sat in front of screens watching red candles cascade down a chart like confetti at a funeral. I’ve panic-sold at the bottom. I’ve bought at the top. I once held a stock for six months because I liked the logo. The logo! Friend, I am the cautionary tale they should show in business school. But here’s the thing — I learned. I studied. I dug into the research. And now I’m going to share everything I know so you don’t have to go through what I went through. You’re welcome. This is your definitive, academically-grounded, comedically-seasoned guide on how to research the stock market.

Why Most People Research the Stock Market Wrong

Here’s the brutal truth: most retail investors — that’s everyday people like you and me — walk into the stock market the same way someone walks into a casino wearing sunglasses thinking that’s gonna help them at the poker table. It is not going to help you at the poker table. You look ridiculous. And your financial strategy is equally ridiculous if it’s based on Reddit threads, TikTok finance gurus, and vibes.

Research published in the Journal of Economic Perspectives by Nobel laureate Robert Shiller (2003) demonstrated that financial markets are far more complex and behaviorally driven than simple rational models suggest. His landmark paper, “From Efficient Markets Theory to Behavioral Finance,” showed that investor psychology, irrational exuberance, and herd behaviour play enormous roles in how markets move — and that understanding these forces is essential for any serious investor [1].

What does that mean for you? It means that before you buy a single share, you need a system. Not a gut feeling. Not a tweet from someone with “crypto expert” in their bio. A system. And building that system starts with understanding the two foundational pillars of stock market research: fundamental analysis and technical analysis.

Pillar One: Fundamental Analysis — Getting Into the Business

Imagine you’re buying a restaurant. Would you just walk past the window, see it looks busy on a Saturday night, and hand over your life savings? No. You’d want to see the books. How much revenue does it make? What are its debts? Who’s running it, and do they actually know what they’re doing? That, in essence, is fundamental analysis.

Fundamental analysis is the process of evaluating a company’s intrinsic value by examining its financial statements, competitive position, industry dynamics, and macroeconomic context. According to a comprehensive review published in Procedia Economics and Finance (Nariswari & Nugraha, 2020), fundamental analysis remains one of the most widely used and academically validated methods for making long-term investment decisions [2].

Reading Financial Statements

There are three core financial documents every trader worth their salt must read before investing in a company:

The Income Statement tells you how much money the company made and spent over a period. You’re looking for revenue growth, gross margin, operating income, and net profit. If a company’s revenue is going up but its profits keep shrinking, something is wrong in that kitchen. The food might be good, but they keep burning through butter.

The Balance Sheet is a snapshot of what the company owns (assets) and owes (liabilities) at a specific point in time. Pay particular attention to the debt-to-equity ratio. A company drowning in debt is like a person who takes out a loan to pay off their credit card, to then take out another loan. We’ve all known that person. Don’t be that person. Don’t invest in that company.

The Cash Flow Statement is arguably the most important document of the three. It tells you whether the business is generating actual cash or just playing accounting games. Profits can be manipulated; cash is harder to fake. As the old saying goes in finance: revenue is vanity, profit is sanity, but cash flow is reality.

In the United States, publicly traded companies are required to file 10-K annual reports and 10-Q quarterly reports with the Securities and Exchange Commission (SEC). These documents are freely available at SEC EDGAR and are the primary source for any serious fundamental analysis [3].

Key Ratios to Know

Once you have your hands on those financial statements, you need to know what to calculate. Here are the essential ratios every stock market researcher uses:

Price-to-Earnings (P/E) Ratio: This compares a company’s stock price to its earnings per share. A P/E of 15 means investors are paying £15 for every £1 of profit. High P/E stocks might be overvalued or expected to grow fast. Low P/E stocks might be bargains — or value traps. The market is sneaky like that.

Price-to-Book (P/B) Ratio: Compares market price to book value (assets minus liabilities). Anything under 1 can signal an undervalued stock, but don’t get too excited — it might be cheap for a reason. Like that flat you toured that seemed suspiciously affordable and then you noticed the neighbour.

Return on Equity (ROE): How efficiently is the company using shareholders’ money? Anything consistently above 15% is generally considered good. Warren Buffett famously looks for businesses with sustainable ROE above 20%.

Debt-to-Equity (D/E) Ratio: Measures financial leverage. A ratio above 2 in most industries should make you nervous. Although in capital-intensive industries like utilities, higher leverage is expected and normal.

