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Market sentiment

Market sentiment, also known as investor sentiment, refers to the general attitude or prevailing mood of investors toward a particular security or the broader financial market, which influences their investment decisions and arises from beliefs about future cash flows and risks that deviate from fundamental facts.^[1] This sentiment is often shaped by psychological biases, emotional responses, and external factors such as news events or economic data, leading to bullish (optimistic) or bearish (pessimistic) trends that drive market dynamics beyond rational valuation.^[2] Despite its central role in finance, market sentiment remains somewhat ill-defined in scholarly literature, with ongoing debates about its precise boundaries and measurement, drawing from behavioral economics and psychology to explain deviations from efficient market hypotheses.^[3] In financial markets, sentiment manifests through collective investor behavior, amplifying price movements in speculative or hard-to-value assets like small-cap stocks or emerging sectors, where it can cause temporary over- or undervaluation.^[1] High sentiment levels, for instance, correlate with increased initial public offering (IPO) activity and mutual fund inflows, while low sentiment may trigger sell-offs and heightened volatility.^[1] Over the past four decades, the study of market sentiment has evolved significantly, incorporating advanced techniques such as lexicon-based analysis of news and social media, machine learning models like support vector machines, and deep learning approaches including long short-term memory networks to quantify and predict its impacts on stock returns, trade volumes, and risks.^[2] The implications of market sentiment extend to both individual investors and institutional players, contributing to noise trading, herding behavior, and broader economic phenomena, as evidenced in events like the 2021 GameStop surge driven by social media discussions.^[2] Understanding and measuring sentiment—through proxies like consumer confidence indices, trading volume anomalies, or sentiment indices derived from textual data—enables better forecasting of market trends and informs regulatory policies aimed at mitigating irrational exuberance or undue pessimism.^[3]^[1] While predominantly studied in equity markets, particularly in the United States, China, and the United Kingdom, its principles apply across asset classes, underscoring its enduring relevance in modern finance.^[2]

Definition and Concepts

Core Definition

Market sentiment refers to the overall attitude or prevailing mood among investors toward a particular security, sector, or the broader financial market, often reflecting collective optimism or pessimism that influences buying and selling pressures.^[4] This aggregate emotional tone, shaped by crowd psychology, manifests in market behaviors such as increased demand during periods of enthusiasm, driving prices upward, or heightened selling amid fear, leading to declines.^[5] Bullish sentiment signals expectations of rising prices, while bearish sentiment anticipates falls, thereby creating short-term momentum that can deviate from underlying asset values.^[4] Unlike fundamental analysis, which evaluates an asset's intrinsic worth through objective metrics like financial statements, earnings, and economic indicators to inform long-term investment decisions, market sentiment is inherently psychological and often short-term oriented, prioritizing investor emotions over rational valuation.^[4]^[6] This distinction highlights how sentiment can amplify market volatility, as emotional biases—such as overconfidence or panic—temporarily override factual assessments of value.^[7] The term "market sentiment" gained prominence in the 20th century alongside the emergence of behavioral economics, which began challenging traditional rational actor models in the 1970s through works like those of Daniel Kahneman and Amos Tversky on cognitive biases.^[5] Earlier references appear in 1920s studies of market psychology, including analyses of economic expansions and crises where investor moods played a key role, as documented in historical accounts of the U.S. economy from 1920 to 1934.^[8] Illustrative examples of sentiment-driven extremes include the 1929 stock market crash, where initial euphoria from speculative buying on margin propelled the Dow Jones Industrial Average to record highs, only for panic to ensue as prices collapsed, eroding confidence and exacerbating the Great Depression.^[9] Similarly, the 2000 dot-com bust exemplified irrational exuberance, as warned by Federal Reserve Chairman Alan Greenspan in 1996, with rampant optimism fueling overvaluation of internet stocks until sentiment shifted, leading to a sharp market correction.^[10]^[11]

Bullish and Bearish Sentiment

Bullish sentiment in financial markets refers to a prevailing optimism among investors, typically manifesting in sustained rising asset prices, heightened trading volumes, and widespread confidence in future economic prospects.^[12] This positive outlook encourages greater participation, as investors anticipate further gains, leading to increased buying pressure that reinforces the upward trend. Key observable signs include elevated initial public offering (IPO) activity, where companies rush to capitalize on favorable valuations, and rising margin buying, as traders leverage borrowed funds to amplify positions in expectation of continued appreciation.^[13]^[14] In contrast, bearish sentiment is characterized by pervasive pessimism, resulting in declining prices, accelerated selling driven by fear, and a shift toward risk aversion where investors prioritize capital preservation over growth opportunities.^[15] This mood often amplifies market downturns through herd behavior, with participants rushing to exit positions to mitigate losses, thereby exacerbating price drops and reducing liquidity. A prominent indicator is panic selling during economic recessions, as seen in the 2008 financial crisis, where widespread fear of systemic collapse prompted massive liquidations across global markets.^[16] Transitions between bullish and bearish sentiment frequently occur at tipping points, such as when over-optimism in bull phases builds excessive leverage and valuations, eventually triggering corrections as reality diverges from expectations.^[17] These shifts can be abrupt, with prolonged high bullish readings—such as investor optimism exceeding 50-55%—often signaling impending reversals to bearish conditions.^[18] Real-world examples illustrate these dynamics starkly. The 2021 meme stock rally, exemplified by GameStop's explosive surge, embodied bullish frenzy fueled by retail investor enthusiasm and social media coordination, driving shares up over 1,500% in weeks amid unchecked optimism.^[19] Conversely, the 2022 crypto winter represented bearish capitulation, with the market losing approximately $2 trillion in value as cascading failures like the Terra-Luna collapse and FTX bankruptcy instilled deep fear, leading to prolonged risk aversion and capitulation selling.^[20]

