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The Efficient Market Hypothesis and Its Critics
Abstract
Revolutions often spawn counterrevolutions and the efficient market hypothesis in finance is no exception. The intellectual dominance of the efficient-market revolution has more been challenged by economists who stress psychological and behaviorial elements of stock-price determination and by econometricians who argue that stock returns are, to a considerable extent, predictable. This survey examines the attacks on the efficient market hypothesis and the relationship between predictability and efficiency. I conclude that our stock markets are more efficient and less predictable than many recent academic papers would have us believe.
... Gayatri et al. (2024) also find that markets tend to be efficient in the semi-strong form because investors respond positively to information about policy changes in interest rates. Malkiel (1989Malkiel ( , 2003 explains that changes in effective interest rates have an impact on investors' investment preferences. Malkiel (2003) explains that changes in effective interest rates tend to have a negative impact on stock returns, causing mean reversion in the EMH assumption. ...
... Malkiel (1989Malkiel ( , 2003 explains that changes in effective interest rates have an impact on investors' investment preferences. Malkiel (2003) explains that changes in effective interest rates tend to have a negative impact on stock returns, causing mean reversion in the EMH assumption. This condition is similar to cases in Turkey from January 2001 to April 2017 when investor's preferences changed as the impact of the policy of setting effective interest rates is deregulated in following the dynamic market model (Tursoy, 2019). ...
... Consistent with Malkiel (1989Malkiel ( , 2003, some empirical evidence in Indonesia also shows that changes in effective interest rates tend not to be in the same direction as stock returns. During the 2014 to 2018 period, Nugroho & Hermuningsih (2020) found that the increase in effective interest rates was significant and not in line with the returns of the construction and building firms. ...
Research Purposes. Objective of study is examining market efficiency in Indonesia under high interest rates issue.Research Methods. Observation period on Indonesia stock composite index starts from June 7, 2023 to February 29, 2024. Several procedures are applied in objective of hypothesis testing, which are: (1) mean difference test supported by Cohen's test and normality test by Anderson-Darling in terms of detecting how market returns differ in the two observation periods; (2) runs test runs test in terms of detecting the randomness of market returns after the risk-free rate; (3) ARIMA is supported by the Augmented Dickey-Fuller test in terms of detecting whether randomness is just noise (or white noise); and (4) variance ratio test in terms of confirming the results of runs test and ARIMA to determine whether the market returns are efficient or just a noise.Research Results and Findings. Consistent with efficient market hypothesis, findings show that the market condition during the effective interest rate period of 6% has higher returns, riskier, better risk-return trade-off, and less efficient. During that period, noise seemed to play a role in creating market gaps. This study concludes that monetary policy in maintaining high effective interest rates cannot determine whether the market is more efficient. As implication, the high effective interest rate tends not to result in a shift in investor behavior to allocate stock investments to risk-free assets. This study contributes to develop the finance and accounting science, especially in the fair presentation of financial information including investment decisions.
... The difference between stock prices and intrinsic values is reported as having a random meaning (Fama, The behavior of stock-market prices, 1965). (Malkiel, 2003) in this research, have an idea that current prices are variations having a stochastic character by comparison with old prices. ...
... In case the prices deviate from their intrinsic values, prices diverge randomly from intrinsic values (Fama, 1965). According to random walk theory in the finance literature, the current prices are random departures from previous prices (Malkiel, 2003). ...
This study aims to test a weak-form efficiency of selected financial markets over the period of 2023-2024. Sample study consists of financial market indices for a number of African markets. The African countries concerned in this study are Algeria, Ghana, Morocco, Nigeria, Tunisia and BRVM (a regional market for eight 8 African countries). Applying variance ratio test, runs test and autocorrelation test to assess whether stock prices follow a random walk, we revealed that only two financial markets, namely, Morocco and BRVM, exhibit a weak-form efficiency. The other markets are inefficient based on the results of three tests. This study is one of the first to include the Algiers Stock Exchange in the sample. ملخص اسة الدر هذه تهدف إلى من املمتدة الفترة خالل املختارة املالية األسواق لبعض الضعيفة الكفاءة اختبار 2023 الى 2024 هذه االفريقية. الدول من لعدد املالية األسواق ات مؤشر من التطبيقية اسة الدر عينة وتتكون ائر الجز هي اسة الدر هذه في املعنية الدول , غانا , املغرب , نيجيريا , وبورصة تونس BRVM تشمل سوق وهي يقية. افر بلدان ثمان كانت اذا ما لتقييم الذاتي االرتباط واختبار ات ار التكر واختبار التباين اختبار وبتطبيق و (املغرب فقط ماليين سوقين أن كشفنا العشوائي السير نموذج تتبع ات املؤشر خالل من األسهم أسعار (BRVM ات االختبار نتائج الى استنادا وهذا كفؤة غير فهي األخرى األسواق أما الشكل ضعيفة كفاءة ان يظهر العينة. ضمن ائر الجز بورصة أدرجت التي اسات الدر أولى بين من اسة الدر هذه وتعد املطبقة. الثالثة الكلمات املفتاحية: ات ار التكر اختبار ، الذاتي االرتباط اختبار ، الت اختبار باين ، املالي األسواق ة اإلفريقية، السير نموذج العشوائي .
... EMH, coined by Fama (1965), suggests that financial markets efficiently reflect information about individual bonds/or the bond market as a whole. This hypothesis is related to the concept of random walk, which characterises subsequent price changes as random deviations from previous prices (Malkiel, 2003). The efficient market hypothesis is based on the view that new information is incorporated into security prices without a lag and that the new information received determines the level of efficiency. ...
... The efficient market hypothesis is based on the view that new information is incorporated into security prices without a lag and that the new information received determines the level of efficiency. Therefore, there are three assumptions of EMH: a weak-form efficient market, a semi-strong-form efficient market and a strong-form efficient market (Malkiel, 2003). Of the three versions of EMH, the semistrong form efficient market illustrates that macroeconomic fundamentals did not allow for the prediction of bond prices/yields; hence, a linear relationship existed between macroeconomic factors and bond prices/yields. ...
This paper examines the effect of macroeconomic variables on government bond yields of different maturities under two regimes in South Africa. The study employs a Two-Stage Markov regime-switching model to analyze monthly time series data from March 2009 to October 2022. It attempts to explain variations in 1-3 year, 3-7 year, 7-12 year and +12 year government bond yields with six independent variables such as inflation, real GDP, real short-term interest rates, real long-term interest rates, real money supply, and the real Rand/Dollar exchange rate. As a result, the study finds that the performance of government bond yields varies with market conditions, as per the adaptive market hypothesis (AMH). More specifically, the returns of the 1-3 year bond index are influenced by real GDP in a bull regime, while the performance of the 3-7 year government bond yield is affected by real GDP in a bear market condition. Additionally, the inflation growth rate influences the performance of the 7-12 year government bond yield in a bull market regime, but not in a bear regime. It also documents that the bear market conditions prevail among selected bond index returns, with the 12-year government bond yield staying in a bull state for 12 months, while the 7-12 year government bond yield stays the longest in a bear state (19 months). These findings demonstrate that the South African bond market is affected by changing conditions. Therefore, the interaction between the macroeconomy and bond performance is better explained by AMH, and there is potential for improved explanatory power through the use of nonlinear modeling techniques.
... This was associated with the development of the "theory of random walks" in the finance literature and the "rational expectations theory" in economics by Jensen (1978). In this regard, Fama (1965b); Malkiel (2003) relate the EMH to the notion of a random walk process in stock price series where all subsequent price changes are represented as random and independent of previous prices Alexander (1961). Two main claims are associated with the Efficient Market Hypothesis (EMH). ...
