Dividend persistence and dividend behaviour

This article demonstrates how a spurious regression problem caused by dividend persistence is compounded by a spurious correlation problem when the dependent and independent variables in dividend behaviour regressions are ratios composed of common component variables. This article utilises a simulation procedure to take account of these problems, with the findings implying that extreme care should be taken when using ratios as predictor or explanatory variables in time series regression. This article introduces a reformulated Lintner first difference dividend behaviour model that is not subject to spurious regression in which past prices predict subsequent changes in dividends.     

House prices,bank instability,and economic growth: Evidence from the threshold model

This paper examines the effects of house prices on bank instability when gauged at various levels of income growth. Bank stability may respond differently to house price changes or deviations from fundamental values in an economic boom environment than in a bust circumstance. A threshold estimation technique developed by Hansen (1999) is applied to a panel of 286 U.S. Metropolitan Statistical Areas (MSAs) over the period 1990Q1–2010Q4. We consider two house price indicators: the house price changes and the house price deviations from long-run equilibrium. The results suggest the existence of income growth threshold effects in the relationship between house prices and bank instability. Specifically, there are two income growth thresholds when using the house price changes and one income growth threshold when the house price deviations are applied. Robustness results using the non-MSAs sample from 1995Q1 to 2010Q4 provide further evidence of income growth threshold effects.     

住房价格、金融稳定与宏观审慎政策

本文通过建立包含异质性家庭、异质性厂商和金融机构的DSGE模型,分析对预期房价作出反应的货币政策和宏观审慎政策的传导机制和政策效果,研究不同政策的选择和协调问题。研究发现,首先,由于政策的作用范围不同,不同政策会对金融稳定和经济稳定产生不同影响。对预期房价作出反应的货币政策能够抑制住房需求和信贷供给,但也会抑制消费需求和产出;而对预期房价作出反应的逆周期LTV政策和逆周期资本充足率政策在应对房价波动导致的金融稳定问题时更加有的放矢。其次,外生冲击的来源会影响政策的选择和协调——当经济波动来源于需求冲击时,固定LTV政策搭配逆周期资本充足率的宏观审慎政策、不对预期房价作出反应的货币政策表现最优;当经济波动来源于供给冲击时,固定LTV政策搭配逆周期资本充足率的宏观审慎政策、对预期房价作出反应的货币政策表现最优。     

How do house prices affect consumption? Evidence from micro data

Housing is a major component of wealth. Since house prices fluctuate considerably over time, it is important to understand how these fluctuations affect households’ consumption decisions. Rising house prices may stimulate consumption by increasing households’ perceived wealth, or by relaxing borrowing constraints. This paper investigates the response of household consumption to house prices using UK micro data. We estimate the largest effect of house prices on consumption for older homeowners, and the smallest effect, insignificantly different from zero, for younger renters. This finding is consistent with heterogeneity in the wealth effect across these groups. In addition, we find that regional house prices affect regional consumption growth. Predictable changes in house prices are correlated with predictable changes in consumption, particularly for households that are more likely to be borrowing constrained, but this effect is driven by national rather than regional house prices and is important for renters as well as homeowners, suggesting that UK house prices are correlated with aggregate financial market conditions.     

Cryptocurrency price prediction using traditional statistical and machine-learning techniques: A survey

Cryptocurrencies are decentralized electronic counterparts of government-issued money. The first and best-known cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machine-learning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight.     

How well do investor sentiment and ensemble learning predict Bitcoin prices?

Investor sentiment is widely recognized as the major determinant of cryptocurrency prices. Although earlier research has revealed the influence of investor sentiment on cryptocurrency prices, it has not yet generated cohesive empirical findings on an important question: How effective is investor sentiment in predicting cryptocurrency prices? To address this gap, we propose a novel prediction model based on the Bitcoin Misery Index, using trading data for cryptocurrency rather than judgments from individuals who are not Bitcoin investors, as well as bagged support vector regression (BSVR), to forecast Bitcoin prices. The empirical analysis is performed for the period between March 2018 and May 2022. The results of this study suggest that the addition of the sentiment index enhances the predictive performance of BSVR significantly. Moreover, the proposed prediction system, enhanced with an automatic feature selection component, outperforms state-of-the-art methods for predicting cryptocurrency for the next 30 days.     