Measurement Methods

Technical Indicators

Technical indicators provide objective, data-driven insights into market sentiment by analyzing price, volume, and breadth metrics from trading activity. These tools derive signals from historical and real-time market data, such as option prices and stock performance, to quantify investor fear, greed, or indecision without relying on subjective opinions. Widely used in technical analysis, they help traders identify potential reversals or confirm trends by highlighting imbalances in market participation. The Volatility Index (VIX), commonly known as the "fear gauge," quantifies expected near-term volatility in the S&P 500 index over the next 30 days, serving as a barometer for investor anxiety. It is calculated from the implied volatilities of a wide range of S&P 500 index options, using the formula VIX = 100 \times \sqrt{\text{expected 30-day variance}}, where the variance reflects the weighted prices of out-of-the-money puts and calls to estimate future market fluctuations.^[21]^[22] Elevated VIX levels, typically above 30, signal heightened fear and bearish sentiment, as investors seek protection through options amid uncertainty, while readings below 20 suggest complacency and bullish confidence.^[23] The Put/Call Ratio measures the trading volume of put options (bearish bets on price declines) relative to call options (bullish bets on price rises), offering a direct gauge of options market sentiment. A ratio exceeding 1 indicates bearish dominance, with more traders anticipating downturns, whereas a value below 1 reflects bullish optimism through higher call activity. Historically, ratios between 0.7 and 1.2 are viewed as a neutral range, balancing hedging and speculative positions without extreme directional bias.^[24]^[25] Market breadth indicators like the Advance-Decline Line (ADL) track the health of overall market participation by cumulatively summing the daily difference between advancing and declining stocks on major exchanges. The ADL is computed as the current day's net advances (advancing stocks minus declining stocks) added to the previous period's value, creating a running total that rises with broad strength and falls with widespread weakness. Divergences, such as the ADL declining while an index like the S&P 500 rises, signal eroding sentiment and potential bearish shifts, as fewer stocks support the uptrend.^[26] Similarly, the High-Low Index assesses sentiment through the ratio of stocks hitting 52-week highs to those reaching new lows, typically smoothed with a 10-day simple moving average and scaled to a 0-100 percentage. This breadth measure highlights momentum extremes: readings below 30 denote bearish conditions dominated by new lows and selling pressure, while values above 70 indicate bullish fervor with surging new highs and buying enthusiasm.^[27] An illustrative example of these indicators' power occurred during the 2020 COVID-19 market crash, when global lockdowns triggered panic selling and the VIX surged to a peak of 82.69 on March 16, embodying extreme fear and profoundly bearish sentiment across equities.^[28] In tandem, the Put/Call Ratio climbed above 1.5 in mid-March, underscoring the rush to protective puts, while the ADL plummeted with widespread declines, confirming the sentiment collapse.^[24] Such synchronized spikes underscore how technical indicators capture acute shifts in collective investor psychology during crises.

Survey and Index-Based Measures

Survey and index-based measures of market sentiment rely on direct polling of investor opinions or aggregated data from market participants to gauge collective attitudes toward future price movements. These approaches capture qualitative and behavioral insights that quantitative price-based indicators may overlook, providing a window into the psychological state of investors. By aggregating responses from surveys or positional data, such measures often serve as contrarian signals, where extreme optimism or pessimism suggests potential market reversals.^[29]^[30] The American Association of Individual Investors (AAII) Sentiment Survey is a prominent example, conducted weekly since 1987 among AAII members to assess short-term stock market expectations over the next six months. Respondents indicate whether they are bullish, bearish, or neutral, with results expressed as percentages of each category; for instance, bullish sentiment above its historical average of 37.5% may signal growing optimism. Investors often use it contrarianly, interpreting readings above 60% bullish as a potential market top due to excessive complacency, while bearish extremes below 30% can foreshadow rallies.^[31]^[32]^[29] Another key survey is the Investors Intelligence Advisory Sentiment Index, which analyzes recommendations from over 130 independent investment newsletters each week, classifying them as bullish, bearish, or expecting a correction. The index calculates the percentage of bullish versus bearish views, with historical data showing that bullish readings exceeding 55% often indicate overcrowding on the buy side and precede market reversals, as excessive agreement among advisors reflects herd behavior. Bearish extremes above 55% are rarer and have similarly signaled bottoms in seven instances since 1987.^[33]^[34]^[33] Composite indices like the CNN Money Fear & Greed Index synthesize multiple sentiment signals into a single score ranging from 0 (extreme fear) to 100 (extreme greed), updated daily using seven equally weighted indicators: market momentum, stock price strength, stock price breadth, put/call options, market volatility (VIX), safe-haven demand, and junk bond demand. Junk bond demand, for example, measures the yield spread between high-yield and investment-grade bonds, where narrower spreads signal greed as investors chase risk. Scores below 25 denote extreme fear, often correlating with undervaluation and buying opportunities, while above 75 indicates greed-driven overvaluation.^[35]^[36] The Commitment of Traders (COT) Report, published weekly by the U.S. Commodity Futures Trading Commission (CFTC), provides insight into futures market sentiment by disclosing aggregate positions held by large traders, including speculators and hedgers, across commodities, currencies, and indices. Net long positions—where long contracts exceed shorts among non-commercial speculators—typically reflect bullish sentiment, as they show bets on price increases; conversely, net shorts indicate bearishness. Traders monitor changes in these positions to detect shifts in market positioning, such as building optimism in equity index futures.^[37]^[38]^[39] Despite their utility, survey and index-based measures have notable limitations, including a lagging nature due to weekly reporting cycles that may miss intraday or rapid sentiment shifts, and self-reporting biases from voluntary participation, which can introduce self-selection where more extreme views are overrepresented. Academic evaluations confirm that while these measures predict returns in aggregate, their reliability varies with market conditions and requires cross-validation to mitigate noise from respondent subjectivity.^[40]^[30]

