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Noise Trader Risk in Financial Markets
Abstract
The authors present a simple overlapping generations model of an asset market in which irrational noise traders with erroneous stochastic beliefs both affect prices and earn higher expected returns. The unpredictability of noise traders' beliefs creates a risk in the price of the asset that deters rational arbitrageurs from aggressively betting against them. As a result, prices can diverge significantly from fundamental values even in the absence of fundamental risk. Moreover, bearing a disproportionate amount of risk that they themselves create enables noise traders to earn a higher expected return than rational investors do. The model sheds light on a number of financial anomalies. Copyright 1990 by University of Chicago Press.
... Behavioral finance theory points out that irrational individual investors are likely to be swayed by emotions, thus making asset prices deviate from fundamentals. De Long et al. [1] believe that investor sentiment is a systemic risk affecting the pricing of the capital market, and the influence of market investor sentiment on the cross-section and time series characteristics of stock returns has therefore become a topic of great research significance. In addition, emotionally retail traders tend to act collectively, who tend to trade the same stock at the same time and in the same direction. ...
... De Long [1] first put forward: investor sentiment is a key factor must be considered in the process of capital asset pricing, put forward the famous DSSW model, that investors is not the neoclassical economics think rational, not the interference of emotion, calm and rational analysis of valuable information, strictly in strict accordance with the theoretical model to calculate the reasonable price of securities. There are many noise traders in the actual stock market, and the investment behavior will be seriously restricted by the investment sentiment. ...
... If high uncertainty is associated with high pricing when sentiment is high, one should expect a negative coefficient of the interaction term, reflecting a larger subsequent correction of mispricing. Figure 1 shows the parameter estimation of the market cumulative excess return forecast using equation (1) for the next 12 months, that is, the prediction ability of sentiment to return reversal β 1 .High and uncertainty correspond to parts of the uncertainty proxy variable distribution above the 90th and below the lower 10th percentile, respectively, with CISCI on the X-axis and the cumulative excess return of the market over the next 12 months on the Y-axis. When TVOL is used to measure the degree of uncertainty, under the condition of high uncertainty, the decline of sentiment predicts the increase of income. ...
Due to internal and external events, the stock market will fluctuate frequently, which makes investors face the uncertainty of the stock market. However, the majority of investors in Chinese stock market are retail investors. As an important investor noise, investors will make irrational investment behavior when the uncertainty is high. Therefore, it is necessary to analyze the correlation between uncertainty, investor sentiment and the stock market yield. Based on this, this paper chooses the Shanghai Composite Index as the research object, and uses a simple time series prediction regression to explore the prediction ability of investor sentiment to predict the cumulative excess returns of the future stock market under different degrees of stock market uncertainty. The empirical results suggest that investor sentiment, amid high stock market uncertainty, will lead to greater mispricing, thus leading to more corrections in the future.
... First, the sentiment connectedness among firms is studied, and a corresponding sentiment spillover network is constructed. While many prior studies focus on the economic impacts of investor sentiments (see, e.g., Kumar and Lee (2006) [45], De et al. (1990) [46], and Han (2008) [47]), they overlook the interplay of individual sentiments. This paper investigates the sentiment interactions between firms using the network structure and measures the investor sentiment connectedness with a novel aggregated centrality index. ...
... First, the sentiment connectedness among firms is studied, and a corresponding sentiment spillover network is constructed. While many prior studies focus on the economic impacts of investor sentiments (see, e.g., Kumar and Lee (2006) [45], De et al. (1990) [46], and Han (2008) [47]), they overlook the interplay of individual sentiments. This paper investigates the sentiment interactions between firms using the network structure and measures the investor sentiment connectedness with a novel aggregated centrality index. ...
... During severe market stress, investor emotions become highly divided, causing investors to act similarly. This process quickly spreads sentiment shocks across the market [46,75]. As a result, the effect of sentiment changes on stock prices becomes weaker. ...
Using a sample of S&P 500 stocks, this paper examines the investor sentiment spillover network between firms and assesses how the sentiment connectedness in the network impacts stock price crash risk. We demonstrate that firms with higher sentiment connectedness are more likely to crash as they spread more irrational sentiment signals and are more sensitive to investor behaviors. Notably, we find that the effect of investor sentiment on crash risk mainly stems from sentiment connectedness among firms rather than firms’ individual sentiment, especially when market sentiment is surging or declining. These findings remain robust after controlling for other determinants of crash risk, including stock price synchronicity, accounting conservatism, and internal corporate governance strength. Our results underscore the importance of sentiment connectedness among firms and provide valuable insights for risk management among investors and regulatory authorities involved in monitoring risk.
... Investors' attitudes and perceptions are reflected in stock prices through daily trading activities leading to increases and decreases in stock prices and stock market anomalies (Kurov, 2008). Keynes (1936) and De Long et al. (1990) argue that investors' 'animal spirits' lead stock prices to fluctuate from their fundamental value, which draws irrational investors into the market, making it more difficult to eradicate mispricing, as prices do not return to their equilibrium level. ...
The objective of the study is to examine the effects of investor sentiment on the Johannesburg Stock Exchange (JSE) index returns in bull and bear market conditions. Accordingly, this study uses monthly data to construct a new market-wide investor sentiment index and test its effects on the JSE aggregated and disaggregated index returns in alternating market conditions for the period March 2007 to January 2024. The findings of the Markov regime-switching model reveal that when the JSE is in a bull market condition, the JSE oil and gas sector returns and the JSE telecommunication sector returns are affected positively by investor sentiment. Similarly, in a bearish state, the JSE health sector returns and JSE telecommunication sector returns are negatively affected by investor sentiment. Collectively, the findings suggest that the effects of investor sentiment on JSE index returns are regime-specific and time-varying, such that they are dependent on the market conditions (bull or bear) and the type of JSE index (aggregated or disaggregated index). Accordingly, investors must consider this information to ensure resilient investment decisions and risk management strategies in sentiment-induced markets and alternating market conditions.
... Simply put, noise traders, albeit known as liquidity traders, cannot consistently beat the markets, as their success is often due to luck and taking more risks. As long as taking risks is rewarded by the market, they will earn money [25]. Although some new evidence from Bogousslavsky and Muravyey [26] shows that retail traders may not just be uninformed traders who lose money, they still might not engage in sustainable investment planning. ...
