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Earnings Yields, Market Values, and Stock Returns
Abstract
Earlier evidence concerning the relation between stock returns and the effects of size and earnings to price ratio (E/P) is not clear‐cut. This paper re‐examines these two effects with (a) a substantially longer sample period, 1951–1986, (b) data that are reasonably free of survivor biases, (c) both portfolio and seemingly unrelated regression tests, and (d) an emphasis on the important differences between January and other months. Over the entire period, the earnings yield effect is significant in both January and the other eleven months. Conversely, the size effect is significantly negative only in January. We also find evidence of consistently high returns for firms of all sizes with negative earnings.
... The existing propositions in the literature for stock price movement and stock price predictions can be broadly classified into three broad categories based on the choice of variables and approaches and techniques adopted in modeling. The first category includes approaches that use simple regression techniques on cross sectional data [12][13][14][15][16]. These models don't yield very accurate results since stock price movement is a highly non-linear process. ...
... This variable is coded into a numeric data, with "1" referring to the month of January and "12" referring to the month of December. The value of the variable month lies in the closed interval [1,12]. b) day_month: this variable refers to the day of the month to which a given record belongs. ...
Prediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted, on the other hand, there are propositions illustrating that, if appropriately modelled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of four years: 2015 – 2018. Based on the NIFTY data during 2015 – 2018, we build various predictive models using machine learning approaches, and then use those models to predict the “Close” value of NIFTY 50 for the year 2019, with a forecast horizon of one week, i.e., five days. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual “Close” values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network (CNN) with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.
... The existing propositions in the literature for stock price movement and stock price predictions can be broadly classified into three broad categories based on the choice of variables and approaches and techniques adopted in modeling. The first category includes approaches that use simple regression techniques on cross sectional data [12][13][14]. These models don't yield very accurate results since stock price movement is a highly non-linear process. ...
... This variable is coded into a numeric data, with "1" referring to the month of January and "12" referring to the month of December. The value of the variable month lies in the range [1,12]. b) day_month: this variable refers to the day of the month to which a given record belongs. ...
Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, and collect its daily price movement over a period of three years (2015 – 2017). Based on the data of 2015 – 2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week. For predicting the price movement patterns, we use a number of classification techniques, while for predicting the actual closing price of the stock, various regression models have been used. We also build a Long and Short-Term Memory (LSTM)-based deep learning network for predicting the closing price of the stocks and compare the prediction accuracies of the machine learning models with the LSTM model. We further augment the predictive model by integrating a sentiment analysis module on Twitter data to correlate the public sentiment of stock prices with the market sentiment. This has been done using Twitter sentiment and previous week closing values to predict stock price movement for the next week. We tested our proposed scheme using a cross validation method based on Self Organizing Fuzzy Neural Networks (SOFNN) and found extremely interesting results.
... The existing propositions in the literature for stock price movement and stock price predictions can be broadly classified into three broad categories based on the choice of variables and approaches and techniques adopted in modeling. The first category includes approaches that use simple regression techniques on cross sectional data [12][13][14]. These models don't yield very accurate results since stock price movement is a highly non-linear process. ...
... This variable is coded into a numeric data, with "1" referring to the month of January and "12" referring to the month of December. The value of the variable month lies in the range [1,12]. b) day_month: this variable refers to the day of the month to which a given record belongs. ...
Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, and collect its daily price movement over a period of three years (2015 – 2017). Based on the data of 2015 – 2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week. For predicting the price movement patterns, we use a number of classification techniques, while for predicting the actual closing price of the stock, various regression models have been used. We also build a Long and Short-Term Memory (LSTM)-based deep learning network for predicting the closing price of the stocks and compare the prediction accuracies of the machine learning models with the LSTM model. We further augment the predictive model by integrating a sentiment analysis module on Twitter data to correlate the public sentiment of stock prices with the market sentiment. This has been done using Twitter sentiment and previous week closing values to predict stock price movement for the next week. We tested our proposed scheme using a cross validation method based on Self Organizing Fuzzy Neural Networks (SOFNN) and found extremely interesting results.
... In general, it can be divided into 3 classes. The first category is where researchers focus on approaches that use simple regression techniques on cross sectional data [33]- [35]. These models don't yield very accurate results since stock price movement is a highly nonlinear process. ...
... Consequently, our final sample includes 488 firms and 37,938 observations. Jaffe et al. (1989) reveal that the look-ahead occurs when the researchers apply the historical information while the investors have yet to reach it. As a result, the 6-month period between the end of the fiscal year and the return testing is suitable. ...
We are the first to determine the effect of intangible intensity (INTANG) on cross-sectional stock returns after controlling financial constraints in the Vietnam stock market. Our sample includes 37,938 firm-month observations from 488 non-financial firms from October 2008 to February 2021. We employ Fama and MacBeth regressions and portfolio analysis methodologies to estimate the impact of intangible assets and financial constraints on stock returns. Our findings show that a percentage increase in INTANG empowers stock returns by 0.922%. Meanwhile, the cross-sectional stock returns decrease by 0.506% when the financial constraints index increases by a percentage point. Moreover, the results suggest that intangible assets in the entire sample and before COVID-19 empower the stock return cross-sectionally. Our findings are robust after employing alternative INTANG proxies. Our findings support the risk-based explanation, the pecking order theory, and prior literature. Our findings suggest governments should promote intellectual property and copyright regulations to encourage Small and Medium Enterprises (SMEs) to expand intangible assets. Furthermore, investors can utilize our suggested models to construct their portfolios efficiently.
