Dollar Volume Research Articles

ISDS disputes, adjudication and cross-border M&As

ISDS claims are associated with reductions in the likelihood, frequency, and dollar volume of M&As from claimant home countries to respondent states. The host-state expropriation events underlying ISDS claims show no significant effects. The impact of ISDS claims remains after including traditional country risk metrics. Thus, ISDS claims play a unique role in guiding international investment. They prompt foreign acquirers to adopt risk-avoiding strategies, which affect various deal characteristics. We detect strong substantiation effects of investor-win cases and some acquittal effects of state-win cases. The ISDS claims related to direct expropriations and strong-institution respondent states typically produce more striking effects.

Retail ETF investing

AbstractUsing marketable order flow data, we analyze key characteristics of aggregate retail exchange‐traded fund (ETF) investing from 2010 to 2021, including allocations, holding period and investment performance. Retail traders allocate 12% more dollar volume to leveraged and inverse ETFs versus nonretail traders. Retail ETF trades distinctly increase with prior ETF returns, in contrast to contrarian stock trading. Estimated ETF holding periods are longer for retail investors versus nonretail. Finally, retail and nonretail ETF trades perform similarly over hypothetical holding periods up to one quarter. Overall, we provide policy‐relevant insights into retail investing behaviours, which have been the subject of recent concern.

Do InsurTech startups disrupt the insurance industry?

Since 2010, InsurTech development has significantly impacted the insurance industry. This study employs an InsurTech-specific dataset, including the number of deals, dollar volume of funding, and human capital investment, to examine the extent of the disruption of InsurTech startups in the insurance industry. The findings reveal that InsurTech has had a substantial disruptive impact on the competitiveness of incumbents while exerting a weakly stimulating effect on premium growth. The evidence indicates that firm size is critical in determining the degree of stimulation. Specifically, smaller insurers with limited investments in InsurTech are more vulnerable to disruption.

Does venture-backed innovation support carbon neutrality?

PurposeThis paper aims to answer three questions: (1) Which countries invest more capital in green firms? (2) What kind of industries do venture capitals (VCs) invest in? (3) Do VCs invest more capital in green firms?Design/methodology/approachFirst, the authors provide summary statistics of the key variables for green and non-green firms. Then the authors use figures to plot the growth of green firms over time. Next, the authors use descriptive data to study VC-invested firms for the top 10 countries and industries for all firms, green firms and non-green firms. Finally, the authors compare the VC investors' characteristics and investment behavior between green and non-green firms.FindingsThis study documents that venture-backed investments in clean technologies have increased dramatically in the number of deals and in the total amount of dollar volume over time. This paper provides evidence that VC firms invest more in green firms in each deal than in non-green firms. The United States and European countries play an important role in funding clean technologies across countries, and this study’s results suggest that VC investors play a considerable role in shaping the development of green finance.Originality/valueThis paper makes the first attempt to investigate the role of VCs in clean technologies to support carbon neutrality, providing initial evidence on venture capitalists' investment efforts towards carbon neutrality. The paper also has practical implications for start-up firms that raise capital and venture capitalists who finance green start-ups.

Cryptocurrency regulation and market quality

We examine the effects of the 2021 Chinese cryptocurrency ban on several aspects of crypto market quality, namely, prices, volatility, and liquidity. We find that average crypto prices plunge and liquidity deteriorates, while volatility spikes in response to the announcement of the ban. While the volatility surge is short-lived, the fall in crypto values and liquidity persists. The results are robust across dollar trading volume sorts, and remain significant after taking into account the inter-connectedness between the market quality measures in a vector autoregressive framework. We present the results in comparison with the 2017 Chinese cryptocurrency ban, which did not result in the same broad changes in the crypto market. We also discuss the potential household ownership and environmental implications of the ban.

Herding in foreign direct investment

This paper, to our knowledge, is the first to examine herding in foreign direct investment (FDI). We investigate it from two perspectives, first the number of countries investing in the host country and then the dollar volumes of those investments. Our results provide strong evidence of herding in FDI. We also show herding in the divestures of these investors. We show that herding in FDI is related to host country characteristics and governance parameters.