... De Long et al. (1990) proposed a noise trader model to explain how irrational investors, driven by sentiment, can create price movements unrelated to fundamentals, resulting in excess volatility. Their work laid the groundwork for sentiment-driven asset pricing models. ...
With an emphasis on the psychological and empirical factors influencing investing choices, this study examines the complex link between investor sentiment and stock market volatility in the Indian setting. Key psychological elements including media impact, herd behaviour, and risk aversion are identified as important determinants of investor mood in this study, which uses a descriptive research approach and primary data gathered from 100 individual investors using structured questionnaires. Strong positive relationships between investor sentiment and market performance were found through empirical study, which included correlation and regression approaches. This suggests that emotional and cognitive biases have a substantial influence on stock market movements. The results support improved investor education and the use of sentiment research techniques, and they highlight the necessity of integrating behavioural finance insights into investing decision-making. This study adds to the expanding corpus of behavioural finance knowledge and provides useful advice for investors, advisors, and legislators on how to handle sentiment-driven market volatility.
... Kyle's (1985) noise trader model shows how the behaviour of noise traders impacts market prices and liquidity. De Long et al. (1990) proposes that higher investor sentiment causes increased noise trading, highlighting how sentiment affects market liquidity, volatility, and price discovery. Combining the insights from both models, it can be deduced that higher investor sentiment can result in higher noise trading and liquidity (Liu, 2015). ...
Behavioural finance theories contend that market anomalies, driven by human biases and heuristics, link liquidity to investor sentiment in asset markets. The irrational investor underreaction increases liquidity and explains the time-series relationship between liquidity and market returns. Based on data from the Colombo Stock Exchange (CSE), this study examines the extent to which market-wide illiquidity and sentiment proxied by turnover measure can forecast the short-term expected returns of the frontier market Sri Lanka during the period 2010-2021, using OLS time series regression methodology. Research findings show that investor sentiment proxied by turnover is positively related to expected market returns, contrary to the observations based on the US market. Regression estimates indicate that expected illiquidity significantly negatively impacts expected returns in a value-weighted specification. Unexpected illiquidity shocks depress the contemporaneous market returns. Small-cap stock returns show greater sensitivity to market illiquidity indicating that they face greater illiquidity risk compared to large-cap stocks. These findings offer insight into how investor sentiment can influence market liquidity and the impact of liquidity risk pricing on market returns over time in a frontier market.
... Thus, market volatility during difficult times is highly influenced by investors' sentiments (Huynh et al., 2021), leading them to choose the safest options for investment, such as gold, in the crisis period. According to psychological economics, humans make their decisions based on their emotions (Elster, 1998;Loewenstein, 2000), which can sometimes be rational or irrational according to behavioral finance studies (Forlani, 2002;Kahneman, 2003) and psychological factors drive investor behavior, causing increased demand for gold as a perceived haven during crises (Barberis et al., 2006;De Long et al., 1990). However, decisions can also be based on preferences or foreseen risks (Finucane et al., 2000;Nasir et al., 2021). ...
This research examines how significant world events affect the gold future commodity market, giving investors a grasp of the fundamental dynamics influencing precious metal investments in precarious market conditions. This paper evaluates the risk and return of gold futures in India from 2020 to 2024, focusing on significant events - COVID-19 pandemic, US Presidential election, Russia-Ukraine war, G20 summit, Israel-Hamas conflict, Ram Mandir inauguration, and the 2024 Assembly elections. Risk is measured using Garman- Klass Volatility, while returns are analysed using event study method using 1210 daily data points from the multi commodity exchange (MCX India). The market experienced a significant positive abnormal return in the adjustment window of the first wave of COVID-19, followed by the Russia-Ukraine and Israel Hamas wars and sensitive to geopolitical events providing policymakers and investors with insightful insights to improve their portfolio management during economic and political unpredictability. This study examines the impact of seven major global events on gold futures including the latest occurrences of 2024. Unlike previous research, which has often focused on historical events, this study integrates a comprehensive analysis of adverse and positive global disruptions, offering a fresh perspective on gold’s role as a haven asset and its price volatility.
... The items that loaded onto Scale6 correspond to this tendency and it was therefore labelled trading knowledge. The factor labelled Scale7_ self-attribution was named as such because the items loading onto this construct reflect the common tendency of investor to mistakenly attribute their success to their own ability (see also , De Long et al., 1990). Another construct of overconfidence bias was termed as Scale9_ self-confidence because its items correspond to susceptibility to self-confidence bias, which is a variant of better-than-average effect within overconfidence bias. ...
The purpose of this paper was to describe the development of a survey instrument designed to directly measure the effects of the underlying psychological constructs on the behaviour of retail-investors engaging in stock trading at the Dar es Salaam stock exchange. The paper adopts a survey research approach to outline the process of developing, validating and testing as survey instrument to help researchers better understand how retail investors make decisions when trading stocks. This study is guided by six behavioural theories, namely availability bias, representativeness bias, overconfidence bias, ambiguity aversion, regret aversion, and loss aversion. Empirical data for these variables were collected through a questionnaire administered to a sample of 280 respondents. Convenience and snowball sampling techniques were employed to recruit participants. To ensure reliability, the study employed the Cronbach’s alpha (α) coefficient, while Principal Component Analysis (PCA) was performed to assess the construct validity of various behavioural biases. The results indicate that the final survey instrument comprises 49 items developed from eight behavioural constructs, organised into seventeen scales, all of which demonstrate acceptable levels of content validity, reliability and construct validity. The paper concludes that the developed instrument is valuable for both academic and practitioner communities, particularly interested in studying trading behaviour of retail stock investors. The primary contribution of this study is the development of a reliable instrument for measuring retail investor trading behaviour in frontier markets. Therefore, the study recommends that researchers, professionals, policy makers and other stakeholders to utilise these constructs in future investigations of investors trading behaviour and into educational programmes to mitigate the negative impacts of biases on decision-making.