... While testing the informational content of SAS No. 59, the results of Holder-Webb and Wilkins (2000) found a connection between the Altman Z-score and excess returns around the bankruptcy announcement. Basu (1997), Fama and French (1992), Jaffe et al. (1989), and Lakonishok et al. (1994) have documented the presence of a PE effect on stock prices in different time periods. They found that low PE stocks are associated with future high stock returns. ...
The financial literature regarding bankruptcy prediction models includes the application of several statistical tools which have progressively become accessible. This paper explores the impact of the Altman score on the stock prices of the Romanian companies listed on the Bucharest Stock Exchange during the period 2014-2022. The Altman score is calculated based on the annual reports of the analyzed companies collected from the Bloomberg database, while the stock exchange prices are collected from the Euromonitor database. The methodology used in the analysis is panel regression realized with the Generalized Method of Movements technique. The data is processed using the EViews software. This research study shows that there is a negative correlation between the Altman Z score and the companies’ share price. In terms of theoretical contribution, this study investigates one of the bankruptcy prediction models that is used more often than other models, namely the Altman Z-Score.
... For example, to cleave to a current position because it is the "current" one refers to the status quo bias. In general, the investor clings to the current position because he somehow tends his rationality towards its popularity (Jaffe, Keim, & Westerfield, 1989). Moreover, the situations to which game theory has truly been applied imitate its discerning usefulness for problems and solutions of an idiosyncratic and modest nature, building in the values of the status quo. ...
The aim of this study is to provide an understanding regarding the investor's situation of loss-aversion and stress during investment decisions in stock market, and the use of various mathematical models (game theories) to eliminate those situations. The objectives of this study are to know about the irrationalities in investor's behavior in stock market, due to stress and loss-averse behavior of investors, and find-out the ways to deal with these behaviors. Qualitative research style used to gather data from the participants of the study. Semi-structured interviews designed to know about the experience and thoughts of each respondent in detail. A sample of 16 experienced stock marketers from Pakistan and USA selected for this study. The study found the specific kind of tensed and biased investor's behaviors in the market, and found their solutions. This study obviously highpoints the ways to deal with stress and loss-averse behavior through mathematical analysis and some other suggestions. Jel Classifications: C7, G02, H54.
... Research shows that Monday returns are strongly associated with the corresponding Friday returns and the average return for the previous week (Ajayi et al. 2004). Jaffe et al. (1989) consider that the Monday returns are positively SN Bus Econ (2023) 3:114 Page 7 of 22 114 associated with the previous week's returns as calculated on the last second Friday close to the last Friday. Even after first-order serial correspondence, the well-known positive Friday-month relationship remains, especially for the neoclassical event. ...
Good and bad news plays a crucial role in the stock market, significantly influencing
investor sentiment, market expectations, and trading decisions. Positive news
can boost market confidence and upward price movements. On the contrary, negative
news can erode investor confidence and cause downward price movements. This
study examines the impact of good and bad news on the effect of day-of-week in
the Pakistan stock market, which has been largely overlooked in previous research
focusing mainly on macro factors. The study applies different ARCH and GARCH
models to investigate the influence of news and day-of-week patterns on stock market
outcomes. The findings reveal a significant day-of-week effect, with the highest
returns on Friday and the lowest returns on Monday. The negative shock has a more
substantial impact than the positive shock, contributing to high future volatility, and
bad news has a more significant influence than good news. The study highlights the
role of news and day-of-week patterns in shaping stock market outcomes and fills
the gap in previous research by emphasizing the importance of these factors.
... In 1989 Jaffe et. all [13] created a multivariate model to test the existence of seasonality in the US market. Their model was used as a base for the creation of this paper's model, from now on function (1), which is estimated twice for both indices = 1 + 2 • 2 + 3 • 3 + 4 • 4 + 5 • 5 + 6 • 6 + 7 • 7 + 8 • 8 + 9 • 9 + 10 • 10 + 11 • 11 + 12 • 12 + (1) ...
As stock market trading has become accessible to everyone, the topic of market efficiency and seasonality has become hot again. Therefore, this paper aims to empirically study the existence of seasonality effects such as calendar events in two different indices of the Dow Jones to confirm that high-capitalization stocks tend to have the exact opposite calendar effects than their low-capitalization counterparts. In this study, various data analytics techniques have been incorporated including dummy variable timeseries regressions. The results of this analysis demonstrate that even now that algorithmic trading is prevailing, there are still market inefficiencies that people can take advantage of to create excess returns.
... Specifically, we rank firm-year observations by their E/P ratios (= EPS t + 1 /P t ). For this table, we omit observations with negative E/P ratios (Jaffe et al. 1989). Each year, we rank firm-years based on E/P ratios into nine portfolios with an equal number of observations. ...
This paper investigates the association between current dividends and analysts’ subsequent earnings forecast errors. This investigation is motivated by the evidence on analyst optimism and Ohlson’s (1991, 1995) fundamental valuation theory that dividends displace future permanent earnings. For the sample period 1985–2016, we document that current dividends are positively correlated with analysts’ future forecast errors, suggesting that analysts potentially ignore the displacement effect of dividends on future earnings. Consistent with theory, this association persists in settings with stable dividends, (i.e., where dividends have limited or no signaling implications) and varies predictably with dividend payouts and cost of capital. We also find that this empirical regularity that analysts do not fully incorporate the effect of dividends on future earnings provides opportunities for arbitrage. More interestingly, we find that the strength of the association between dividends and analysts’ forecast errors has declined over the sample period; this decline appears to correspond with the underlying change in the discount rate over time. The finding that analysts’ do not fully incorporate the implications of current dividends on future earnings is consistent with previously documented inefficiencies in analysts’ use of publicly available information. However, such a systematic association with dividends also suggests that it may be a source of the persistence in analysts’ optimistic bias and thus offers new insights into analyst behavior.
