Research articles for the 2020-07-25
Are Foreclosure Spillover Effects Universal? Variation over Space and Time
SSRN
Government intervention in the housing market was partially driven by research showing that foreclosures lower neighboring housing values and thus increase neighbors’ risk of foreclosure. While prior research has consistently identified a negative spillover effect of foreclosures on nearby housing values, the magnitude of the effect varies widely across studies. We argue that the spillover effect on nearby housing prices varies across space and over time. To capture the extent of spatial variation, we employ geographically weighted regression, which allows modeled relationships to vary locally within a geographic area. We find heterogeneous foreclosure spillover effects both across and within metropolitan areas and that the magnitude and range of said effects vary over time. These findings raise the possibility that policies and programs designed to intervene in the housing market analyze and use local variation in the negative externalities of foreclosure to best target scarce resources within and across communities.
SSRN
Government intervention in the housing market was partially driven by research showing that foreclosures lower neighboring housing values and thus increase neighbors’ risk of foreclosure. While prior research has consistently identified a negative spillover effect of foreclosures on nearby housing values, the magnitude of the effect varies widely across studies. We argue that the spillover effect on nearby housing prices varies across space and over time. To capture the extent of spatial variation, we employ geographically weighted regression, which allows modeled relationships to vary locally within a geographic area. We find heterogeneous foreclosure spillover effects both across and within metropolitan areas and that the magnitude and range of said effects vary over time. These findings raise the possibility that policies and programs designed to intervene in the housing market analyze and use local variation in the negative externalities of foreclosure to best target scarce resources within and across communities.
Auto-Correlation of Returns in Major Cryptocurrency Markets
SSRN
This paper is the first of a series of short articles that explore the efficiency of major cryptocurrency markets. A number of statistical tests and properties of statistical distributions will be used to assess if cryptocurrency markets are efficient, and how their efficiency changes over time. In this paper, we analyze auto-correlation of returns in major cryptocurrency markets using the following methods: Pearson’s auto-correlation coefficient of different orders, Ljung-Box test, and first-order Pearson’s auto-correlation coefficient in a rolling window. All experiments are conducted on the BTC/USD, ETH/USD, ETH/BTC markets on Bitfinex exchange, and the XBT/USD market on Bitmex exchange, each on 5-minute, 1-hour, 1-day, and 1-week time frames. The results are represented visually on charts. Statistically significant auto-correlation is persistently present on the 5m and 1H time frames on all markets. The tests disagree on the 1D and 1W time frames. The results of this article are fully reproducible. Used datasets, source code, and a runnable Jupyter Notebook are available on GitHub.
SSRN
This paper is the first of a series of short articles that explore the efficiency of major cryptocurrency markets. A number of statistical tests and properties of statistical distributions will be used to assess if cryptocurrency markets are efficient, and how their efficiency changes over time. In this paper, we analyze auto-correlation of returns in major cryptocurrency markets using the following methods: Pearson’s auto-correlation coefficient of different orders, Ljung-Box test, and first-order Pearson’s auto-correlation coefficient in a rolling window. All experiments are conducted on the BTC/USD, ETH/USD, ETH/BTC markets on Bitfinex exchange, and the XBT/USD market on Bitmex exchange, each on 5-minute, 1-hour, 1-day, and 1-week time frames. The results are represented visually on charts. Statistically significant auto-correlation is persistently present on the 5m and 1H time frames on all markets. The tests disagree on the 1D and 1W time frames. The results of this article are fully reproducible. Used datasets, source code, and a runnable Jupyter Notebook are available on GitHub.
Effects of Age, Size, Sponsor and Government Shareholdings on Profitability: Evidence from Engineering Industry of Bangladesh
SSRN
Purpose- This paper aims at investigating the effects of age, size, fixed assets utilization, sponsor and government shareholdings on the profitability of engineering industry of Bangladesh for the period of 2000-2019. Methodology- This paper analyzed 37 out of 39 companies under engineering industry listed on Dhaka Stock Exchange. Fixed effects model has been applied after deciding this from Hausman test to estimate the effects of age, size, fixed asset utilization, sponsor and government shareholdings on the profitability.Findings- Size, fixed asset utilization, and sponsor shareholding have significant impact on profitability. While fixed asset utilization has positive impact and age, size, sponsor shareholding and government shareholding have negative impact on it. Mixed influences of learning effect and size effect are experienced among the firms.Conclusion- The findings from the analysis are diversified in nature. The investors and policy makers should have in depth insight to make better decision.
SSRN
Purpose- This paper aims at investigating the effects of age, size, fixed assets utilization, sponsor and government shareholdings on the profitability of engineering industry of Bangladesh for the period of 2000-2019. Methodology- This paper analyzed 37 out of 39 companies under engineering industry listed on Dhaka Stock Exchange. Fixed effects model has been applied after deciding this from Hausman test to estimate the effects of age, size, fixed asset utilization, sponsor and government shareholdings on the profitability.Findings- Size, fixed asset utilization, and sponsor shareholding have significant impact on profitability. While fixed asset utilization has positive impact and age, size, sponsor shareholding and government shareholding have negative impact on it. Mixed influences of learning effect and size effect are experienced among the firms.Conclusion- The findings from the analysis are diversified in nature. The investors and policy makers should have in depth insight to make better decision.