... The emergence of the agent-based computational approach in asset pricing was initially motivated on the one hand by observations of empirical regularities in asset returns that were hard to reconcile with the distributional properties of fundamental values, and on the other hand by the developments in the so-called noise trader literature (Long et al., 1990). As the idea of non-linear chaotic systems was explored in natural sciences, some researchers in economics and fi nance began to wonder whether macroeconomic and fi nancial time series were also characterized by the presence of chaos (Brock, 1993), loosely defi ned as a dynamic system the behaviour of which is highly sensitive to initial conditions. ...
Since the 1980s asset pricing in the traditional neoclassical paradigm has been confronting empirical evidence contradicting both the predictions of the models as well as their microfoundations. Simultaneously, the market microstructure literature started probing the details of the trading process, turning the spotlight onto the effects of asymmetric information, clearing mechanisms and agents’ learning and belief formation. These details, which were “abstracted away” in the earlier models, are becoming ever more important as the complexity of markets grows due to proliferation of algorithmic and high frequency trading and markets turn into ecologies of strategic, but not necessarily perfectly rational, co-evolving agents. In this review article I argue that the paradigms of agent-based and evolutionary finance are ideally suited to handle the modelling of markets as these complex ecologies. I review the most prominent contributions of evolutionary and agent- based modelling to asset pricing, specifically, categorizing them into three main streams: the research on the effects of institutional details of the markets, the research on the effects of agent heterogeneity, and the research of market selection. Furthermore, I argue that further progress can be made by combining the evolutionary and agent-based paradigms and highlight research questions for which such a mixed-method approach is likely to be the most fruitful.
С 1980-х годов традиционная неоклассическая парадигма оценки активов сталкивалась с эмпирическими данными, ставящими под сомнение как предсказания моделей, так и их микроэкономические основания. В то же время исследователи микроструктуры финансовых рынков начали изучать детали транзакционного процесса, обращая внимание на эффекты асимметричной информации, рыночных механизмов, а также на обучение и формирование убеждений агентов. Эти детали, которым не уделялось должного внимания в традиционных моделях, становятся все более важными в изучении современных финансовых рынков, превратившихся в сложные экосистемы, в которых взаимодействуют стратегические, но необязательно идеально рациональные, коэволюционирующие агенты. В этом обзоре литературы, посвященном эволюционному и агент-ориентированному моделированию финансовых рынков, рассматриваются наиболее значимые результаты исследований в данном направлении. Предложена классификация исследований, подразделяющая их на три направления: исследования влияния институциональной составляющей финансовых рынков на оценку активов, исследования влияния гетерогенности агентов и исследования, связанные с теорией рыночного отбора. Также в работе приводятся аргументы в пользу сочетания эволюционного и агент- ориентированного моделирования в изучении финансовых рынков и выявляются открытые вопросы в теории оценки активов, изучение которых в эволюционной и агент-ориентированной парадигме было бы, по мнению автора, наиболее продуктивным.
... For example, the work of LeBaron (2000) has demonstrated that ABMs can generate asset price series that exhibit excess volatility similar to that observed in real markets, a feature that emerges naturally from the interaction of agents following simple trading rules, including those based on noise and information misinterpretation. This aligns with the findings of De Long et al. (1990). This further suggests that the actions of noise traders can contribute to market volatility. ...
This master thesis explores the reproduction of rough volatility, characterized by the
jagged, discontinuous paths observed in financial market volatility, typically modeled by
a fractional Brownian motion with Hurst exponent less than 0.5. Using an agent-based
model, we investigate how micro-level interactions within a simulated limit order book can
generate macro-level volatility patterns that resemble patterns of rough volatility of asset
prices. By varying key market parameters, we provide an account of the mechanisms that
contribute to the emergence of rough volatility and its implications for market behavior.
... For instance, bubbles arise in infinite-horizon growing economies with rational traders (O'Connell and Zeldes 1988;Tirole 1985;Weil 1989). Bubbles can also emerge in economies where rational traders hold divergent beliefs or behave myopically (Tirole 1982) or in markets with irrational traders (De-Long et al. 1990). Moreover, bubbles can occur when arbitrageurs are unable to synchronize their trades (Abreu and Brunnermeier 2003) or in markets with constraints on borrowing (Santos and Woodford 1997;Scheinkman and Xiong 2004). ...
Deviation of an asset price from its fundamental value, commonly referred to as a price bubble, is a well-known phenomenon in financial markets. Mathematically, a bubble arises when the deflated price process transitions from a martingale to a strict local martingale. This paper explores price bubbles using the framework of optional semimartingale calculus within nonstandard probability spaces, where the underlying filtration is not necessarily right-continuous or complete. We present two formulations for financial markets with bubbles: one in which asset prices are modeled as càdlàg semimartingales and another where they are modeled as làdlàg semimartingales. In both models, we demonstrate that the formation and re-emergence of price bubbles are intrinsically tied to the lack of right continuity in the underlying filtration. These theoretical findings are illustrated with practical examples, offering novel insights into bubble dynamics that hold significance for both academics and practitioners in the field of mathematical finance.
We examine how investor sentiment influences KOSPI 200 futures mispricing under varying market conditions, including volatility, short‐sale restrictions, and regulatory changes. The sentiment–mispricing relationship intensifies during high volatility, as arbitrageurs face greater constraints in correcting price distortions, leading to increased mispricing. Short‐sale restrictions further amplify mispricing and the impact of pessimistic sentiment. Regulatory reforms reducing domestic trading activity also shape sentiment–mispricing dynamics, suggesting that lower participation by irrational investors enhances market efficiency. Our findings underscore the importance of market‐specific factors—arbitrage constraints and regulations—in understanding sentiment‐driven mispricing.
This study examines the evolutionary aspects of portfolio investment involving risky and risk-free assets in an uncertain market environment. We consider an investment game in which profit maximization by adjusting the number of risky assets in the portfolio is the basis of investment dynamics, as is similarly found in the governing rule for resource competition between hawks and doves in population dynamics. The investment dynamics exhibit a stable Nash equilibrium in which investors who hold as many risky assets as feasible accumulate more wealth in the repeated game. Since an increase in individual investor wealth owes much to risk-taking behavior, investment dynamics are susceptible to recurrent bubbles and crises. Given that wealth accumulation directly results from increased risky assets, the portfolio choice for risky assets seems rational (i.e., optimal) when investors face uncertainty. This situation provides insight into why financial institutions and investors are exposed to financial fragility and structural instability during periods of economic uncertainty.