... When running the empirical test on CAPM, Basu finds that the model fails to show that stocks with higher earnings/price ratios have higher returns than stocks with lower earnings/price ration, and vice versa [5]. Jaffe et al. further confirm the study of Basu [6]. In addition, Banz indicates that the average return for stocks of firms with low level of market capitalizations is higher than average return for stocks with high level of market capitalizations [7]. ...
As the most developed financial market in the world, liquidity in the market remains in a very strong position despite the recent rise in long-end US bond rates. This paper compares the usefulness and characteristics of Capital Asset Pricing Model and Three Factor Model by using the regression model to carry out bigdata analysis from the US market between 2021 and 2022. Based on the analysis, Three Factor Model is better suited to today’s market with the characteristic of variability and uncertainty. However, the concerns raised by previous studies regarding the momentum effect still remain unresolved. In order to enhance the reliability and precision, data spanning a longer period of time should be selected. This paper can be of great help to investors who need to make financial decisions, as it compares the beta of each stock with the beta calculated from two models to find out which model gives a more accurate forecast. Overall, these results shed light on beta evaluation model selection for the state-of-art approaches.
... Despite long theoretically and empirically investigation, the preceding literature evidenced dissimilar and unpredictable results regarding the asset pricing models especially in emerging equity markets where investors are hyperconscious to make rational investment decision. Moreover, various patterns such as earning-to-price ratio (Basu, 1977;Jaffe, Keim & Westerfield, 1989); leverage (Bhandari, 1988); size (Benz, 1981;Basu, 1983); value (Stattman, 1980;Rosenberg et al., 1985) divert the singlefactor model into multi-factor model. Among them, the most popular one introduced by (Fama & French, 1992, 1993 who augmented two additional factors named size and value factor that became more popular in the academic literature of finance known as Fama-French three-factor model (henceforth FF3FM). ...
Despite the strong growing popularity of Asset Pricing Models, it is difficult to estimate which factor contributes significantly in explaining average excess portfolio returns particularly in emerging equity market. Using an extensive sample over Jan-1994-Dec-2020 period, this paper aims to extend the literature by augmenting Tobin-Q adjusted risk premium with various unconditional standard asset pricing models which seeks to postulate the nexus between expected portfolios stock returns and risk-factors using monthly data of 521 enlisted financial and non-financial firms from Pakistan Stock Exchange. The multiple time-series OLS regression analysis models are employed to analyze Tobin-q risk-factor augmented with various factors models. Fama and French (2015) five-factor model excessively explains average equity returns however, our results reveal that size, value, profitability and particularly Tobin-q factor are significant while market and investment factor are redundant in Pakistan Stock Exchange. The momentum factor shows weak results in describing average equity returns in the market. Based on Gibbons, Ross and Shanken (1989) test, our findings support Tobin-Q augmented Fama and French (2015) five-factor model as appropriate for pricing stocks returns in emerging market of Pakistan. The investors, portfolio managers and policy-makers should assume the Tobin-q factor while constructing diversified portfolios for investments in Pakistan Stock Exchange.
... According to Jaffe et al. (1989), the look-ahead bias occurs when the researcher uses historical data that is not yet available to investors. Therefore, we follow Fama and French (1992) to mitigate the look-ahead bias by matching returns for the July of year t to June of year t+1 with accounting data at the end of December of year t-1. ...
We are the first ever to examine the financial constraints and distress risk puzzle of listed manufacturing firms in Vietnam. We employ different estimation methods such as portfolio sorting, Fama Macbeth regression, and asset pricing models to analyse a sample containing 27 300 firm-month observations from 2008 to 2021. Our empirical evidence figures out that the Z-score anomaly exists in the Vietnam stock market before the Covid-19 pandemic. The distress risk puzzle also exists after controlling for financial constraints and other firm characteristics. Moreover, the asset pricing model results conjecture that the distress risk is a priced factor. The average raw returns difference and risk-adjusted returns difference between stocks in the highest and lowest Z-score terciles are around 1% per month. However, we figure out that the Z-score puzzle disappears during the pandemic. Finally, our study employs a two-way sorting methodology to examine the causality between the distress puzzle and financial constraints. Our study figures out that distress risks cause higher financial constraints, not the other way around. Our findings support managers and policymakers in managing the default risk, especially during the pandemic.
... The literature trying to prove or disprove the efficient market hypothesis can be classified in three strands according to choice of variables and techniques of estimation and forecasting. The first strand consists of studies using simple regression techniques on cross sectional data (Basu, 1983;Jaffe et al., 1989;Rosenberg et al., 1985;Fama & French, 1995;Chui & Wei 1998). The second strand of the literature has used time series models and techniques to forecast stock returns following economic tools like Autoregressive Integrated Moving Average (ARIMA), Granger Causality Test, Autoregressive Distributed Lag (ARDL) and Quantile Regression to forecast stock prices (Jarrett & Kyper, 2011;Adebiyi et al., 2014;Mondal et al., 2014;Mishra, 2016). ...
Prediction of future movement of stock prices has been the subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted accurately. On the other hand, there are propositions that have shown that, if appropriately modelled, stock prices can be predicted fairly accurately. The latter have focused on choice of variables, appropriate functional forms and techniques of forecasting. This work proposes a granular approach to stock price prediction by combining statistical and machine learning methods with some concepts that have been advanced in the literature on technical analysis. The objective of our work is to take 5 minute daily data on stock prices from the National Stock Exchange (NSE) in India and develop a forecasting framework for stock prices. Our contention is that such a granular approach can model the inherent dynamics and can be fine-tuned for immediate forecasting. Six different techniques including three regression-based approaches and three classification-based approaches are applied to model and predict stock price movement of two stocks listed in NSE - Tata Steel and Hero Moto. Extensive results have been provided on the performance of these forecasting techniques for both the stocks.
