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The Stock Market Crash of 2008 Caused the Great Recession: Theory and Evidence
Abstract
This paper argues that the stock market crash of 2008, triggered by a collapse in house prices, caused the Great Recession. The paper has three parts. First, it provides evidence of a high correlation between the value of the stock market and the unemployment rate in U.S. data since 1929. Second, it compares a new model of the economy developed in recent papers and books by Farmer, with a classical model and with a textbook Keynesian approach. Third, it provides evidence that fiscal stimulus will not permanently restore full employment. In Farmer's model, as in the Keynesian model, employment is demand determined. But aggregate demand depends on wealth, not on income.
... The 2008 financial crisis was a large-scale global meltdown that "affected nearly all organizations and businesses globally, including governments, and the public, and private sectors" (Jin et al., 2018, p. 575). Short-term effects of the crisis included destabilized banking and securities industries, widespread home foreclosures, high unemployment, and stock market turbulence (Farmer, 2012). Following a years-long complex layering of a housing price bubble, introduction of mortgage-backed financial instruments, excess risk taking, insufficient oversight, and increased corporate debt (NCCFECUS, 2011), the situation developed into "one of the longest and most significant economic crises that the world has ever seen" (Brem et al., 2020, p. 360). ...
... From Friday, September 12 -Sunday, September 14, 2008, amid an economic freefall widely compared to the Great Depression leadup (see Craig et al., 2008;Farmer, 2012), the United States government convened an emergency weekend meeting for leaders of the "big five" Wall Street investment firms: Bear Stearns, Lehman Brothers, Merrill Lynch, Goldman Sachs, and Morgan Stanley. As a result, Bear Stearns was sold to JP Morgan Chase for pennies on the dollar in a deal both encouraged and funded by the U.S. government, Merrill Lynch was sold to Bank of America in a similar fashion, Lehman Brothers filed for bankruptcywhich at the time was the largest Chapter 11 on recordand the remaining two firms, Goldman Sachs and Morgan Stanley, received an infusion of government money, colloquially known as a bailout (Craig et al., 2008). ...
... The Weekend Wall Street Died serves as not only as a bastion of the situation's overall severity, but also an optical demonstration of the roots of the crisis (Craig et al., 2008). The failure of three long-standing American institutionssaddled by toxic debt of their own volitioncaused overall stock markets to plummet (Farmer, 2012). While Goldman Sachs and Morgan Stanley technically survived the ordeal, the firms remained enmeshed in the controversy and financial damage created by collateralized debt obligations (NCCFECUS, 2011). ...
This paper explores the potential to address mid-crisis communication needs in longitudinal crises by using a paired renewal discourse and inoculation messaging strategy. While renewal discourse focuses on inherent opportunities and the possibility for organizations to improve following a crisis, insulating stakeholder views from ongoing and future crisis effects with inoculation messaging during the crisis can improve the durability of positive outcomes. Theoretically driven rhetorical analysis is used to examine stakeholder communication from one of the two firms that survived The Weekend Wall Street Died in September 2008, a midpoint in the 18-month global financial meltdown. The results demonstrate mid-crisis as a distinct communication exigency and the applied use of a paired renewal discourse and inoculation strategy as a crisis and ongoing risk management approach.
... During the last three decades, stock price crashes have occurred frequently in the Chinese security market. Stock price crashes significantly damage market investors' wealth and confidence [2]. Furthermore, they can cause dramatic fluctuations in stock prices, which can quickly induce systemic financial risks and threaten financial security and the development of the Chinese social economy [3,4]. ...
... In summary, the main contributions of this research are summarized as follows: (1) A novel prediction method based on the XGBoost-NSGA-II-SHAP model is proposed to accurately and efficiently predict the occurrences of stock price crashes. (2) This study utilizes many financial indicators as the features for predicting stock price crashes, and it could provide explanations about the effectiveness of company financial indicators in the prediction of stock price crashes. (3) We divide the whole dataset by market capitalization size, and we analyze the differences in their prediction effectiveness by using the proposed model. ...
... Therefore, from the results of four categories divided by market capitalization size, it is found that the proposed XGBoost-NSGA-II prediction model produced the greatest predictive accuracy among all the investigated methods. (2) Compared to the other benchmark methods, the XGBoost model produced a better ACC in predicting stock price crashes, indicating that XGBoost was more effective in identifying stock price crashes. ...
Stock price crashes have occurred frequently in the Chinese security market during the last three decades. They have not only caused substantial economic losses to market investors but also seriously threatened the stability and financial safety of the security market. To protect against the price crash risk of individual stocks, a prediction and explanation approach has been proposed by combining eXtreme Gradient Boosting (XGBoost), the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and SHapley Additive exPlanations (SHAP). We assume that financial indicators can be adopted for stock crash risk prediction, and they are utilized as prediction variables. In the proposed method, XGBoost is used to classify the stock crash and non-crash samples, while NSGA-II is employed to optimize the hyperparameters of XGBoost. To obtain the essential features for stock crash prediction, the importance of each financial indicator is calculated, and the outputs of the prediction model are explained by SHAP. Compared with the results of benchmarks using traditional machine learning methods, we found that the proposed method performed best in terms of both prediction accuracy and efficiency. Especially for the small market capitalization samples, the accuracy of classifying all samples reached 78.41%, and the accuracy of identifying the crash samples was up to 81.31%. In summary, the performance of the proposed method demonstrates that it could be employed as a valuable reference for market regulators engaged in the Chinese security market.
... Recently, in a series of papers, Farmer (2010Farmer ( , 2012Farmer ( , 2013Farmer ( , 2015 hypothesises that long after the financial crises or stock market crash there is a persistence high unemployment rate in the United States. Farmer (2015) demonstrates that there is a positive long-run relationship between the stock market and the unemployment rate in the U.S. for the period 1953 to 2011; and that the causal effect running from the stock market to the unemployment rate. ...
... If the public pessimism persists and their confidence stays low believing that the value of the assets will be lower and will never recover, then this new equilibrium is sustainable as a steady state. Farmer (2012) asserts that by incorporating the role of animal spirit in the form of belief function in his model can explains the high and persistent unemployment as an equilibrium phenomenon. ...
... Farmer's hypothesis that there is a long-run relationship between the stock market and the unemployment rate and that there is one-way causal effect of stock market to the unemployment rate, has been tested by several researchers. Arabaci (2017) supports the earlier findings of Farmer (2012Farmer ( , 2015 that the stock market demonstrate unidirectional causal effect from the stock market to unemployment rate in the U.S. Pan (2018) examines the relationship between the stock market and unemployment rate in 30 advanced and 11 developing and emerging countries; and found strong and one-way causal direction from stock market to unemployment in the advanced countries but Farmer hypothesis does not apply to the developing countries. On the other hand, using German data, Fritsche and Pierdzioch (2016) found that the stock market Granger cause unemployment both in the short-run and the long-run, thus supporting the Farmer hypothesis. ...
Farmer hypothesised that there is a linear long-run relationship between the movement of the stock market and movement in the unemployment rate, and there is a unidirectional causality running from stock market to unemployment rate. In this study we test the Farmer hypothesis by using Malaysias daily data on stock price and unemployment rate during the COVID-19 pandemic. We performed unit root, cointegration and short-run as well long-run non-causality tests between the stock market and unemployment rate using high frequency data for the period January to October 2020. Our results suggest that both stock market and unemployment rate are non-stationary and cointegrated. The causality test results suggest that using shorter lag, Farmer hypothesis is rejected while at longer lags, Farmer hypothesis cannot be rejected.
... Los precios de los activos que colapsan tienden a tener rendimientos semanas antes al día de la caída asi como aquellas cuyo precio aumentó entre tres meses y tres años. Farmer (2012) plantea que la causa de las recesiones económicas casi siempre son los colapsos en el mercado bursátil, a partir del estudio de los cambios de la riqueza debidos a las caídas del mercado y el aumento de la tasa de desempleo inmediata a este suceso, concluyendo que la forma de mejorar los efectos de los colapsos en la economía es por medio de mejores políticas fiscales de intervención en el mercado de bursátil. Bates (2012) plantea que los rendimientos son más sensibles cuando son negativos que cuando son positivos; en un estudio sobre los colapsos presentados entre 1926 y 2010, encuentra una relación excesiva de los rendimientos negativos durante los días de negociación en que el mercado ha caído. ...