Prior studies have explored the link between ESG ratings and firms' financial performance, but few have focused on its interplay with investor sentiment. This study examines how investor sentiment moderates the relationship between ESG ratings and financial performance. We use a fixed‐effect regression model applied to panel data from Chinese listed firms from 2011 to 2020. We find strong evidence that investor sentiment can reinforce the impact of ESG ratings on financial performance. We further investigate how investor sentiment moderates the relationships between the dimensions of ESG—environmental (E), social (S), and governance (G)—performance, and financial performance, respectively. Evidence shows that investor sentiment can enhance the negative impact of E on financial performance, while it enhances the positive impacts of S and G on financial performance. Our results are robust across various econometric techniques, including generalised‐least‐squares regression, first‐difference regression, and system‐generalised method of moments estimation. We provide insights for firm managers on leveraging investor sentiment to enhance ESG strategies to improve financial performance and offer implications for policymakers in crafting effective regulations that account for investor sentiment dynamics.
Background of the study
This research examines how psychological and social biases affect individual investors’ investing decisions. Investor sentiment significantly influences financial markets, frequently causing stock prices to deviate from their intrinsic values. In rising economies such as India, where retail investors are significantly affected by psychological factors, comprehending these attitudes is crucial.
Methods
This study analyses data from a comprehensive questionnaire that was conducted throughout the nation and included 552 retail investors. The investigation employed structural equation modelling (SEM) to identify the elements that influence the decision-making of individuals who invest in the Indian stock market.
Findings
The research offers insight on the influence that investor attitude has on investment decision-making as well as the factors that precede it. The study demonstrates that investors make financial decisions based on sentiment. In addition to assessing the efficacy of the Indian financial market, this study sought to ascertain the rationality of investors’ choices by exploring the factors that influence their decision-making process.
Conclusion
The outcome of the study shows that information seeking, anchoring, herding, representativeness, and overconfidence all have a big impact on investors. Moreover, the study has proven investors’ irrationality and stock market inefficiency. The findings may be employed to further examine the trading practices of international investors and encourage further study in the field of behavioural finance.
The Multimodal Semi- Supervised Attention-based Long Short-Term Memory (MS-SSA-LSTM) model introduces an improvement in stock price forecasting by combining sentiment analysis, swarm intelligence algorithms, deep learning methods, and multi-source data. This model builds a dictionary of sentiments and calculates a sentiment index based on aggregation from East Money forum postings for sentiment analysis. As a result, this kind of study is insightful in providing analyses on how changes in market sentiment influence stock prices.In order to optimize the prediction accuracy, the LSTM hyperparameters are tuned using the Sparrow Search Algorithm (SSA). Empirical findings indicate that the MS-SSA-LSTM model outperforms the other models evaluated. This methodology is an effective approach for generating accurate stock price forecasts. Excellent, the program, which was specially designed for China's erratic financial market, excels in the prediction of short-term stock prices and provides useful information to help investors make an informed decision. A hybrid model of LSTM+GRU has also been introduced for the classification of stock emotion. The approach also utilized a strong ensemble, which consisted of a voting regression for stock price prediction: LinearRegression + RandomForestRegressor + KNeighborsRegressor, and a voting classifier for sentiment analysis: AdaBoost + RandomForest. All these ensembles were combined to enhance the overall predictive performance by being easily integrated with the existing models (MLP, CNN, LSTM, MS-LSTM, and MS-SSA-LSTM). A user-friendly Flask frame work with SQ Lite support was developed to streamline the sign up, sign in, and model assessment procedures and enable user engagement and testing.
This paper studies the market microstructure implications of uninformed trading volume. We capture uninformed volume using spikes in retail trading triggered by weekly advertisements (ads) in the Wall Street Journal that are largely duplicates. We report three findings. First, consistent with a positive volume-volatility relation, stock price volatility amplifies on recurring ad days. Second, informed investors time liquidity to trade more aggressively on recurring ad days. Third, despite the increase in informed trading on such days, price impact is lower, yielding a negative volume–price impact relation. Collectively, the evidence supports the theoretical predictions of Collin-Dufresne and Fos (2016).
This study replicates and extends Baker and Wurgler's (2006) analysis on investor sentiment's impact on stock returns. We confirm their findings by demonstrating the significant cross‐sectional effect of sentiment in both their original sample (1963–2002) and a new sample (2002–2023). Expanding the scope, we introduce a monthly sentiment measure and analyze the U.S. market (2002–2023) and the Chinese market. Our results show that the predictive strength of sentiment indicators can shift or invert over time. In the Chinese market, sentiment's expected cross‐sectional effects disappear when foundational conditions, such as stock valuation variance, are not met.
Welfare quality is usually a stochastic outcome, as attempts at improving social welfare cannot be predicted in advance. The advances in stock market participation conclude that equity market performance is able to reflect investors’ mass reactions and therefore can fairly reflect the empiricism of welfare quality. In this paper, the pillars of the Happy Planet Index (hereinafter HPI) are used as proxies for countries’ welfare quality. The data cover 57 countries where equity markets exist over the annual period of 2006–2020. The results indicate that (a) the three pillars of HPIs have historical positive impacts on market capitalization and stock turnover; (b) stochastically, life satisfaction has an expected positive impact on market capitalization and stock turnover; (c) firms located in high (low) HPIs, life satisfaction, and life expectancy have significant (insignificant) stochastic impacts on market capitalization; and (d) the historical ecological footprints have positive impacts on market capitalization and stock turnover, whereas stochastic impacts are statistically insignificant.
Purpose
This study aims to investigate the textual analysis of accounting annual reports and the economic policy uncertainty (EPU) of the cost of equity for companies listed on the Tehran Stock Exchange (TSE). In addition, it explored the impact of the annual report readability on the cost of equity moderated by EPU.
Design/methodology/approach
To this aim, information about 178 companies during 2014–2021 was examined. EPU was calculated based on the inflation rate, interest rate, exchange rate and economics growth. The industry-adjusted earnings price ratio (IndEP) and Gordon’s growth model were used for the cost of equity.
Findings
The results showed that annual report readability has a negative impact on the cost of equity, while EPU has a positive impact. EPU significantly increases the negative impact of annual reports readability on the cost of equity. To further validate and check whether the findings are robust to COVID-19, a robustness check was used which confirmed the previous results.
