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This paper investigates financial attention using novel panel data on daily investor online account logins. We find support for selective attention to portfolio information. Account logins fall by 9.5% after market declines. Investors also pay less attention when the VIX volatility index is high. The level of attention and the attention/return correlation are strongly related to investor demographics (gender, age) and financial position (wealth, holdings). Using a new statistical decomposition, we show how aggregate and individual household trading are related to investor attention.

We use exogenous scheduling of Wall Street Journal columnists to identify a causal relation between financial reporting and stock market performance. To measure the media's unconditional effect, we add columnist fixed effects to a daily regression of excess Dow Jones Industrial Average returns. Relative to standard control variables, these fixed effects increase the R² by about 35%, indicating each columnist's average persistent \"bullishness\" or \"bearishness.\" To measure the media's conditional effect, we interact columnist fixed effects with lagged returns. This increases explanatory power by yet another one-third, and identifies amplification or attenuation of prevailing sentiment as a tool used by financial journalists.

Efficient management of bankruptcy risk requires treating distant-to-default (DD) stochastically as long as historical stock prices move randomly and, thus, do not guarantee that history may repeat itself. Using long-term data that date back to 1952–2023, including the nonfinancial companies listed in the Dow Jones Industrial Average and National Association of Securities Dealers Automated Quotations indexes, this study estimates the historical and stochastic DDs via the geometric Brownian motion (GBM). The results show that (a) the association between the debt-to-equity ratio and the stochastic DD can be used as an indicator of excessive debt financing; (b) debt tax savings have a positive effect on stochastic DD; (c) bankruptcy costs have negative effects on stochastic DD; (d) in terms of the size of the company being proxied by sales revenue and the equity market value of the company, the DD is a reliable measure of bankruptcy costs; (e) in terms of macroeconomic influences, increases in the percentage change in manufacturing output are associated with lower observed and stochastic DD; and (f) in terms of the influences of industry, the stochastic DD is affected by the industry average retail inventory to sales. This paper contributes to related studies in terms of focusing on the indicators that a company’s management can focus on to address the stochastic patterns inherent in the estimation of the DD.

A large and violent tornado/multiple-vortex mesocyclone (MVMC) tracked east and northeastward near El Reno, Oklahoma, on 31 May 2013, causing eight fatalities, including storm chasers/researchers attempting to deploy in situ instrumentation. Subvortices moved within and near the MVMC, some in trochoidal-like patterns, with ground-relative translational velocities ranging from 0 to 79 m s−1, the fastest ever documented. Doppler on Wheels (DOW) measurements in one of these subvortices exceeded 115 m s−1at 114 m AGL. With assumptions concerning radar-unobserved components of the velocity, peak wind speeds of 130–150 m s−1are implied, comparable to the strongest ever measured. Only enhanced Fujita scale 3 (EF-3) damage was documented, likely because of a paucity of well-built structures and the most intense winds being confined to small, rapidly moving subvortices, resulting in only subsecond gusts. The region enclosing the maximum winds of the tornado/MVMC extended ∼2 km. DOW-measured winds > 50 m s−1(> 30 m s−1) extended far beyond the radius of maximum winds (RMW) extending >5 km (7 km), comparable to the widest ever documented. A strong multiple-vortex anticyclonic tornado with dual-polarization debris signatures is documented. A subvortex tracking eastward within the larger tornado/MVMC intensified, moved north, and then moved northwestward, becoming briefly nearly stationary near/over a research team's vehicle, transporting it ∼600 m generally eastward, killing the team. An experienced media team's vehicle was destroyed inside the tornado/MVMC, resulting in injuries. The circumstances leading to these incidents are analyzed using DOW data. The anomalous—and likely unpredictable in real time—path of the interior subvortex likely contributed to these deaths and injuries. The risks associated with chasing and scientific missions near and particularly inside large and complex MVMC/tornado vortices are discussed.

Using an axisymmetric model of tornado structure tightly constrained by high-resolution wind field measurements collected by Doppler on Wheels (DOW) mobile radars, the potential impacts of intense tornadoes crossing densely populated urban areas are evaluated. DOW radar measurements combined with in situ low-level wind measurements permit the quantification of lowlevel tornadic winds that would impact structures. Axisymmetric modeled wind fields from actual and hypothetical tornadoes are simulated to impact high-density residential and commercial districts of several major cities. U.S. census block data, satellite imagery, and other sources are used to characterize and count the number of structures impacted by intense winds, up to 132 m s−1, and estimate the level and cost of resulting damage. Census data are used to estimate residential occupancy and human casualties. Results indicate that a large and intense tornado crossing through residential portions of Chicago, Illinois, could result in tragic consequences with winds in excess of 76 m s−1impacting 99 km², substantially destroying up to 239,000 single-and dual-family housing units, occupied by up to 699,000 people, resulting in 4,500–45,000 deaths, and causing substantial damage to over 400,000 homes occupied by over 1,100,000 people. Widespread damage caused by winds exceeding 102 m s−1could occur over a broad area of the high-rise office and apartment districts causing permanent structural damage to many such buildings. Smaller and less intense tornadoes would cause lesser, but still substantial, levels of damage and mortality. Tornadoes crossing Houston and Dallas–Fort Worth, Texas; New York, New York; Saint Louis, Missouri; Washington, D.C., and Atlanta, Georgia, could cause varying levels of damage and mortality.