The purpose of this study was to determine the market reaction to changes in stock prices on the Russian-Ukraine war event on the Indonesia Stock Exchange in 2022. This research using Bayesian analysis Approach on banking stocks. The results show that the AAR and CAR Before-After of the three models do not show significant results or market anomaly. The implications of the results of this research for investors and researchers that the events of the Russian-Ukraine war in 2022 can be taken into consideration for investors to be more rational in responding to political events in making investments in both the financial sector or other sectors, and for other researchers the EMH theory is no longer relevant to recent world developments. The limitations in this study are only in the financial sector in banking stocks, and the future big agenda will be expanded in several sectors, namely manufacturing, infrastructure and technology on the Indonesia stock exchange. The novelty in this research is that it uses the Bayesian analysis approach to the three models of approaches that have been and are often used, but in this case it is very different from previous researchers who have never used the Bayesian analysis approach
... Moreover, investors tend to make decisions based on what they think is appropriate b a s e d o n information that might be immaterial, diverging the actual share prices away from its fair value [13]. On the other hand, various theories are attempting to explain the misconception of the Efficient Market Hypothesis but none of them is perfect [14]. In practicality, share markets are neither perfect nor completely inefficient. ...
Most investors view the stock market as a place to trade shares where everything is well organized based on data and economic factors. Investors behave rationally after analyzing the market based on related data and news. However, behavioural finance shifts the traditional view, emphasising human emotions and cognitive bias. This review explores the intersection of how human psychology, such as cognitive bias, emotion, and cultural background, drives retail investment decisions across global stock markets in the past decades. Important psychological aspects of loss aversion, overconfidence, herding, and anchoring are examined to determine how they affect market efficiency and the pricing of assets. In the review, we outline the evolution, strengths, and weaknesses of financial theory and models from classical paradigms like efficient markets hypothesis, CAPM, through behavioral finance to emerging neurofinance. In addition, we consider both qualitative and quantitative research concerning cognitive and emotional biases, such as overconfidence, herding, and loss aversion, which influence the decision-making processes of individual investors. By combining empirical data and theoretical models, this review offers a better view of the reasons behind unregulated markets and the role played by cognitive biases in destabilizing the market. The findings point to applying approaches in investment and financial literacy to help curb the detrimental effects of irrational thinking. Keywords: Behavioral Finance, Cognitive Biases, Investor Psychology, Stock Market Investments, Market Volatility, Loss Aversion, Overconfidence, Herd Behavior, etc.
... Fundamental and technical analysis are the primary methods in financial investment. Given the limitations of the efficient market hypothesis in real financial markets (Ball, 2009;Malkiel, 2003;Stout, 2002), the significance of technical analysis is recognized (Blume et al., 1994;Taylor and Allen, 1992;Lo et al., 2000;Knight, 2010). Trading range breakouts, a key aspect of technical analysis, have * Correspondence to Weiran Huang. ...
... Elle confirme ainsi l'intérêt de considérer le fractionnement comme un événement à impact potentiel sur la liquidité et la perception des investisseurs, surtout dans les environnements où les asymétries informationnelles restent prégnantes. Toutefois, il convient de rappeler que même dans les marchés réputés efficients, de nombreuses anomalies subsistent (Malkiel, 2003 ;Shiller, 2000), ce qui relativise la frontière entre marchés développés et émergents. Une lecture critique de cette hypothèse suggère que l'efficience, loin d'être un état, serait plutôt une aspiration -une forme idéale rarement atteinte. ...
Cette étude se propose d’examiner la manière dont un marché financier en
développement réagit aux opérations de fractionnement d’actions, en prenant pour cadre d’analyse la Bourse Régionale des Valeurs Mobilières (BRVM). L’analyse porte sur 36 opérations réalisées entre 2017 et 2019, période marquée par une vague réglementaire inédite qui a profondément modifié la structure des titres cotés.
En mobilisant la méthodologie des études d’événement, l’objectif est de tester la validité de l’hypothèse d’efficience informationnelle au sens semi-fort : les prix reflètent-ils immédiatement et correctement l’annonce d’un fractionnement ? Loin d’observer une réponse uniforme ou instantanée, les résultats mettent en lumière une réaction hétérogène et souvent différée du marché, trahissant ainsi des signes d’inefficience informationnelle. La dynamique de prix semble en effet modulée par les caractéristiques propres aux sociétés émettrices et aux modalités techniques des opérations.
Ce travail apporte une contribution originale à la littérature en documentant les réactions boursières sur un marché africain encore peu exploré, tout en soulignant
les spécificités structurelles et comportementales de tels environnements. Il interpelle également les régulateurs et les entreprises sur l’importance cruciale d’une
communication financière claire, accessible et proactive pour améliorer la transparence et favoriser une meilleure intégration de l’information dans les cours boursiers.
... Financial time series analysis is crucial in making investment decisions, assessing market risks, and developing robust trading strategies. Despite the Efficient Market Hypothesis (Fama, 1970) and later research supporting the thesis (Malkiel, 2003), there is a broad body of studies focused on applying techniques to predict future market movements (Hsieh et al., 2011;Hsu et al., 2016;Weng et al., 2017). However, owing to the low signal-to-noise ratio and inherently chaotic nature of market data, the task of financial time series forecasting requires the development of a complex and detailed methodology appropriate to these challenges (De Prado, 2015). ...
This research systematically develops and evaluates various hybrid modeling approaches by combining traditional econometric models (ARIMA and ARFIMA models) with machine learning and deep learning techniques (SVM, XGBoost, and LSTM models) to forecast financial time series. The empirical analysis is based on two distinct financial assets: the S&P 500 index and Bitcoin. By incorporating over two decades of daily data for the S&P 500 and almost ten years of Bitcoin data, the study provides a comprehensive evaluation of forecasting methodologies across different market conditions and periods of financial distress. Models' training and hyperparameter tuning procedure is performed using a novel three-fold dynamic cross-validation method. The applicability of applied models is evaluated using both forecast error metrics and trading performance indicators. The obtained findings indicate that the proper construction process of hybrid models plays a crucial role in developing profitable trading strategies, outperforming their individual components and the benchmark Buy&Hold strategy. The most effective hybrid model architecture was achieved by combining the econometric ARIMA model with either SVM or LSTM, under the assumption of a non-additive relationship between the linear and nonlinear components.
... Due to its simplicity, the largest volume of scientific literature addressed the topic of empirical verification of market efficiency in the weak form. Acceptance of EMH in weak form signifies that technical analysis techniques (analyzing past price movements to forecast their future values) are characterized by no predictive power (Malkiel, 2003). In other words, based only on historical prices, it should not be possible to create a profitable trading strategy that would manage to outperform the market systematically. ...
We compare traditional approach of computing logarithmic returns with the fractional differencing method and its tempered extension as methods of data preparation before their usage in advanced machine learning models. Differencing parameters are estimated using multiple techniques. The empirical investigation is conducted on data from four major stock indices covering the most recent 10-year period. The set of explanatory variables is additionally extended with technical indicators. The effectiveness of the differencing methods is evaluated using both forecast error metrics and risk-adjusted return trading performance metrics. The findings suggest that fractional differentiation methods provide a suitable data transformation technique, improving the predictive model forecasting performance. Furthermore, the generated predictions appeared to be effective in constructing profitable trading strategies for both individual assets and a portfolio of stock indices. These results underline the importance of appropriate data transformation techniques in financial time series forecasting, supporting the application of memory-preserving techniques.
... These results are in perfect alignment with existing literature, which has shown that crises, such as the one triggered by Covid-19, disrupt market efficiency due to increased volatility, behavioral biases, and investor uncertainty (Malkiel, 2003). Studies (Çelik, 2021;Liew et al., 2022;Bassiouny et al., 2023;Tauseef, 2023, Sahu et al., 2024Kumar, 2024) suggest that these anomalies can emerge or disappear depending on investor reactions to exceptional conditions, thus explaining the changes observed in African markets. ...