Originality/value
Empirical research fails to produce conclusive evidence about report readability and cost of equity capital, and the lack of consensus in the literature suggests that this effect remains an empirical question. There has been a significant increase in EPU globally, which can affect managers’ views about uncertain and complex tones especially in annual reports. Substantial work is yet to be done on the moderating role of EPU. This study fills the existing research gap. This paper has practical implications not only for firms and investors but also for policymakers.
The objective of this chapter is the integration of machine learning (ML) techniques and investor sentiment analysis for emotion-based asset allocation strategies. It addresses challenges in noisy and imbalanced sentiment data by highlighting the effectiveness of models of supervised learning, which include fuzzy support vector machines (FSVM) and new fuzzy support vector machines (NFSVM) proposed in Kaur et al. These models use fuzzy membership functions to denoise and better classify assets with greater growth potential. The crucial methodologies/trends include effective feature extraction techniques like principal component analysis (PCA), which can streamline sentiment data and capture investor emotions effectively. This chapter further shows how such ML-driven insights can be used to integrate them with Markowitz’s mean-variance portfolio optimization model (MV model), with and without cardinality constraints, to create optimal portfolios. Experimental analysis based on datasets taken from the Nifty 50 and Euro Stoxx 50 indices reveals the fact that emotion-driven strategy, NFSVM-based, outperforms traditional SVM and FSVM models in terms of risk-adjusted returns and portfolio diversification. This work opens up possibilities for combining sentiment analysis and ML to navigate turbulent financial markets and offers new portfolio optimization frameworks. Future studies could involve expanding datasets, hybrid machine-learning techniques, and advanced deep-learning models that are applied in real-time applications.
Noisy traders are market participants whose decisions often deviate from rational behavior, influenced instead by emotions, speculation, and social cues rather than fundamental financial information. Traditional financial theories tend to assume that markets are efficient and that investors act rationally to maximize utility. However, in reality, financial markets are frequently influenced by various behavioral biases, especially those exhibited by noisy traders, leading to unpredictable market dynamics. The Adaptive Markets Hypothesis (AMH) offers a framework for understanding markets that accounts for the changing and adaptive nature of investor behavior over time. According to AMH, investors continuously adjust their strategies based on environmental changes, learning from past experiences, and adapting to new circumstances. This approach allows for the inclusion of different types of market participants, including noisy traders, whose biases can impact market efficiency and create volatility. This research explores how decision-making biases among noisy traders, such as overconfidence, confirmation bias, and herd behavior, influence market behavior in adaptive markets. By examining the role of these biases, we gain insight into how they contribute to market anomalies and influence the pricing and volatility of financial assets.
We propose a novel class of asset price models in which the crash hazard rate is determined by a function of a non-local estimation of mispricing. Rooted in behavioral finance, the non-local estimation embodies in particular the characteristic of ‘anchoring’ on past price levels and the ‘probability judgment’ about the likelihood of a crash as a function of the self-referential mispricing, enabling us to disentangle the risk-return relationship from its instantaneous connection. By describing drawdowns and crashes as market regimes with correlated negative jumps clustering over a finite period of time, our model provides a solution to the problem plaguing most crash jump models, which are in general rejected in calibrations of real financial time series because they assume that crashes occur in a single large negative jump, which is counterfactual. The model estimation is implemented on synthetic time series and real markets, shedding light on the estimation of the ‘true’ expected return, which is usually confounded by the entanglement between volatility and jump risks. Estimated from the daily time series of three stock indexes, the hidden expected return exhibits a secular increase over time and tends to be larger than the realized return, suggesting that financial markets have been overall underpriced.
This study finds that accounting standards play an important role in cross‐border investor reactions to peer firm earnings. Specifically, we document that when international peer firms report under the same accounting standards, investors overreact to peer firms' earnings announcements. Using a sample of 35,116 firm‐pair‐years from 51 countries between 2000 and 2010, we show that heightened information transfers for international same‐standard firms are followed by predictable price reversals when investors observe own‐firm earnings. However, overreactions are not present for international firm‐pairs that follow different accounting standards. While we find that institutional investors learn over time, overreactions do not decline among retail investors. Additional tests suggest that overreactions cause significant excess volatility, which results in economically significant costs. Collectively, our findings document an unintended consequence of financial reporting harmonization in the form of increased investor overreactions.
O presente estudo teve como objetivo verificar se é possível obter retornos anormais maiores em eventos políticos em comparação com eventos que refletem os fundamentos da empresa. Para tanto utilizou-se a metodologia de estudo de eventos no qual os retornos anormais foram calculados através do modelo de mercado. Os dados de retornos utilizados foram das ações preferencias da Petrobrás no período de 2021 a 2024. Os eventos políticos considerados na janela de eventos foram as mudanças de presidente na empresa e as eleições nacionais de 2022. Os eventos que representaram os fundamentos da companhia, foram a divulgação trimestral do resultado da empresa. Os resultados indicaram que as trocas de presidentes geraram retornos anormais maiores do que a divulgação de resultados financeiros, enquanto as eleições resultaram em retornos anormais menores. No entanto, os testes realizados não apresentaram evidências de que as diferenças entre os retornos anormais desses eventos sejam diferentes de zero, o que refuta a hipótese do estudo.
A new measure of investor sentiment is introduced and tested in this study. Different from using search volume of certain macroeconomic terms to capture investor attention/sentiment, this new measure is crafted from analyzing and classifying the sentiment contents of textual comments of retail investors active in a major Chinese stock forum. Different from the traditional sentiment studies linking market‐level sentiment to market‐wide reactions, this uncomplicated measure is constructed for individual stocks and subsequently, reactions of the same stocks are tracked and examined, offering a more direct and precise correlation test. In our validation tests, we show a significantly positive correlation between investor sentiment and three stock market parameters that is, stock return, price volatility, and information efficiency. Specifically, a positive sentiment is associated with higher stock returns and a higher degree of information efficiency as well as higher price volatility. These associations seem to attenuate with improvement in the information environment, such as better investor protection.