Determinants of House Prices: A Quantile Regression Approach

OLS regression has typically been used in housing research to determine the relationship of a particular housing characteristic with selling price. Results differ across studies, not only in terms of size of OLS coefficients and statistical significance, but sometimes in direction of effect. This study suggests that some of the observed variation in the estimated prices of housing characteristics may reflect the fact that characteristics are not priced the same across a given distribution of house prices. To examine this issue, this study uses quantile regression, with and without accounting for spatial autocorrecation, to identify the coefficients of a large set of diverse variables across different quantiles. The results show that purchasers of higher-priced homes value certain housing characteristics such as square footage and the number of bathrooms differently from buyers of lower-priced homes. Other variables such as age are also shown to vary across the distribution of house prices.

房价调控、地方政府债务与宏观经济波动

在地方政府债务高企的背景下,房价调控能否使资本流向非房地产部门？房价调控如果触发地方政府债务违约,宏观政策应如何应对？本文基于中国宏观经济的特征事实,引入地方政府的土地财政行为,将房价变动与地方政府的偿债能力联系起来。研究表明,由于地方政府依赖土地出让和土地抵押贷款筹集收入,房价管控导致的地价下降会带来地方政府收入的下降,直接影响地方政府的偿债能力。如果地方政府债务不出现违约,那么房价管控带来的地价下降会降低地方政府从金融部门获得的抵押融资额,使非基建部门的融资成本下降,非基建部门投资和产出上升。而如果调控房价带来的地价下降导致地方政府出现债务违约,金融部门资产受损,使金融中介减少贷款和提高贷款成本,带来整个社会的信贷紧缩,经济中各个部门的产出大幅下降。进一步的政策分析表明,有必要在避免地方政府债务违约的同时,使用财政资金补充银行资本金等多种方式稳定金融中介的资产负债表,从而将房价调控对经济的负面影响程度降到最低。     

The Dynamics of Location in Home Price

It is well established that house prices are dynamic. It is also axiomatic that location influences such selling prices, motivating our objective of incorporating spatial information in explaining the evolution of house prices over time. In this paper, we propose a rich class of spatio-temporal models under which each property is point referenced and its associated selling price modeled through a collection of temporally indexed spatial processes. Such modeling includes and extends all house price index models currently in the literature, and furthermore permits distinction between the effects of time and location. We study single family residential sales in two distinct submarkets of a metropolitan area and further categorize the data into single- and multiple-transaction observations. We find the spatial component is very important in explaining house price. Moreover, the relative homogeneity of homes within the submarket and the frequency with which homes sell affects the pattern of variation across space and time. Differences between single and repeat sale data are evident. The methodology is applicable to more general capital asset pricing when location is anticipated to be influential.     

General Insurance Deductible Ratemaking

Insurance claims have deductibles, which must be considered when pricing for insurance premium. The deductible may cause censoring and truncation to the insurance claims. However, modeling the unobserved response variable using maximum likelihood in this setting may be a challenge in practice. For this reason, a practitioner may perform a regression using the observed response, in order to calculate the deductible rates using the regression coefficients. A natural question is how well this approach performs, and how it compares to the theoretically correct approach to rating the deductibles. Also, a practitioner would be interested in a systematic review of the approaches to modeling the deductible rates. In this article, an overview of deductible ratemaking is provided, and the pros and cons of two deductible ratemaking approaches are compared: the regression approach and the maximum likelihood approach. The regression approach turns out to have an advantage in predicting aggregate claims, whereas the maximum likelihood approach has an advantage when calculating theoretically correct relativities for deductible levels beyond those observed by empirical data. For demonstration, loss models are fit to the Wisconsin Local Government Property Insurance Fund data, and examples are provided for the ratemaking of per-loss deductibles offered by the fund. The article discovers that the regression approach is actually a single-parameter approximation to the true relativity curve. A comparison of selected models from the generalized beta family discovers that the usage of long-tail severity distributions may improve the deductible rating, while advanced frequency models such as 01-inflated models may have limited advantages due to estimation issues under censoring and truncation. In addition, in this article, models for specific peril types are combined to improve the ratemaking.     