The Covid-19 pandemic profoundly disrupted global economies and financial systems, altering investor behavior and challenging traditional market dynamics. Among these disruptions, calendar anomalies, which challenge the efficient market hypothesis, offer a unique lens to assess market efficiency during crises. The objective of this study is to examine the impact of Covid-19 on calendar anomalies in the main African stock markets, an area largely overlooked in existing research despite the region’s increasing importance in global financial systems. Using daily closing prices from January 1, 2009 to December 31, 2021, and employing a GJR-GARCH (1,1) model, the findings indicate that calendar anomalies exhibit temporal variation within the sample markets, influenced by trends that shift markets between periods of efficiency and inefficiency. Additionally, the study highlights the emergence of new calendar anomalies coinciding with the onset of the Covid-19 pandemic. These results offer lasting insights for investors, suggesting the need for dynamic trading strategies that can adapt to calendar anomalies during global crises. For policymakers, the research underscores the importance of reducing information asymmetry to enhance market resilience in times of crisis. The study also emphasizes the need for further research to explore how systemic shocks, such as Covid-19, can disrupt traditional market patterns and affect stock market behavior.
... In contrast, portfolio optimization models prefer to concentrate on choosing the best portfolio, as initially encouraged by Modern Portfolio Theory (MPT) [1]. The Efficient Market Hypothesis [43] states that it is impossible to constantly beat the market by timing the stock prices. Hence, researchers often seek to identify a group of assets that will likely outperform the market on a near-term basis rather than directly forecasting market changes. ...
Portfolio optimization is a widely studied topic in quantitative finance. Recent advances in portfolio optimization have shown promising capabilities of deep reinforcement learning algorithms to dynamically allocate funds across various potential assets to meet the objectives of prospective investors. The reward function plays a crucial role in providing feedback to the agent and shaping its behavior to attain the desired goals. However, choosing an optimal reward function poses a significant challenge for risk-averse investors aiming to maximize returns while minimizing risk or pursuing multiple investment objectives. In this study, we attempt to develop a risk-adjusted deep reinforcement learning (RA-DRL) approach leveraging three DRL agents trained using distinct reward functions, namely, log returns, differential Sharpe ratio, and maximum drawdown to develop a unified policy that incorporates the essence of these individual agents. The actions generated by these agents are then fused by employing a convolutional neural network to provide a single risk-adjusted action. Instead of relying solely on a singular reward function, our approach integrates three different functions aiming at diverse objectives. The proposed approach is tested on daily data of four real-world stock market instances: Sensex, Dow, TWSE, and IBEX. The experimental results demonstrate the superiority of our proposed approach based on several risk and return performance metrics when compared with base DRL agents and benchmark methods.
... Although the EMH differs from the random walk hypothesis, which states that prices follow a random walk and thus do not contain any exploitable patterns (Campbell et al., 1996, p. 32), the two are often informally linked. This association arises from the reasoning that if the EMH holds, prices should be unpredictable, reflecting only the immediate impact of news, ie inherently unpredictable information (Malkiel, 2003). ...
A common issue faced by investors who use technical analysis is the reconciliation of conflicting trading signals, especially when these signals are highly correlated, such as those generated by multiple moving averages. This study expands on a model-free algorithm inspired by reinforcement learning to address the challenge of reconciling trading signals while taking transaction costs into account. The algorithm is trained to optimize alpha, a widely used measure of risk-adjusted return. Principal component analysis is utilized to reduce the dimensionality of a modified version of moving average signals, which are then used to define the input state. A policy network, represented by a feedforward neural network, is trained using historical data to convert states into trading actions. An evaluation network calculates and optimizes alpha by adjusting the policy network’s parameters. The algorithm utilizes a zero-arbitrage portfolio to accurately isolate alpha from the underlying asset’s return. By combining 199 simple moving average signals in a systematic manner, the algorithm was able to maximize the q5 asset pricing model alpha using a high-volatility United States stock portfolio as a risky asset. The algorithm demonstrates superior performance compared to both individual moving average signals and existing combination algorithms.
... Campbell [6] used financial indicators that were a combination of technical and fundamental indicators to select optimal stocks in the Taiwan stock market. However, Campbell and Shiller [7] challenged the efficient market hypothesis Campbell and Viceira [8] and the accepted random walk hypothesis Malkiel [9] using various artificial intelligence algorithms and found that future changes in stock prices cannot be predicted from historical data because they are independent and random fluctuations. Therefore, future changes in stock prices are widely considered unpredictable. ...
Predicting stock prices has long been a topic of interest for analysts and researchers. The aim of this study is to develop a comprehensive model for predicting stock prices in the Tehran Stock Exchange using a combined approach of fuzzy Delphi interpretive structural modeling with the use of technical, fundamental, macroeconomic, and emotional factors. In this study, first, using the fuzzy Delphi method, 15 key criteria were identified from among 54 prediction criteria extracted from research literature according to investors' perspectives. Then, using the interpretive structural modeling approach, the relationships between them were examined and a hierarchical model was presented. Based on the findings in the ISM model, it is observed that the price is placed at the end of the hierarchy with high driving power, depending on earnings per share and cash flow index. The criteria that are placed at the bottom of the hierarchy are exchange rates and relative strength index and exponential moving average, which are the most influential indicators. This study is the first of its kind to identify stock price prediction criteria by considering all dimensions and developing hierarchical relationships between them using the ISM approach
... Geopolitical events, as significant information signals, should, therefore, be promptly reflected in stock valuations. However, the semi-strong form of EMH acknowledges that information asymmetry and processing limitations may cause delayed market reactions to complex geopolitical developments (Lo, 2004;Malkiel, 2003). ...
This paper examines the impact of various uncertainty channels on stock market returns in Saudi Arabia, with a focus on the Tadawul All Share Index (TASI). It examines factors such as Saudi-specific Geopolitical Risk, Global Oil Price Uncertainty, Climate Policy Uncertainty, and U.S. Monetary Policy Uncertainty. Using monthly data from November 1998 to June 2024 and the Local Projections (LP) methodology, the study examines how these uncertainties impact market returns across various time horizons, taking into account potential structural breaks and nonlinear dynamics. Our findings indicate significant variations in the market’s response to the uncertainty measures across two distinct periods. During the first period, geopolitical risks have a strong positive impact on market returns. Conversely, the second period reveals a reversal, with negative cumulative effects, suggesting a shift in risk–return dynamics. Oil Price Uncertainty consistently exhibits a negative impact in both periods, highlighting the changing nature of oil dependency in the Saudi market. Additionally, Climate Policy Uncertainty is becoming more significant, reflecting increased market sensitivity to global environmental policy changes. Our analysis reveals significant asymmetries in the effects of various uncertainty shocks, with Monetary Policy Uncertainty exhibiting nonlinear effects that peak at intermediate horizons, while commodity-related uncertainties exhibit more persistent impacts. These findings, which remain robust across various tests, offer critical insights for portfolio management, policy formulation, and risk assessment in emerging markets undergoing substantial economic changes.
... Sustained, non-random return structures which are not explained by model risk, can signal meaningful departures from market efficiency (Campbell et al., 1998;Malkiel, 2003). While EMH remain foundational theories in finance, the integration of behavioural insights, adaptive models, and now LLMs as pattern recognition tools reflects growing recognition of market complexity and bounded rationality, potentially identifying inefficiencies invisible to traditional statistical methods (Kahneman & Tversky, 1979;Lo, 2004). ...
This study examines whether Large Language Models (LLMs) can support momentum-based breakout strategies for retail investors by analysing technical charts and validating statistical patterns. We analysed 3,621 price charts (2023–2025) to assess LLMs’ ability to identify trading setups and deviations from random walk behaviour. 13,624 tests were run on 1,204 trades using various statistical tests. While 70% of patterns conformed to random walk behaviour in the short term (7–15 days), momentum signals rose to 26.42% over longer periods (50–250 days). Complex prompting produced both higher random walk classification (72.41% vs. 69.29%) and better returns than single-shot prompting. Incorporating Gamma Exposure (GEX) improved caution but reduced return rates. Discrepancies of up to 20% between JavaScript and Python implementations underscored LLMs’ current limitations in statistical precision. Overall, LLMs may democratise technical analysis but still require human oversight, structured prompting, and validation through traditional methods.