Earnings Per Share (EPS) Growth: Is the company growing its earnings year over year? Consistent EPS growth is one of the strongest indicators of a healthy, compounding business.

Case Study 1: Warren Buffett and Coca-Cola

You want proof that fundamental analysis works? Look no further than Warren Buffett’s 1988 acquisition of Coca-Cola stock. Buffett famously purchased approximately $1 billion worth of Coca-Cola shares after analysing the company’s brand moat, consistent earnings growth, high return on equity, global distribution network, and pricing power. He didn’t look at a chart. He didn’t check Twitter — mainly because it didn’t exist yet, but you get the point.

By 2024, Berkshire Hathaway’s Coca-Cola stake was worth over $25 billion — a 2,400% return. And Buffett didn’t do anything after buying. He just sat there. Reading annual reports. Drinking Cherry Coke. Being absolutely insufferable in the best possible way.

The lesson? Deep fundamental research on business quality — not just numbers — is the bedrock of long-term wealth creation in the stock market.

Pillar Two: Technical Analysis — Reading the Crowd

Now, I know what some of you are thinking: “But what about charts? What about all those lines people draw?” Welcome to technical analysis — the art of studying price movements and trading volumes to identify patterns and forecast future price direction.

And before the fundamentalists in the room roll their eyes, let me quote some science. A peer-reviewed study published in Artificial Intelligence Review by Hu et al. (2019) conducted a systematic review of 122 research papers spanning over a decade and concluded that both technical and fundamental analysis, when combined with machine learning, demonstrated significant predictive power in stock market forecasting [4].

So no, technical analysis is not astrology. Well — it’s a little like astrology. But the profitable kind.

Key Technical Concepts

Support and Resistance Levels: These are price zones where a stock tends to stop and reverse. Support is a floor — buyers tend to step in here. Resistance is a ceiling — sellers dominate. When a stock breaks through resistance on high volume, that’s called a breakout. When it falls through support, that’s called a breakdown — and it’s about as fun as it sounds.

Moving Averages: The 50-day and 200-day simple moving averages (SMA) are among the most widely tracked indicators in the world. When the 50-day crosses above the 200-day, traders call it a “Golden Cross” — historically a bullish signal. When it crosses below, it’s called a “Death Cross.” The names are melodramatic, but they work.

Relative Strength Index (RSI): This momentum oscillator measures whether a stock is overbought (above 70) or oversold (below 30). An RSI above 70 means the market might be getting a little too excited. Like when everyone suddenly becomes a property expert at a dinner party. The stock is probably due for a pullback.

Volume Analysis: Price moves mean more when they come with high volume. A stock jumping 5% on thin volume is suspicious. The same jump on three times average volume is much more convincing. Always check the volume. Always.

Candlestick Patterns: Each trading day forms a “candle” showing the open, high, low, and close price. Patterns like the “Hammer,” “Doji,” and “Engulfing Candle” can signal reversals or continuations. Yes, they have wild names. No, that doesn’t make them less useful.

Case Study 2: The GameStop Short Squeeze of 2021

Let’s talk about GameStop, shall we? In January 2021, GameStop (GME) was a struggling brick-and-mortar video game retailer heavily shorted by hedge funds who believed — reasonably, based on fundamentals — that the company was in terminal decline. Enter a community of retail traders on Reddit who noticed the technical setup: extraordinarily high short interest (over 140% of the float was shorted — which, by the way, is mathematically wild) combined with a company whose shares were trading near multi-year lows.

Using technical momentum analysis and social sentiment research, retail traders co-ordinated a buying surge that triggered a short squeeze. The stock went from around $20 to nearly $500 in a matter of weeks. Hedge funds lost billions. Small traders became overnight millionaires. And then the stock crashed back down.

The lesson here is twofold. First, understanding short interest data — a crucial part of market research — can identify powerful explosive setups. Second, and this is critical: knowing when to exit is just as important as knowing when to enter. Many people who rode GME up also rode it back down because they got emotionally attached. I’m not judging. Emotions are human. But the market? The market has no soul.

Macroeconomic Research: The Big Picture That Moves Everything

Here’s something most beginners completely ignore: the stock market doesn’t exist in a vacuum. It lives inside an economy, and that economy is affected by interest rates, inflation, GDP growth, employment data, central bank policy, geopolitical events, and a dozen other macro variables.