Theoretical Foundations

Investor Attention Theory

Investor attention theory posits that attention is a scarce cognitive resource for investors, leading to selective processing of information that can introduce biases into market sentiment. In models of investor behavior, limited attention causes investors to overweight recent or salient information, resulting in sentiment-driven deviations from fundamental values. For instance, Barberis and Shleifer's 2003 framework illustrates how investors categorize assets into styles based on representativeness heuristics, fostering overreaction to style-specific news and underreaction elsewhere.^[41] One primary mechanism through which attention influences sentiment is via spikes in media coverage, which heighten investor focus and amplify emotional responses. Increased media attention draws investors to specific stocks or sectors, often correlating with heightened volatility as sentiment swings become more pronounced. A seminal study using Google Trends' Search Volume Index (SVI) demonstrates that surges in search queries for firm names predict future trading volume and volatility, indicating that media-driven attention exacerbates sentiment effects by mobilizing uninformed investors.^[42] Retail investor attention, often proxied by surges in trading volume, similarly shapes sentiment through herding dynamics during high-attention periods. When retail traders concentrate on popular assets amid volume spikes, they tend to mimic each other's actions, leading to clustered buying or selling that reinforces bullish or bearish sentiment. Empirical evidence shows a positive link between retail attention—measured by abnormal trading activity—and herding behavior, particularly in less liquid stocks where sentiment propagation is stronger.^[43] Institutional investor attention, gauged by the issuance of analyst reports, can distort prices by concentrating focus on select firms, thereby skewing sentiment toward overvaluation or undervaluation. When analysts issue clustered reports, institutional trading intensifies around those recommendations, amplifying sentiment biases as attention narrows to highlighted attributes. Research indicates that higher analyst coverage correlates with greater institutional attention shocks, reducing underreaction to news but occasionally leading to price distortions from herd-like institutional responses.^[44] Social media metrics, such as Twitter mentions, serve as modern proxies for investor attention, with 2010s studies linking spikes in platform activity to rapid sentiment swings. Elevated tweet volumes around firms signal heightened attention, often preceding abnormal returns as retail sentiment diffuses virally. For example, analyses of Twitter sentiment from that decade reveal that positive or negative mention surges predict stock price movements, underscoring how digital chatter amplifies attention-driven biases in sentiment formation.^[45] Event-driven attention, particularly around earnings announcements, further illustrates sentiment dynamics, where sudden information releases capture investor focus and intensify reactions. Such events spike search volumes and media hits, leading to sentiment overreactions as attention converges. A formula for constructing an attention index in these contexts from the study is \text{ASVI}_t = \log(\text{SVI}_t) - \log[\text{Med}(\text{SVI}_{t-1}, \dots, \text{SVI}_{t-8})], which measures abnormal search volume relative to recent medians to quantify focus intensity and its role in sentiment amplification.^[42] Post-2020 developments in digital attention models have expanded this theory by integrating machine learning to parse multifaceted online signals, enhancing predictions of sentiment in volatile markets. These models incorporate real-time social media and search data to capture nuanced attention patterns, revealing stronger links between digital proxies and sentiment-driven volatility in emerging assets like cryptocurrencies.^[46]

Behavioral Finance Perspectives

Behavioral finance integrates psychological insights to explain how cognitive biases and emotional responses shape market sentiment, often leading to irrational collective behaviors that deviate from efficient market predictions. Investors' overconfidence in their abilities prompts excessive trading, which intensifies bullish sentiment during market uptrends by increasing buy orders and volume, while ignoring risks. Similarly, confirmation bias drives individuals to favor information aligning with preexisting views, reinforcing extreme sentiments—such as prolonged optimism in bubbles or pessimism in crashes—by discounting contradictory evidence.^[47] A foundational perspective is the noise trader model, which posits that sentiment-driven noise traders, influenced by beliefs uncorrelated with fundamentals, cause temporary price deviations and increased volatility, as rational arbitrageurs face limits to correction.^[48] Herding behavior further exacerbates sentiment swings, as investors suppress private information to mimic the crowd, fostering asset bubbles in euphoric phases and abrupt crashes during fear-driven sell-offs. This phenomenon can be modeled using a herding index calculated as the correlation between individual trades and the market average, capturing the degree of synchronized actions beyond fundamental signals.^[49] Prospect theory, developed by Kahneman and Tversky in 1979, underscores loss aversion—where losses impact utility more than equivalent gains—as a core driver of sentiment dynamics. During downturns, this bias amplifies bearish sentiment, prompting panic selling and heightened volatility as investors overweight potential further losses relative to recovery prospects.^[50] The disposition effect compounds these imbalances, with investors selling winning stocks prematurely to realize gains and holding losing positions too long in hopes of recovery, thereby sustaining bearish sentiment by delaying capital reallocation from underperformers. This pattern, rooted in regret aversion, prolongs market corrections and hinders efficient pricing adjustments. Empirical studies from the 1990s established that sentiment, driven by these biases, predicts cross-sectional return anomalies, such as undervaluation in closed-end funds and momentum effects, challenging rational expectations models.^[51] Post-2008 financial crisis research has integrated neurofinance, using brain imaging to reveal neural correlates of biases like loss aversion in real-time sentiment shifts, enhancing understanding of emotional contagion in markets.^[52]