... The considerable variation in estimates can be succinctly explained by assuming that the degree of correlations undergoes fluctuations over time [35,86]. This contradicts the efficient market hypothesis (EMH) which, within the paradigm of an ordinary Brownian setting, dictates that market prices incorporate all available information instantaneously [87,88]. Indeed, as a consequence, this has led to the development of qualitative models such as behavioral finance [89,90], which is based on the study of psychological influence on the behavior of market practitioners, or adaptive market hypothesis [91], which relies on the concepts of evolutionary biology. ...
The diversity of diffusive systems exhibiting long-range correlations characterized by a stochastically varying Hurst exponent calls for a generic multifractional model. We present a simple, analytically tractable model which fills the gap between mathematical formulations of multifractional Brownian motion and empirical studies. In our model, called telegraphic multifractional Brownian motion, the Hurst exponent is modeled by a smoothed telegraph process which results in a stationary beta distribution of exponents as observed in biological experiments. We also provide a methodology to identify our model in experimental data and present concrete examples from biology, climate, and finance to demonstrate the efficacy of our approach.
Published by the American Physical Society 2025
... In efficient markets, the hypothesis posits that commodity prices are perfectly integrated, leading to immediate adjustments and corrections based on available information (Malkiel 2003). Augmented Dickey-Fuller (ADF) test was employed to determine the presence of a unit root in the price series and stationarity was assessed after first differencing (Table 5). ...
This paper examined the price dynamics and market integration in wholesale and retail sugar markets of India using monthly data from the Food and Agriculture Organization for four major markets from 2014-2015 to 2023-2024. Understanding these dynamics across spatially separated markets is crucial for developing policies that enhance market efficiency and promote sustainable growth. Various analytical tools were employed viz. compound annual growth rate, instability index, seasonal price index, correlation analysis, Johansen's co-integration test, and Granger causality test and impulse response function. The analysis demonstrated that the selected sugar markets exhibit suboptimal integration due to limited market intelligence, slow information flow and inadequate infrastructure. An upward trend was observed in sugar prices in all the markets over the study period. Seasonal price fluctuations were observed, linked to the crushing season and higher demand during summer months. Co-integration analysis confirmed a long-run equilibrium, Granger causality test revealed unidirectional, bidi-rectional and no causality price influence between the markets. Moreover, impulse response analysis concluded relatively well interconnected markets with potential inefficiencies, particularly in Patna market. This study uniquely highlights the interplay between market efficiency and seasonal fluctuations in the Indian sugar sector, providing a nuanced understanding of spatial integration and its policy implications. To enhance market integration and price convergence, policy recommendations include modernizing marketing systems, fostering public-private partnerships, improving infrastructure, strengthening supply chains and facilitating external trade.
... His efficient market hypothesis (EMH) posits that securities prices fully reflect all available information, making it impossible for investors to consistently outperform the market through stock selection or market timing. The theory assumes that investors are rational and maximize their utility, information is freely available to all market participants simultaneously, there are no transaction costs or taxes, investors can borrow and lend at the riskfree rate, and all investors have homogeneous expectations regarding returns (Malkiel, 2003). ...
This study examined the impact of financial reporting quality on the cost of capital among listed Iraqi banks, recognizing the cost of capital as a vital determinant of financial stability, investment decisions, and economic growth. The research focused on how faithful representation, timeliness, and value relevance influence financing costs. The investigation adopted ex-post facto research methodology as the utilized data were pre-existing and not intended for alteration. The study encompassed a population of 43 listed banks in Iraq. The sample size was 43 banks, determined through census sampling techniques. The research spanned from 2015 to 2024. Panel regression analysis was conducted and the FGLS regression model was used to examine the relationship between the variables studied. The findings revealed that both faithful representation and timeliness have a positive and statistically significant effect on the cost of capital, suggesting that greater financial transparency may expose underlying risks, leading investors to demand higher returns. 660 However, value relevance had a negative but statistically insignificant effect, indicating that although decision-useful information could lower capital costs, this relationship lacks strong evidence in the Iraqi context. The results showed how different dimensions of financial reporting quality affect investors' perceptions and capital costs in a developing financial market. This study concludes that faithful representation and timeliness significantly increase the cost of capital, potentially due to enhanced risk awareness among investors in a volatile environment. In line with the findings of this study, this study recommends that regulators and financial institutions should invest in programs that improve investors' understanding of financial statements to foster better use of disclosed information in pricing decisions.
... Perusahaan secara bertahap mengadopsi jejaring sosial, seperti Facebook, Twitter, dan Instagram yang digunakan sebagai saluran untuk mendistribusikan berita perusahaan dan mendukung strategi pemasaran mereka (Hasan & Wang, 2017 (Malkiel, 2003). ...
This study aims to examine the effect of Instagram users response to corporate Instagram post (likes and comments) toward individual ownership and institutional ownership of sharia stocks in Indonesia. The data collection method used is purposive sampling technique. The number of samples in this study were 258 companies registered in ISSI (Indonesia Sharia Stock Index) and having an active Instagram account. Data processing using SPSS.26 with Ordinary Least Square (OLS). The results showed that Instagram user respond increasing the institutional investor and individual investor in sharia stock. The interpretation for this findings that such Instagram activities increase the visibility of the company across a more diverse group of investors. This increase visibility makes information more accessible to individual and institutional investors, so that they are attracted to invest more in sharia stock. Sharia issuer companies should also use social media to provide valid information to potential investors. So that it can be one of the foundations of investors in choosing Islamic stocks. This research is the first study that used social media to measure Sharia Stock Ownership
... The considerable variation in estimates can be succinctly explained by assuming that the degree of correlations undergoes fluctuations over time [35,86]. This contradicts the Efficient Market Hypothesis (EMH), which within the paradigm of an ordinary Brownian setting, dictates that market prices incorporate all available information instantaneously [87,88]. Indeed, as a consequence, this has led to the development of qualitative models such as Behavioural finance [89,90], which is based on the study of psychological influence on the behaviour of market practitioners, or Adaptive Market Hypothesis [91] which relies on the concepts of evolutionary biology. ...
The diversity of diffusive systems exhibiting long-range correlations characterized by a stochastically varying Hurst exponent calls for a generic multifractional model. We present a simple, analytically tractable model which fills the gap between mathematical formulations of multifractional Brownian motion and empirical studies. In our model, called telegraphic multifractional Brownian motion, the Hurst exponent is modelled by a smoothed telegraph process which results in a stationary beta distribution of exponents as observed in biological experiments. We also provide a methodology to identify our model in experimental data and present concrete examples from biology, climate and finance to demonstrate the efficacy of our approach.
... Second, longer periods reduce the overall market noise as compared to shorter periods, and they also account for business cycles (Shiller 2005). Lastly, longer period data may experience a minimum impact of outliers as compared to shorter periods, and are useful in capturing the long-term risk and market behavior of markets (Malkiel 2003). ...
The primary objective of this empirical study is to investigate the Indian equity market network by analyzing its topological properties using the disparity filtering technique, and a minimum spanning tree. It investigates the backbone structure of the reduced weighted equity network and highlights the sector-based cluster formation. This study also examines the relative importance of each sector by utilizing different key network metrics, with comparative analysis against other emerging markets. It observes a high sector-specific dominance, power imbalance, disparity, and risk concentration in the healthcare and technology sectors. It also finds that fast-moving consumer goods and the healthcare sector can play important roles in maintaining economic stability, public health, and social wellbeing. The findings of this study are highly useful in understanding the market structure, risk management, and investment decisions in the emerging market context of India.
... Economists analyze and predict the equity and debt market prices and manage the associated risk (Matilla-García et al. 2005). Before the boom of AI, equity and debt market analysis was based on human decision-making, fundamental analysis, technical analysis, and news and information analysis (Malkiel 2003). The study of the equity and debt market has undergone drastic transformations such as algorithmic trading, sentiment analysis, risk assessment and management, robo advisor, quantitative models, and fraud detection (Lo and Wang 2014). ...