PurposeThe purpose of this paper is to examine changes in stock returns, liquidity, institutional ownership, analyst following and investor awareness for companies added to and deleted from the Dow Jones Industrial Average (DJIA) index. Previous studies report conflicting evidence regarding the market reactions to changes in the DJIA index membership.Design/methodology/approachThis study uses the event-study methodology to calculate abnormal returns and trading volume around the announcement and effective days of DJIA index changes from 1929 to 2015. It also tests for significant changes in liquidity, institutional ownership, analyst following and investor awareness in the 1990–2015 period. Multivariate regressions are used to perform a simultaneous analysis of competing hypotheses.FindingsThis study resolves the mixed results of previous DJIA index papers by documenting different stock price and trading volume reactions over the 1929–2015 period. Focusing on the most recent period, 1990–2015, the study finds that stocks added to (deleted from) the index experience a significant permanent stock price gain (loss). The observed stock price reaction seems to be associated with changes in liquidity proxies thus lending support for the liquidity hypothesis.Research limitations/implicationsLimited data availability for the periods prior to 1990 prevents this study from identifying the exact reasons for different stock price and trading volume reactions across subperiods of the 1929–2015 period.Originality/valueThis study provides the most comprehensive examination of market reactions to changes in the DJIA index and resolves the mixed results of previous studies. A better understanding of market reactions around the DJIA index changes can help both individual and institutional investors with developing effective trading strategies and index managing companies with designing optimal announcement policies.

The impact of ground-based glaciogenic seeding on wintertime orographic, mostly stratiform clouds is analyzed by means of data from an X-band dual-polarization radar, the Doppler-on-Wheels (DOW) radar, positioned on a mountain pass. This study focuses on six intensive observation periods (IOPs) during the 2012 AgI Seeding Cloud Impact Investigation (ASCII) project in Wyoming. In all six storms, the bulk upstream Froude number below mountaintop exceeded 1 (suggesting unblocked flow), the clouds were relatively shallow (with bases below freezing), some liquid water was present, and orographic flow conditions were mostly steady. To examine the silver iodide (AgI) seeding effect, three study areas are defined (a control area, a target area upwind of the crest, and a lee target area), and comparisons are made between measurements from a treated period and those from an untreated period. Changes in reflectivity and differential reflectivity observed by the DOW at low levels during seeding are consistent with enhanced snow growth, by vapor diffusion and/or aggregation, for a case study and for the composite analysis of all six IOPs, especially at close range upwind of the mountain crest. These low-level changes may have been affected by natural changes aloft, however, as evident from differences in the evolution of the echo-top height in the control and target areas. Even though precipitation in the target region is strongly correlated with that in the control region, the authors cannot definitively attribute the change to seeding because there is a lack of knowledge about natural variability, nor can the outcome be generalized, because the sample size is small.

A National Science Foundation sponsored educational deployment of a Doppler on Wheels radar called the Hawaiian Educational Radar Opportunity (HERO) was conducted on O‘ahu from 21 October to 13 November 2013. This was the first-ever deployment of a polarimetric X-band (3 cm) research radar in Hawaii. A unique fine-resolution radar and radiosonde dataset was collected during 16 intensive observing periods through a collaborative effort between University of Hawai‘i at Mānoa undergraduate and graduate students and the National Weather Service’s Weather Forecast Office in Honolulu. HERO was the field component of MET 628 “Radar Meteorology,” with 12 enrolled graduate students who collected and analyzed the data as part of the course. Extensive community outreach was conducted, including participation in a School of Ocean and Earth Science and Technology open house event with over 7,500 visitors from local K–12 schools and the public. An overview of the HERO project and highlights of some interesting tropical rain and cloud observations are described. Phenomena observed by the radar include cumulus clouds, trade wind showers, deep convective thunderstorms, and a widespread heavy rain event associated with a cold frontal passage. Detailed cloud and precipitation structures and their interactions with O‘ahu terrain, unique dual-polarization signatures, and the implications for the dynamics and microphysics of tropical convection are presented.

In this paper we utilize White's Reality Check bootstrap methodology (White (1999)) to evaluate simple technical trading rules while quantifying the data-snooping bias and fully adjusting for its effect in the context of the full universe from which the trading rules were drawn. Hence, for the first time, the paper presents a comprehensive test of performance across all technical trading rules examined. We consider the study of Brock, Lakonishok, and LeBaron (1992), expand their universe of 26 trading rules, apply the rules to 100 years of daily data on the Dow Jones Industrial Average, and determine the effects of data-snooping.

Financial data (e.g., intraday share prices) are recorded almost continuously and thus take the form of a series of curves over the trading days. Those sequentially collected curves can be viewed as functional time series. When we have a large number of highly correlated shares, their intraday prices can be viewed as high-dimensional functional time series (HDFTS). In this paper, we propose a new approach to forecasting multiple financial functional time series that are highly correlated. The difficulty of forecasting high-dimensional functional time series lies in the \"curse of dimensionality.\" What complicates this problem is modeling the autocorrelation in the price curves and the comovement of multiple share prices simultaneously. To address these issues, we apply a matrix factor model to reduce the dimension. The matrix structure is maintained, as information contains in rows and columns of a matrix are interrelated. An application to the constituent stocks in the Dow Jones index shows that our approach can improve both dimension reduction and forecasting results when compared with various existing methods.