... Y aunque desde el 2014, muchas personas están esperando este colapso, por el contrario, los precios de las acciones y los índices del mercado han seguido aumentando, mostrando en las graficas de precios una inclinación alcista pronunciada. El temor sobre un posible colapso del mercado se debe a que los colapsos bursátiles tienen un impacto en la economía de los países (Farmer, 2012), y al ser el mercado bursátil americano un referente para muchas economías del mundo, también afectaría nuevamente el sistema económico internacional. ...
... El comportamiento del mercado bursátil en Estados Unidos también tiene relación con la tasa de desempleo del país (Farmer, 2012); cuando el mercado está subiendo, la tasa de desempleo tiende a subir y cuando se han presentado colapsos del mercado, el desempleo ha bajado también, y se ha mantenido bajo durante un periodo importante de tiempo. ...
La presente investigación tuvo como objetivo estudiar el mercado estadounidense, la posible formación de una burbuja financiera y el colapso presentado entre enero y febrero del 2018; se utiliza una metodología de análisis del comportamiento de los inversionistas en el mercado donde se identifican tres tipos principales inversionistas, institucionales, de ruido y especuladores, que toman sus decisiones basados en la interacción observada y el volumen de cada uno en el mercado. Se pudo identificar la influencia de la toma de decisiones de los tres participantes en los precios; sobre todo el peso que tienen los mismos cuando se realizan ventas en corto, a la espera de que haya una caída en los precios como la situación presentada a principios de 2018. Con respecto a estas fechas, los resultados demostraron que los precios no cayeron a un valor que indicara un nuevo colapso en las finanzas de Estados Unidos. Sin embargo, el escepticismo por parte de muchos participantes del mercado continúa.
... The debate about the interrelations between stock-market and unemployment fluctuations, studied in several papers by Farmer (2012Farmer ( , 2013Farmer ( , 2015, has expanded with several theoretical or empirical studies. Farmer comes to several conclusions, one of which is that the absence of relevant price signals can have the effect of trapping a market economy in an equilibrium with a high unemployment rate. ...
... To investigate the links between a macroeconomic 'belief function', substituted by stock-market swings and the unemployment rate, we use the same approach as Farmer (2012Farmer ( , 2015. The data from 1960m1 to 2020m10 are calculated as follows: ...
In this paper, we look at the connection between the stock market and the unemployment rate in the United States. Using a recent time-varying Granger causality framework covering the period from January 1960 to October 2020, tests reveal that lagged realizations of the stock market have predictive power regarding unemployment, and vice et versa, but that the predictive ability only occurs sporadically over time, particularly during ‘crash’ periods. These results are in line with the literature on the information spillover between finance markets and the real-life economy, with changes of causality across time.
... The debate about the interrelations between stock-market and unemployment fluctuations, studied in several papers by Farmer (2012Farmer ( , 2013Farmer ( , 2015, has expanded with several theoretical or empirical studies. Farmer comes to several conclusions, one of which is that the absence of relevant price signals can have the effect of trapping a market economy in an equilibrium with a high unemployment rate. ...
... To investigate the links between a macroeconomic "belief function", substituted by stock-market swings and the unemployment rate, we use the same approach as Farmer (2012Farmer ( , 2015. The data from 1960m1 to 2020m10 are calculated as follows: ...
In this paper, we look at the connection between the stock market and the unemployment rate in the United States. Using a recent time-varying Granger causality framework covering the period from January 1960 to October 2020, tests reveal that lagged realizations of the stock market have predictive power regarding unemployment, and vice et versa, but that the predictive ability only occurs sporadically over time, particularly during "crash" periods. These results are in line with the literature on the information spillover between finance markets and the real-life economy, with changes of causality across time.
... Y aunque desde el 2014, muchas personas están esperando este colapso, por el contrario, los precios de las acciones y los índices del mercado han seguido aumentando, mostrando en las graficas de precios una inclinación alcista pronunciada. El temor sobre un posible colapso del mercado se debe a que los colapsos bursátiles tienen un impacto en la economía de los países (Farmer, 2012), y al ser el mercado bursátil americano un referente para muchas economías del mundo, también afectaría nuevamente el sistema económico internacional. ...
... El comportamiento del mercado bursátil en Estados Unidos también tiene relación con la tasa de desempleo del país (Farmer, 2012); cuando el mercado está subiendo, la tasa de desempleo tiende a subir y cuando se han presentado colapsos del mercado, el desempleo ha bajado también, y se ha mantenido bajo durante un periodo importante de tiempo. ...
El Centro de Estudios e Investigaciones en Desarrollo Regional (CEIDER) de la Facultad de Ciencias Económicas y Empresariales de la Universidad Santiago de Cali, coordina sus actividades de investigación en la línea de Ciencia, Tecnología e Innovación con responsabilidad social; su eje-centro de actividades se enmarca en el desarrollo regional, medio ambiente y sociedad, para el impulso de sus líneas de investigación en temas de sostenibilidad ambiental, gestión organizacional, responsabilidad social empresarial, contabilidad internacional, teoría y pedagogía contable, comercio internacional y competitividad. La siguiente compilación hace parte de un trabajo de investigación y colaboración de pares, que busca contribuir desde la academia para enriquecer la temática de las monedas disruptivas.
... Y aunque desde el 2014, muchas personas están esperando este colapso, por el contrario, los precios de las acciones y los índices del mercado han seguido aumentando, mostrando en las graficas de precios una inclinación alcista pronunciada. El temor sobre un posible colapso del mercado se debe a que los colapsos bursátiles tienen un impacto en la economía de los países (Farmer, 2012), y al ser el mercado bursátil americano un referente para muchas economías del mundo, también afectaría nuevamente el sistema económico internacional. ...
... El comportamiento del mercado bursátil en Estados Unidos también tiene relación con la tasa de desempleo del país (Farmer, 2012); cuando el mercado está subiendo, la tasa de desempleo tiende a subir y cuando se han presentado colapsos del mercado, el desempleo ha bajado también, y se ha mantenido bajo durante un periodo importante de tiempo. ...
... Z různých úhlů pohledu zdůrazňuje relevantnost agregátní poptávky při vytváření nových volných pracovních míst celá řada teoretických i empirických studií (Benigno, Fornaro, 2018;Cynamon, Fazzari, 2015;Čížek, 2017;Eriksson, Stadin, 2017;Farmer, 2008Farmer, , 2012Guerrazzi, Gelain, 2015;Heathcote, Perri, 2018;Mian, Sufi , 2011;Murphy, 2017;Naastepad, Storm, 2006;Vargas, Luna, 2014). Ball (1999) empiricky analyzoval země ve skupině G7 v období 1979Q1-1984Q4 a dochází k závěru, že monetární politika a další determinanty agregátní poptávky po fi nálních statcích mají silný efekt v krátkodobém i dlouhodobém vývoji nezaměstnanosti. ...
... Existence více než jedné rovnovážné míry nezaměstnanosti je častým projevem zabudováním mechanismu agregátní poptávky po fi nálních statcích do modelu trhu práce (Čížek, 2017;Diamond, 1980;Farmer, 2011Farmer, , 2012Kaplan, Menzio, 2015). ...
... In the last few decades, the financial world has witnessed a series of extreme events ranging from stock market crashes, economic downturns and geopolitical wars, throwing the global supply chain into disarray, as well as situations arising from a health crisis that created havoc all over the world. Such events, apart from their primary adverse impact on life and livelihood (Farmer, 2012;Ahmad et al., 2022;and Zhang et al., 2021), lead to a large amount of wealth erosion from the financial markets as well as damaging effects on trade and economic growth (Gencay and Selçuk, 2004;Blankenburg and Palma, 2009;and McKibbin and Stoeckel, 2010). Moreover, due to the increasing interconnectedness between financial markets worldwide (Gareci and Gnabo, 2018; Mohti et al., 2019;and Raddant and Kenett, 2021), the risk originating from an extreme event does not remain confined to its point of origin but amplifies across several other connected markets (Abuzayed et al., 2021;Hansen, 2021;and Abduraimova, 2022), with varying degree of impacts. ...