In this paper, we introduce a hybrid sentiment analysis model, FinBERT-BiLSTM-TextCNN, which integrates deep learning and machine learning techniques to accurately analyze the sentiment strength of comments in financial forums. We conduct a regression analysis of stock prices and apply economic analysis methods to evaluate the impact of social media sentiment on market dynamics. By processing user comments, we construct a comprehensive investor sentiment measurement indicator known as the Social Media Sentiment Index (SMI). This index innovatively combines two key metrics: sentiment strength and user engagement, offering a novel analytical tool for quantifying and characterizing investor sentiment. Our experimental results indicate two main findings: (1) Compared to current sentiment classification approaches, the hybrid FinBERT-BiLSTM-TextCNN sentiment analysis model achieves over a 3% improvement in sentiment classification accuracy on stock market forum datasets; (2) Based on real trading data from the Chinese stock market over two years and an analysis of 20 million comments from the East Money Forum, the proposed Social Media Sentiment Index more accurately reflects changes in individual stock characteristics and tracks market sentiment fluctuations more efficiently, with a correlation to stock price movements improving by more than 4% compared to traditional methods. This study demonstrates the significant role of integrating economic analysis with sentiment analysis in predicting stock market trends, highlighting the value of the Social Media Sentiment Index as a robust indicator of investor behavior and market sentiment.
This study examines the impact of ESG sentiment on the forecast of excess stock returns. We construct a monthly ESG sentiment index ( S ESG ) that captures the tone of ESG news coverage, distinguishing between positive and negative sentiment. Our findings indicate that S ESG predictions of market excess returns are statistically significant in both in‐sample and out‐of‐sample analyses, with stronger predictive power during high sentiment periods compared to low sentiment periods. Furthermore, economic tests demonstrate that S ESG generates a high Sharpe ratio and utility gains for investors, highlighting its potential economic benefits as a predictor in the increasingly important field of ESG investments.
Much of the information available to participants in speculative markets is in the nature of expert opinion, analysis, professional advice, and so on. Markets discount widely held factual information very well; this paper studies market efficiency with respect to subjective information. We examine the "market" for bets on thoroughbred horse races to determine whether the published forecasts of professional handicappers are completely discounted. A multinomial logit probability model is used to measure the information content of the forecasts, and we find that they do contain considerable information but that the track odds generated by betting discount almost all of it. Within the population of bettors, those betting at the track appear to discount the handicapper information fully, but those betting through New York's off-track betting system do not.
"Nowhere does history indulge in repetitions so often or so uniformly as in Wall Street," observed legendary speculator Jesse Livermore. History tells us that periods of major technological innovation are typically accompanied by speculative bubbles as economic agents overreact to genuine advancements in productivity. Excessive run-ups in asset prices can have important consequences for the economy as firms and investors respond to the price signals, resulting in capital misallocation. On the one hand, speculation can magnify the volatility of economic and financial variables, thus harming the welfare of those who are averse to uncertainty and fluctuations. But on the other hand, speculation can increase investment in risky ventures, thus yielding benefits to a society that suffers from an underinvestment problem.
Changes in variance, or volatility, over time can be modeled using the approach based on autoregressive conditional heteroscedasticity. Another approach is to model variance as an unobserved stochastic process. Although it is not easy to obtain the exact likelihood function for such stochastic variance models, they tie in closely with developments in finance theory and have certain statistical attractions. This article sets up a multivariate model, discusses its statistical treatment, and shows how it can be modified to capture common movements in volatility in a very natural way. The model is then fitted to daily observations on exchange rates.
The power of dividend yields to forecast stock returns, measured by regression R2, increases with the return horizon. We offer a two-part explanation. (1) High autocorrelation causes the variance of expected returns to grow faster than the return horizon. (2) The growth of the variance of unexpected returns with the return horizon is attenuated by a discount-rate effect - shocks to expected returns generate opposite shocks to current prices. We estimate that, on average, the future price increases implied by higher expected returns are just offset by the decline in the current price. Thus, time-varying expected returns generate ‘temporary’ components of prices.
Open-ending a closed-end fund forces the price of the fund's shares to their net asset value. Open-ending behavior is shown to correspond in predictable ways to the incentive to open-end and to potential resistance to open-ending. Moreover, closed-end fund share prices begin to generate statistically significant positive abnormal returns well in advance of the formal announcement of the open-ending. Although a small part of the total abnormal return is not entirely exhausted until after the announcement, such market price performance is broadly consistent with a semi-strong form efficient market.
Frozen concentrated orange juice is an unusual commodity. It is concentrated not only hydrologically, but also geographically; more than 98 percent of U.S. production takes place in the central Florida region around Orlando.' Weather is a major influence on orange juice production and un- like commodities such as corn and oats, which are produced over wide geographical areas, orange juice output is influenced primarily by the weather at a single location. This suggests that frozen concentrated orange juice is a relatively good candidate for a study of the interaction between prices and a truly exogenous determinant of value, the weather.
In this paper I derive a risk-adjusted measure of portfolio performance (now known as Jensen's Alpha) that estimates how much a manager's forecasting ability contributes to the fund's returns. The measure is based on the theory of the pricing of capital assets by Sharpe (1964), Lintner (1965a) and Treynor (Undated). I apply the measure to estimate the predictive ability of 115 mutual fund managers in the period 1945-1964 - that is their ability to earn returns which are higher than those we would expect given the level of risk of each of the portfolios. The foundations of the model and the properties of the performance measure suggested here are discussed in Section II. The evidence on mutual fund performance indicates not only that these 115 mutual funds were on average not able to predict security prices well enough to outperform a buy-the-market-and-hold policy, but also that there is very little evidence that any individual fund was able to do significantly better than that which we expected from mere random chance. It is also important to note that these conclusions hold even when we measure the fund returns gross of management expenses (that is assume their bookkeeping, research, and other expenses except brokerage commissions were obtained free). Thus on average the funds apparently were not quite successful enough in their trading activities to recoup even their brokerage expenses.
This study documents substantial gains accuring to shareholders of discounted closed-end investment companies when these funds are reorganized to allow shareholders to obtain the market value of the fund's assets. The findings indicate that the discounts on closedend funds are real, i.e., they are not the sole result of inaccurate reporting of the fund's net asset value. The study also documents significant abnormal returns after the announcement of management-sponsored proposals to reorganize. This finding is inconsistent with the joint hypothesis of market efficiency and that the market model (as estimated) is the correct return bench mark for funds undertaking reorganization.