收入差距、信贷约束与房价变动

本文通过构建一个异质性代理人模型,刻画了收入差距通过信贷渠道影响房价的作用机制。研究表明,收入差距的缩小提升了低收入者的收入占比,使该类人群获得了更多的外部融资进行购房,由此产生了两方面效应:(1)信贷约束放松降低了住房流动性溢价,从而对房价产生负向影响;(2)收入上涨增加了住房边际效用较高的低收入者对房价正向影响的权重,从而使住房需求上升的效应抵消了此前的负向影响,最终促进房价上涨。通过对1970-2017年44个国家的进一步分析发现,相比于高收入者收入的下降,低收入者收入占比的上升在放松信贷约束和提升房价方面具有更显著的作用。据此本文认为:一方面要通过增加住房供给来化解城市化率提升与高房价之间的内在矛盾;另一方面,在经济增速放缓的时期,缩小收入差距,推动以“人”为核心的高质量城市化,并引导信贷资源向低收入群体倾斜是当前促进国内大循环、稳定社会融资规模和房地产市场的重要手段。     

高房价是否导致了区域高技能人力资本流出?

我国正处于由人口大国向人力资本强国转型的关键阶段,高技能人才资源已成为当今区域经济社会发展的第一资源;与此同时,我国已经历了一段房价全面上涨的时期,由房价高速上涨所导致的人才流出问题引起政府与公众的广泛关注。在此背景下,本文通过对大学生就业地选择的关注,考察了高房价对地区高技能人力资本流出的影响。本文的结果稳健地表明,高房价显著地提高了大学毕业生离开本地就业的概率;根据本文的估计,在2010年至2015年期间,房价的上升至少增加了区域高技能人才流出21.5%;特别地,房价的挤出作用对于较弱家庭背景的学生以及非一线城市更为严重,此外,高房价也影响了大学毕业生的职业选择。本文的研究结果指出了中国高房价可能影响长期经济社会发展的一个新的重要渠道;同时,通过对房价的关注,为地区与国家减少高技能人才流出,实现经济发展由要素驱动向创新驱动转变提供了新的启示。     

House prices,consumption and the role of non-Mortgage debt

This paper evaluates the strength of the relationship between house prices and consumption, through the use of debt. Whereas the existing literature has largely studied the effects of house prices on homeowner total or mortgage debt, we focus on the non-mortgage component of household borrowing, using Canadian household-level data for 1999–2007. We rely on variation in regional house prices, homeownership status and age to establish the relationship between house prices and non-mortgage debt. Then, using direct information on debt uses, we determine that house price growth was associated with a non-trivial fraction of concurrent aggregate non-housing consumption growth.     

我国股市与汇市之间关系的再检验——基于滚动时间窗口技术和阈值误差修正模型的证据

本文采用滚动时间窗口的技术,基于协整检验和Granger因果检验的方法,检验我国股票市场和人民币兑美元汇率之间的联动关系。实证结果表明,股价与汇率之间长期均衡关系是随时间变化的。2008年之前,股价与汇率之间总体上不存在长期均衡关系,而且仅存在汇率到股价的单向引导关系。2008年之后,二者之间总体上存在显著的长期均衡关系,而且股价与汇率互为对方变动的Granger原因。运用阈值误差修正模型,我们发现股价与汇率之间的短期均衡存在显著的非对称效应。汇率对股价的短期影响要远远大于股价对汇率的影响。     

Do economic statistics contain information to predict stock indexes futures prices and returns? Evidence from Asian equity futures markets

This research examines the impact of local and international market factors on the pricing of stock indexes futures in East Asian countries. The purpose of this paper is to present a study of the significant factors that determine the major stock indexes futures’ prices of Hong Kong, Malaysia, Singapore, South Korea and Taiwan. This study first investigates the relationships between Hang Seng Index Futures, KLCI Futures, SiMSCI Futures, KOSPI Futures, Taiwan Exchange Index Futures and local interest rates, dividend yields, local exchange rates, overnight S&P500 index and a newly constructed index, Asian Tigers Malaysia Index (ATMI). 11 years historical data of stock indexes futures and the economic statistics are studied; 10 years in-sample data are used for testing and developing the pricing models, and 1 year out-of-sample data is used for the purpose of verifying the predicted values of the stock indexes futures. Using simple linear regressions, local interest rates, dividend yields, exchange rates, overnight S&P500 and ATMI are found to have significant impact on these futures contracts. In this research, the next period close is predicted using simple linear regression and non-linear artificial neural network (ANN). An examination of the prediction results using nonlinear autoregressive ANN with exogenous inputs (NARX) shows significant abnormal returns above the passive threshold buy and hold market returns and also above the profits of simple linear regression (SLR). The empirical evidence of this research suggests that economic statistics contain information which can be extracted using a hybrid SLR and NARX trading model to predict futures prices with some degree of confidence for a year forward. This justifies further research and development of pricing models using fundamentally significant economic determinants to predict futures prices.