Motivated by the recent boom in artificial intelligence (AI) playing a significant role in the economics of individuals, firms, and government bodies, we investigate the role of AI in economics by reviewing the literature (2231 articles) during the last 34 years (1990 to November 2024). We identify five research streams: (1) AI and economic modeling, (2) AI and macroeconomics (eight sub‐streams), (3) AI and equity and debt market, (4) AI and prediction modeling (three sub‐streams), and (5) AI and economics of innovation. Further, we offer suggestions for future research (20 questions). Additionally, we outline a framework to consider changes in economics before and after AI adoption. Further, the critical AI‐based methods are identified and discussed.
... According to Jensen (1968), active management of sectoral indices may lead to better performance during periods of market inefficiency, but the costs associated with active management can offset these gains. Malkiel (2003) argues that broad indices generally outperform actively managed portfolios over the long term due to their lower costs and inherent diversification. This view is supported by Ellis (2017), who emphasizes the importance of low-cost index funds in achieving market-average returns. ...
... This situation also undermines the efficient market hypothesis formulated by Fama (1970). According to it, stock prices reflect all information available at a given time, which means that investors cannot expect above-average profits (Malkiel, 2003). ...
... Moreover, the complex nature of bond valuation, involving factors such as interest rate expectations, credit risk, and liquidity premiums, creates potential opportunities for informed investors to exploit market inefficiencies (Fama & French, 1993). (Malkiel, 2003) states that while markets may not be perfectly efficient, they are efficient enough that the average investor cannot consistently outperform the market. ...
This dissertation provides a comprehensive analysis of U.S. fixed income instruments over the past decade, focusing on their performance and the implications for investment strategies. The study examines various types of fixed income securities, including Treasury securities, corporate bonds, and municipal bonds, analysing their risk-return profiles and the impact of significant economic events such as the COVID-19 pandemic. Utilizing data from Bloomberg Terminal, the research employs descriptive statistics, regression analysis, correlation coefficients, and event-based analysis to assess the performance of U.S. fixed income instruments relative to their U.K. counterparts. The findings reveal distinct performance trends across different bond indices, highlighting the superior stability of U.S. Treasuries and the higher returns, albeit with increased volatility, of corporate bonds. The study also uncovers a moderate positive correlation between U.S. and U.K. bond markets, suggesting partial synchronization influenced by global economic conditions. Furthermore, the event-based analysis demonstrates significant shifts in bond prices and coupon rates during the pandemic, underscoring the importance of dynamic asset allocation in volatile market environments. This research contributes to the field of investment management by providing evidence-based insights into the strategic role of fixed income instruments in diversified portfolios. It offers valuable guidance for investors seeking to optimize their asset allocation, particularly in the context of risk management and the pursuit of stable, predictable returns.
... Fama (1970) argued that such information reveals underlying risks, thereby influencing investors' expected returns. Malkiel (2003) contended that market risk is affected by unpredictable economic events. In testing for normal optimal returns, the weak form of the EMH, as proposed by Fama (1970), is employed, which posits that information relevant to current stock prices is independent of past prices. ...
From an accounting perspective, taxes reduce profits and are often perceived as diminishing shareholders’ rights. Consequently, government support through tax reductions plays a crucial role in enhancing the effectiveness of corporate strategies aimed at minimizing tax burdens. From the perspective of the Efficient Market Hypothesis (EMH), government tax incentive policies serve as vital signals to investors, shaping their expectations and influencing investment decisions. This study focuses on Indonesia’s tax incentive policy introduced at the onset of the COVID-19 pandemic on April 1, 2020, and continuing until September 30, 2023. To assess market efficiency during this period, portfolios were constructed from the top 21 firms listed on the IDX Quality 30 and IDX High Dividend 20 indices, categorized by their systematic risk and cost of equity. The findings indicate that portfolios with higher systematic risk and cost of equity exhibit more optimal returns, greater volatility, and better risk-return trade-offs. Conversely, portfolios with lower systematic risk and cost of equity tend to yield suboptimal returns due to their passive investment characteristics. Overall, the returns from all portfolios during the tax incentive period align with the weak form of the EMH, albeit showing negative autocorrelation instead of a purely random walk pattern. These findings imply that information regarding tax incentives influences prices primarily among firms with higher cost of equity or systematic risk. This study contributes to the understanding of the EMH by examining the impact of tax incentives during the pandemic while controlling for both the cost of equity and systematic risk.
This study examined the impact of the adoption of International Financial Reporting Standards (IFRS) on the financial performance of consumer goods manufacturing firms and the market efficiency in Nigeria. Data from the Nigerian Exchange Group (NXG) and Bloomberg (2000-2023) were analyzed using descriptive statistics, correlation matrix, and panel regression. Model diagnostics confirmed the validity of the data. Results indicated that IFRS adoption positively influenced performance, showing a significant relationship between market returns and individual stock prices before and after adoption. The study also found that Nigeria's capital market exhibited moderate efficiency in both periods. It concludes that IFRS adoption has significantly impacted the financial performance of manufacturing firms and recommends that companies leverage IFRS benefits to improve operations. Investors should focus on market returns, and companies should enhance communication of market trends.
This study examines the efficiency of Iranian capital market after the imposition of economic sanctions, currency crisis and Covid 19 pandemic. Some events after 2008 have fundamentally changed the Iranian capital market environment and we expect these reforms to change the level of market efficiency. This research investigates the daily returns behavior in Tehran Stock Exchange (TSE) utilizing autocorrelation, augmented Dickey-Fuller, and runs tests over the period 2008-2022. The statistical significance of the data was assessed using both parametric and nonparametric test statistics. The results confirm that the Tehran Stock Exchange index has been affected by the Covid-19 pandemic crisis. It was also further revealed that economic and banking sanctions have had a direct impact on the Iranian economy and the Tehran Stock Exchange market, such that after the approval of each of the sanctions, significant structural failures were observed in the trend of the Tehran Stock Exchange index. And also the results indicate that all tests don't support that TSE daily returns follow random walk. Therefore, we conclude that in the Iranian capital market, abnormal returns can still be obtained by using technical and fundamental analysis. The study's contribution might have been compromised by the absence of socio-demographic, technical, financial and other significant policy factors from the analysis. This paper is the first to examine the efficiency of the capital market at a weak form, during the period of the Covid-19 pandemic and the imposition of international sanctions against the Iranian economy.
The Model of Intuitive Morality and Exemplars (MIME) suggests that news audiences, including investors, evaluate news based on their moral frames, and that these moral evaluations shape behavior. We extracted moral signals from 382,185 news articles across an 8-month period and examined their predictive effect on stock market movement. Results indicate that morality is a strong predictor during low economic periods and is driven by subversion and sanctity. Overall, our study suggests that moral framing and its foundations are important considerations for research on news effects, especially during periods of economic instability. The study provides an additional theoretical perspective on stock market fluctuations as well as practical implications for stakeholders with an interest in dampening collective panics and stabilizing investor sentiment.
Mixed‐ownership reform (the Reform) is crucial for the construction of a modern market economy. Through a proprietary data set of 32 provinces in China, we find that government intelligent transformation (GIT) can attract private firms acquiring local state‐owned enterprises (SOEs), and such link is stronger among target firms in central and western regions, and acquirers with lower competitive positions and higher levels of digital transformation. Additionally, we also find this effect exists both in local and nonlocal private firms. Further tests suggest that GIT can regulate government behaviors, reduce relational transactions, and enhance local policy environment quality and the level of local market legal governance, thereby promoting private firms to participate in the Reform. Moreover, in the provinces with higher levels of GIT, there is a greater likelihood that private firms will gain control rights over SOEs and obtain more resources following the Reform. Our findings may have policy implications for the world's largest emerging market.