Let me put it plainly: you could do the most perfect fundamental analysis on the most beautiful company in the world, buy its stock, and then the Federal Reserve announces an unexpected rate hike — and your stock drops 15% in a day. Not because anything is wrong with the company. But because the macro environment shifted the tide on every boat.

This is not hypothetical. This is 2022.

Interest Rates: When central banks raise interest rates, borrowing becomes more expensive, corporate profits shrink, and the present value of future earnings — which is what a stock price represents — decreases. The inverse is also true: rate cuts are generally bullish for stocks. Understanding where we are in the interest rate cycle is non-negotiable research.

Inflation Data: The Consumer Price Index (CPI) and Producer Price Index (PPI) are released monthly and closely watched by every serious market participant. High inflation erodes corporate margins and forces central banks to raise rates. Low inflation encourages stimulus. The balance is everything.

GDP Growth: Strong GDP growth means businesses are thriving and consumers are spending. Weak GDP, especially two consecutive quarters of contraction (a technical recession), tends to be bearish for risk assets. Keep an eye on quarterly GDP releases from the Office for National Statistics (ONS) in the UK, and the Bureau of Economic Analysis (BEA) in the US.

Yield Curve: The relationship between short-term and long-term government bond yields is one of the most reliable leading indicators in finance. An inverted yield curve — where short-term yields exceed long-term yields — has preceded every US recession since the 1950s. When that curve inverts, experienced traders get nervous. Very nervous. The kind of nervous where you start buying tinned goods.

Sector and Industry Analysis: Not All Stocks Are Created Equal

One of the most important — and most overlooked — parts of researching the stock market is understanding that different sectors perform very differently at different points of the economic cycle. This concept is called sector rotation.

During economic expansions, cyclical sectors like technology, consumer discretionary, and industrials tend to outperform. During slowdowns or recessions, defensive sectors like healthcare, utilities, and consumer staples hold up better — because people still need their medicine and their electricity no matter what the GDP is doing.

Researching sector performance means asking:

Which sectors are leading the current market rally?
Which sectors are showing relative weakness?
What is the current economic environment suggesting about which sectors should outperform next?

Tools like the SPDR Sector ETFs allow you to track sector performance in real time. Free screeners like Finviz and Stockanalysis.com allow you to sort stocks by sector and compare key metrics within an industry quickly and efficiently.

A peer-reviewed study in the Journal of Finance by Fama and French (1992) demonstrated that size and value factors — which are inherently connected to sector and industry characteristics — explain a significant portion of stock return variation beyond what the market beta alone accounts for. Their three-factor model became one of the most cited and applied frameworks in all of quantitative finance [5].

Sentiment Analysis: When Everyone Is Talking About It, Run

There’s a famous story — possibly apocryphal, but the point stands — that Joseph Kennedy Sr. knew it was time to get out of the stock market in 1929 when his shoeshine boy started giving him stock tips. When taxi drivers, barbers, and people at family Christmas dinners are excitedly telling you about a stock, you are likely near a top. The sophisticated money moved in early. You’re arriving at the party just as the best people are leaving and the cleaning crew is putting on their coats.

Sentiment research involves monitoring market psychology to gauge whether investors are overwhelmingly bullish (greedy) or bearish (fearful). Here are the tools:

CNN Fear & Greed Index: A composite indicator that measures seven factors including market momentum, stock price breadth, and put/call ratios to produce a single “fear to greed” reading. Free and updated daily at CNN Markets.

AAII Investor Sentiment Survey: The American Association of Individual Investors surveys its members weekly on whether they are bullish, neutral, or bearish over the next six months. Extreme readings are often contrarian signals — when 60%+ of respondents are bearish, the market frequently rallies. Humans are predictably irrational.

Put/Call Ratio: Options traders buy “puts” to bet on stocks falling and “calls” to bet on rising prices. When the put/call ratio spikes above 1.2, it signals extreme fear and can be a bullish contrarian signal.

Short Interest: Available through FINRA and reported bi-monthly, this data shows what percentage of a stock’s float is currently sold short. Extremely high short interest can either validate bearish fundamentals — or create the conditions for a violent short squeeze, as we learned from GameStop.

Research published in the Journal of Behavioral Finance has repeatedly shown that investor sentiment carries statistically significant predictive power for short-term market returns. Put simply: the crowd’s emotions are measurable, and they are exploitable [6].