Influencing Factors

Economic and Macro Events

Macroeconomic events play a pivotal role in driving shifts in market sentiment by altering investors' perceptions of economic health, future growth prospects, and risk levels. Interest rate changes by central banks, such as the U.S. Federal Reserve, are among the most direct influencers. Rate hikes typically induce bearish sentiment by increasing borrowing costs for consumers and businesses, which dampens economic activity and corporate profitability. For instance, during the 2022-2023 rate-hiking cycle, the Federal Reserve raised rates aggressively to combat inflation, leading to pronounced negative impacts on the NASDAQ Index and equity sell-offs as actual index values fell below expected trends.^[53]^[54] Conversely, the Federal Reserve's rate cuts beginning in September 2024, which lowered the federal funds rate by 100 basis points through December 2024, boosted bullish sentiment by reducing borrowing costs and supporting corporate earnings, contributing to record highs in major indices like the S&P 500.^[55] GDP and employment data releases further shape sentiment by signaling the economy's trajectory. Strong GDP growth reports foster bullish sentiment by indicating robust production and potential for higher corporate earnings, encouraging investor optimism and increased buying activity. Conversely, weak employment figures or contracting GDP trigger fear and risk aversion, often amplifying bearish turns. The 2001 dot-com recession exemplifies this, where declining GDP and rising unemployment amid the tech bust led to widespread market panic and sharp equity declines.^[56] Geopolitical events introduce uncertainty that can rapidly pivot market sentiment, particularly in affected sectors. Wars, invasions, or major elections heighten volatility and risk premiums, prompting shifts toward safe-haven assets. The 2022 Russian invasion of Ukraine starkly illustrated this, spiking energy market sentiment as oil and gas prices surged due to supply disruptions, while global equities experienced heightened volatility and a potential 1% reduction in 2022 world GDP growth.^[57] Inflation developments erode or bolster confidence depending on their trajectory and magnitude. Persistently high inflation undermines bullish sentiment by eroding purchasing power, raising input costs for firms, and prompting expectations of tighter monetary policy, often leading to bearish market turns. Quantitative analyses reveal a negative relationship, where sentiment scores adjust downward in response to inflation surprises, modeled as \Delta S = -\beta \times \Delta I (with \beta > 0), as evidenced by experimental data showing a 1 percentage point reduction in stock return expectations upon learning of historical low returns during high-inflation periods.^[58] Central bank communications, including forward guidance, serve as key pivots for sentiment by clarifying policy intentions and anchoring expectations. The Federal Open Market Committee (FOMC) meetings often act as turning points, where announcements of future rate paths influence investor outlooks on growth and inflation. Sentiment indices derived from FOMC communications explain variations in asset prices, with surprises in policy tone driving shifts in equities, bonds, and currencies from 1999 to 2022.^[59]^[60] Traditional media outlets, such as newspapers and television broadcasts, play a pivotal role in shaping market sentiment by disseminating information that influences investor perceptions and prompts immediate reactions. News headlines often drive short-term sentiment shifts, with studies showing that positive or negative tones in financial news can predict stock returns over horizons of 1-2 days, as positive news elevates returns quickly while negative news has a more prolonged dampening effect.^[61] Tone analysis of news articles, which quantifies sentiment through the ratio of positive to negative words, has been found to correlate significantly with subsequent stock market movements, enabling better forecasting of volatility and returns.^[62] Social media platforms have amplified the speed and reach of sentiment formation, particularly among retail investors, by enabling rapid information sharing and collective mobilization. Platforms like Reddit's r/WallStreetBets have fueled retail-driven sentiment surges, as seen in the 2021 GameStop short squeeze, where heightened posting activity and both positive and negative sentiments on Reddit strongly correlated with increased trading volume (e.g., positive sentiment coefficient of 4.209*** during the squeeze period), outpacing Twitter's influence and demonstrating social informedness in driving market dynamics.^[63] This event exemplified how viral social media narratives can create bullish momentum, leading to a stock price increase of over 1,500% in January 2021 as retail investors coordinated against institutional short sellers.^[63] Influencers, including financial analysts and online personalities, significantly sway market crowds through recommendations that alter sentiment and trading behavior. Analyst upgrades and downgrades reliably boost trading volume and induce short-term price movements, with herding effects among investors amplifying these shifts and potentially causing overreactions or mispricing.^[64] Echo chambers on social platforms reinforce biases by exposing users to aligned views, intensifying sentiment polarization and contributing to sustained bullish or bearish trends among follower networks.^[64] Misinformation and fake news propagated through media channels can rapidly spread fear or euphoria, exacerbating sentiment extremes and leading to volatile market responses. During the 2016 Brexit referendum, widespread misinformation amplified bearish sentiment in forex markets, contributing to sharp declines in the British pound and broader equity drops, with negative fake news events shown to have significant short-term negative impacts on returns across EU markets.^[65] Sentiment metrics derived from natural language processing (NLP) on social media, such as Twitter, provide quantifiable insights into market mood. One common approach uses NLP to classify tweets as positive, negative, or neutral, yielding a media sentiment score calculated as:

\text{Media Sentiment} = \frac{\text{Positive Mentions} - \text{Negative Mentions}}{\text{Total Volume}}

This formula, applied to tweet volumes, has demonstrated predictive power for stock movements by capturing public mood shifts that precede price changes.^[66] Post-2020, platforms like TikTok have emerged as potent influencers of market sentiment through short-form videos and algorithmic feeds that virally promote investment ideas. Finfluencers on TikTok shape crowd sentiment by driving attention to specific stocks, with studies showing that mega-influencers increase volatility and trading volume, though not always returns, as seen in spikes in option implied volatility following viral TikTok trends.^[67] This rise has democratized access to financial discourse but also heightened risks from unverified advice in echo-like algorithmic bubbles.^[68]

Market Applications

Equity and Commodity Markets

In equity markets, earnings seasons often intensify investor sentiment, as positive surprises can fuel optimism and drive stock prices higher, while disappointments trigger widespread pessimism. For instance, during quarterly earnings reports, aggregated sentiment from analyst forecasts and media coverage has been shown to predict short-term stock returns, with overly optimistic extrapolations leading to subsequent corrections. Sector rotations, such as the bullish tilt toward technology stocks throughout the 2010s, further illustrate how prevailing market mood influences capital flows, with investors shifting toward perceived growth sectors amid favorable economic narratives.^[69]^[70] Contrarian strategies in equities leverage extreme sentiment levels as reversal signals, where peaks in bearishness often precede market bottoms. A notable example occurred in early 2009, when the American Association of Individual Investors (AAII) survey recorded a record-high bearish reading of 70.3% on March 5, coinciding with the bear market trough and signaling an impending rebound in stock prices. Such sentiment extremes, derived from investor polls, have historically provided contrarian buy opportunities by highlighting overreactions among retail participants.^[71]^[72] In commodity markets, supply shocks prominently shape sentiment, as seen with oil prices in 2022, when geopolitical tensions and production disruptions led to heightened volatility and bullish expectations amid fears of shortages.^[73] Gold, conversely, functions as a safe-haven asset during periods of bearish equity sentiment, attracting inflows when stock markets decline due to its perceived stability and inverse correlation with risk assets. Empirical analysis across multiple countries confirms gold's role as a reliable hedge in turmoil, bolstering demand when equity sentiment sours.^[74]^[75] Sentiment analysis enhances equity and commodity trading by overlaying psychological insights onto technical indicators, aiding in precise entry and exit decisions. For example, combining sentiment scores from news and social data with moving averages or relative strength index (RSI) has improved return predictions and risk-adjusted performance in backtested equity strategies. In 2023, the hype surrounding artificial intelligence propelled tech sector sentiment, contributing to a $2.4 trillion increase in major U.S. tech firms' market capitalizations as investor enthusiasm drove valuations higher.^[76]^[77]^[78] Equity markets exhibit more retail-driven sentiment dynamics compared to the institutional dominance in commodities, where individual investors' polls like AAII heavily influence short-term stock movements, whereas commodity sentiment is shaped by large-scale hedging and supply chain data from professional traders. This retail emphasis in equities amplifies volatility during sentiment swings, contrasting with the more measured responses in commodity futures.^[79]