Non-fungible token (NFT) markets on the Ethereum blockchain: temporal development, cointegration and interrelations

ABSTRACT The market for non-fungible tokens (NFTs), transferrable and unique digital assets on public blockchains, has received widespread attention and experienced strong growth since early 2021. This study provides an introduction to NFTs and explores the 14 largest submarkets using data from the Ethereum blockchain between June 2017 and May 2021. The analyses rely on (a) the number of NFT sales, (b) the dollar volume of NFT trades and (c) the number of unique blockchain wallets that traded NFTs. Based on the number of transactions and wallets, the Ethereum-based NFT market peaked at the end of 2017 due to the success of the CryptoKitties project. As of 2021, fewer transactions occur but the traded value is much higher. We find that NFT submarkets are cointegrated and feature various causal short-run connections between them. The success or adoption of younger NFT projects is influenced by that of more established markets. At the same time, the success of newer markets has an impact on the more established projects. The results contribute to the overall understanding of the NFT phenomenon as an emerging asset class and suggest that NFT markets are immature or even inefficient.

Discounting Restricted Securities

AbstractWe examine the costs of trading restrictions by exploiting an SEC rule change eliminating an approximately 80-day restriction period in private placements for small issuers. Using a difference-in-differences specification, we find that the restriction is binding, as dollar volume increases 19 percentage points vis-à-vis proceeds, and costly, as offering discounts fall by 8 percentage points. Discounts fall more for issuers with higher information asymmetry or longer restriction periods. We account for endogenous responses to the rule change. Overall, our findings suggest that trading restrictions are costly and have large effects on firms’ cost of capital.

Spillovers of funding dry-ups

We uncover a new channel for spillovers of funding dry-ups. The US money market fund (MMF) reform led to an exogenous reduction in unsecured MMF funding for some banks. We use novel data to trace those banks to a corporate deposit funding platform. As they sought to replace the lost dollar funding, the funding squeeze spilled over to other banks with no MMF exposure. The latter paid more for corporate dollar deposits, and their pool of funding providers deteriorated. Their dollar lending volumes and margins declined. Our results suggest banks’ competitiveness in funding markets affect their competitiveness in lending markets.

Nowcasting Growth Rates of Russia’s Export and Import by Commodity Group

In this paper, we apply a set of machine learning and econometrics models, namely: Elastic Net, Random Forest, XGBoost, and SSVS to nowcasting (estimate for the current period) the dollar volumes of Russian exports and imports by a commodity group. We use lags in the volumes of export and import commodity groups, and exchange prices for some goods and other variables, due to which the curse of dimensionality becomes quite acute. The models we use have proven themselves well in forecasting in the presence of the curse of dimensionality, when the number of model parameters exceeds the number of observations. The best-performing model appears to be the weighted machine learning model, which outperforms the ARIMA benchmark model in nowcasting the volume of both exports and imports. According to the Diebold– Mariano test, in the case of the largest commodity groups our model often manages to obtain significantly more accurate nowcasts relative to the ARIMA model. The resulting estimates turn out to be quite close to the Bank of Russia’s historical forecasts built under comparable conditions.

Cross-cryptocurrency Return Predictability

Using the minute-frequency data of the top 30 coins listed on Binance, which represent 86% of the total dollar trading volume of the cryptocurrency market, we document strong evidence of cross-cryptocurrency return predictability. The lagged returns of other cryptocurrencies serve as significant predictors of focal cryptocurrencies up to ten minutes without return reversals, in line with gradual information diffusion. To alleviate statistical issues, we employ the adaptive LASSO method to identify informative predictors and find consistent results. Furthermore, even after accounting for the highest transaction costs on Binance, a long-short portfolio formed based on our findings with a ten-minute rebalancing frequency generates a significant out-of-sample return of 1.14 bps on a minute basis for the futures trading, indicating a profitable investment strategy.

Firing frictions and the U.S. mergers and acquisitions market

Following the adoption of state laws that increase firing costs, there is an immediate and persistent 30% reduction in total mergers and acquisitions (M&A) dollar volume and average M&A size as well as an immediate increase in withdrawn deals. Firing costs do not affect M&A announcement returns, but there are negative returns surrounding the announcement of state laws that increase firing frictions, especially for future M&A targets. These findings suggest that post-merger employee turnover is a first-order source of value for U.S. mergers.

