3.9K
Downloads
22
Episodes
The Finance Professor Podcast is hosted by Linus Wilson. Dr. Wilson earned his Ph.D. in 2007 from Oxford University. He has taught thousands of finance students at all levels. His research is in banking, financial crises, CEO pay, and corporate finance. He has been a source for over two hundred major news stories in the Wall Street Journal, New York Times, Financial Times, and other news organizations. He is a leading scholar on the TARP bank bailouts of the great recession.
Episodes
Jul 26, 2026
Jul 26, 2026
39 min
Abstract
In this paper, we look at the daily weather data on the highest peak of North America to predict daily summit success rates. Denali (also known as Mt. McKinley) is 6,190 meters high or 20,310 feet. This is the first study to analyze daily weather to predict daily summit success. Higher temperatures and lower wind speeds at 14,200-foot camp, the highest weather station on the mountain during the period studied, 2015-2023, are associated with significantly higher summit rates. While weather does matter, more of the variance is explained by the date, indicating private and guided parties should plan their expeditions so they can have a chance at the summit in early June well in advance of reliable weather reports.
"Weather and Summit Success on Denali" by Dr. Linus Wilson
Date Written: July 25, 2026
Keywords: Denali, Mount McKinley, seasonality, mountaineering, climbing, Seven Summits, highpointing, weather JEL Codes: Z20, Z30
Suggested Citation:
Jul 4, 2026
Jul 4, 2026
50 min
The Seasonality of Climbing Mt. McKinley (Denali)
29 Pages Posted: 8 Apr 2026
Date Written: March 17, 2026
Abstract
This note finds that climbers are significantly more likely to summit Mount McKinley (Denali) in Alaska, the highest point in North America, in June or early July than other parts of the Mt. McKinley climbing season of late April to late July. Since climbs are typically 17-to-21 days round trip with a 15-to-18-day ascent, the highest chance of summitting will be for climbers arriving on the mountain in the last two weeks of May to the first two weeks of June.
Keywords: Denali, Mount Mckinley, Seasonality, Mountaineering, Climbing, Seven Summits, Highpointing
Suggested Citation:
Dec 27, 2023
Dec 27, 2023
18 min
Saving the Toughest for Last: 50th Completers’ Final U.S. State High Points
21 Pages Posted:
Date Written: December 27, 2023
Abstract
This paper finds that the most difficult U.S. state high point climbs are saved for the end. 50th completers are significantly more likely to save more difficult U.S. state high points for their final high point ascent. There is also some more limited evidence that 50th completers save more distant high points for their final ascent.
Keywords: altitude, Appalachian Trail, ascent, climbing, Denali, difficulty index, high point, highpointing, hiking, Mauna Kea, mount, mountain, mountaineering, peak, peak bagging, state high point, through-hike, trekking, Yosemite Decimal System
JEL Classification: R42, Q38, Z2, & Z3
Suggested Citation:
Jul 21, 2023
Jul 21, 2023
51 min
Does Difficulty Affect U.S. State High Points Ascents?
24 Pages Posted:
Date Written: July 20, 2023
Abstract
We develop a six-factor ranking of U.S. state high points. While this index is significantly associated with fewer ascents, only one of the six factors drives that result. Technical difficulty is the only measure of high point difficulty that significantly discourages summits. Climbers seem indifferent to physical effort required and are significantly attracted to higher elevation U.S. state high points. We also show popular through hikes near the high point are associated with significantly more summits. This indicates that mountain communities could see surges in tourism if a route to the summit was made less technically difficult.
Keywords: altitude, Appalachian Trail, ascent, climbing, Denali, difficulty index, high point, highpointing, hiking, mount, mountain, mountaineering, Mt. Whitney, Pacific Crest Trail, peak, peak bagging, state high point, through-hike, trekking, Yosemite Decimal System
JEL Classification: R42, Q38, Z2, Z3
Suggested Citation:
Dr. Linus Wilson[1]
Professor of Finance
Department of Economics & Finance
B.I. Moody III College of Business
University of Louisiana at Lafayette
Moody Hall, Room 253
P.O. Box 43709
Lafayette, LA 70504
(337) 482-6209
linus [dot] wilson {at} louisiana [dot] edu
Apr 8, 2023
Apr 8, 2023
59 min
Dr. Linus Wilson reads his latest paper about the bank runs at SVB and Signature Bank and the Federal Reserve's emergency loan program to save the banks from further uninsured deposit runs.
The Fed Funds Risk-Premium after the Silicon Valley
Bank Run and the Bank Term Funding Program (BTFP)
28 Pages Posted:
Date Written: April 8, 2023
Abstract
We find the emergency lending program introduced on March 12, 2023, called the Bank Term Funding Program (BTFP) coincided with a statistically significant increase in the risk-premium on Fed funds loans relative to the shortest-term T-bills. We find that the risk-premium on Fed funds loans less 28-day T-bills increased by between 39 to 56 basis points in the wake of the Silicon Valley Bank and Signature Bank runs. This led to a stealth loosening of monetary conditions without a Fed funds rate cut in part due to the incentives created by the BTFP to have banks hoard Treasuries and other eligible collateral.
Keywords: Bank Term Funding Program, BTFP, emergency lending Fed, Federal Reserve, Fed funds rate, Signature Bank, Silicon Valley Bank, T-bills, Treasuries
JEL Classification: E43, E51, E52, G21, & G28
Suggested Citation:
See all of Dr. Linus Wilson's research at www.financeprofessor.org or www.linuswilson.com
This is not investment advice.