Currency and Forex Markets

Market sentiment in the currency and forex markets is profoundly influenced by carry trades and fluctuations in global risk appetite, where investors borrow in low-yield currencies like the Japanese yen to invest in higher-yield assets, amplifying movements during periods of heightened uncertainty.^[80] In bearish global moods characterized by risk aversion, the US dollar often strengthens as a safe-haven currency, drawing flows away from higher-risk emerging market currencies and exacerbating depreciation pressures on pairs like AUD/USD.^[81] Key indicators of forex sentiment include net speculative positions reported in the Commitment of Traders (COT) data from the US Commodity Futures Trading Commission, particularly for major pairs such as EUR/USD, where extreme net long or short positions by non-commercial traders signal potential reversals in market bias.^[37] The risk-on/risk-off framework further captures sentiment dynamics, with risk-on environments favoring commodity currencies like the Australian dollar amid positive global outlooks, while risk-off shifts propel flows toward safe havens like the USD and CHF.^[82] Sentiment shocks in forex often stem from central bank policy divergences, as exemplified by the Swiss National Bank's abrupt unpegging of the Swiss franc from the euro on January 15, 2015, which triggered a 20-30% franc appreciation and widespread volatility across European pairs due to disrupted expectations.^[83] The forex market's near-24-hour operation heightens the role of retail traders relative to institutions, as social media platforms amplify sentiment through rapid dissemination of news and opinions, often leading to herd behavior among retail participants who comprise a significant portion of trading volume.^[84] This was evident in the 2024 unwind of the yen carry trade following the Bank of Japan's rate hike, where social media-driven panic accelerated yen strengthening and global risk-off flows, contrasting with more measured institutional responses.^[85] Quantitatively, implied volatility from FX options serves as a proxy for market sentiment, with higher levels indicating fear or uncertainty; the JPMorgan Global FX Volatility Index (CVIX), an analog to the VIX for equities, is computed as the square root of the average 1-month at-the-money implied volatility across major currency pairs.^[86]

\text{CVIX} = \sqrt{\frac{1}{N} \sum_{i=1}^{N} \sigma_i^2}

where \sigma_i represents the implied volatility for pair i, and N is the number of pairs in the basket.^[86]

Investor Implications

Trading Strategies

Traders leverage market sentiment to inform offensive strategies that capitalize on psychological extremes, often integrating it as a timing tool alongside other analyses to exploit mispricings driven by crowd behavior. Contrarian investing counters prevailing sentiment by purchasing assets during periods of excessive pessimism, positioning for an anticipated rebound as fear subsides. A key example involves the American Association of Individual Investors (AAII) Sentiment Survey, where bullish readings below 20%—indicating extreme bearishness—have served as reliable buy signals; historical analysis shows the S&P 500 delivering average six-month returns of approximately 14% following such lows, with positive outcomes in every instance examined.^[29] Momentum trading seeks to profit from sustained directional moves amplified by bullish sentiment, entering long positions as positive crowd psychology accelerates price trends. Traders monitor sentiment shifts to ride these waves, often setting stop-loss orders triggered by early signs of reversal, such as deteriorating news tone, to limit exposure when momentum fades.^[87]^[88] Sentiment timing refines trade execution by overlaying sentiment data on fundamental valuations, entering positions only when both align to confirm undervaluation or overvaluation. For instance, traders may forgo unhedged directional bets when the VIX surpasses 30—signaling elevated fear and volatility—opting instead for hedged approaches to navigate the heightened risk of whipsaws.^[89]^[90] Algorithmic approaches automate sentiment exploitation through bots that parse news feeds and textual data via natural language processing to score market mood in real time, generating buy or sell signals. Backtested models incorporating news sentiment have demonstrated alpha generation with enhanced returns during volatile periods, outperforming benchmarks by capturing short-term inefficiencies.^[91] These strategies, however, are prone to risks such as false signals in entrenched trends, where sentiment extremes persist longer than typical, leading to premature entries or exits. In 2020, for example, early bullish interpretations of sentiment amid the COVID-19 downturn created bull traps—false upward breakouts that reversed sharply—trapping optimistic traders in losses before the broader recovery materialized.^[92]^[93]

Risk Assessment Techniques

Market sentiment serves as a key barometer for assessing investment risks by signaling potential bubbles when bullishness reaches extreme levels. High levels of investor optimism often precede market corrections, as excessive positivity can detach asset prices from underlying fundamentals, increasing vulnerability to downturns. For instance, studies have shown that surges in investor sentiment significantly explain the probability and expansion of stock market bubbles, with optimistic sentiment amplifying price deviations from intrinsic values.^[94] Research treats elevated CAPE values alongside sentiment proxies as indicators of impending reversals, as seen in historical bubbles.^[95] In stress testing, market sentiment informs simulations of bearish scenarios to evaluate portfolio resilience under adverse conditions. By incorporating sentiment-driven volatility, financial institutions can model how shifts from euphoria to pessimism might exacerbate losses, revealing hidden exposures in normal-state assessments. Empirical evidence confirms that sentiment influences VaR through its impact on return volatility and cross-sectional risks.^[96] This method enhances the robustness of stress tests by capturing behavioral amplifiers of market shocks. Portfolio diversification strategies leverage sentiment readings to balance exposure to sentiment-sensitive assets, mitigating risks from correlated downturns. In periods of bearish sentiment, investors may reduce allocations to equities, which are prone to sentiment swings, and shift toward less volatile assets like bonds or commodities to maintain equilibrium. Research demonstrates that sentiment-aware diversification, such as using news-based sentiment scores in portfolio optimization, improves risk-adjusted returns by avoiding overconcentration in high-sentiment assets that later underperform.^[97] Dynamic adjustments based on diversification patterns in investor portfolios further serve as a sentiment gauge, prompting rebalancing when sentiment extremes signal reduced hedging effectiveness.^[98] Sentiment provides early warnings of drawdowns when it diverges from economic fundamentals, offering a predictive edge for risk management. Notable divergences, such as overly optimistic housing market sentiment in the mid-2000s despite weakening fundamentals like rising subprime defaults, foreshadowed the 2007-2008 crisis; surveys from that era revealed widespread investor overconfidence in perpetual price appreciation, ignoring affordability strains and credit risks, which contributed to the bubble's burst and subsequent market collapse.^[99] Such gaps between sentiment and metrics like income growth or leverage ratios have been shown to reliably anticipate equity drawdowns, enabling proactive hedging. Post-2008 regulatory frameworks, including Basel III, emphasize systemic risk monitoring. Academic analyses post-crisis integrate sentiment into systemic risk measures, such as spillover models, to assess contagion risks across institutions, aligning with macroprudential goals.^[100] This incorporation helps regulators mitigate moral hazard and ensure resilience against sentiment-fueled crises.