The Night and Day of Amihud’s (2002) Liquidity Measure

Abstract Amihud’s stock (il)liquidity measure averages daily ratios of the absolute close-to-close return to dollar volume, including overnight returns. Our modified measure uses open-to-close returns matching return and trading volume measurement windows. It is more strongly correlated with trading-cost measures (by 8%–37%) and better explains cross-sections of returns, doubling estimated liquidity premiums. Using nonsynchronous trading near close, we show overnight returns are primarily information driven: including them in Amihud’s proxy for price impacts of trading magnifies measurement error, understating liquidity premiums. Our modification helps wherever Amihud’s measure is required. Our measures are publicly available for 1964–2019 and can be updated. (JEL G12, G14) Received June 2, 2020; editorial decision September 11, 2020 by Editor Jeffrey Pontiff.

Allocation of National Institutes of Health Funding by Disease Category in 2008 and 2019

Prior research suggests an association between burden of disease and National Institutes of Health (NIH) funding. The allocation of NIH funding should reflect, to some extent, the health needs of the population, along with other factors. To examine the factors associated with NIH funding in 2019 for 46 diseases. This cohort study used disability-adjusted life-years to measure the 2008 and 2019 US burden of disease and compared them with NIH categoric funding for 46 diseases. Disability-adjusted life-years to measure the 2008 and 2019 US burden of disease, 2016 health spending, and 2008 NIH funding levels for 46 diseases. 2019 NIH funding levels for 46 diseases. The 46 diseases accounted for 62 392 713 of 94 399 784 disability-adjusted life-years (66.1%) in 2008 and 75 706 718 of 111 074 472 disability-adjusted life-years (68.2%) in 2019, representing more than 66% of all disability-adjusted life-years in both years. By dollar volume, Alzheimer and dementia increased the most, with approximately $1.8 billion more funding in 2019 than 2008 (from $530 million in 2008 to $2398 million in 2019, a 352% increase), whereas interpersonal violence had the greatest decrease, $95 million, in 2019 NIH funding (from $236 million in 2008 to $141 million in 2019, a 40% decrease). For the 46 diseases in this study, the variable with the greatest association with NIH funding in 2019 was the level of NIH funding in 2008, with a simple correlation of 0.88. Burden of disease and changes in burden of disease were not statistically significantly associated with NIH funding levels once the prior level of funding was included in the model. The models suggested that a 1% higher level of NIH funding in 2008 was associated with a 0.91% higher level of NIH funding in 2019. In this study, NIH spending for most diseases seemed to be based primarily on the level of NIH spending more than 10 years earlier, despite changes in burden of disease. Congress and the NIH should examine the allocation process to ensure NIH investments are responsive to changes in the health of the population.

Non-fungible token (NFT) markets on the Ethereum blockchain: Temporal development, cointegration and interrelations

The market for non-fungible tokens (NFTs), transferrable and unique digital assets on public blockchains, has received widespread attention and experienced strong growth since early 2021. This study provides an introduction to NFTs and explores the 14 largest submarkets using data from the Ethereum blockchain between June 2017 and May 2021. The analyses rely on (a) the number of NFT sales, (b) the dollar volume of NFT trades and (c) the number of unique blockchain wallets that traded NFTs. Based on the number of transactions and wallets, the Ethereum-based NFT market peaked at the end of 2017 due to the success of the CryptoKitties project. As of 2021, fewer transactions occur but the traded value is much higher. We find that NFT submarkets are cointegrated and feature various causal short-run connections between them. The success or adoption of younger NFT projects is influenced by that of more established markets. At the same time, the success of newer markets has an impact on the more established projects. The results contribute to the overall understanding of the NFT phenomenon and suggest that NFT markets are immature or even inefficient.