Finding the Risk-Return Trade-Off With Google
SSRN
Investor attention is central to explaining the mean-variance puzzle. Using Google Search Volumes as a proxy to attention, I document a positive trade-off during low attention periods that is significantly undermined when attention is high. The negative association between on-line searches and the trade-off is also present in the time-varying analysis. I also find that this deterioration can be explained by the escalation of risk brought about by the entry of retail investors into the market. The results are robust for several alternative explanations, such as data periodicity, conditional variance measures, on-line search terminologies and macroeconomic variables, and provide further support for the importance of noise-traders to stock market inefficiency.
SSRN
Investor attention is central to explaining the mean-variance puzzle. Using Google Search Volumes as a proxy to attention, I document a positive trade-off during low attention periods that is significantly undermined when attention is high. The negative association between on-line searches and the trade-off is also present in the time-varying analysis. I also find that this deterioration can be explained by the escalation of risk brought about by the entry of retail investors into the market. The results are robust for several alternative explanations, such as data periodicity, conditional variance measures, on-line search terminologies and macroeconomic variables, and provide further support for the importance of noise-traders to stock market inefficiency.
Short-Term Market Changes and Market Making with Inventory
SSRN
We study optimal trading strategy of a market maker with stock inventory in the presence of short-term market changes, especially changes in trading intensity of market participants and stock volatility. We employ Poisson jump processes in modelling such market condition changes. We provide closed form optimal bidding and asking strategies of the market maker, and analyze the market maker’s inventory changes accordingly.
SSRN
We study optimal trading strategy of a market maker with stock inventory in the presence of short-term market changes, especially changes in trading intensity of market participants and stock volatility. We employ Poisson jump processes in modelling such market condition changes. We provide closed form optimal bidding and asking strategies of the market maker, and analyze the market maker’s inventory changes accordingly.
The Future of Financial Fraud
SSRN
Is financial fraud becoming a bigger or smaller problem over time? Current empirical approaches to this question generate mixed inferences. As an alternative, I use two theoretical constructs that isolate several factors that motivate fraud, and use them to consider the impact of technological and wealth changes over time. Some changes, such as an increase in anonymity in some financial transactions, facilitate new fraud innovations and increase the possibility of fraud. The COVID-19 pandemic and resulting economic shutdown has fostered major disruptions in relative demands and organizational capital that also increase the likelihood of fraud over the next few years. Viewed over a longer time scale, however, the majority of technological and wealth changes seem likely to increase the use and effectiveness of reputational capital, third-party enforcement, and ethical motivations as fraud deterrents. I predict that, on net, these changes will work to drive a long-term decrease in the incidence of fraud.
SSRN
Is financial fraud becoming a bigger or smaller problem over time? Current empirical approaches to this question generate mixed inferences. As an alternative, I use two theoretical constructs that isolate several factors that motivate fraud, and use them to consider the impact of technological and wealth changes over time. Some changes, such as an increase in anonymity in some financial transactions, facilitate new fraud innovations and increase the possibility of fraud. The COVID-19 pandemic and resulting economic shutdown has fostered major disruptions in relative demands and organizational capital that also increase the likelihood of fraud over the next few years. Viewed over a longer time scale, however, the majority of technological and wealth changes seem likely to increase the use and effectiveness of reputational capital, third-party enforcement, and ethical motivations as fraud deterrents. I predict that, on net, these changes will work to drive a long-term decrease in the incidence of fraud.
To Own or Not to Own: Stock Loans Around Dividend Payments
SSRN
In a standard stock loan, the borrower reimburses the lender any dividends paid while the loan is outstanding. Since these substitute dividends may be taxed differently than dividend payments themselves, some investors have incentives to either remove their shares from lend-able supply â€" if they pay high taxes on substitute dividends â€" or lend out their shares to arbitrageurs â€" if they pay high taxes on dividends. Consistent with these incentives, we find a significant tightening of the equity lending market on dividend record days driven by both a contraction of supply and an expansion of demand â€" although the demand effect appears to dominate. We then exploit the plausibly exogenous nature of these shifts to causally link tightness in the lending market to wider effective spreads in the stock market.
SSRN
In a standard stock loan, the borrower reimburses the lender any dividends paid while the loan is outstanding. Since these substitute dividends may be taxed differently than dividend payments themselves, some investors have incentives to either remove their shares from lend-able supply â€" if they pay high taxes on substitute dividends â€" or lend out their shares to arbitrageurs â€" if they pay high taxes on dividends. Consistent with these incentives, we find a significant tightening of the equity lending market on dividend record days driven by both a contraction of supply and an expansion of demand â€" although the demand effect appears to dominate. We then exploit the plausibly exogenous nature of these shifts to causally link tightness in the lending market to wider effective spreads in the stock market.