Extreme events such as a market crash, an economic downturn, a geopolitical war, or a situation emanating from a health crisis have proved to inflict far-reaching adverse impacts on an economy, depending on the country's market microstructure. This becomes more important due to the increasing interconnectedness across global financial markets and subsequent financial contagion. As such, it becomes imperative to quantify the relevant risk (i.e., tail risk) arising out of such events. This study examines the phenomenon of tail risk in the Indian stock market from sectoral returns, using extreme value theory (EVT). The study uses returns of 11 sectoral indices of Nifty from 2006 to 2023, and estimates the tail risk measure each quarter using a peak-over threshold approach following prior literature (Hill, 1975). It also investigates the impact of estimated tail risk on expected future returns of the respective sectoral indices. The results revealed the tail risk estimates (at 5% threshold of cross-sectional sectoral returns) to be significant. Further, tail risk positively impacts the expected Nifty sectoral returns, and the impact is robust even after controlling for macroeconomic factors like inflation, short-term interest rate, and long-term interest rate. The results of the study would help investors adopt better risk management practices and help regulators conduct stress tests to understand potential risk scenarios.
... Deep learning models often demonstrate instability when confronted with extreme market fluctuations, such as those experienced during the 2008 financial crisis [39] and the 2019 COVID-19 pandemic [40]. In response, reinforcement learning models have gained prominence due to their adaptability and capacity for continuous learning. ...
As financial markets grow increasingly complex in the big data era, accurate stock prediction has become more critical. Traditional time series models, such as GRUs, have been widely used but often struggle to capture the intricate nonlinear dynamics of markets, particularly in the flexible selection and effective utilization of key historical information. Recently, methods like Graph Neural Networks and Reinforcement Learning have shown promise in stock prediction but require high data quality and quantity, and they tend to exhibit instability when dealing with data sparsity and noise. Moreover, the training and inference processes for these models are typically complex and computationally expensive, limiting their broad deployment in practical applications. Existing approaches also generally struggle to capture unobservable latent market states effectively, such as market sentiment and expectations, microstructural factors, and participant behavior patterns, leading to an inadequate understanding of market dynamics and subsequently impact prediction accuracy. To address these challenges, this paper proposes a stock prediction model, MCI-GRU, based on a multi-head cross-attention mechanism and an improved GRU. First, we enhance the GRU model by replacing the reset gate with an attention mechanism, thereby increasing the model's flexibility in selecting and utilizing historical information. Second, we design a multi-head cross-attention mechanism for learning unobservable latent market state representations, which are further enriched through interactions with both temporal features and cross-sectional features. Finally, extensive experiments on four main stock markets show that the proposed method outperforms SOTA techniques across multiple metrics. Additionally, its successful application in real-world fund management operations confirms its effectiveness and practicality.
... In recent research, [10] proposed the StockNet model based on GRU, which includes an injection module to prevent overfitting and a survey module for stock analysis. However, these deep learning models often exhibit instability when facing extreme market fluctuations [7,14]. Therefore, models based on reinforcement learning have attracted attention due to their adaptability and continuous learning capabilities. ...
Stock price prediction is a challenging problem in the field of finance and receives widespread attention. In recent years, with the rapid development of technologies such as deep learning and graph neural networks, more research methods have begun to focus on exploring the interrelationships between stocks. However, existing methods mostly focus on the short-term dynamic relationships of stocks and directly integrating relationship information with temporal information. They often overlook the complex nonlinear dynamic characteristics and potential higher-order interaction relationships among stocks in the stock market. Therefore, we propose a stock price trend prediction model named LSR-IGRU in this paper, which is based on long short-term stock relationships and an improved GRU input. Firstly, we construct a long short-term relationship matrix between stocks, where secondary industry information is employed for the first time to capture long-term relationships of stocks, and overnight price information is utilized to establish short-term relationships. Next, we improve the inputs of the GRU model at each step, enabling the model to more effectively integrate temporal information and long short-term relationship information, thereby significantly improving the accuracy of predicting stock trend changes. Finally, through extensive experiments on multiple datasets from stock markets in China and the United States, we validate the superiority of the proposed LSR-IGRU model over the current state-of-the-art baseline models. We also apply the proposed model to the algorithmic trading system of a financial company, achieving significantly higher cumulative portfolio returns compared to other baseline methods. Our sources are released at https://github.com/ZP1481616577/Baselines\_LSR-IGRU.
... However, a stock market crash could also affect some people who do not directly own stock if the crash influences people's expectations or beliefs. Thus, the second transmission mechanism proposed relates to the value of the stock market response not only to fundamentals (preferences, technology and endowments, etc.), but also to beliefs or expectations (Romer, 1990;Farmer, 2011Farmer, , 2013Farmer, , 2015. If market participants sell stocks because they believe that future market prices will be lower, this will generate a large self-fulfilling shock to beliefs about future asset prices, in turn causing a permanent increase in the unemployment rate. ...
Las crisis bursátiles se han producido con mayor frecuencia en España. Sin embargo, en el periodo 1850-2006, España tuvo una menor probabilidad, que otros países, de que una depresión económica estuviera asociada a una crisis, a pesar de que las depresiones económicas también fueron comunes durante el periodo. Este resultado podría explicarse por el pequeño tamaño y bajo nivel de desarrollo del mercado bursátil durante la mayor parte de este periodo. Sin embargo, la probabilidad aumenta al considerar un lapso más largo (1850-2018 y también hasta 2020), que incluye los años de mayor desarrollo financiero e integración internacional. En este último periodo, España se parece más a otros países desarrollados y las crisis bursátiles parecen predecir en mayor medida la posibilidad de que se produzca una depresión, especialmente cuando la crisis bursátil va acompañada de una crisis bancaria. En consecuencia, una mayor inestabilidad financiera parece ser el principal mecanismo de transmisión entre las crisis bursátiles y las depresiones.
... There are some protentional reasons. From the graph in Figure 5 unemployment rate reach about 10% [9]. As many people lose jobs, the demand for production will decrease. ...
Oil Price is important to countries and people. As Black Gold, Oil is considered the blood of the industry. People in modern cities are surrounded by crude oil and its derivatives, such as petrol for cars, plastic products for storing food, etc. Thus, the changing price of oil can have a profound effect on people and countries. The prediction of oil prices can lessen the impact. Once people and the government know the future price of oil, then people and government can adjust their behavior. This study leverages the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to find the parameters of estimation and forecasts. The study uses Brent crude oil prices from May 1987 to July 2022. The study result shows that the price of oil is hard to predict based on the Brent crude oil prices. The study also discussed some protentional reasons why the SARIMA model cannot predict the price so accurately even if the method is correct.
... Market crashes have been ubiquitous during the last two decades. The most notable of these is the recession of 2008 subprime mortgage crisis (Farmer, 2012), and the most recent Coronavirus crash (Ashraf, 2020). As a result, the ability to predict financial market behaviour has become of paramount importance (Orús et al., 2019). ...
This paper introduces a new hybrid deep quantum neural network for financial predictions, the QuantumLeap system. This system consists of an encoder that transforms a partitioned financial time series into a sequence of density matrices; a deep quantum network that predicts the density matrix at a later time; and a classical network that measures, from the output density matrix, the maximum price reached by a security at a later time. The deep quantum network is isomorphic to a deep classical network and is computationally tractable. A hybrid deep network is associated with each time stride, allowing for parallelisation of the learning process. The classical network is a learnable measurement apparatus which infers, from the output density matrix, the maximum price reached by a security for a given time. Experimental results, associated with 24 securities, clearly demonstrate the accuracy and efficiency of the system in both the regression and extrapolation regimes.
... The housing sector is essential for the rest of the economy because real estate constitutes the largest asset class. The Great Recession was triggered by the bursting of a housing bubble in the United States (Farmer (2012)). The housing market has also been linked to many social and economic issues. ...