... The literature trying to prove or disprove the efficient market hypothesis can be classified in three strands according to choice of variables and techniques of estimation and forecasting. The first strand consists of studies using simple regression techniques on cross sectional data (Basu, 1983;Jaffe et al., 1989;Rosenberg et al., 1985;Fama & French, 1995;Chui & Wei 1998). The second strand of the literature has used time series models and techniques to forecast stock returns following economic tools like Autoregressive Integrated Moving Average (ARIMA), Granger Causality Test, Autoregressive Distributed Lag (ARDL) and Quantile Regression to forecast stock prices (Jarrett & Kyper, 2011;Adebiyi et al., 2014;Mondal et al., 2014;Mishra, 2016). ...
Prediction of future movement of stock prices has been the subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted accurately. On the other hand, there are propositions that have shown that, if appropriately modelled, stock prices can be predicted fairly accurately. The latter have focused on choice of variables, appropriate functional forms and techniques of forecasting. This work proposes a granular approach to stock price prediction by combining statistical and machine learning methods with some concepts that have been advanced in the literature on technical analysis. The objective of our work is to take 5 minute daily data on stock prices from the National Stock Exchange (NSE) in India and develop a forecasting framework for stock prices. Our contention is that such a granular approach can model the inherent dynamics and can be fine-tuned for immediate forecasting. Six different techniques including three regression-based approaches and three classification-based approaches are applied to model and predict stock price movement of two stocks listed in NSE - Tata Steel and Hero Moto. Extensive results have been provided on the performance of these forecasting techniques for both the stocks.
The current study scrutinizes the calendar anomalies in the context of the local market by analyzing the Pakistan Stock Exchange (PSX). For this purpose, closing prices of KSE-100, KSE-30 and KSE-All share Index from January, 2009 to June, 2021 have been used as well as a thorough individual firm level analysis is done, taking average log-returns of selected sample firms returns using OLS regression, general GARCH (1,1), asymmetric TGARCH and PGARCH models. The results indicate monthly seasonality, with significant April, July, and September effect in PSX indices returns. The findings of the study reveal that weak form inefficiency exists in Pakistan Stock Market, which implies the possibility of earning abnormal returns by investors using timing strategies. Due to global pandemic conditions, investor psychology investors turned circumspect. Consequently, the individual firms’ trading has also reduced.
Traditional value measures performed poorly over the past three decades. We reevaluate the value strategy using a new measure—the ratio of cash-based operating profitability to price (COP/P)—and find a zero-investment portfolio that buys the highest-COP/P stocks and shorts the lowest-COP/P stocks earns monthly returns of 0.78% on a value-weighted basis and 1.04% on an equal-weighted basis. The COP/P effect holds even for large-capitalization stocks and exists even in the post-1990 period, when book-to-market does not predict returns. The COP/P measure subsumes many widely used value measures and the conservative-minus-aggressive investment factor. (JEL G02, G12)
This research analyzes the impact of firm-specific characteristics on the cross-section of equity returns of insurance companies in Nepal. For this purpose, the study used 22 insurance companies listed in the Nepal Stock Exchange (NEPSE), comprising seven life and 15 non-life insurance companies. To make a balanced panel, those companies are selected as samples that have completed at least seven years of operation until the end of 2021 and are listed in NEPSE. The secondary data from 2015 to 2021 is analyzed using panel regression models. The Breusch-Pagan and Hausman tests are used to select the best panel model, and the random effect model is the best estimation model. The study reveals that systematic risk associated with the stock can explain the stock’s risk premium, proving that the notion of CAPM holds in the context of Nepal as stock beta is positive and significant in all tested models. Similarly, the book-to-market ratio is found to be negatively affecting the stock return. On the other hand, earning yield positively affects the stock return, and firm size negatively impacts equity returns. However, the investment rate and leverage are insignificant to affect equity returns. Therefore, stock investors are recommended to select alternatives based on systematic risk, earnings yield, and BM ratio. The findings also suggest to corporate policymakers that merger and acquisition cannot maximize shareholders’ wealth because it only increases paid-up capital, and this leads to decreased equity returns due to an increase in the book-to-market ratio and size of the firm.
As traders, investors, and analysts try to decide whether to buy, sell, or hold Apple Inc. shares, Apple Stock Prediction is a crucial component of the financial market. It is difficult to forecast the future value of Apple's stock due to market volatility and a variety of unknown events, including shifts in customer tastes, political unpredictability, and global economic trends. Therefore, it is essential to use a variety of techniques to find the most effective strategy for forecasting Apple's stock price. To ascertain the intrinsic value of a stock, fundamental analysis examines financial statements, market patterns, and economic conditions. This method looks at the sales, profit margins, and cash flow of Apple Inc. as well as its overall financial performance. On the other hand, technical analysis examines past market data, such as price and volume, to spot patterns and trends that can predict future price movements. Charts, graphs, and other visual aids are used in this strategy to pinpoint potential entry and exit positions for trading Apple's stock. Multi-Layer Perceptron is a kind of artificial neural network that mimics the actions of the human brain and has been successfully used to analyze large amounts of complex data. In contrast, XGBoost is a machine learning algorithm that makes predictions using previous data, making it perfect for predicting the future movement of Apple's stock.