I once ignored sentiment indicators and bought into a “hot tech stock” that every YouTube channel was covering. The stock was up 200% in three months before I touched it. I bought it because I thought I was late but not too late. I was too late. I bought the absolute top. The stock then fell 70% over the next year. It was like showing up to a party, helping yourself to the last chip, and then watching the lights come on and everyone leave. Do not be me. Learn from me.

Case Study 3: Amazon’s Long Game

In 1997, Amazon went public at a split-adjusted price of approximately $1.50 per share. The company wasn’t profitable for years. Traditional fundamental analysts using short-term P/E analysis would have run screaming in the other direction. Its P/E ratio was, at various points, literally infinite — because the “E” (earnings) were negative.

But investors who took a longer view and researched Amazon’s total addressable market (TAM), its customer acquisition engine, the power of Prime’s flywheel economics, and its nascent cloud computing division (AWS) — which is now the most profitable segment by far — were rewarded beyond imagination.

By 2024, Amazon’s stock had appreciated from that $1.50 IPO price to over $185 per share. That’s a return of over 12,000%. Those who held through the dot-com crash, the 2008 financial crisis, and multiple corrections along the way were rewarded for their research and conviction.

The lesson: research is not just about what a company is today. It’s about what it’s building toward. Revenue trajectory, addressable market size, competitive moats, and management vision matter enormously for long-term investors.

How to Use Stock Screeners Effectively

You don’t have to manually sift through thousands of stocks. Stock screeners let you filter the entire market based on your specific criteria — P/E ratio, revenue growth, dividend yield, RSI levels, short interest, sector, and dozens of other variables.

Here are the best free and paid tools:

Finviz — The gold standard free screener. You can filter by fundamentals, technicals, and descriptive criteria simultaneously. Excellent heat maps showing market performance at a glance.

Stock Analysis — Clean, comprehensive, and free. Excellent for viewing historical financial statements and earnings history.

Simply Wall St — Visual, intuitive, and great for quick fundamental research. Particularly useful for beginners who want a visual breakdown of a company’s health.

TradingView — The best charting platform available, period. Free tier is generous. Unmatched for technical analysis, with thousands of community-created indicators.

Bloomberg Terminal / Refinitiv Eikon — The professional standard. These cost thousands per year. If you have access through an employer or university, use them. If not, the free tools above are genuinely excellent for individual investors.

The Efficient Market Hypothesis: Know Thy Enemy (Or Friend)

No article on stock market research would be complete without addressing the Efficient Market Hypothesis (EMH) — the theory that has probably caused more heated arguments in finance departments than any other idea in the past 60 years.

Proposed by Nobel laureate Eugene Fama in his landmark 1970 paper “Efficient Capital Markets: A Review of Theory and Empirical Work,” the EMH posits that stock prices at any given moment fully reflect all available information. The implication is devastating if true: no amount of research should consistently produce returns above the market average, because all public information is already priced in [7].

There are three forms:

Weak form: Past prices cannot predict future prices (i.e., technical analysis doesn’t work)
Semi-strong form: All publicly available information is already priced in (i.e., fundamental analysis can’t consistently beat the market either)
Strong form: Even insider information is reflected in prices (illegal in most countries, and empirically refuted)

Now, Fama and his colleague Kenneth French spent decades testing these ideas. Their three-factor model identified that value stocks and small-cap stocks historically outperform the broader market — which is itself an anomaly under strict EMH. The great irony is that Fama’s own research partially undermined his hypothesis [5].

Robert Shiller, who shared the 2013 Nobel Prize in Economics with Fama (yes, they shared the same prize while disagreeing on everything — academia is wild), argued that markets exhibit excess volatility relative to fundamentals, suggesting systematic patterns of irrational behaviour that informed investors can exploit [1].

The practical takeaway? The market is mostly efficient, most of the time. But inefficiencies exist — especially in small-cap stocks, during earnings surprises, in the short term following major news events, and in sectors temporarily out of favour. Research doesn’t guarantee you’ll beat the market, but it dramatically reduces the chance that you’ll blow up your portfolio on a company you never actually looked at properly.

Building Your Research Routine: A Step-by-Step Framework

Okay. You’ve absorbed the theory. Now let me give you a practical, repeatable research process you can use before investing in any stock.