This thesis consists of three chapters that study frictional markets. The first chapter asks the question of what are the sources of labor income shocks, with a special focus on the scarring effects of recessions. I develop and estimate a dynamic frictional model of the labor market with heterogeneous workers and firms. The economic contribution of the first chapter is to show that sorting — the degree of complementarity between firms and workers — is a key component of idiosyncratic labor income risk. A technical contribution is to show that, while the determination of wage is a priori complex in a dynamic search model with heterogeneity, an efficient and robust algorithm exists. The second chapter explores to what extent a localized drop in commuting costs may lead to an increase in local employment. This chapter makes use of a discontinuity introduced by a French reform in September 2015 in the Paris metropolitan area. I find that cities that enjoyed a decrease in commuting costs experienced an increase in local employment. While the first two chapters analyze the labor market, the last chapter focuses on another key frictional market: the housing market. Little is known on the rental market because there are no comprehensive datasets recording rental agreements. To circumvent this issue, I collected data on rental ads in the Paris metropolitan area using web scraping techniques for a period of three months. I show that the rental housing market is well described by a directed search model. However, a non-negligible proportion of landlords use a two-step pricing approach when setting the rent, which raises interesting welfare and modeling questions.
... The fact that DeepPocket could manage the coronavirus crisis is to be found, in part, in such a long-term memory mechanism. Indeed, the offline training, which was performed on historical data, involved the early 2000's recession where the market reached a low point in 2002 as well as the 2009 market crisis (Langdon, McMenamin, & Krolik, 2002;Farmer, 2012). The knowledge gained by DeepPocket over these two crisis periods was instrumental in the management of the coronavirus crisis. ...
Portfolio management aims at maximizing the return on investment while minimizing risk by continuously reallocating the assets forming the portfolio. These assets are not independent but correlated during a short time period. A graph convolutional reinforcement learning framework called DeepPocket is proposed whose objective is to exploit the time-varying interrelations between financial instruments. These interrelations are represented by a graph whose nodes correspond to the financial instruments while the edges correspond to a pair-wise correlation function in between assets. DeepPocket consists of a restricted, stacked autoencoder for feature extraction, a convolutional network to collect underlying local information shared among financial instruments and an actor-critic reinforcement learning agent. The actor-critic structure contains two convolutional networks in which the actor learns and enforces an investment policy which is, in turn, evaluated by the critic in order to determine the best course of action by constantly reallocating the various portfolio assets to optimize the expected return on investment. The agent is initially trained offline with online stochastic batching on historical data. As new data become available, it is trained online with a passive concept drift approach to handle unexpected changes in their distributions. DeepPocket is evaluated against five real-life datasets over three distinct investment periods, including during the Covid-19 crisis, and clearly outperformed market indexes.
... The fact that DeepPocket could manage the coronavirus crisis is to be found, in part, in such a long-term memory mechanism. Indeed, the offline training, which was performed on historical data, involved the early 2000's recession where the market reached a low point in 2002 as well as the 2009 market crisis (Langdon et al., 2002;Farmer, 2012). The knowledge gained by DeepPocket over these two crisis periods was instrumental in the management of the coronavirus crisis. ...
Portfolio management aims at maximizing the return on investment while minimizing risk by continuously reallocating the assets forming the portfolio. These assets are not independent but correlated during a short time period. A graph convolutional reinforcement learning framework called DeepPocket is proposed whose objective is to exploit the time-varying interrelations between financial instruments. These interrelations are represented by a graph whose nodes correspond to the financial instruments while the edges correspond to a pair-wise correlation function in between assets. DeepPocket consists of a restricted, stacked autoencoder for feature extraction, a convolutional network to collect underlying local information shared among financial instruments, and an actor-critic reinforcement learning agent. The actor-critic structure contains two convolutional networks in which the actor learns and enforces an investment policy which is, in turn, evaluated by the critic in order to determine the best course of action by constantly reallocating the various portfolio assets to optimize the expected return on investment. The agent is initially trained offline with online stochastic batching on historical data. As new data become available, it is trained online with a passive concept drift approach to handle unexpected changes in their distributions. DeepPocket is evaluated against five real-life datasets over three distinct investment periods, including during the Covid-19 crisis, and clearly outperformed market indexes.
... In this research context, a resilient micropolitan area is one that has undergone a rapid decline (Fogli et al., 2012); (ii) the recession became a national problem in the fourth quarter of 2008 with the collapse of several large investment and commercial banks (Farmer, 2012); and (iii) the global recession started in 2009 as international trade and commodity prices fell, leading to sharp gains in unemployment (Bacchetta and van Wincoop, 2016). The BEA produces annual estimates of employment and income by industry at the county level. ...
Micropolitan areas (between 10,000 and 50,000 people) are not immune to economic shocks that threaten their vitality. Factors related to economic shocks can range from local companies leaving a town or national economic crises affecting local economies. Using the perspective of local micropolitan area stakeholders, this research seeks to identify why certain micropolitan areas recover from an economic shock while others do not. The research included the case study of two micropolitan areas in the U.S. Midwest (one resilient and one vulnerable), based on 22 interviews with key stakeholders representing diverse for-profit and government organizations. Our results reveal differences in the collective capacity and its underlying practices in the two micropolitan areas. We found that stakeholders built collective capacity by aligning effort, interacting face-to-face, supporting participation, sharing identity and building organizational capacity. Collective capacity ultimately enhanced the resilient micropolitan area’s ability to adopt place-based, or localized, strategies at a higher rate and larger scale than the vulnerable micropolitan area. The results contribute to theory of constitutive collaboration and help policy makers and stakeholders make informed decisions regarding practices to promote economic resilience.
... Los desplomes financieros son un ejemplo de eventos cuya ocurrencia presenta una duración no periódica. Estos tienen una gran variedad de consecuencias económicas negativas que van desde pérdidas de ingreso persona, hasta bancarrotas masivas y recesiones (Farmer, 2012). Las duraciones no periódicas dan lugar a tiempos transcurridos de manera aleatoria por lo que para su estudio se utilizan metodologías estadísticas propias del análisis de tiempo de vida. ...
El objetivo fundamental del modelo autorregresivo de duración condicional (ADC) es el
modelamiento de series de tiempo con periodos no equidistantes. Dada la naturaleza leptocúrtica de los retornos de la tasa representativa del mercado (TRM) y el comportamiento de las duraciones asociado a estos, se usó una distribución de Rayleigh con transmutación, que permite aproximar de forma ajustada a una distribución de colas pesadas, coherente con este hecho estilizado en los retornos. Se concluye que el tiempo transcurrido entre caídas del dólar, en promedio, se encuentra entre 3 y 6 días.
... Self-fulfilling beliefs advocates (e.g., Azariadis, 1981;Cass and Shell, 1983;Farmer, 1999Farmer, , 2012bFarmer, , 2012cFarmer, , 2013Bacchetta and Van Wincoop, 2013;Benhabib and Wang, 2013;Benhabib et al., 2016; Asriyan et al. (2019a) There is adverse selection in the dynamic asset market and potential asset buyers not only care about the unobserved quality of the asset they bid for but also about future market liquidity in case they want to resell the asset. Sentiments here pertain to rational expectations about future liquidity driven by a sunspot process. ...
Economists, observers, and policy‐makers often emphasize the role of sentiment as a potential driver of the business cycle. In this paper, we provide three contributions to this debate. First, we give an overview of the recent literature on the nexus between sentiment (considering both confidence and uncertainty) and economic activity. Second, we review existing empirical measures of sentiment, in particular consumer confidence, stock market volatility (SMV) and Economic Policy Uncertainty (EPU), on monthly data for 27 countries, 1985–2016. Third, we identify some new stylized facts based on international evidence. While different measures are surprisingly lowly correlated on average in each country, they are typically highly positively correlated across countries, suggesting the existence of a global factor or sizeable international spillovers of sentiment. Consumer confidence has the closest co‐movement with economic and financial variables, and most of the correlations are contemporaneous or forward‐looking, consistent with the view that economic sentiment is indeed a driver of activity.