The Role of Leveraged ETFs and Option Market Imbalances on End-of-Day Price Dynamics

Leveraged ETFs and market makers who are active in option markets must ad-just imbalances arising from market movements. Establishing delta-neutrality may cause either return momentum or reversal depending on the sign and size of the imbalance vis-a-vis market prevailing liquidity. We find that a large and negative (positive) aggregated gamma imbalance, relative to the average dollar volume, gives rise to an economically and statistically significant end-of-day momentum (rever-sal). We compare this channel to the rebalancing of leveraged ETFs and find that the effect generated by leveraged ETFs is economically larger. Consistent with the notion of temporary price pressure, the documented effects quickly revert at the next day’s open. Information-based explanations are unlikely to cause the results, suggesting a non-informational channel through which leveraged ETFs and option markets affect underlying stocks towards the market close.

A BIT goes a long way: Bilateral investment treaties and cross-border mergers

We examine whether bilateral investment treaties (BITs), an external governance mechanism, stimulate cross-border mergers by protecting the property rights of foreign acquirers. Exploiting the staggered adoption and bilateral nature of the treaties, we find that BITs have a large positive effect on cross-border mergers. The probability and dollar volume of mergers between two given countries more than doubles after the signing of a BIT. The increase is driven by deals flowing from developed economies to developing economies and is concentrated in target countries with medium levels of political risk. The results suggest BITs are effective in expanding the global market for corporate control, particularly in the developing world.

Insider Trading and Strategic Disclosure

I show that public companies disproportionately disclose positive news on days when corporate executives sell shares under predetermined Rule 10b5-1 plans. I find that the likelihood, share volume and dollar volume of insider sales under 10b5-1 plans are higher when good news is disclosed, and each of these are higher when the disclosed news is better. Disclosure of good news on Rule 10b5-1 selling days is greatest in the health care sector and among mid-cap firms. I show that stock prices reverse after high levels of Rule 10b5-1 selling on positive news days, and that the price reversal increases with the share volume of Rule 10b5-1 selling. I show that, whatever might be said about health care executives’ advantageous stock sales as they developed vaccines during the pandemic of 2020, those sales were not uncommon.

Improved MRO Inventory Management System in Oil and Gas Company: Increased Service Level and Reduced Average Inventory Investment

This study proposes a methodology for the oil and gas businesses to keep their production plant productive with a minimum investment in carrying maintenance, repair, and operating inventory planning. The goal is to assist the exploration and production companies in minimizing the investment in keeping maintenance, repair, and operating (MRO) inventory for improving production plant uptime. The MRO inventory is the most expensive asset and it requires substantial investment. It helps in keeping the oil and gas production plant productive by performing planned and unplanned maintenance activities. A (Q, r) model with a stock-out and backorder cost approach is combined with a continuous inventory review policy for the analysis of class A items of oil and gas production plant MRO inventory. The class A items are identified through popular ABC analysis based on annual dollar volume. The demand for the inventory is modeled through Poisson distribution with consideration of constant lead time. The (Q, r) model in both stock-out cost and backorder cost approaches assigned higher order frequency and lower service level to low annual demand and highly expensive items. The stock-out cost approach shows an 8.88% increase in the average service level and a 56.9% decrease in the company average inventory investment. The backorder cost approach results in a 7.77% increase in average service level and a 57% decrease in average inventory investment in contrast to the company’s existing inventory management system. The results have a direct impact on increasing plant uptime and productivity and reducing company maintenance cost through properly managing maintenance stock. The analysis is carried out on the oil and gas production plant’s MRO inventory data, but it can be applied to other companies’ inventory data as well. All the results reflected in this research are based on the inventory ordering policy of two orders per year. The inventory ordering frequency per year may be other than two orders per year depending on the type of organization.

An Invariance Relationship in the Number of Buy-Sell Switching Points

Market microstructure invariance predicts business time to unfold at a rate proportional to the 2~3 power of the product of dollar volume and returns volatility. Define a “switching point” as an investor changing the direction of trading from buying to selling or selling to buying. For a specific market, the aggregate number of switching points is a good indicator of the pace of business time. Using data from the Korea Exchange (KRX) from 2008 to 2010, we calculate the number of switching points for each stock for each month. The estimated exponent is 0.675 (standard error 0.005, R2 = 0.93) validates the business time clock predicted by invariance. Most variation reflects variation in the number of accounts trading a stock, not variation of switching points per account.