Psychologists have been observing and interpreting economic behaviour for at least fifty years, and the last decade, in particular, has seen an escalated interest in the interface between psychology and economics. The Cambridge Handbook of Psychology and Economic Behaviour is a valuable reference resource dedicated to improving our understanding of the economic mind and economic behaviour. Employing empirical methods – including laboratory experiments, field experiments, observations, questionnaires and interviews – the Handbook covers aspects of theory and method, financial and consumer behaviour, the environment and biological perspectives. With contributions from distinguished scholars from a variety of countries and backgrounds, the Handbook is an important step forward in the improvement of communications between the disciplines of psychology and economics. It will appeal to academic researchers and graduates in economic psychology and behavioural economics.
Psychologists have been observing and interpreting economic behaviour for at least fifty years, and the last decade, in particular, has seen an escalated interest in the interface between psychology and economics. The Cambridge Handbook of Psychology and Economic Behaviour is a valuable reference resource dedicated to improving our understanding of the economic mind and economic behaviour. Employing empirical methods – including laboratory experiments, field experiments, observations, questionnaires and interviews – the Handbook covers aspects of theory and method, financial and consumer behaviour, the environment and biological perspectives. With contributions from distinguished scholars from a variety of countries and backgrounds, the Handbook is an important step forward in the improvement of communications between the disciplines of psychology and economics. It will appeal to academic researchers and graduates in economic psychology and behavioural economics.
Psychologists have been observing and interpreting economic behaviour for at least fifty years, and the last decade, in particular, has seen an escalated interest in the interface between psychology and economics. The Cambridge Handbook of Psychology and Economic Behaviour is a valuable reference resource dedicated to improving our understanding of the economic mind and economic behaviour. Employing empirical methods – including laboratory experiments, field experiments, observations, questionnaires and interviews – the Handbook covers aspects of theory and method, financial and consumer behaviour, the environment and biological perspectives. With contributions from distinguished scholars from a variety of countries and backgrounds, the Handbook is an important step forward in the improvement of communications between the disciplines of psychology and economics. It will appeal to academic researchers and graduates in economic psychology and behavioural economics.
Psychologists have been observing and interpreting economic behaviour for at least fifty years, and the last decade, in particular, has seen an escalated interest in the interface between psychology and economics. The Cambridge Handbook of Psychology and Economic Behaviour is a valuable reference resource dedicated to improving our understanding of the economic mind and economic behaviour. Employing empirical methods – including laboratory experiments, field experiments, observations, questionnaires and interviews – the Handbook covers aspects of theory and method, financial and consumer behaviour, the environment and biological perspectives. With contributions from distinguished scholars from a variety of countries and backgrounds, the Handbook is an important step forward in the improvement of communications between the disciplines of psychology and economics. It will appeal to academic researchers and graduates in economic psychology and behavioural economics.
Psychologists have been observing and interpreting economic behaviour for at least fifty years, and the last decade, in particular, has seen an escalated interest in the interface between psychology and economics. The Cambridge Handbook of Psychology and Economic Behaviour is a valuable reference resource dedicated to improving our understanding of the economic mind and economic behaviour. Employing empirical methods – including laboratory experiments, field experiments, observations, questionnaires and interviews – the Handbook covers aspects of theory and method, financial and consumer behaviour, the environment and biological perspectives. With contributions from distinguished scholars from a variety of countries and backgrounds, the Handbook is an important step forward in the improvement of communications between the disciplines of psychology and economics. It will appeal to academic researchers and graduates in economic psychology and behavioural economics.
Psychologists have been observing and interpreting economic behaviour for at least fifty years, and the last decade, in particular, has seen an escalated interest in the interface between psychology and economics. The Cambridge Handbook of Psychology and Economic Behaviour is a valuable reference resource dedicated to improving our understanding of the economic mind and economic behaviour. Employing empirical methods – including laboratory experiments, field experiments, observations, questionnaires and interviews – the Handbook covers aspects of theory and method, financial and consumer behaviour, the environment and biological perspectives. With contributions from distinguished scholars from a variety of countries and backgrounds, the Handbook is an important step forward in the improvement of communications between the disciplines of psychology and economics. It will appeal to academic researchers and graduates in economic psychology and behavioural economics.
Psychologists have been observing and interpreting economic behaviour for at least fifty years, and the last decade, in particular, has seen an escalated interest in the interface between psychology and economics. The Cambridge Handbook of Psychology and Economic Behaviour is a valuable reference resource dedicated to improving our understanding of the economic mind and economic behaviour. Employing empirical methods – including laboratory experiments, field experiments, observations, questionnaires and interviews – the Handbook covers aspects of theory and method, financial and consumer behaviour, the environment and biological perspectives. With contributions from distinguished scholars from a variety of countries and backgrounds, the Handbook is an important step forward in the improvement of communications between the disciplines of psychology and economics. It will appeal to academic researchers and graduates in economic psychology and behavioural economics.
Psychologists have been observing and interpreting economic behaviour for at least fifty years, and the last decade, in particular, has seen an escalated interest in the interface between psychology and economics. The Cambridge Handbook of Psychology and Economic Behaviour is a valuable reference resource dedicated to improving our understanding of the economic mind and economic behaviour. Employing empirical methods – including laboratory experiments, field experiments, observations, questionnaires and interviews – the Handbook covers aspects of theory and method, financial and consumer behaviour, the environment and biological perspectives. With contributions from distinguished scholars from a variety of countries and backgrounds, the Handbook is an important step forward in the improvement of communications between the disciplines of psychology and economics. It will appeal to academic researchers and graduates in economic psychology and behavioural economics.