... Evaluation Figure 6.2 illustrates the cumulative rewards and the prediction error power are illustrated Observe that the agent is highly correlated with the market (i.e., S&P 500) and overall collects lower cumulative returns. Moreover, note that the market crash in 2009 (Farmer, 2012), affects the agent significantly, leading to a decline by 179.6% (drawdown), taking as many as 2596 days to recover, from 2008-09-12 to 2015-10-22. ...
In this thesis, we develop a comprehensive account of the expressive power, modelling efficiency, and performance advantages of so-called trading agents (i.e., Deep Soft Recurrent Q-Network (DSRQN) and Mixture of Score Machines (MSM)), based on both traditional system identification (model-based approach) as well as on context-independent agents (model-free approach). The analysis provides conclusive support for the ability of model-free reinforcement learning methods to act as universal trading agents, which are not only capable of reducing the computational and memory complexity (owing to their linear scaling with the size of the universe), but also serve as generalizing strategies across assets and markets, regardless of the trading universe on which they have been trained. The relatively low volume of daily returns in financial market data is addressed via data augmentation (a generative approach) and a choice of pre-training strategies, both of which are validated against current state-of-the-art models. For rigour, a risk-sensitive framework which includes transaction costs is considered, and its performance advantages are demonstrated in a variety of scenarios, from synthetic time-series (sinusoidal, sawtooth and chirp waves), simulated market series (surrogate data based), through to real market data (S\&P 500 and EURO STOXX 50). The analysis and simulations confirm the superiority of universal model-free reinforcement learning agents over current portfolio management model in asset allocation strategies, with the achieved performance advantage of as much as 9.2\% in annualized cumulative returns and 13.4\% in annualized Sharpe Ratio.
Purpose
A novel psychology-based framework is proposed to investigate the causes of stock market crashes.
Design/methodology/approach
Our approach builds upon the dynamic capital mobility model, expanding it by incorporating bidirectional relationships between investor sentiment and the average stock market price level. Investor sentiment is measured in terms of jumping rates of investors’ attitudes to the stock market in our model. The role and impact of investor sentiment on the average stock market price level are further clarified across various scenarios through simulations.
Findings
Our model provides a direct quantification of the influence of investor sentiment on stock market price dynamics, shedding light on an alternative mechanism contributing to stock market crashes alongside fundamental changes. Our simulation results confirm that optimistic investor sentiment can mitigate the magnitude of negative stock market price movements triggered by adverse fundamental changes. Our model also indicates that the stock market price level may exhibit higher volatility than can be attributed to fundamental changes.
Research limitations/implications
Our study only briefly touches on the origins of shifts in investor sentiment by introducing a self-fulfilling feedback loop from the average stock market price level to investor sentiment. In future research, we will identify other key factors contributing to shifts in investor sentiment and subsequently analyze their influence on investor sentiment.
Practical implications
Our findings carry practical implications for recent and future movements in the US stock market. For instance, despite the Federal Reserve’s continuous interest rate hikes since March 2022, the overall optimistic investor sentiment has helped mitigate a severe downward movement in the stock market. However, in the absence of clear indications of a loose monetary policy, a rapid shift of investor sentiment from optimism to pessimism in the US stock market could potentially lead to a historic stock market crash.
Originality/value
While the significance of investor sentiment in shaping stock market price dynamics, particularly regarding cross-sectional variations of stock returns, is widely recognized, quantifying directly its impact on the average stock market price level has remained a persistent challenge. Only very limited literature has delved into this subject, often hindered by a lack of theoretical support or yielding inconsistent results.
Stock crash risk often propagates through various interconnected relationships between firms, amplifying its impact across financial markets. Few studies predicted the crash risk of one firm in terms of its relevant firms. A common strategy is to adopt graph neural networks (GNNs) with some predefined firm relations. However, many relations remain undetected or evolve over time. Restricting to several predefined relations inevitably makes noise and thus misleads stock crash predictions. In addition, these relationships are not independent during the process of propagating information and interacting with each other. This study proposes the multi-relation reconstruction and fusion graph neural network (MRRFGNN) to predict stock crash risk by capturing complex relations among listed companies. First, the model employs self-supervised learning and contrastive learning to reconstruct and infer implicit relationships between companies. Second, the model incorporates a relation self-attention mechanism to integrate various types of relationships, enabling a more nuanced understanding of the multiple spillover effects. Empirical evidence from a series of experiments demonstrates the superiority of the proposed method, which achieves the best performance with improvements of at least 2.14% in area under the curve (AUC) and 2.64% in Matthews correlation coefficient (MCC), highlighting its potential for practical application in financial markets.
This paper offers a starting point for reflection on the similarities and differences of the impact on financial markets of the Great Recession of 2008 and of the Covid-19 pandemic of 2020 in terms of volatility and correlation risk among the most significant financial indexes in Europe. More precisely, the dataset employed includes the daily returns of Ftse100, CaC40, Dax30 and FtseMib40, with reference to the two time periods in which the two major crises manifested their effects on the markets. We use two different methodological approaches: the analysis of the daily conditional variance using various families of GARCH models and the study of the weekly realized volatility using HAR models. Furthermore, the estimation of the dependence structure of the GARCH residuals using copula functions is performed.
This study introduces multimodal data machine learning framework to predict stock crashes. It encapsulates market data, graph data cultivated from industry affiliations through node2vec, and text data derived from sentiment analysis. The LightGBM is utilized, marking an improvement by 7.13% over preceding studies, achieving 75.85% balanced accuracy. An innovative long-short portfolio construction approach is articulated, demonstrating the practical significance of the predictions, with a 4.75% portfolio return in 2022 — a 27.26% advancement over the CSI 300. This endeavour in leveraging multimodal data machine learning for stock crash prediction offers a promising performance, serving as a valuable reference for investors.
In this paper, we investigate house price shocks on the macroeconomic variables (financial market, inflation, and real sector) of the G7 economies. We use the GVAR to capture the spillover effects from the U.S. housing market and oil prices on these economies from 1991M3–2022M10. We identify the U.S. house price shock using the Structural Generalized Impulse Response Function, house supply and demand variables, and regional divergence. We find that the domestic stock markets and industrial production are the most sensitive to house price shocks. We further compare the importance of house and oil prices on domestic fluctuations. The estimates reinforce the previous findings: U.S. house prices are responsible for a quarter of the domestic volatility of the stock markets and industrial production. In the other macroeconomic segments, the effects of house prices are present, but in lower values. Our results show that house prices provoke more domestic fluctuations than oil prices. Finally, we also found that short and long‐term credit markets, as well as stock markets, transmit the house price shock to industrial production. Consequently, we provide potential channels to comprehend the spillover effect of U.S. house prices on international markets.
This paper studies the connection between the stock market and real output in China and compares it with benchmark countries, employing a novel vector autoregression with asymmetric leads (VARwAL) model. It makes two contributions. First, it finds that the time profile of the Chinese stock market's response to real output shocks suggests no evidence of a distorted relationship due to manipulation of Chinese real output data or domination of the Chinese stock market by individual investors. Rather, the Chinese stock market is relatively more responsive to real output, in line with the larger share of manufacturing in the Chinese economy. Electricity output and industrial profits, two different, less‐manipulable time series, yield similar results. Second, it presents the first use of VARwAL impulse responses to detect stock market bubbles: VARwAL captures the 2015 bubble in China successfully. Over the full sample period, China's stock market appears to have been less prone to bubbles than the US stock market.
It is essential to understand the relationship between stock market returns and economic growth. We investigate whether the stock market is a leading indicator, a lagging indicator, or it has no relation to economic growth. Identifying this can help households plan their investments for business cycle contractions and expansions, as well as provide legislators and federal government agencies with the information they could use to maximize the effectiveness of their policies. Through our research, we identified the relationship between stock market returns and the changes in the percentage of gross domestic product (GDP) growth in the prior and the year following the market returns. By running a regression analysis of GDP percent change on the prior-year S&P 500 annual return, we find a significance level of 0.00159, which is highly statistically significant. Therefore, we conclude that the stock market is a leading indicator of a recession, which further allows us to better understand how policymakers can make optimal decisions.