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Understanding Bear Markets: Phases, Examples, and Investment Tips
A bear market occurs when financial markets experience a decline of 20% or more. This is often marked by falling prices and growing investor pessimism. These ...
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London suffers third biggest fall in 'great crash of 2008' - The Guardian
Oct 10, 2008 · "What we are witnessing is mass selling on a global scale due to a combination of sheer panic and fear, combined with complete uncertainty over ...
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Understanding Market Sentiment: Definition, Indicators, and Analysis
Nov 22, 2024 · Trader Insight: Bullish sentiment can support uptrends, but extreme optimism often leads to overbought markets and increased correction risk.Missing: transition | Show results with:transition
[18]
Analyze Stock Market Sentiment To See What Others Are Thinking ...
Aug 5, 2025 · Bullish sentiment above 50–55% for multiple weeks has often preceded short-term peaks. Bearish sentiment above 50% has historically occurred ...
[19]
SEC says GameStop meme stock frenzy fueled by 'investor sentiment'
Oct 18, 2021 · SEC says GameStop meme stock frenzy fueled by 'investor sentiment' ... "The extreme volatility in meme stocks in January 2021 tested the ...
[20]
Why the 2022 'crypto winter' is unlike previous bear markets - CNBC
Jul 13, 2022 · Cryptocurrencies have suffered a brutal comedown this year, losing $2 trillion in value since the height of a massive rally in 2021.
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[PDF] Cboe Volatility Index® Mathematics Methodology
Introduction. This document covers the mathematics of calculations for the VIX® Index and other Cboe volatility indices that use this methodology.
[22]
VIX Volatility Products - Cboe Global Markets
The Cboe Volatility Index® (VIX® Index) is a leading measure of market expectations of near-term volatility conveyed by S&P 500 Index®(SPX) option prices.VIX FAQs · VIX Options · Historical Data · VIX Futures
[23]
Understanding the CBOE Volatility Index (VIX) in Investing
Oct 21, 2025 · The CBOE Volatility Index (VIX) measures the market's expectation of 30-day volatility for the S&P 500. VIX values above 30 indicate greater ...How It Works · Applying VIX Volatility to the... · How VIX Values Are Calculated
[24]
Put-Call Ratio Meaning and How to Use It to Gauge Market Sentiment
Apr 26, 2025 · The put-call ratio is an indicator of whether investors as a whole are placing more bets on prices falling or rising.
[25]
How to Trade Better with the Put/Call Ratio
For instance, a systematic strategy might involve buying stocks when the ratio falls below 0.7 (signaling bullish sentiment) and selling when it rises above 1.2 ...<|separator|>
[26]
Advance-Decline Line - Overview, Formula, Example
The advance-decline line (ADL) is a technical indicator that plots the difference between the number of advancing stocks to declining stocks on a daily basis.
[27]
High-Low Index - ChartSchool - StockCharts.com
Jun 7, 2024 · In general, a stock index is deemed strong (bullish) when the High-Low Index is above 50, which means new highs outnumber new lows. Conversely, ...
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Use The VIX To Measure Fear And Confirm Key Stock Market Bottoms
At its peak during the coronavirus bear market, the VIX topped out at 85.47 on March 18, 2020, just points away from the 89.53 peak price that occurred during ...
[29]
Is the AAII Sentiment Survey a Contrarian Indicator?
Most of the time, the readings from our survey provide insight into the moods of individual investors, but are well within their typical historical ranges.Historical Sentiment... · Bullish Sentiment · Sentiment's Role As A Market...Missing: methodology | Show results with:methodology
[30]
[PDF] An Evaluation of Investor Sentiment Measures - NYU Stern
Abstract: This paper evaluates investment sentiment measures by testing their predictability for future returns. Different measures are combined to form ...
[31]
AAII Investor Sentiment Survey
A bullish indicator means there's a general investor sentiment that a stock or market will trend upward based on signals, patterns or the overall tone of the ...Historical Data · Model Portfolios · The AAII Journal · AAII Stock Screens
[32]
Sentiment Survey Past Results | AAII
The sentiment survey measures the percentage of individual investors who are bullish, bearish, and neutral on the stock market short term.
[33]
Help | Advisors Sentiment - Investors Intelligence
The Advisors Sentiment report surveys the market views of over 100 independent investment newsletters (those not affiliated with brokerage houses or mutual ...
[34]
Investors Intelligence Sentiment Index (Advisor & Strategy)
bullish, bearish, or correction.
[35]
Fear and Greed Index - Investor Sentiment - CNN
The Fear & Greed Index uses junk bond demand as a signal for Greed. FEAR ... Every component and the Index are calculated as soon as new data becomes available.
[36]
https://money.cnn.com/data/fear-and-greed/
[37]
Commitments of Traders | CFTC
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COT Report for Traders | MRKT Positioning Dashboard
Sep 9, 2025 · A positive net position means speculators are predominantly long (bullish), while a negative net position signals net shorts (bearish). Many ...<|separator|>
[39]
S&P 500 COT Index - MacroMicro
An upward trend in the index indicates bullish sentiment among large traders in the S&P 500 market, while a downward trend suggests bearish sentiment. Note: The ...
[40]
Latest Archive - AAII
AAII Sentiment Survey: Bullish Sentiment Jumps to a 58-Week High February 09 2023 by AAII Staff Bullish sentiment rose to a 58-week high, while bearish ...Missing: preceding | Show results with:preceding
[41]
Differences between NZ and U.S. individual investor sentiment
Aug 2, 2023 · Another word of caution is that both surveys allow members to participate 'whenever they feel like it' and thus a degree of self-selection bias ...
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[PDF] Style investing - Nicholas Barberis
We study asset prices in an economy where some investors categorize risky assets into different styles and move funds among these styles depending on their ...
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[PDF] A Model of Investor Sentiment
The model is based on psychological evidence and produces both underreaction and overreaction for a wide range of parameter values. Nicholas Barberis. Graduate ...Missing: 2003 | Show results with:2003
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[PDF] In Search of Attention - University of Notre Dame
We propose a new and direct measure of investor attention using search frequency in Google (Search Volume Index (SVI)). In a sample of Russell 3000 stocks ...Missing: amplification volatility<|separator|>
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Retail investor attention and herding behavior - ScienceDirect.com
The degree of investor attention to a particular underlying company is positively linked with herding behavior for retail investors.Missing: surges | Show results with:surges