Step 1: Screen for Candidates (15 minutes) Use Finviz or Stock Analysis to run a screen based on your strategy. If you’re a value investor, screen for P/E under 15, positive earnings growth, low debt. If you’re a growth investor, screen for revenue growth above 20%, expanding margins, and a strong earnings trend.

Step 2: Read the Annual Report and Last Two Earnings Calls (45-60 minutes) For UK stocks, these are available on the company’s investor relations page and via the London Stock Exchange. For US stocks, go directly to SEC EDGAR. Read the CEO’s letter. Read the management discussion section. Listen to the earnings call transcript — management’s tone and language reveal a great deal about their confidence and honesty.

Step 3: Analyse the Financials (30 minutes) Calculate or verify the key ratios: P/E, P/B, ROE, D/E, free cash flow yield. Compare these ratios to the company’s own 5-year history and to sector peers. Context is everything.

Step 4: Assess the Competitive Moat (20 minutes) Ask: What stops a competitor from doing exactly what this company does? Is there a cost advantage? Network effects? Intellectual property? Brand loyalty? Switching costs? A business without a moat is a business at constant risk of being disrupted.

Step 5: Check the Technical Picture (15 minutes) Pull up the chart on TradingView. Is the stock in an uptrend, downtrend, or range? Where is the RSI? Is there a meaningful support level nearby that could define your risk? Is the stock near a 52-week high (potential breakout) or low (potential value trap or recovery play)?

Step 6: Check Macro and Sentiment Context (10 minutes) What is the current interest rate environment? Is the sector in favour or out of favour? What does the CNN Fear & Greed Index say? What is the short interest in this stock?

Step 7: Define Your Trade Parameters Before You Buy Before you press the buy button — I cannot stress this enough — know exactly: (a) why you are buying, (b) what price would prove your thesis wrong, (c) where your stop loss is, and (d) what your target is. If you cannot answer all four questions, you are not investing. You are gambling. And the casino has nicer carpets.

Common Research Mistakes (And How to Avoid Them)

Confirmation bias: You like a stock, so you only read the bullish case. You actively avoid the bearish arguments. This is like asking your friend who already bought a car whether you should buy the same car. You know what they’re going to say.

Recency bias: The market went up for three years, so it’ll go up next year too. The last three trades worked, so I’m a genius. No. Those things might be true. They might not be. Use evidence, not momentum of luck.

Anchoring: “I bought this at £50, so I won’t sell until it gets back to £50.” The stock is now at £20. The stock does not know — or care — what you paid for it. The stock is not sitting at home thinking about you. Nobody is thinking about you. Let it go.

Over-diversification: Owning 47 stocks because you’ve researched all 47 of them sounds responsible. In practice, your best ideas are buried under a pile of mediocre ones. True diversification is achieved with 15-25 well-researched positions, not 50 loosely followed ones.

Neglecting position sizing: Even a brilliant research call means nothing if you risk 40% of your portfolio on a single stock. Professional traders typically risk 1-2% of capital per position. This sounds boring. Boring is how you stay in the game long enough to win.

Case Study 4: The 2008 Financial Crisis — What Research Could Have Shown

The 2008 global financial crisis is often portrayed as an unforeseeable black swan. It was not unforeseeable. Several investors saw it coming, did the research, and made extraordinary profits.

Michael Burry — a trained physician turned fund manager — conducted deep fundamental research into the US mortgage-backed securities market in 2005 and 2006. He read thousands of pages of mortgage prospectuses, analysed default rates, studied the composition of mortgage pools underlying CDOs, and concluded that the entire subprime mortgage market was a house of cards. He then bought credit default swaps — essentially insurance policies against these instruments — and eventually made over $700 million for his investors when the market collapsed.

John Paulson did the same, making approximately $15 billion in 2007-2008 — one of the greatest trades in financial history.

The lesson is not that you should bet on apocalypses. The lesson is that the research was available. The data was public. The signs were there for anyone willing to do the unglamorous work of actually reading the documents. Most people didn’t bother. They assumed prices would keep going up because prices had been going up. That’s not research. That’s wishful thinking wearing a suit.