Psychologists have been observing and interpreting economic behaviour for at least fifty years, and the last decade, in particular, has seen an escalated interest in the interface between psychology and economics. The Cambridge Handbook of Psychology and Economic Behaviour is a valuable reference resource dedicated to improving our understanding of the economic mind and economic behaviour. Employing empirical methods – including laboratory experiments, field experiments, observations, questionnaires and interviews – the Handbook covers aspects of theory and method, financial and consumer behaviour, the environment and biological perspectives. With contributions from distinguished scholars from a variety of countries and backgrounds, the Handbook is an important step forward in the improvement of communications between the disciplines of psychology and economics. It will appeal to academic researchers and graduates in economic psychology and behavioural economics.
Psychologists have been observing and interpreting economic behaviour for at least fifty years, and the last decade, in particular, has seen an escalated interest in the interface between psychology and economics. The Cambridge Handbook of Psychology and Economic Behaviour is a valuable reference resource dedicated to improving our understanding of the economic mind and economic behaviour. Employing empirical methods – including laboratory experiments, field experiments, observations, questionnaires and interviews – the Handbook covers aspects of theory and method, financial and consumer behaviour, the environment and biological perspectives. With contributions from distinguished scholars from a variety of countries and backgrounds, the Handbook is an important step forward in the improvement of communications between the disciplines of psychology and economics. It will appeal to academic researchers and graduates in economic psychology and behavioural economics.
Psychologists have been observing and interpreting economic behaviour for at least fifty years, and the last decade, in particular, has seen an escalated interest in the interface between psychology and economics. The Cambridge Handbook of Psychology and Economic Behaviour is a valuable reference resource dedicated to improving our understanding of the economic mind and economic behaviour. Employing empirical methods – including laboratory experiments, field experiments, observations, questionnaires and interviews – the Handbook covers aspects of theory and method, financial and consumer behaviour, the environment and biological perspectives. With contributions from distinguished scholars from a variety of countries and backgrounds, the Handbook is an important step forward in the improvement of communications between the disciplines of psychology and economics. It will appeal to academic researchers and graduates in economic psychology and behavioural economics.
Psychologists have been observing and interpreting economic behaviour for at least fifty years, and the last decade, in particular, has seen an escalated interest in the interface between psychology and economics. The Cambridge Handbook of Psychology and Economic Behaviour is a valuable reference resource dedicated to improving our understanding of the economic mind and economic behaviour. Employing empirical methods – including laboratory experiments, field experiments, observations, questionnaires and interviews – the Handbook covers aspects of theory and method, financial and consumer behaviour, the environment and biological perspectives. With contributions from distinguished scholars from a variety of countries and backgrounds, the Handbook is an important step forward in the improvement of communications between the disciplines of psychology and economics. It will appeal to academic researchers and graduates in economic psychology and behavioural economics.
The financial markets have been suffering from unforeseen and sudden economic turbulences that have been directly or indirectly influences the assets values. To analyze such these market influences, the separate discipline, investment management was formed in Finance and developed chronologically up to current professional and scientific phase. It is the fact that the market has been stimulated by the financial indicators which is in number as well as by the behavioral characteristics of the investment communities. Moreover, while considering the financial market as the mirror of economy it is equally important to note that the market moves on the sentiments and the trading behavior of the market participants. In the meantime, the trading behaviors depend upon the market information such as financial and non-financial - media, politics, hypes, etc. The study analyzes the market information and stock returns in Nepalese context in 2012. The study primarily focus on the usefulness of the historical database, the financial news coverage and its effects on stock returns, the political leadership effects on stock returns, and the study also determine the factors of investment decision.
This chapter concerns the efficiency of capital markets, which is at the source of trading and the potential for trading strategies to produce consistent profits. Market efficiency is defined and its relationship to the random behavior of security prices is explained. Weak form efficiency tests are described along with its relationship to technical analysis and calendar effects. Illustrations of momentum and mean reversion tests are provided. Semi-strong efficiency tests are described along with their relationships with fundamental analysis and corporate announcements. Event study methodology is illustrated with a small sample example. Strong form efficiency studies are described with an emphasis on insider trading. Prediction markets are offered to illustrate the transmission of information into prices.
O agronegócio brasileiro contribuiu em 21,6% para o PIB brasileiro em 2017, entretanto, mesmo com sua importância, não se tem no Brasil um índice de mercado que reflita a realidade deste setor. Apesar da crescente popularidade de temáticas ligadas ao agronegócio, há poucos estudos existentes na performance dos preços dos ativos agropecuários. Sabe-se que pequenos e grandes produtores, gestores de fundos e grandes investidores buscam diversificar seus investimentos e veem o agronegócio como essencial para sua composição de carteira. O objetivo desta pesquisa é propor um índice de mercado para o agronegócio brasileiro, a partir da proposta de uma carteira, comparando assim seus retornos e volatilidade com outros índices de mercado amplamente utilizados. O método empregado para analisar a eficiência das carteiras construídas foi a análise de regressão com heteroscedasticidade corrigida pelo software Gretl®, sendo respeitadas as premissas de estacionariedade e ausência de autocorrelação. O estudo possibilita entregar uma carteira e, consequentemente, um índice de mercado para o agronegócio brasileiro que apresente uma maior correlação deste setor de com suas respectivas atividades. Este estudo busca trazer um novo índice para o setor, contribuindo com outros trabalhos acadêmicos, melhores práticas de investimento de mercado e, indiretamente, para um melhor desenvolvimento da sociedade.