This study targets the development through the motivation of the research to explore the impact of the KLSE market index from Malaysia in the influence towards the stock price performance among the leading technology companies globally. The lack of the study on the similar scope of study had been triggering the need for the exploration for this study to investigate the potential significant in the relationship between the KLSE market index and the stock price performance for the leading technology companies. The previous study had been suggesting there should be significant positive correlation and relationship between the market indices against the performance of the technology stock prices developing the initial hypothesis for the study. However, the quantitative study on the 10 years timeline for the study pattern for the KLSE market and the technology stock price performance had revealed that the empirical evidence had pointed the opposite result where the significant negative correlation and relation was found based on the historical data through the quantitative analysis using the correlation analysis and regression analysis leading to the rejection of the initial hypothesis as drawn. This had been providing fresh insight for the contribution of study expanding the opportunities for the future study of similar area of expertise.
This paper investigates the spillover effect of lagged US daily returns on stock return predictability across 17 developed markets from January 1st, 1972 through August 31st, 2022. Using daily returns series, we find that lagged US returns is a superior predictor for future returns in international markets while including the lagged domestic returns and considering US negative or extreme returns. The predictive power of lagged US daily returns, nonetheless, substantially weakens during the recent COVID period. Our results imply that the degrees of stock return predictability and spillovers across markets are driven by the evolutionary market conditions, the channels of information transmission, and information leadership.
This paper explores the complementary benefits of embedding a deep learning model as a fully data-driven fuzzy implication operator of a five-layer neuro-fuzzy system for learning and explanations for the predictions of both steady-state and dynamically changing data. In traditional Mandani-type neuro-fuzzy systems, the entailment performed by the implication is realized using the fuzzy implication operator based on the fuzzy rules formed in the rule base during encoding and recall. Given the presence of a group of test data that are significantly different from the training data, the realization of entailments through the use of the implication operator based on the fuzzy rules formed in traditional neuro-fuzzy systems may not be adequate. This paper attempts to adopt a more direct approach by embedding a deep learning model in the neuro-fuzzy system to serve as a fuzzy implication operator, thereby allowing the data-driven learning of fuzzy implication using the deep structure to provide a close correspondence to the real-world entailment of data. In addition, embedding the neuro-fuzzy architecture within the deep learning model allows the comprehension of the learning and explanation of the reasoning of the deep network. The induced fuzzy association rules impart transparency to the deep learning based implication using a common set of semantic meanings, which are amenable to human interpretability. The effectiveness of the proposed model is evaluated on a continuously stirred tank reactor dataset and three financial stock prediction datasets. Experimental results showed that the proposed model outperformed other state-of-the-art techniques based on the four datasets, which contain high levels of uncertainties.
Background
The association between stock volatility and cardiovascular diseases (CVD) was described during the 2008 Global Stock Market Crash; however, whether the finding in an occasional stock market crash is spurious remains unclear.
Methods
A time-series design was used to evaluate the association between short-term exposure to daily returns of two major indices and daily hospital admissions for CVD and its subtypes based on claims data from the National Insurance Claims for Epidemiological Research (NICER) study covering 174 major cities in China. The average percentage change in daily hospital admissions for cause-specific CVD per 1% change in daily index returns was calculated because the Chinese stock market policy limits its change by 10% of the previous day's closing price. A Poisson regression in a generalised additive model was used to assess the city-specific association; then, overall national estimations were pooled by random-effects meta-analysis.
Findings
Totally 8,234,164 hospital admissions for CVD were recorded during 2014–2017. Points of the Shanghai closing indices ranged from 1991·3 to 5166·4. A U-shaped association was observed between daily index returns and CVD admissions. Changes of 1% in daily returns of the Shanghai index corresponded to 1·28%(95%CI: 1·04%–1·53%), 1·25%(0·99%–1·51%), 1·42%(1·13%–1·72%), and 1·14%(0·39%–1·89%) increases in hospital admissions for total CVD, ischaemic heart disease, stroke, or heart failure on the same day, respectively. Similar results were observed for the Shenzhen index.
Interpretation
Stock market volatility is associated with an increased CVD admission.
Funding
Chinese Ministry of Science and Technology (2020YFC2003503) and National Natural Science Foundation of China (81973132, 81961128006).
This study examines a unit root process in lower-bounded time series with additive outliers. A standard unbounded unit root test may be unsuitable when applied to nonnegative or lower-bounded data; therefore, lower-bounded unit root tests that handle outliers are proposed. The results demonstrate that these tests reject less frequently the null hypothesis of unit root because of a nuisance parameter associated with the lower bound. The empirical study of inflation and stock market volatility in South America reveals the presence of outliers related to the hyperinflation crisis and stock markets owing to multiple shocks, such as the recent COVID-19 pandemic. Moreover, these series cannot reject the unit root hypothesis if the lower bound is considered.
This paper investigates how supply noise and demand noise contribute to business cycle fluctuations in three major European economies. A structural vector autoregressive model is used to identify supply, demand, supply noise and demand shocks. The identification scheme is built on nowcast errors of output growth and the inflation rate that are derived from the Consensus Economics Survey. The results indicate that positive supply noise and positive demand noise shocks have an expansionary effect on output, but their magnitude differs across countries. The two shocks contribute equally to business fluctuations, and jointly, they account for around one quarter of the total variation in GDP in each of the three countries.
This paper investigates the causal effect of economic sentiment on consumption using micro-data between 2008 and 2017. After showing that individuals update their beliefs about the national state of the economy in response to local housing and labor market fluctuations, I use the Social Connectedness Index (SCI) to construct an SCI-weighted index of housing price growth that captures individuals’ exposure to housing market shocks in friends’ zip codes. Using plausibly exogenous variation in individuals’ exposure to friends who experience heterogeneous housing price growth, I find that a standard deviation rise in economic sentiment is associated with a 17.3–24.6% rise in consumption of non-durables.
يقوم موضوع هذه الدراسة بإلقاء الضوء على سوق الأوراق المالية وهو عبارة عامة تشير إلى شراء وبيع
الأوراق المالية الاستثمارية، حيث يمكن أن تشمل هذه الاستثمارات الأسهم والسندات والخيارات والعقود الآجلة
والصناديق المشتركة والصناديق المتداولة في البورصة، حيث يتم تداول العديد من هذه الأوراق المالية في بورصات
الأوراق المالية، ولكن يتم تداول البعض الآخر من خلال مؤسسات الوساطة المالية، وتأتي معظم الأعمال التي
يتم إجراؤها في سوق الأوراق المالية من مستثمرين مؤسسيين، لكن التقنيات الجديدة مثل التداول عبر الإنترنت
جعلت سوق الأوراق المالية أكثر سهولة بالنسبة للمستثمرين الأفراد.
تسمح بورصة نيويورك ( )NYSEأكبر بورصة قائمة على الأسهم في العالم بالسماح للمستثمرين
والمتداولين بجني الأموال من خلال توفير سوق لهم لتداول الأوراق المالية، كما أنها تسمح للشركات بجمع الأموال
من خلال إدراج أنواع مختلفة من الأوراق المالية فيها، ولتوفير مثل هذه الخدمات والسوق تقوم بورصة نيويورك
بالعمل قدما لقدم مع مؤسسات الوساطة المالية وذلك من أجل تنشيط أسوق الأوراق المالية عامة، وبورصة نيويورك
.خاصة، لتنويع المحافظ الاستثمارية وتقليل المخاطر
Examining the interaction between the stock market and the real sector is a critical issue in terms of providing financial markets the driving force in economic growth through various channels. In the process following the increase of regional integrations on a global scale and then the liberalization of financial movements, the relationship between financial markets and economic growth has attracted attention, and research on the relationship between these two sizes has intensified, especially in recent years. While the liberalization movements in the financial markets which also include stock markets, may positively affect the economies of the country in terms of creating funds; at the same time, it may have the potential to affect negatively, as it may open up financial crises outside the country. For this purpose, the study examines the existence of the relationship between economic growth and the improving of the stock market for the BRICS-T countries in the period 1995-2017. In order to examine the relations, first of all, cross-section dependence and homogeneity tests were applied. Unit root and cointegration tests which are in accordance with the results of these tests, were conducted. Finally, Common Correlated Effects (CCE) and Extended Average Group (Augmented Mean Group (AMG)) cointegration estimators were used to estimate the impact's long-term coefficient. According to the findings obtained in the study, the effect of stock market development on economic growth may differ according to the specific dynamics of the countries concerned. So as a result, positively related in Brazil, Russia and China, Turkey and South Africa in a negative related, and points out that there is no statistically significant relationship in India.