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[PDF] Institutional Investor Attention and Underreaction to News
We find that larger firms with greater analyst coverage are associated with significantly more institutional attention shocks, on average. The results are ...
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Informational role of social media: Evidence from Twitter sentiment
This paper examines the information content of firm-specific sentiment extracted from Twitter messages. We find that Twitter sentiment predicts stock returns ...
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Attention-augmented hybrid CNN-LSTM model for social media ...
Sep 26, 2025 · This conversation is used as a monitoring tool to predict crypto prices based on public sentiments. According to the existing research, ...
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[PDF] A Mind is a Terrible Thing to Change: Confirmatory Bias in Financial ...
Aug 29, 2016 · This paper studies the impact of the confirmatory bias on financial markets. Building on Rabin and Schrag (1999), we propose a model in which ...
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[PDF] Herd Behavior in Financial Markets
Herding clearly leads to correlated trading, but the reverse need not be true. The LSV measure is based on trades conducted by a subset of market partic-.
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[PDF] Prospect Theory: An Analysis of Decision under Risk - MIT
This tendency, called the certainty effect, contributes to risk aversion in choices involving sure gains and to risk seeking in choices involving sure losses.Missing: sentiment | Show results with:sentiment
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[PDF] Investor Sentiment in the Stock Market - NYU Stern
This paper outlines a “top down” approach to behavioral finance and the stock market. ... “Consumer Confidence and Asset Prices: Some Empirical Evidence.
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(PDF) The Impact of the Federal Reserve Raises the Interest Rates ...
The Federal Reserve announced a 75-basis point rate hike to raise the benchmark rate to a range of 2.25%-2.50% to curb another spike in inflation on July 28.<|separator|>
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Impact of Federal Reserve Interest Rate Changes - Investopedia
When the Fed cuts rates, yields on everything from U.S. Treasurys to corporate bonds tend to fall, making them less attractive to new investors. Bond prices ...
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Macroeconomic Indicators That Affect the US Stock Market
Macroeconomic factors like GDP, inflation, employment, and retail sales affect the value of your portfolio. Understanding these economic indicators is vital ...
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[PDF] impacts of the russian invasion of ukraine on financial market ...
The shock from Russia's invasion of Ukraine has cast doubt on a strong global economic recovery from the COVID-19. Considerable uncertainty remains for global ...
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Inflation and Trading - ScienceDirect.com
We study how investors respond to inflation combining a customized survey experiment with trading data at a time of historically high inflation.
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Gauging the Sentiment of Federal Open Market Committee ...
Jul 7, 2025 · The surprises in the sentiment index around FOMC meetings announcements explain variation in major asset prices classes between May 1999 and ...
[59]
What is forward guidance, and how is it used in the Federal ...
Aug 22, 2025 · Forward guidance is a tool that central banks use to tell the public about the likely future course of monetary policy.
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[PDF] News versus Sentiment: Predicting Stock Returns from News Stories
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[PDF] Measuring News Sentiment - Federal Reserve Bank of San Francisco
Mar 13, 2020 · News sentiment is measured using text analysis tools, a new time-series measure, and a new lexicon built specifically for economic news ...
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Social informedness and investor sentiment in the GameStop short ...
May 23, 2023 · To assess how market activities changed during the squeeze, we will examine the effects of social media activities on stock market transactions.
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Full article: Implications of analyst recommendations on stock market
This comprehensive review paper offers scholarly insights into financial analysts' recommendations and their impact on stock markets.
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Does fake news impact stock returns? Evidence from US and EU ...
Negative fake news has a significant short-term negative impact on stock returns, while positive or neutral news has no clear impact.
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[PDF] Stock Prediction Using Twitter Sentiment Analysis | CS229
The method uses Twitter sentiment analysis to predict public mood, then uses this mood and past DJIA values to predict future stock movements.Missing: NLP scholarly
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The Role of Finfluencers in Shaping Crowd Sentiment
Jun 2, 2025 · The paper utilizes empirical data to investigate finfluencers – key actors in financial social media – and their impact on crowd sentiment.
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(PDF) TikTok Influence on Affect Option Prices: Attention Dynamics ...
Sep 18, 2025 · Results indicate that sudden internet popularity can significantly increase a stock's option price and implied volatility even without new ...
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Earnings Extrapolation and Predictable Stock Market Returns
Mar 31, 2025 · The market return in the first month of a quarter is strongly negatively predicted by the return in the preceding 12 months, whereas the returns ...
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How Traders Are Using Sector Rotation to Plan Trades - Investopedia
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Feature: Investor Sentiment as a Contrarian Indicator - AAII
For example, bearish sentiment reached a record high of 70.3% on March 5, 2009, just as the bear market was reaching a bottom. The current historical ...
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[PDF] Trading the market's mood - Fidelity Investments
FIGURE 1: SENTIMENT EXTREMES. Extreme AAII survey readings are contrarian trade signals, according to conventional wisdom. However, investors were bearish at ...
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World markets on oil watch as Middle East tensions flare | Reuters
Jun 20, 2025 · Blocked shipping routes would compound any supply shock, as any increased output from OPEC+ may not reach the international market, said hedge ...