This chapter highlights the existing research works carried out in India and abroad by the scholars exploring the microeconomic, macroeconomic and industry-specific factors affecting the firm-level performance. Comprehensive review of the existing literature on the effect of these factors on the efficiency, profitability and stock prices was accomplished. The research gap in the existing literature was identified in this chapter by using Evidence Gap Map. The chapter also outlines the objectives of the study in the perspective of such research gap.KeywordsManufacturingMicroeconomic factorsMacroeconomic factorsIndustry-specific factorsEfficiencyProfitabilityStock priceEvidence gap map
The Arbitrage Pricing Theory implies that portfolios with small R2 should have large alphas. We show that, as a consequence, the prominent asset pricing anomalies share a common trait: abnormal returns are driven mainly by stocks having smaller and less stable correlations with the market portfolio. Univariate sorts based on five-year rolling-window correlations with the market excess return produce patterns similar to those based on size, value, profitability, investment, price ratios, and earnings and price momenta. A correlation-driven factor that captures this common property makes some of the Fama–French factors redundant in regressions with the univariate sorts.
This chapter emphasizes that understanding the sectoral characteristics is an important task before making a practical portfolio choice. The work described in the chapter also demonstrates that the sectors are different in terms of their trend, seasonal and random components of their respective time series of historical index values. The R programming language is used to decompose the time series of the historical index values of the fifteen critical sectors of the Indian economy, and the decomposition results are analyzed to understand their characteristics further. Several stocks from each sector are also analyzed in a similar line. Using the seasonal component only for illustrative purposes, we present extensive company-wise results to show that the individual companies, most of the time, mimic the corresponding sectoral seasonal patterns.
This chapter studies the behavior of India’s information technology (IT) sector as it is one of the important sectors of the country’s economy. A gamut of statistical, machine learning, and deep learning models is proposed to analyze the close values of the IT sector index. Both regression and classification methods are used for predicting the future stock index and its future movement patterns. We use the daily stock price index values of the Indian IT sector from the NSEIndia website and use these values to predict the movement of the prices on a forecast horizon of one week. An LSTM regression model is proposed using the univariate stock index data of close index values. The LSTM model is found to outperform all machine learning models. For building the models, we use the historical daily index values from the NSEIndia website from December 29, December 2008 to December 27, 2019. The performance results of the LSTM models show that it is possible to very accurately forecast the daily stock index close values for the next week using the last week’s stock price records.
This chapter proposes a granular approach to stock price prediction by combining several statistical and machine learning methods of prediction on technical analysis of stock prices. We present various approaches for short-term stock price movement forecasting using multiple classification approaches and regression techniques and compare their performance in predicting stock price movement. We believe this approach will provide several helpful information to the investors in the stock market who are particularly interested in short-term investments for profit. The initial version of the work was published earlier in a conference proceedings. In the present work, we have extended the predictive model by including an additional five classification and five regression models, including an advanced deep learning model.
This chapter proposes several machine learning and deep learning-based predictive models for predicting the NIFTY 50 index movement in the National Stock Exchange (NSE) of India. We use the daily stock price values for the period January 4, 2010, to December 31, 2018, as the training dataset for building the models, and apply the models to predict the daily stock price movement and actual closing value of the stock for the period January 1, 2019, to December 31, 2019. We further augment the predictive model by incorporating a sentiment analysis module that analyses public sentiment on Twitter on the NIFTY 50 stocks. The sentiment analysis module's output is used as the second input to the model with the historical NIFTY 50 data to predict future stock price movements.
This paper investigates whether the organizational form of a property-liability insurer influences its risk-taking. We investigate the investment and underwriting behavior of 62 German property-liability insurers in the period from 2000 to 2019. We find that stock insurers take higher risks, both in underwriting and in investments than mutual insurers. Our findings are relevant to customers, investors, and regulators, as they provide insights into the fundamental differences between stock and mutual insurers in the German property-liability.insurance market.
This study, utilising aspects of event methodology, focuses on the phenomenon of over-reaction which is defined as the over-response to new information. The over-reaction hypothesis suggests that the greater the magnitude of initial price change, the more extreme the offsetting reaction.
Cox and Peterson’s (1994) methodology, adapted to local conditions on the Johannesburg Stock Exchange, is used. Events are defined as single day price declines in excess of ten percent, fifteen percent and twenty percent for companies trading on the JSE between 1973 to 1998. Abnormal returns are computed for the twenty trading day period following the price decline. Abnormal returns are computed using both the trade-to-trade and the standard market model approaches. The regression parameters are estimated using a 150 day pre-event period and a 150 day post-event period. The period spanning six days before to twenty days after the event is not used for parameter estimation. Abnormal returns are then calculated for the event window, and average abnormal returns (AARs) and cumulative average abnormal returns (CAARs) computed and tested for significance over varying “time windows” from the day after the event to twenty days thereafter. A series of cross-sectional regressions are also employed to control for liquidity (size), book-to-market and price earnings ratio effects. In contrast to Cox and Peterson (1994) who concluded that over-reaction per se does not independently and significantly account for abnormal returns, this study finds evidence of significant over-reaction with a positive price reversal over the three trading days following the significant price decline. The study therefore supports prior studies on the JSE suggesting the presence of market over-reaction (Page and Way, 1994; Muller, 1999).
Are equity anomalies a product of p-hacking in the asset pricing literature? To shed new light on this question, we perform a true out-of-sample study of 30 well-known anomalies in the cross-section of returns. We replicate these anomalies in a novel hand-collected dataset of firms listed on the historical Stock Exchange of Melbourne in the years 1926 to 1987. The vast majority of return-predictive signals cannot be confirmed. Those which are observed are commonly driven by small firms with marginal economic significance. Only a handful of anomalies survive our tests, namely, the dividend yield, value uncertainty, and short-term residual reversal effects. Overall, our findings support the view that many anomalies are statistical artifacts resulting from data mining.