This paper builds a parsimonious model of nominal wage stickiness based on wage dispersion induced by search frictions. The paper shows that this model can explain those salient features of wage adjustment in the data, including the average frequency of wage adjustment, the average duration of a wage, and the size distribution of wage changes.
Cryptocurrencies—digital assets—are discussed from the viewpoint of the medium of exchange and the store of value with a focus on Bitcoin. The issue of trust towards the unit of accounts is viewed in the perspective of historical events including fiat currency reforms in the past all over the world. It is argued that a major test of the present global financial system and its emerging new technology alternatives has not occurred yet. The notion of intrinsic value of currency is strongly attached to the stability of the social and economic system, which landscape will be probably drastically altered in the next few decades due to the rise of artificial intelligence. The chapters in this book which are focusing on the current aspects of financial innovations also point out the possible directions and dilemmas the future may bring us.
The present article studies a semi-parametrical method of macro-economic forecast in periods of sharp changes in economy put forward by the authors. Data blocks are chosen on the basis of clusterization methods, which are as close to the current economic conditions as possible. The key method of clusterization is the method of the closest neighbor. Time series is split into blocks and then the closest block is chosen for the last block of observation, which demonstrates the idea of coordination of directive series movements. As a basic model of forecasting the authors use ARIMA model. The authors show advantages of this approach for forecasting during the great recession – the economic slump of 2008 for such variables as inflation rate, unemployment and real private income. This method demonstrates its superiority in comparison with parametrical linear, non-linear, single-dimension and multidimension alternative methods for the period 2007–2019. The article provides calculation s obtained as a result of computer experiment using Python language for data on inflation rate and oil prices for the mentioned period. This approach in future can be used in intellectual methods of machine teaching, such as neuron networks.
We estimate a forward-looking monetary policy reaction function for the postwar United States economy, before and after Volcker's
appointment as Fed Chairman in 1979. Our results point to substantial differences in the estimated rule across periods. In
particular, interest rate policy in the Volcker-Greenspan period appears to have been much more sensitive to changes in expected
inflation than in the pre-Volcker period. We then compare some of the implications of the estimated rules for the equilibrium
properties of inflation and output, using a simple macroeconomic model, and show that the Volcker-Greenspan rule is stabilizing.
European unemployment has been steadily increasing for the last 15 years and is expected to remain very high for many years to come. In this paper, we argue that this fact implies that shocks have much more persistent effects on unemployment than standard theories can possibly explain. We develop a theory which can explain such persistence, and which is based on the distinction between insiders and outsiders in wage bargaining. We argue that if wages are largely set by bargaining between insiders and firms, shocks which affect actual unemployment tend also to affect equilibrium unemployment. We then confront the theory to both the detailed facts of the European situation as well as to earlier periods of high persistent unemployment such as the Great Depression in the US.
It is widely agreed that the natural unemployment rate recovered strongly in several OECD economies in the 1990s while not yet in the others. This paper draws on models by the authors endogenizing the path of the natural rate in order to trace the causes and apportion the credit. The results suggest that the five structural reforms of the labor market urged by the OECD Secretariat were effective and account for some of the recovery where adopted. However, the paper sees most of the recoveries as largely the emergence from structural slump to a structural boom--a boom brought by revived investment in employees, customers and fixed capital. Behind this revival is a recovery of business-asset valuations resulting from improved expectations for productivity growth and, judging by the stock market, hopes for a future lift in business earnings that exceeds extrapolations of the past--both driven by the "new economy."
This paper considers a prototypical New Keynesian model, in which the equilibrium is undetermined if monetary policy is "passive." The likelihood-based estimation of dynamic equilibrium models is extended to allow for indeterminacies and sunspot fluctuations. We construct posterior weights for the determinacy and indeterminacy region of the parameter space and estimates for the propagation of fundamental and sunspot shocks. According to the estimated model, U.S. monetary policy post-1982 is consistent with determinacy, whereas the pre-Volcker policy is not. We find that before 1979 indeterminacy substantially altered the propagation of shocks.
While conventional farming systems face serious problems of sustainability, organic agriculture is seen as a more environmentally friendly system as it favours renewable resources, recycles nutrients, uses the environment’s own systems for controlling pests and diseases, sustains ecosystems, protects soils, and reduces pollution. At the same time organic farming promotes animal welfare, the use of natural foodstuffs, product diversity and the avoidance of waste, among other practices. However, the future of organic agriculture will depend on its economic viability and on the determination shown by governments to protect these practices. This paper performs panel regressions with a sample of Catalan farms (Spain) to test the influence of organic farming on farm output, costs and incomes. It analyses the cost structures of both types of farming and comments on their social and environmental performance.
All files needed to replicate the results of the article
We estimate a three equation model of the monetary transmission mechanism using data from 1970:Q1 to 1999:Q4. In earlier work [2] we focused on the low frequency properties of these data by studying a cointegrated VAR. In the current paper we extend our analysis by estimating a class of models that Farmer and Uhlig [8] call the recoverable structure. The recoverable structure is a subset of the equations of a structural linear rational expectations model with parameters that remain constant when there is a break in the policy rule. Since these equations include future expectations as right-hand-side variables their parameters are not subject to the Lucas [19] critique of econometric policy evaluation. We estimate the recoverable structure of a monetary model of the U.S. economy and we use our estimates to carry out counterfactual experiments across policy regimes.
This paper presents a model of the macroeconomy in which any unemployment rate may be a steady-state equilibrium and every equilibrium unemployment rate is associated with a different value for the price of assets. To select an equilibrium, I construct a theory in which asset price bubbles are caused by the self-fulfilling animal spirits of market participants, selected by a belief function. In contrast to my earlier work on this topic, asset prices may be unbounded. All of the actors in my model have rational expectations and the asset price bubbles that occur are individually rational, even though the equilibria of the model are socially inefficient. My work opens the door for a new class of theories in which market psychology, captured by the belief function, plays an independent role in helping us to understand economic crises.
This paper presents a theory of the monetary transmission mechanism in an old-Keynesian model with multiple equilibrium unemployment rates. The model has two equations in common with the new-Keynesian model; the optimizing IS curve and the policy rule. It differs from the new-Keynesian model by replacing the Phillips curve with a belief function to determine expectations of nominal income growth. I estimate the new and old-Keynesian models using U.S. data and I show that the old-Keynesian model fits the data better than its new-Keynesian competitor.
This paper presents a model of the macroeconomy that reformulates what I take to be two important ideas from Keynes General Theory. The first is that there may be a continuum of steady state unemployment rates. The second is that beliefs select an equilibrium. I argue that search and matching costs in the labor market lead to the existence of a continuum of equilibria and I resolve the resulting indeterminacy by assuming that the beliefs of stock market participants are self-fulfilling. The paper reconciles Keynesian economics with general equilibrium theory without invoking the assumption of frictions that prevent wages and prices from reaching their equilibrium levels.
This paper uses a model with a continuum of equilibrium steady state unemployment rates to explore the effectiveness of fiscal policy. The existence of multiple steady state equilibria is explained by the presence of search and recruiting costs. I use the model to explain the current financial crisis as a shift to a high unemployment equilibrium, induced by the self-fulfilling beliefs of market participants about asset prices. I ask two questions. (1) Can fiscal policy help us out of the crisis? (2) Is there an alternative to fiscal policy that is less costly and more effective? The answer to both questions is yes.