     

Trades and Quotes: A Bivariate Point Process

This article formulates a bivariate point process to jointlyanalyze trade and quote arrivals. In microstructure models,trades may reveal private information that is then incorporatedinto new price quotes. This article examines the speed of thisinformation flow and the circumstances that govern it. A jointlikelihood function for trade and quote arrivals is specifiedin a way that recognizes that an intervening trade sometimescensors the time between a trade and the subsequent quote. Modelsof trades and quotes are estimated for eight stocks using Tradeand Quote database (TAQ) data. The essential finding for thearrival of price quotes is that information flow variables,such as high trade arrival rates, large volume per trade, andwide bid–ask spreads, all predict more rapid price revisions.This means prices respond more quickly to trades when informationis flowing so that the price impacts of trades and ultimatelythe volatility of prices are high in such circumstances.     

Housing Price Dynamics in Time and Space: Predictability,Liquidity and Investor Returns

It is widely accepted that aggregate housing prices are predictable, but that excess returns to investors are precluded by the transactions costs of buying and selling property. We examine this issue using a unique data set—all private condominium transactions in Singapore during an eleven-year period. We model directly the price discovery process for individual dwellings. Our empirical results clearly reject a random walk in prices, supporting mean reversion in housing prices and diffusion of innovations over space. We find that, when house prices and aggregate returns are computed from models that erroneously assume a random walk and spatial independence, they are strongly autocorrelated. However, when they are calculated from the appropriate model, predictability in prices and in investment returns is completely absent. We show that this is due to the illiquid nature of housing transactions. We also conduct extensive simulations, over different time horizons and with different investment rules, testing whether better information on housing price dynamics leads to superior investment performance.     

House Prices and Government Spending Shocks

We show that dynamic stochastic general equilibrium (DSGE) models with housing and collateralized borrowing predict a fall in house prices following positive government spending shocks. By contrast, we show that house prices in the United States rise persistently after identified positive government spending shocks. We clarify that the incorrect house price response is due to a general property of DSGE models—approximately constant shadow value of housing—and that modifying preferences and production structure cannot help in obtaining the correct house price response. Properly accounting for the empirical evidence on government spending shocks and house prices using a DSGE model therefore remains a significant challenge.     

The equilibrium effect of fundamentals on house prices and rents

Using a dynamic equilibrium model of housing tenure choice with fully specified markets for homeownership and rental properties, and endogenous house prices and rents, this paper studies the effect of fundamentals on equilibrium house prices and rents. Lower interest rates, relaxed lending standards, and higher incomes are shown to account for approximately one-half of the increase in the U.S. house price–rent ratio between 1995 and 2006, and to generate the pattern of rapidly growing house prices, sluggish rents, increasing homeownership, and rising household indebtedness observed in the data.     

Share price anticipation of earnings and management's discussion of operations and financing

This paper combines research on the measurement of disclosure quality and the measurement of share price anticipation of earnings to produce a new test of the usefulness of the information disclosed in management discussions of operations and financing for predicting future earnings. Market-Based Accounting Research has shown that earnings changes are anticipated and impounded in prices well before the financial year for which earnings are reported. This price anticipation leads to downward biased earnings response coefficients (ERCs) in the commonly estimated regression model of returns on contemporaneous earnings changes. We exploit predictable differences in the biasedness of the ERC estimate across firm-years to test the hypothesis that share prices are better informed when the annual report contains a detailed discussion of the firm's operations and financing. Our results suggest that such voluntary disclosure may have been useful in predicting future earnings changes. The effect would appear to be strongest (1) in models that examine one-period-ahead and two-period-ahead share price anticipation and (2) when we employ a disclosure index that captures forward-looking information.