This article summarizes our text Empirical Asset Pricing: The Cross Section of Stock Returns . The text provides an overview of the empirical asset pricing research, with a focus on cross‐sectional studies of stock returns. The main objective of this research is to identify patterns in stock returns and understand the drivers of these patterns. The text has two main parts. The first part discusses in detail the main statistical and econometric techniques used in empirical asset pricing research, the most important of which are portfolio analysis and Fama and MacBeth regression analysis. The second part summarizes the main findings in empirical asset pricing research, beginning with the seminal research on the Capital Asset Pricing Model and ending with the most contemporary work as of the time of publication, 2016. The text is intended for use in a doctoral‐level or advanced masters‐level empirical asset pricing or investments course, as well as by professional portfolio managers, risk managers, and other practitioners.
JEL Classifications: G11, G12, G13, G17
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.
Studies of size and earnings/price ratio effects together have produced contradictory results. Does one effect subsume the other or are there two separate effects? This paper demonstrates that equity returns are related to both size and earnings/price ratio as well as the month of January. Reinganum [20] and Basu [4] are reexamined to find the reasons for their contradictory results. Reinganum's finding that size subsumes earnings/price ratio is caused by a fortuitous choice of methods. Basu's finding that earnings/price ratio subsumes size appears to be sample-specific.This paper examines the implied standard deviation (ISD) estimated from transactions data on options, using the Black-Scholes pricing model. It was found that the distribution of the ISD is symmetric, though not normal. Also, the ISD based on the last daily observation deviates significantly from the daily average ISD. It is suggested that the daily average is a more reliable estimate of the standard deviation.
This study examines the empirical relation between stock returns and (long-run) dividend yields. The findings show that much of the phenomenon is due to a nonlinear relation between dividend yields and returns in January. Regression coefficients on dividend yields, which some models predict should be non-zero due to differential taxation of dividends and capital gains, exhibit a significant January seasonal, even when controlling for size. This finding is significant since there are no provisions in the after-tax asset pricing models that predict the tax differential is more important in January than in other months.
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 is concerned with the size-related anomalies in stock returns reported by Banz (1981) and Reinganum (1981). They showed that small firms have tended to yield returns greater than those predicted by the traditional CAPM. We find that the size effect is linear in the logarithm of size, but reject the hypothesis that the ex ante excess return attributable to size is stable through time. We briefly analyze the Seemingly Unrelated Regression Model (SURM) and a two-step procedure as two alternative estimators of the size effect. Due to the instability of the effect, we find that the estimates are sensitive to the time period studied.
Studies relating accounting and price data often use the COMPUSTAT or related PDE data base as the source for the accounting data. This practice may introduce a look‐ahead bias and an ex‐post‐selection bias into the study. We examine this problem by comparing results from the standard COMPUSTAT data base with those from a data base which suffers from neither bias. We find that rates of return from portfolios chosen on the basis of accounting data from the two data bases differ significantly. Further, we find that these differences imply different conclusions when we test a specific hypothesis relating accounting and price data. Finally, we propose a number of remedies which may reduce the bias when the standard COMPUSTAT data base is used.
Previous estimates of a 'size effect' based on daily returns data are biased. The use of quoted closing prices in computing returns on individual stocks imparts an upward bias. Returns computed for buy-and-hold portfolios largely avoid the bias induced by closing prices. Based on such buy-and-hold returns, the full-year size effect is half as large as previously reported, and all of the full-year effect is, on average, due to the month of January.
Recent empirical work by Banz (1981) and Reinganum (1981) documents abnormally large risk-adjusted returns for small firms listed on the NYSE and the AMEX. The strength and persistence with which the returns appear lead both authors to conclude the single-period, two-parameter capital asset pricing model is misspecified. This study (1) confirms that total market value of common stock equity varies inversely with risk-adjusted returns, (2) demonstrates that price per share does also, and (3) finds that transaction costs at least partially account for the abnormality.
This study examines the empirical relationship between the return and the total market value of NYSE common stocks. It is found that smaller firms have had higher risk adjusted returns, on average, than larger firms. This ‘size effect’ has been in existence for at least forty years and is evidence that the capital asset pricing model is misspecified. The size effect is not linear in the market value; the main effect occurs for very small firms while there is little difference in return between average sized and large firms. It is not known whether size per se is responsible for the effect or whether size is just a proxy for one or more true unknown factors correlated with size.
Faculty of Business, McMaster University. The author is indebted to Professors Harold Bierman, Jr., Thomas R. Dyckman, Roland E. Dukes, Seymour Smidt, Bernell K. Stone, all of Cornell University, and particularly to this Journal's referees, Nancy L. Jacob and Marshall E. Blume, for their very helpful comments and suggestions. Of course, any remaining errors are the author's responsibility. Research support from the Graduate School of Business and Public Administration, Cornell University is gratefully acknowledged.
University of Pennsylvania, and Donaldson, Lufkin, and Jenrette, Inc., respectively. The authors wish to thank Professors Fisher Black, Eugene Fama, Irwin Friend, Stephen Ross, and Randolph Westerfield for their much appreciated comments, and the Rodney L. White Center for Financial Research for financial support
Misspecification in Capital Asset Pricing: Empirical Anomalies Based on Earnings' Yields and Market Values
- Mark
Price, Beta and Evidence Listing
- Marshall
Dividend Yields and Stock Returns
- Donald