Restrictions that a class of general equilibrium models place upon the average returns of equity and Treasury bills are found to be strongly violated by the U.S. data in the 1889–1978 period. This result is robust to model specification and measurement problems. We conclude that, most likely, an equilibrium model which is not an Arrow-Debreu economy will be the one that simultaneously rationalizes both historically observed large average equity return and the small average risk-free return.
In any voluntary trading process, if agents have rational expectations, then it is common knowledge among them that the equilibrium trade is feasible and individually rational. This condition is used to show that when risk-averse traders begin at a Pareto optimal allocation (relative to their prior beliefs) and then receive private information (which disturbs the marginal conditions), they can still never agree to any non-null trade. On markets, information is revealed by price changes. An equilibrium with fully revealing price changes always exists, and even at other equilibria the information revealed by price changes “swamps” each trader's private information.
The stability of the rational expectations equilibrium of a simple asset market model is studied in a situation where a group of traders learn about the relationship between the price and return on the asset using ordinary least squares estimation, and then use their estimates in predicting the return from the price. The model which they estimate is a well-specified model of the rational expectations equilibrium, but a misspecified model of the situation in which the traders are learning. It is shown that for appropriate values of a stability parameter the situation converges almost surely to the rational expectations equilibrium.
The General Theory of Employment, Interest, and Money / John Maynard Keynes Note: The University of Adelaide Library eBooks @ Adelaide.
ABSTRACT. In an attempt to capture the buying and selling behaviour of different investors in the vicinity of interim earnings announcements, Vieru et al. separate the individuals into five trading frequency (activity) classes. Shivakumar disaggregates the commonly used proxy for earnings surprise into cash flow and accrual components and evaluates the ability of each of these components in predicting future stock returns. Ekholm investigates how different types of investors react to new earnings information. González analyzes the relevance of two different reasons for banks to acquire firms' stock: the increase of agency costs in the lending relationship, and participation in the expected profits of undervalued firms.
A slowly mean-reverting component of stock prices tends to induce negative autocorrelation in returns. The autocorrelation is weak for the daily and weekly holding periods common in market efficiency tests but stronger for long-horizon returns. In tests for the 1926-85 period, large negative autocorrelations for return horizons beyond a year suggest that predictable price variation due to mean reversion accounts for large fractions of 3 to 5-year return variances. Predictable variation is estimated to be about 40 percent of 3 to 5-year return variances for portfolios of small firms. The percentage falls to around 25 percent for portfolios of large firms. Copyright 1988 by University of Chicago Press.
Introducing more speculators into the market for a given commodity leads to improved risk sharing but can also change the informational content of prices. This inflicts an externality on those traders already in the market, whose ability to make inferences based on current prices will be aff ected. In some cases, the externality is negative: the entry of new s peculators lowers the informativeness of the price to existing trader s. The net result can be one of price destabilization and welfare red uction. This is true even when all agents are rational, risk-averse c ompetitors who make the best possible use of their available informat ion. Copyright 1987 by University of Chicago Press.
It is sometimes asserted that rational speculative activity must result in more stable prices because speculators buy when prices are low and sell when they are high. This is incorrect. Speculators buy when the chances of price appreciation are high, selling when the chances are low. Speculative activity in an economy in which all agents are rational, have identical priors, and have access to identical information may destabilize prices, under any reasonable definition of destabilization. It takes extremely strong conditions to ensure that speculative activity (of the commodity storage variety) "stabilizes" prices, even in a very weak sense.
The authors present a model of portfolio allocation by noise traders with incorrect expectations about return variances. For such misperceptions, noise traders who do not affect prices can earn higher expected returns than rational investors with similar risk aversion. Moreover, such noise traders can come to dominate the market in that the probability that they eventually have a high share of total wealth is close to one. Noise traders come to dominate despite their taking of excessive risk and their higher consumption. The authors conclude that the case against their long-run viability is not as clear-cut as is commonly supposed. Coauthors are Andrei Shleifer, Lawrence H. Summers, and Robert J. Waldmann. Copyright 1991 by University of Chicago Press.
Assessing the price evolution of houses on the basis of average sales prices, as is current practice in Belgium, might be misleading due to changing characteristics of the houses sold in the periods observed. A hedonic index which takes into account changes in characteristics is more appropriate. We use the budget surveys of the Belgian Statistical Institute to illustrate how this also applies for Belgium. The estimated hedonic price index for house sales on the secondary market is practically always below the index based on average sales values for the period considered. This demonstrates the need to collect more extensive data on the characteristics of the dwellings sold in Belgium.
Analyses of rational speculation usually presume that it dampens fluctuations caused by "noise" traders. This is not necessarily the case if noise traders follow positive-feedback strategies--buy when prices rise and sell when prices fall. It may pay to jump on the bandwagon and purchase ahead of noise demand. If rational speculators' early buying triggers positive-feedback trading, then an increase in the number of forward-looking speculators can increase volatility about fundamentals. This model is consistent with a number of empirical observations about the correlation of asset returns, the overreaction of prices to news, price bubbles, and expectations.
This paper derives and estimates an equilibrium model of stock price behavior in which exogenous "noise traders" interact with risk-averse "smart money" investors. The model assumes that changes in exponentially detrended dividends and prices are normally distributed, and that smart money investors have constant absolute risk aversion. In equilibrium, the stock price is the present value of expected dividends, discounted at the riskless interest rate, less a constant risk premium, plus a term which is due to noise trading. The model expresses both stock prices and dividends as sums of unobserved components in continuous time. The model is able to explain the volatility and predictability of U.S. stock returns in the period 1871-1986 in either of two ways. Either the discount rate is 4% or below, and the constant risk premium is large; or the discount rate is 5% or above, and noise trading, correlated with fundamentals, increases the volatility of stock prices. The data are not well able to distinguish between these explanations.