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Is gold a hedge or a safe haven against stock markets? Evidence ...
Gold is a strong safe haven if these hold under market turmoil. We empirically examine the property of gold in 24 countries for a sample period spanning over 40 ...
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Public attention, oil and gold markets during the COVID-19
Apart from macroeconomic fundamentals, supply and demand, the impact of sentiment on crude oil and gold markets has also garnered increased scholarly interest ( ...
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Enhancing Trading Performance Through Sentiment Analysis with ...
Jul 13, 2025 · These results demonstrate that integrating sentiment analysis with technical indicators enhances trading performance, with GPT-2 excelling ...
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[PDF] Explaining US Stock Market Directions using Technical Indicators ...
Sep 15, 2021 · This paper provides a stock market forecasting model that utilizes technical indicators and investor sentiment as part of the analysis. We ...
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AI gave tech giants a $2.4 trillion boost to their market caps in 2023
Oct 17, 2023 · US tech giants added $2.4 trillion to their market capitalizations in a year defined by the hype around generative artificial intelligence.Missing: sentiment | Show results with:sentiment
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How Retail vs. Institutional Investor Sentiment Differ in Affecting ...
The impact of retail and institutional investor sentiment on stock returns differs due to the disparities in their market influence and investment goals.
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[PDF] Understanding Currency Carry Trades
Dec 19, 2024 · Depending on the mandate and risk appetite, hedge funds, asset managers, and individual investors could all be applying carry trade strategies.
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[PDF] Flight to Safety or Flight from Carry?* - Harvard Business School
By contrast, so-called carry-driven flows are more sensitive to relative interest rate differentials and are often triggered by changes in risk appetite, ...
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[PDF] and the challenge of assessing its evolution by risk appetite indicators
The dynamic stance of the risk appetite of market participants as a sentiment could thus serve as an important contributing factor in the transmission of ...
[82]
Exchange rates and prices: evidence from the 2015 Swiss franc ...
Oct 3, 2018 · We find that border prices invoiced in Swiss francs fell on average by 5% in response to the roughly 15% appreciation of the Swiss franc against the euro by ...<|control11|><|separator|>
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[PDF] Social Communication and FX Retail Trading
Aug 12, 2022 · I also highlight the role of social communication in altering retail traders' return patters and market persistence.
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Unwind of massive yen-funded carry has room to go, analysts say
Aug 6, 2024 · An epic unwinding of the yen-funded carry trade that has reverberated through global markets may have further to go, analysts said on ...<|control11|><|separator|>
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FX volatility indices: useful or not? - Euromoney
Jul 4, 2024 · There is no single measure of implied volatility for FX ... (CVix), using option pricing to measure implied volatility for certain currencies.Missing: proxy | Show results with:proxy
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Momentum Trading: Types, Strategies and More – Part I
Apr 11, 2024 · Bullish sentiment may fuel upward momentum, while bearish sentiment may lead to downward momentum. Momentum traders often monitor sentiment ...Missing: waves | Show results with:waves
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A Comprehensive Guide to Momentum Trading Strategies and Tips
Stop losses are crucial in momentum trading because they protect against sudden reversals that can quickly erode gains. They help traders limit losses by ...
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How to Blend Technical and Fundamental Analysis for Better Market ...
While fundamental analysis often focuses on long-term trends, technical analysis can aid in timing trades by revealing short-term price movements and favorable ...
[89]
How Smart Money Uses the VIX to Beat Market Panic
Jun 30, 2025 · Here's the simple truth: VIX below 15 = complacency (time to be cautious). VIX above 30 = panic (time to be opportunistic). 2025's Story: A ...
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Understanding Bull Traps: Avoid Investment Pitfalls in Bear Markets
From a psychological standpoint, bull traps occur when bulls fail to support a rally above a breakout level, which could be due to a lack of momentum and/or ...
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Decoding Bull Traps: How to Avoid False Bullish Signals
Oct 24, 2025 · A bull trap occurs when a market appears to break higher, drawing in buyers, only for the price to reverse sharply and decline.
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Does Investor Sentiment Drive Stock Market Bubbles? Beware of ...
Apr 29, 2019 · The results indicate that investor sentiment significantly explains stock bubble probability and bubble expansion.
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[PDF] Buffett's indicator and SHiller's CAPE ratio as sentiment proxies
“The CAPE ratio can predict anomaly (size, value, momentum, profitability and investment factors) returns across different countries (the US, the UK, the ...
[94]
Value at risk, cross-sectional returns and the role of investor sentiment
In this paper, we find that the relationship between the value-at-risk (VaR) and expected returns is negative and this negative relationship between the VaR ...
[95]
A novel approach to portfolio selection using news volume and ...
Aug 8, 2023 · In this study, we develop a novel approach to portfolio diversification by integrating information on news volume and sentiment with the k-nearest neighbors ( ...<|separator|>
[96]
Portfolio Diversification Dynamics as a Measure of Market Sentiment
Sep 15, 2011 · We build an original market sentiment index based solely on changes over time in the number of different stocks held by individual investors.
[97]
[PDF] Optimism and Pessimism About the U.S. Housing Market Before the ...
Optimism about future house price growth shoulders much of the blame for the mortgage crisis that swamped the U.S. and world economies starting in 2007. Had ...
[98]
[PDF] measuring systemic risk through investor sentiment
Oct 15, 2015 · In this paper, I develop a systemic risk measure derived from investor sentiment that has predictive power over future economic activity and ...
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Systemic risk spillovers incorporating investor sentiment
This study investigates systemic risk spillovers across 49 Chinese financial institutions through incorporating investor sentiment.