Predicting the highly volatile foreign exchange (FOREX) market is a challenging task influenced by economic, geopolitical, and psychological factors. Sudden market fluctuations and unexpected events further complicate this endeavor. The present study introduces a novel hybrid regression approach for FOREX rate prediction, integrating empirical mode decomposition, a stacked long short-term memory deep learning model, and the particle swarm optimization metaheuristic into a unified framework. The proposed method is evaluated on three major currency pairs, namely EUR/USD, USD/CHF, and EUR/CHF, and benchmarked against its standalone components as well as state-of-the-art machine learning models, including XGBoost and support vector regression. Additionally, its effectiveness as a signal provider is tested through a simulated trading environment focused on the EUR/USD pair. The results demonstrate that the proposed approach outperforms competing models, achieving high profitability while minimizing risk.
The view that a business unit can better compete against product-market rivals if granted funding from its parent firm’s internal capital market (ICM) has lost traction within strategy, despite conflicting evidence. We develop a theory to explain when funding from a parent firm’s ICM should enable a business unit to more effectively capture value (i.e., profit) from its investment opportunities under product-market competition. We depart from prior theories by examining how opportunities relate to competition. Specifically, we propose a typology of opportunities along two strategic dimensions. The first dimension is firm-specificity, a concept derived from the resource-based view. It refers to whether an opportunity stems from unique firm resources and capabilities and is therefore exclusive to a business unit rather than shared with (and contestable by) its product-market rivals. The second dimension is uncertainty about the investment path, a concept derived from the literature on investment under uncertainty and real options. When present, it is impossible (and undesirable) to commit upfront to a fully predetermined set of investments in an opportunity. These dimensions imply that different opportunities may have distinct critical needs in terms of funding—such as secrecy, timeliness, and reliability—that must be satisfied for a business unit to capture value. Ultimately, our theory indicates that receiving funding from a parent firm’s ICM increases a business unit’s chances of capturing value when those critical needs are present, suggesting that units with ICM funding may prevail in some competitive environments.
The causes and consequences of stock market collapses have been the subject of several researches. Some of the explanations might be attributed to economic factors such as “rising interest rates”, “high inflation”, or a “recession”. Political uncertainty, natural disasters, or a crisis in a specific industry might potentially be the cause. These studies primarily concentrate on stock price prediction across all main indexes such as business profits, geopolitical unrest, the financial crisis, and pandemic conditions. The process of predicting a stock crisis is challenging since the stock market is more volatile than usual. The challenge of crisis prediction is difficult for academics and investors. The price history and trend volume of 50 stocks have been collected from “The national stock exchange” of India. First, the irrelevant financial parameters are removed using “principal component analysis”. The second is the bi-directional LSTM deep neural for regression approach, which is used to categorize stocks with solid fundamentals. The third is the identification and detection of bubble and stock crisis events using the “moving average crossover”. The fourth method uses a bi-directional LSTM deep neural “convolutional neural network” and “Bayesian linear regression” technique to anticipate stock crises. “mean squared error”, “mean absolute error”, and “root mean square error (RMSE)” parameters were used for the performance of the model. The stock crises using the bi-directional LSTM deep neural technique, which has outperformed as 198.21% RMSE value of “Adani-Ports”. The researchers can investigate other technological indications in the future to forecast the crisis point. It can more improved using a new optimizer with a hybrid regression model.
This study examines the impact of social media sentiment on stock prices in the banking and finance sector of Turkey, focusing on stocks that have been detected to have been manipulated by the Capital Markets Board (CMB). Using comments from Twitter and Investing.com, the study analyzes both general sentiment trends and specific suspicious actions associated with stocks. Using a binary sentiment classification approach, the study distinguishes between (1) three classification models (positive, neutral, negative) and (2) a binary classification that identifies suspicious and neutral comments. Advanced machine learning and deep learning models are used in the study to generate predictive insights. Testing various feature combinations, the study finds that the most successful models are LSTM and CNN, achieving the highest predictive accuracy when Twitter sentiment is combined with the suspicious score, and outperforming traditional models based solely on historical price trends and Investing.com sentiment data. The results demonstrate the strong predictive potential of Twitter and highlight the role of social media in indicating and potentially driving suspicious market behavior. This study highlights the need to monitor sentiment trends in markets, providing practical insights for investors and regulators.
This article explores the complex mechanisms of financial markets and the evolving patterns in investment behavior. Financial markets are integral to the global economy, encompassing the primary market for issuing new securities and the secondary market for trading existing ones. The primary market facilitates capital rising through methods like initial public offerings (IPOs), while the secondary market allows for the trading of these securities, with prices influenced by supply and demand dynamics. Recent shifts in investment behavior are driven by technological advancements and changes in accessibility. Innovations such as algorithmic trading, robo-advisors, and fractional shares have broadened investment opportunities and made markets more accessible. Social media and fintech have further transformed investment practices, leading to trends in ethical investing and increased short-term trading. Additionally, economic and geopolitical factors, along with demographic changes like an aging population and the preferences of Millennials and Gen Z, are reshaping market dynamics and investment strategies. Understanding these developments is essential for navigating today’s evolving financial landscape.
The challenge of stock market prediction still persists for investors, researchers, portfolio managers, and a diverse
array of stakeholders. This study aims to investigate the effectiveness of artificial neural networks, deep neural
networks, and long-term memory models in predicting stock market indices. The study encompasses daily index
prices of Nifty 50, S&P 500, and Euronext 100, along with macroeconomic data and technical indicators from
January 2015 to September 2023, containing 2201 observations for each market. When evaluated using different
metrics, the results indicate better performance of artificial neural network models than deep neural networks
and long short-term memory models in most cases. The results from the validation set, test set, and an extra
holdout set indicate better performance of artificial neural networks in the context of the Nifty 50 index. In the
case of the S&P 500, the deep neural network performed well on validation set results, and artificial neural
networks performed better than other models in the test set and holdout set predictions. In the case of Euronext
100, the deep neural network again performed better on the validation set; however, the Long short-term
memory model outperformed other models with better predictions on testing and holdout set data. The study
also applies a modified grid search approach to find the optimal hyperparameters, where the hyperparameters
from the simpler model, i.e., artificial neural network using a conventional grid search approach, were used in
more complex models like deep neural network and long short-term memory model.
This study aims to improve the precision of financial modeling in dynamic stock and foreign exchange markets by incorporating Particle Swarm Optimization (PSO) with essential Machine Learning (ML) models. The goal is to achieve a balanced and precise relationship between the complexity of the model and the quality of its fit, highlighting the advantageous effects of advanced optimization techniques in this specific situation. The empirical testing of pivotal models, namely Support Vector Machine (SVM), Decision Tree, and Random Forest, is conducted within a comprehensive framework incorporating PSO. The evaluation process aims to measure the impact of PSO on accuracy metrics, specifically by analyzing the observed changes in Akaike Information Criterion (AIC) values before and after the implementation of PSO. This approach demonstrates the effectiveness of optimization in achieving a balance between the model’s complexity and the fit’s accuracy. Combining PSO with ML models improves forecast accuracy for the overall monthly volume of stocks and foreign exchange markets. The significant reduction in AIC values after optimization highlights the concrete advantages of optimization techniques, confirming their effectiveness in attaining an optimized balance. This research uniquely contributes by creatively combining advanced optimization techniques with ML to develop financial models.
Several predetermined variables that reflect levels of bond and stock prices appear to predict returns on common stocks of firms of various sizes, long-term bonds of various default risks, and default-free bonds of various maturities. The returns on small-firm stocks and low-grade bonds are more highly correlated in January than in the rest of the year with previous levels of asset prices, especially prices of small-firm stocks. Seasonality is found in several conditional risk measures, but such seasonality is unlikely to explain, and in some cases is opposite to, the seasonal found in mean returns.
Considers that intuitive predictions follow a judgmental heuristic-representativeness. By this heuristic, people predict the outcome that appears most representative of the evidence. Consequently, intuitive predictions are insensitive to the reliability of the evidence or to the prior probability of the outcome, in violation of the logic of statistical prediction. The hypothesis that people predict by representativeness was supported in a series of studies with both naive and sophisticated university students (N = 871). The ranking of outcomes by likelihood coincided with the ranking by representativeness, and Ss erroneously predicted rare events and extreme values if these happened to be representative. The experience of unjustified confidence in predictions and the prevalence of fallacious intuitions concerning statistical regression are traced to the representativeness heuristic.