Tools, Platforms, and Resources Summary

To consolidate everything, here is a curated list of the best research tools and resources:

News and Data:

Reuters — Reliable, fast financial news
Bloomberg — The professional standard for financial journalism
The Financial Times — Essential reading for UK and global markets

Fundamental Data:

SEC EDGAR — All US public company filings
Macrotrends — 20+ years of historical financial data, free
Simply Wall St — Visual fundamental analysis

Technical Analysis:

TradingView — Best charting platform available
Finviz — Combined screener and heat map

Macro Research:

FRED Economic Data — Federal Reserve Bank of St. Louis — comprehensive free macroeconomic database
ONS UK — Office for National Statistics for UK economic data

Sentiment:

The Mindset of a Serious Market Researcher

I want to close with something that no charting tutorial or earnings model can teach you: mindset.

The market is one of the most psychologically challenging environments you will ever operate in. It will test your patience when good stocks go nowhere for months. It will test your conviction when perfectly researched positions move against you before reversing in your favour. It will test your humility when you are wrong — and you will be wrong. Often. Even the best investors in the world are wrong 40% of the time. The goal is not to be right every time. The goal is for your winners to be bigger than your losers.

A meta-analysis published in PLOS ONE examining investor behavioural biases across 26 years of research found that the single biggest barrier to successful investing was not lack of information — it was cognitive and emotional biases including overconfidence, loss aversion, herding, and confirmation bias [6]. These are the real enemies. Not the market.

Research is your weapon against these biases. When you have done the work — when you know why you own what you own, when you entered, where your stop is, and what your target is — you operate from a place of informed conviction rather than reactive fear. You become the person at the poker table reading everyone else’s tells instead of nervously checking your own hand every thirty seconds.

I started this article by telling you I once held a stock for six months because I liked the logo. I am embarrassed about that. That company, by the way, went bankrupt. The logo — which I liked very much — did nothing to prevent that outcome. The brand colours did not stop the cash burn. The font choices did not stave off the creditors.

But I learned. I read the papers. I studied the methods. I built a system. And now? I still lose sometimes — because everyone loses sometimes — but I lose for the right reasons. I lose on calculated bets with defined risk, not on vibes and aesthetics.

You have just read several thousand words on how to research the stock market. You now know more than the vast majority of people who will open a brokerage account this year. The question is whether you’ll use it.

The market is open Monday through Friday. Your excuses are available 24/7. Choose wisely.

References

Shiller, R.J. (2003). “From Efficient Markets Theory to Behavioral Finance.” Journal of Economic Perspectives, 17(1), 83–104. DOI: 10.1257/089533003321164967. Available at: https://www.aeaweb.org/articles?id=10.1257/089533003321164967
Nariswari, T.N., & Nugraha, N.M. (2020). “Fundamental Analysis Models in Financial Markets.” Procedia Economics and Finance / ScienceDirect, International Institute for Social and Economics Sciences. Available at: https://www.sciencedirect.com/science/article/pii/S2212567115013441
Bowen, D. (2016). “A Review of Fundamental and Technical Stock Analysis Techniques.” Journal of Stock & Forex Trading / Longdom Publishing. Available at: https://www.longdom.org/open-access/a-review-of-fundamental-and-technical-stock-analysis-techniques-13262.html
Hu, Z., Liu, W., Bian, J., Liu, X., & Liu, T.Y. (2019). “Listening to Chaotic Whispers: A Deep Learning Framework for News-Oriented Stock Trend Prediction.” Cited in: Polamuri, S.R., et al. (2019). “A Systematic Review of Fundamental and Technical Analysis of Stock Market Predictions.” Artificial Intelligence Review, Springer Nature. DOI: 10.1007/s10462-019-09754-z. Available at: https://link.springer.com/article/10.1007/s10462-019-09754-z
Fama, E.F. (1991). “Efficient Capital Markets: II.” The Journal of Finance, 46(5), 1575–1617. DOI: 10.1111/j.1540-6261.1991.tb04636.x. Available at: https://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1991.tb04636.x
Raheja, S., & Dhiman, B. (2024). “Unpacking Investor Psychology: A Systematic Review and Meta-Analysis of Behavioural Biases Shaping Investment Decisions.” PLOS ONE / PMC National Library of Medicine. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12576316/
Fama, E.F. (1970). “Efficient Capital Markets: A Review of Theory and Empirical Work.” The Journal of Finance, 25(2), 383–417. Available at: https://www.bu.edu/econ/files/2011/01/Fama2.pdf

Disclaimer: This article is for educational and informational purposes only and does not constitute financial advice. Always conduct your own research and consider consulting a qualified financial adviser before making investment decisions.