The New Keynesian framework has emerged as the workhorse for the analysis of monetary policy and its implications for inflation, economic fluctuations, and welfare. It is the backbone of the new generation of medium-scale models under development at major central banks and international policy institutions, and provides the theoretical underpinnings of the inflation stability-oriented strategies adopted by most central banks throughout the industrialized world. This graduate-level textbook provides an introduction to the New Keynesian framework and its applications to monetary policy. Using a canonical version of the New Keynesian model as a reference framework, Jordi Galí explores issues pertaining to the design of monetary policy, including the determination of the optimal monetary policy and the desirability of simple policy rules. He analyzes several extensions of the baseline model, allowing for cost-push shocks, nominal wage rigidities, and open economy factors. In each case, the implications for monetary policy are addressed, with a special emphasis on the desirability of inflation targeting policies.
We study the low frequency comovements in unemployment, inflation and the federal funds rate in the U.S. From 1970 through 1979 all three series trended up together; after 1979 they all trended down. The conventional explanation for the buildup of inflation in the 1970s is that the Fed reacted to an increase in the natural rate of unemployment by conducting an overly passive monetary policy. We show that this explanation is difficult to reconcile with the observed comovement of the Fed funds rate and inflation. We argue instead that the source of the inflation buildup in the 1970s was a downward drift in the real interest rate that was translated into a simultaneous increase in unemployment and inflation by passive Fed policy. Our explanation relies on the existence in the data of a positive long-run unemployment–inflation relationship. This is consistent with non-superneutrality of money in the long-run, and hence the title of our paper ‘natural rate doubts’.
We measure the return to capital directly from the NIPA and BEA data and examine the return implications of the real business cycle model. We construct a quarterly time series of the after-tax return to business capital. Its volatility is considerably smaller than that of S&P 500 returns. The standard business cycle model captures almost 50% of the volatility in the return to capital (relative to the volatility of output). We consider several departures from the benchmark model; the most promising is one with stochastic taxes which captures nearly 80% of the relative volatility in the return to capital.
The General Theory of Employment, Interest, and Money / John Maynard Keynes Note: The University of Adelaide Library eBooks @ Adelaide.
We propose a procedure for representing a time series as the sum of a smoothly varying trend component and a cyclical component. We document the nature of the co-movements of the cyclical components of a variety of macroeconomic time series. We find that these co-movements are very different than the corresponding co-movements of the slowly varying trend components.
Twenty five years after the publication of the second edition, this paper describes and evaluates the Contributions to monetary and macroeconomics made in Don Patinkin's Money, Interest, and Prices (MIP). Its first accomplishment was to settle definitively many issues, such as the valid and invalid dichotomies between real and nominal magnitudes, Say's identity, the nature of the Keynesian system, and the requirements for the neutrality of money, which had been disputed for decades. It also opened the road to the future by developing macroeconomic models from a well specified microeconomic foundation. In so doing, it established the base on which subsequent equilibrium macroeconomics built. Beyond that, in Chapter XII, Patinkin pioneered the development of disequilibrium analysis by presenting a fully articulated model that makes the key distinction between notional and effective demands, and using it to explain price and quantity adjustments in conditions of unemployment.
We survey the literature on search-theoretic models of the labor market. We show how this approach addresses many issues, including the following: Why do workers sometimes choose to remain unemployed? What determines the lengths of employment and unemployment spells? How can there simultaneously exist unemployed workers and unfilled vacancies? What determines aggregate unemployment and vacancies? How can homogeneous workers earn different wages? What are the tradeoffs firms face from different wages? How do wages and turnover interact? What determines efficient turnover? We discuss various modeling choices concerning wage determination and the meeting process, including recent models of directed search.
At the microeconomic level, this paper revises and broadens the theory of the equilibrium rate of unemployment, the "natural rate" in a monetary model. The authors begin by recreating Steven Salop's turnover model of the natural rate in its naturally intertemporal version. Useful findings on impact effects and the adjustment process at the individual firm, necessarily excluded by the static version, are shown to derive from the dynamized model. At the macroeconomic level, the authors then provide a general-equilibrium analysis of some shocks showing how they drive the equilibrium unemployment rate and in varying ways also disturb the real rate of interest. Copyright 1992 by American Economic Association.
The paper reviews the recent literature on monetary policy rules. We exposit the monetary policy design problem within a simple baseline theoretical framework. We then consider the implications of adding various real world complications. Among other things, we show that the optimal policy implicitly incorporates inflation targeting. We also characterize the gains from making a credible commitment to fight inflation. In contrast to conventional wisdom, we show that gains from commitment may emerge even if the central bank is not trying to inadvisedly push output above its natural level. We also consider the implications of frictions such as imperfect information.
This paper considers a prototypical New Keynesian model, in which the equilibrium is undetermined if monetary policy is "passive." The likelihood-based estimation of dynamic equilibrium models is extended to allow for indeterminacies and sunspot fluctuations. We construct posterior weights for the determinacy and indeterminacy region of the parameter space and estimates for the propagation of fundamental and sunspot shocks. According to the estimated model, U.S. monetary policy post-1982 is consistent with determinacy, whereas the pre-Volcker policy is not. We find that before 1979 indeterminacy substantially altered the propagation of shocks.
This paper contains the likelihood analysis of vector autoregressive models allowing for cointegration. The author derives the likelihood ratio test for cointegrating rank and finds it asymptotic distribution. He shows that the maximum likelihood estimator of the cointegrating relations can be found by reduced rank regression and derives the likelihood ratio test of structural hypotheses about these relations. The author shows that the asymptotic distribution of the maximum likelihood estimator is mixed Gaussian, allowing inference for hypotheses on the cointegrating relation to be conducted using the Chi(" squared") distribution. Copyright 1991 by The Econometric Society.
This study investigates the impact of the current financial crisis on Canada's potential GDP growth. Using a simple accounting framework to decompose trend GDP growth into changes in capital, labor services and total factor productivity, we find a sizeable drop in Canadian potential growth in the short term. The estimated decline of about 1 percentage point originates from a sharply decelerating capital stock accumulation (as investment has dropped steeply) and a rising long-term unemployment rate (which would raise equilibrium unemployment rates). However, over the medium term, we expect Canada's potential GDP growth to gradually rise to around 2 percent, below the pre-crisis growth rate, mostly reflecting the effects of population aging and a secular decline in average working hours.
Both textbook economics and common sense teach us that the value of household wealth should be related to consumer spending. At the same time, movements in asset values often seem disassociated with important movements in consumer spending, as episodes such as the 1987 stock market crash and the contraction in equity values that occurred in the fall of 1998 suggest. An important first step in understanding the consumption-wealth linkage is determining how closely the two variables are actually correlated, and whether there exist important movements in asset values that are not associated with changes in consumption. This paper provides evidence that a surprisingly small fraction of the variation in household net worth is related to variation in aggregate consumer spending. We use empirical techniques that allow us to quantify the relative importance of permanent and transitory innovations in the variation of consumer spending and wealth and find that transitory shocks dominate post-war variation in wealth, while permanent shocks dominate variation in aggregate consumption. Although transitory innovations are found to have little influence on consumer spending, they have long-lasting effects on wealth , exhibiting a half-life of a little over two years. The findings suggest that most macro models which make no allowance for transitory variation in wealth that is orthogonal to consumption are likely to misstate both the timing and magnitude of the consumption-wealth linkage.
A multivariate regime-switching model for monetary policy is confronted with U.S. data. The best fit allows time variation in disturbance variances only. With coefficients allowed to change, the best fit is with change only in the monetary policy rule and there are three estimated regimes corresponding roughly to periods when most observers believe that monetary policy actually differed. But the differences among regimes are not large enough to account for the rise, then decline, in inflation of the 1970s and 1980s. Our estimates imply monetary targeting was central in the early 1980s, but also important sporadically in the 1970s.
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Identifying the Monetary Transmission Mechanism Using Structural Breaks European Central Bank Working Paper Series Natural Rate Doubts
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Animal Spirits, Rational Bubbles and Unemployment in an Old-Keynesian Model CEPR Discussion Paper, 8439 How to Prevent the Great Depression of
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Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregreeive Models
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How to Prevent the Great Depression of
- R E A Farmer
Does Fiscal Policy Matter? Blinder and Solow Revisited
- R E A Farmer
- D Plotnikov
Remarks to the Federal Reserve Bank of Atlanta Conference
- B S Bernanke