"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.
ABSTRACT Two easily measured variables, size and book-to-market equity, combine to capture the cross-sectional variation in average stock returns associated with market {3, size, leverage, book-to-market equity, and earnings-price ratios. Moreover, when the tests allow for variation in {3 that is unrelated to size, the relation between market {3 and average return is flat, even when {3 is the only explanatory variable. THE ASSET-PRICING MODEL OF Sharpe (1964), Lintner (1965), and Black (1972)
We estimate the equity premium using dividend and earnings growth rates to measure the expected rate of capital gain. Our estimates for 1951 to 2000, 2.55 percent and4.32 percent, are much lower than the equity premium produced by the average stock return,7.43 percent. Our evidence suggests that the high average return for 1951 to 2000 is due to a decline in discount rates that produces a large unexpected capital gain. Our main conclusion is that the average stock return of the last half-century is a lot higher than expected.
This study examines, month-by-month, the empirical relation between abnormal returns and market value of NYSE and AMEX common stocks. Evidence is provided that daily abnormal return distributions in January have large means relative to the remaining eleven months, and that the relation between abnormal returns and size is always negative and more pronounced in January than in any other month — even in years when, on average, large firms earn larger risk-adjusted returns than small firms. In particular, nearly fifty percent of the average magnitude of the ‘size effect’ over the period 1963–1979 is due to January abnormal returns. Further, more than fifty percent of the January premium is attributable to large abnormal returns during the first week of trading in the year, particularly on the first trading day.
Market efficiency survives the challenge from the literature on long-term return anomalies. Consistent with the market efficiency hypothesis that the anomalies are chance results, apparent overreaction to information is about as common as underreaction, and post-event continuation of pre-event abnormal returns is about as frequent as post-event reversal. Most important, consistent with the market efficiency prediction that apparent anomalies can be due to methodology, most long-term return anomalies tend to disappear with reasonable changes in technique.
In this article, the authors argue that financial advising is a prescriptive activity whose main objective should be to guide investors to make decisions that best serve their interests. To advise effectively, advisors must be guided by an accurate picture of the cognitive and emotional weaknesses of investors that relate to making investment decisions: their occasionally faulty assessment of their own interests and true wishes, the relevant facts that they tend to ignore, and the limits of their ability to accept advice and to live with the decisions they make. The authors sketch some parts of that picture as they have emerged from research on judgment, decision-making, and regret over the last three decades. They deal with a selection of judgment biases and with errors of preference, which arise either from mistakes that people make in assigning values to future outcomes, or from improper combinations of probabilities and values. The authors provide recommendations to help financial advisors mitigate the harmful effects of these biases as well as a checklist they can use to measure their effectiveness at dealing with these biases.
Question: How can aggregate stock market valuation ratios - the dividend-price ratio and earnings-price ratio - fail to predict future stock price movements, movements in the denominator, given that these ratios themselves are mean-reverting? Answer: If these ratios predict the growth rates of dividends or earnings, movements in the numerator. Given their analysis of long aggregate historical stock market data ti-om the United States and eleven other countries, the authors conclude that these ratios do not forecast dividends or earnings, certainly not consistently in the right direction. The analysis suggests a gloomy next four or ten years for the United States and some other stock markets.
We document a striking positive stock price reaction to the announcement of corporate name changes to Internet-related dotcom names. This “dotcom” effect produces cumulative abnormal returns on the order of 74 percent for the 10 days surrounding the announcement day. The effect does not appear to be transitory; there is no evidence of a postannouncement negative drift. The announcement day effect is also similar across all firms, regardless of the firm’s level of involvement with the Internet. A mere association with the Internet seems enough to provide a firm with a large and permanent value increase.
The efficient markets hypothesis has been the central proposition in finance for nearly thirty years. It states that securities prices in financial markets must equal fundamental values, either because all investors are rational or because arbitrage eliminates pricing anomalies. This book describes an alternative approach to the study of financial markets: behavioral finance. This approach starts with an observation that the assumptions of investor rationality and perfect arbitrage are overwhelmingly contradicted by both psychological and institutional evidence. In actual financial markets, less than fully rational investors trade against arbitrageurs whose resources are limited by risk aversion, short horizons, and agency problems. The book presents and empirically evaluates models of such inefficient markets. Behavioral finance models both explain the available financial data better than does the efficient markets hypothesis and generate new empirical predictions. These models can account for such anomalies as the superior performance of value stocks, the closed end fund puzzle, the high returns on stocks included in market indices, the persistence of stock price bubbles, and even the collapse of several well-known hedge funds in 1998. By summarizing and expanding the research in behavioral finance, the book builds a new theoretical and empirical foundation for the economic analysis of real-world markets.
The proportion of U.S. firms paying dividends drops sharply during the 1980s and 1990s. Among NYSE, AMEX, and Nasdaq firms, the proportion of dividend payers falls from 66.5% in 1978 to only 20.8% in 1999. The decline is due in part to an avalanche of new listings that tilts the population of publicly traded firms toward small firms with low profitability and strong growth opportunities—the timeworn characteristics of firms that typically do not pay dividends. But this is not the whole story. The authors' more striking finding is that, no matter what their characteristics, firms in general have become less likely to pay dividends.
The authors use two different methods to disentangle the effects of changing firm characteristics and changing propensity to pay on the percent of dividend payers. They find that, of the total decline in the proportion of dividend payers since 1978, roughly one-third is due to the changing characteristics of publicly traded firms and two-thirds is due to a reduced propensity to pay dividends. This lower propensity to pay is quite general—dividends have become less common among even large, profitable firms.
Share repurchases jump in the 1980s, and the authors investigate whether repurchases contribute to the declining incidence of dividend payments. It turns out that repurchases are mainly the province of dividend payers, thus leaving the decline in the percent of payers largely unexplained. Instead, the primary effect of repurchases is to increase the already high payouts of cash dividend payers.
A literature survey reveals consistent excess returns after public announcements of firms' earnings. If the information in publicly-announced earnings is a public good, then these results seem inconsistent with equilibrium in the securities market: public goods, being without private cost, should earn no private return. Alternative explanations of this anomaly are considered. The most likely explanation is that earnings variables proxy for omitted variables or other misspecification effects in the two-parameter model: that the measured market portfolio is not mean-variance efficient. Similar anomalies and explanations apply to other ‘yield-surrogates’, including dividend yields and Value Line ratings.
The empirical relationship between earnings' yield, firm size and returns on the common stock of NYSE firms is examined in this paper. The results confirm that the common stock of high E/P firms earn, on average, higher risk-adjusted returns than the common stock of low E/P firms and that this effect is clearly significant even if experimental control is exercised over differences in firm size. On the other hand, while the common stock of small NYSE firms appear to have earned substantially higher returns than the common stock of large NYSE firms, the size effect virtually disappears when returns are controlled for differences in risk and E/P ratios. The evidence presented here indicates that the E/P effect, however, is not entirely independent of firm size and that the effect of both variables on expected returns is considerably more complicated than previously documented in the literature.
This paper investigates transitory components in stock prices. After showing that statistical tests have little power to detect persistent deviations between market prices and fundamental values, we consider whether prices are mean-reverting, using data from the United States and 17 other countries. Our point estimates imply positive autocorrelation in returns over short horizons and negative autocorrelation over longer horizons, although random-walk price behavior cannot be rejected at conventional statistical levels. Substantial movements in required returns are needed to account for these correlation patterns. Persistent, but transitory, disparities between prices and fundamental values could also explain our findings.
This paper examines two alternative models of the process generating stock returns. Under the calendar time hypothesis, the process operates continuously and the expected return for Monday is three times the expected return for other days of the week. Under the trading time hypothesis, returns are generated only during active trading and the expected return is the same for each day of the week. During most of the period studied, from 1953 through 1977, the daily returns to the Standard and Poor's composite portfolio are inconsistent with both models. Although the average return for the other four days of the week was positive, the average for Monday was significantly negative during each of five-year subperiods.