Analyses of rational speculation usually presume that it dampens fluctuations caused by "noise" traders. This is not necessarily the case if noise traders follow positive-feedback strategies--buy when prices rise and sell when prices fall. It may pay to jump on the bandwagon and purchase ahead of noise demand. If rational speculators' early buying triggers positive-feedback trading, then an increase in the number of forward-looking speculators can increase volatility about fundamentals. This model is consistent with a number of empirical observations about the correlation of asset returns, the overreaction of prices to news, price bubbles, and expectations. Coauthors are Andrei Shleifer, Lawrence H. Summers, and Robert J. Waldmann. Copyright 1990 by American Finance Association.
The relationship between long-term and short-term interest rates is crucial for macroeconomic policy evaluation. Since the short-term interest rate is the opportunity cost of holding money, it is widely believed that the Federal Reserve has more direct control over short-term than over long-term interest rates in the United States. Yet if capital is costly to adjust or takes time to place into use, investment decisions may depend on long-term interest rates. The term structure of interest rates thus appears central to the monetary transmission mechanism. Unfortunately, the determinants of the term structure remain poorly understood. This paper uses data from the United States, Canada, and the United Kingdom, and Germany to examine various hypotheses regarding the term structure. My goal is to see whether the experiences of these four countries since 1960 can help provide a general explanation of the term structure. In the United States many observers believe the large variations in the long-term interest rate since 1979 are not adequately explained by movements in short-term interest rates. Of particular interest is whether the experience of the United States in these and earlier years merely reflects an unusual historical episode. If it does, it would be inappropriate to draw any general conclusions from this experience or to extrapolate this experience into the future. This study is in part motivated by apparent differences between recent experience in the United States and experience elsewhere. In 1985, the rate on long-term government bonds in the United States exceeded the rate on three-month Treasury bills by more than 300 basis points. By contrast, the long-term interest rate in the United Kingdom was more than 100 basis points below the short-term interest rate. Interpreting such divergent national experiences is the primary purpose of studying the term structure more generally.
A dynamic model of insider trading with sequential auctions, structured to resemble a sequential equilibrium, is used to examine the informational content of prices, the liquidity characteristics of a speculative market, and the value of private information to an insider. The model has three kinds of traders: a single risk neutral insider, random noise traders, and competitive risk neutral market makers. The insider makes positive profits by exploiting his monopoly power optimally in a dynamic context, where noise trading provides camouflage which conceals his trading from market makers. As the time interval between auctions goes to zero, a limiting model of continuous trading is obtained. In this equilibrium, prices follow Brownian motion, the depth of the market is constant over time, and all private information is incorporated into prices by the end of trading.
Recent empirical research has identified a significant amount of volatility in stock prices that cannot easily be explained by changes in fundamentals; one interpretation is that asset prices respond not only to news but also to irrational "noise trading." We assess the welfare effects and incidence of such noice trading using an overlapping-generations model that gives investors short horizons. We find that the additional risk generated by noise trading can reduce the capital stock and consumption of the economy, and we show that part of that cost may be borne by rational investors. We conclude that the welfare costs of noise trading may be large if the magnitude of noise in aggregate stock prices is as large as suggested by some of the recent empirical literature on the excess volatility of the market.
Changes in variance, or volatility, over time can be modeled using the approach based on autoregressive conditional heteroscedasticity. Another approach is to model variance as an unobserved stochastic process. Although it is not easy to obtain the exact likelihood function for such stochastic variance models, they tie in closely with developments in finance theory and have certain statistical attractions. This article sets up a multivariate model, discusses its statistical treatment, and shows how it can be modified to capture common movements in volatility in a very natural way. The model is then fitted to daily observations on exchange rates.
The pricing of shares of closed-end investment companies appears to provide a startling counter-example to the general rule. These companies invest in a portfolio of stocks and other securities just as do open-end mutual funds. Unlike the mutual funds, however, closed-end companies neither issue new shares nor redeem outstanding ones. Investors who wish to purchase or sell closed-end shares must do so on the open market at prices reflecting not the net asset values of the companies but rather the supply and demand for the shares. Therein lies the seeming inconsistency with the efficient-markets hypothesis. The shares of closed-end investment companies usually sell at discounts, and sometimes at substantial discounts, from the actual values of the portfolios of stocks they hold. This paper attempts to develop some theoretical principles concerning the valuation of shares of closed-end investment companies. Then, cross-sectional empirical estimates will be presented showing the relationship between fund discounts (or premiums) and the factors isolated in the theoretical analysis. Finally, the behavior of the cross-sectional estimates over time will be examined and a time-series analysis of average discounts (or premiums) will be conducted. I conclude that while the structure of discounts can be partially explained on the basis of theoretical principles, the size of the discounts is far larger than warranted. The pricing of closed-end fund shares does then seem to provide an illustration of a market imperfection in capital-asset pricing.
This paper examines the hypothesis that financial markets are myopic by studying the term structure of interest rates. White rejecting decisively the traditional expectations hypothesis regarding the term structure, our statistical results also lead us to conclude that long term interest rates do not overreact to either the level or the change in short termrates. This finding suggests that participants in bond markets are not myopic or overly sensitive to recent events. Our statistical results also suggest that most variations in the yield curve reflect changes in liquidity premia rather than expected changes in interest rates.
General equilibrium models with representative agents have proved to be inadequate descriptions of U.S. financial data. I present a model with heterogeneous agents, optimizers, and nonoptimizers that exhibits high stock-price volatility and mimics empirical regularities found in U.S. consumption, stock return, and three-month treasury-bill return data. The simulation and estimation of the model are performed using a new technique called "backsolving," which is of independent interest to researchers attempting to solve nonlinear, stochastic models.
The Investor's Guide to Closed-End Funds
- Thomas Herzfeld
Herzfeld, Thomas. The Investor's Guide to Closed-End Funds. New York: McGraw Hill, 1980.
The Case For Flexible Exchange Rates
- Milton Friedman
Friedman, Milton. " The Case For Flexible Exchange Rates. " Essays in Positive Economics.
A Random Walk Down Wall Street References also made to subsequent editions of
- Burton G Malkiel
Malkiel, Burton G. A Random Walk Down Wall Street. New York: Norton, 1973. References also
made to subsequent editions of 1985 and 1989.
Differences of Opinion and the Volume of Trade
- Hal Varian
Varian, Hal. " Differences of Opinion and the Volume of Trade. " Mimeographed. Ann Arbor:
University of Michigan, 1986.
FIGURE 1