Our paper re-examines the profitability of relative strength or momentum trading strategies (buying past strong performers and selling past weak performers). We find that standard relative strength strategies require frequent trading in disproportionately high cost securities such that trading costs prevent profitable strategy execution. In the cross-section, we find that those stocks that generate large momentum returns are precisely those stocks with high trading costs. We conclude that the magnitude of the abnormal returns associated with these trading strategies creates an illusion of profit opportunity when, in fact, none exists.
This study uses 90 years of daily data on the Dow Jones Industrial Average to test for the existence of persistent seasonal
patterns in the rates of return. Methodological issues regarding seasonality tests are considered. We find evidence of persistently
anomalous returns around the turn of the week, around the turn of the month, around the turn of the year, and around holidays.
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.
This paper investigates whether predictable patterns that previous empirical work in finance have isolated appear to be persistent and exploitable by portfolio managers. On a sample that is free from survivorship bias we construct a test wherein we simulate the purchases and sales an investor would undertake to exploit the predictable patterns, charging the appropriate transaction costs for buying and selling and using only publicly available information at the time of decision making. We restrict investment to large companies only to assure that the full cost of transactions is properly accounted for. We confirmed on our sample that contrarian strategies yield sizable excess returns after adjusting for risk, as measured by beta. Using analysts' estimates of long - term growth we construct a test of the Lakonishok, Shleifer, and Vishny (1994) hypothesis. We cannot reject the hypothesis that neither the low - expected - growth portfolio nor the high - expected - growth portfolio yielded any risk - adjusted excess return over the 1980s. Our finding suggests that the superior performance of contrarian strategies cannot adequately be explained by the superior performance of stocks with low expected growth. © 2000 by the President and Fellows of Harvard College and the Massachusetts Institute of Technolog
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.
In monthly U.S. data for 1959–1979 and 1979–1983, the state of the term structure of interest rates predicts excess stock returns, as well as excess returns on bills and bonds. This paper documents this fact and uses it to examine some simple asset pricing models. In 1959–1979, the data strongly reject a single-latent-variable specification of predictable excess returns. There is considerable evidence that conditional variances of excess returns change through time, but the relationship between conditional mean and conditional variance is reliably positive only at the short end of the term structure.
Technical analysis, also known as 'charting,' has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis-the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and we apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution-conditioned on specific technical indicators such as head-and-shoulders or double bottoms-we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value. Copyright The American Finance Association 2000.
Analysis of decision making under risk has been dominated by expected utility theory, which generally accounts for people's actions. Presents a critique of expected utility theory as a descriptive model of decision making under risk, and argues that common forms of utility theory are not adequate, and proposes an alternative theory of choice under risk called prospect theory. In expected utility theory, utilities of outcomes are weighted by their probabilities. Considers results of responses to various hypothetical decision situations under risk and shows results that violate the tenets of expected utility theory. People overweight outcomes considered certain, relative to outcomes that are merely probable, a situation called the "certainty effect." This effect contributes to risk aversion in choices involving sure gains, and to risk seeking in choices involving sure losses. In choices where gains are replaced by losses, the pattern is called the "reflection effect." People discard components shared by all prospects under consideration, a tendency called the "isolation effect." Also shows that in choice situations, preferences may be altered by different representations of probabilities. Develops an alternative theory of individual decision making under risk, called prospect theory, developed for simple prospects with monetary outcomes and stated probabilities, in which value is given to gains and losses (i.e., changes in wealth or welfare) rather than to final assets, and probabilities are replaced by decision weights. The theory has two phases. The editing phase organizes and reformulates the options to simplify later evaluation and choice. The edited prospects are evaluated and the highest value prospect chosen. Discusses and models this theory, and offers directions for extending prospect theory are offered. (TNM)
This paper examines the comovement of stocks with similar ticker symbols. For one such pair of firms, there is a significant correlation between returns, volume, and volatility at short frequencies. Deviations from "fundamental value" tend to be reversed within several days, although there is some evidence that the return comovement persists for longer horizons. Arbitrageurs appear to be limited in their ability to eliminate these deviations from fundamentals. This anomaly allows the observation of noise traders and their effect on stock prices independent of changes in information and expectations. Copyright The American Finance Association 2001.
On the trading day prior to holidays, stocks advance with disproportionate frequency and show high mean returns averaging nine to fourteen times the mean return for the remaining days of the year. Over one third of the total return accruing to the market portfolio over the 1963-82 period was earned on the eight trading days that fall before holiday market closings each year. Examination of hourly preholiday stock returns reveals high returns throughout the day. Preholiday stock returns in the posttest 1983-86 period are also examined. Copyright 1990 by American Finance Association.
This paper presents estimates indicating that, for aggregate U.S. stock market data 1871-1986, a long historical average of real earnings is a good predictor of the present value of future real dividends. This is true even when the information contained in stock prices is taken into account. We estimate that for each year the optimal forecast of the present value of future real dividends is roughly a weighted average of moving average earnings and current real price, with between 2/3 and 3/4 of the weight on the earnings measure. This means that simple present value models of stock prices can be strongly rejected. We use a vector autoregressive approach which enables us to compute the implications of this for the behavior of stock prices and returns. We estimate that log dividend-price ratios are more variable than, and virtually uncorrelated with, their theoretical counterparts given the present value models. Annual returns on stocks are quite highly correlated with their theoretical counterparts, but are two to four times as variable. Our approach also reveals the connection between recent papers showing forecastability of long-horizon returns on corporate stocks, and earlier literature claiming that stock prices are too volatile to be accounted for in terms of simple present value models. We show that excess volatility directly implies the forecastability of long-horizon returns.
Economists have long been puzzled by why firms pay dividends when alternative methods of rewarding shareholders and financiers exist which involve less taxes. This paper will highlight the fact that firms can distribute cash to equity holders in ways more lightly taxed than dividends. The two methods we examine are share repurchase programs and cash-financed mergers and acquisitions. So why should cash distributions from firms to shareholders ever take the form of dividends? This paper first provides evidence on the explosive growth in dividend cash payments, and then discusses how this evidence should affect theories about corporate finance.
This paper provides a survey on studies that analyze the macroeconomic effects of intellectual property rights (IPR). The first part of this paper introduces different patent policy instruments and reviews their effects on R&D and economic growth. This part also discusses the distortionary effects and distributional consequences of IPR protection as well as empirical evidence on the effects of patent rights. Then, the second part considers the international aspects of IPR protection. In summary, this paper draws the following conclusions from the literature. Firstly, different patent policy instruments have different effects on R&D and growth. Secondly, there is empirical evidence supporting a positive relationship between IPR protection and innovation, but the evidence is stronger for developed countries than for developing countries. Thirdly, the optimal level of IPR protection should tradeoff the social benefits of enhanced innovation against the social costs of multiple distortions and income inequality. Finally, in an open economy, achieving the globally optimal level of protection requires an international coordination (rather than the harmonization) of IPR protection.
Burton G Malkiel81 r“Prospect Theory: An Analysis of Decision Un-der Risk
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Review of Robert J. Shiller’s Irrational Exuberance
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Comments: Symposium on Volatility in U.S. and Japanese Stock Markets
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Price-Earnings Ratios as Forecasters of Returns: The Stock Market Outlook in 1996 Unpublished manuscript
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Predicting Returns in Stock and Bond Markets Are Seasonal Anomalies Real? A Ninety-Year Perspective
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Lakonishok, Josef and S. Smidt. 1988. " Are
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Lakonishok, Josef, Andrei Shleifer and Robert Vishny. 1994. " Contrarian Investment, Extrapolation, and Risk. " Journal of Finance. December, 49, pp. 1541–578.
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Princeton Lectures in Finance Proof that Properly Anticipated Prices Fluctuate Randomly
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Ross, Stephen. Forthcoming. Princeton Lectures
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