Jan 20, 2023
Jan 20, 2023
28 min
by
Linus Wilson
Abstract
The loan standards question in the Federal Reserve’s quarterly Senior Loan Officer Survey is shown to be predictive of quarterly stock returns a month or two after its release. This is an apparent violation of semi-strong form stock market efficiency. Out-of-sample, we use this signal and develop a simple risk and alpha model to market time the S&P 500. It outperformed the S&P 500 with a Sharpe (1966) ratio of 1.9 versus 0.34 for passive investment.
Keywords: alpha, commercial and industrial loans, investing, loan standards, market efficiency, market timing, stock market, portfolio theory, returns, risk-model, semi-strong form, Senior Loan Officer Survey, Sharpe ratio, survey
JEL Classification: G11, G14, G17, & G21
Wilson, Linus, Profitable Timing of the Stock Market with the Senior Loan Officer Survey (January 21, 2023). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4332525
See all of Dr. Linus Wilson's research at www.financeprofessor.org or www.linuswilson.com
This is not investment advice.
Dec 28, 2021
Dec 28, 2021
48 min
This is the substantially revised version of this paper.
Estimating the Value of Statistical Life (VSL) Losses from COVID-19 Infections in the United States
29 Pages Posted: 21 Apr 2020 Last revised: 6 Dec 2021
Date Written: December 3, 2021
Abstract
This paper uses the Value of Statistical Life (VSL) literature to weigh the costs and benefits of non-pharmaceutical interventions of the U.S. COVID-19 stay-at-home orders that affected 92 percent of the U.S. workforce at their peak in April 2020. We calculate the pre-vaccine COVID-19 infection fatality rate to have been 0.85 percent. We find that the stay-at-home orders saved most likely about 71,000 lives and led to a net benefit to the United States of 1.7 percent of GDP after accounting for lives saved and drops in workforce participation. Through October 31, 2021, the VSL of U.S. lives lost to COVID-19 was over $8.4 trillion.
Keywords: cost-benefit, CFR, COVID-19, IFR, NPI, SARS-CoV-2, social distancing, stay-at-home orders, VSL
JEL Classification: G22, I1, I18, J31, J65, K32
Suggested Citation:
Sep 12, 2021
Sep 12, 2021
31 min
Dr. Linus Wilson discusses and reads his most recent study about the cryptocurrency mining and computer hardware prices entitled "GPU Prices and Cryptocurrency Returns".
"Abstract
We look at the association between the price of a cryptocurrency and the secondary market prices
of the hardware used to mine it. We find the prices of the most efficient Graphical Processing Units
(GPUs) for Ethereum mining are significantly positively correlated with the daily price returns to
that cryptocurrency.
Journal of Economic Literature Codes: G12, G23, L11, L22, L63
Keywords: 3080, ASIC, Bitcoin, crypto, cryptocurrency, ETH, Ethereum, GeForce, GPU,
mining, Nvidia, RTX
by
Dr. Linus Wilson
Associate Professor of Finance
Department of Economics & Finance
B.I. Moody III College of Business
University of Louisiana at Lafayette "
"1. Introduction
We use a unique data set of scalper prices for graphical processing units (GPUs) to study
the association between the price of Ethereum (ticker ETH) and the hardware used to mine it.
We find the most efficient ETH mining GPUs as measured by secondary market price per
productivity unit (called the hashrate) had secondary market price moves that were positively
correlated with daily returns to ETH.
Most of the prior research into cryptocurrency mining has focussed on Bitcoin and does
not measure the impact between the cryptocurrency’s price’s correlation with key mining
hardware. Dimitri (2017) and Ma et al. (2019) model Bitcoin mining as an all-pay tournament.
Ma et al. (2019) argue that free entry in mining is ultimately wasteful in part because Bitcoin
miners consumed more electricity than all of Australia. Easley et al. (2019) are sceptical about
the usefulness of Bitcoin as a medium of exchange as its network could only process seven
transactions per second versus Visa which can process 50,000 transactions per second. Cong et
al. (2021) find that mining pools help cryptocurrency miners eliminate ideosyncratic risk.
Kristoufek (2020) finds that price of Bitcoin over the long-term impacted the cost of mining
components. Mueller (2020) looks at entry and exit thresholds for both Bitcoin and Ethereum
miners.
In section 2, the GPU mining market for cryptocurrency is discussed and basic model of
GPU pricing with ETH mining is developed. The data sources are discussed in section 3. In
section 4, the statistical analysis indicates that the Nvidia GeForce RTX 3060ti and the RTX
3080 GPUs are significantly more attractively priced for ETH mining, and their prices are
positively correlated with daily price moves in Ethereum. "
The paper link is at
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3922181
(c) Linus Wilson, 2021, Vermilion Advisory Services, LLC
May 1, 2020
May 1, 2020
59 min
Dr. Linus Wilson discusses and reads his new study:
SARS-CoV-2, COVID-19, Infection Fatality Rate (IFR) Implied by the Serology, Antibody, Testing in New York City
Wilson, Linus, SARS-CoV-2, COVID-19, Infection Fatality Rate (IFR) Implied by the Serology, Antibody, Testing in New York City (May 1, 2020). Available at SSRN:
https://ssrn.com/abstract=3590771
"Abstract
The SARS-CoV-2, COVID-19, infection fatality rate (IFR) has been hard to accurately estimate. It is a key parameter for disease modeling and policy decisions. Asymptomatic spread and limited testing have understated infections in hard to predict ways across jurisdictions. We survey serology, antibody, studies of the COVID-19 infection to find official cases are understated by an average of 25-to-1. Further, we analyze the deaths and infections in New York City to estimate an overall IFR for the United States of 0.863 percent.
...5. Conclusion
The COVID-19 pandemic has a lot of uncertainty about the ratio of deaths to total infections. That confounds the calculation of how deadly the novel coronavirus is. The serology sampling in New York City and elsewhere makes estimates of infections more reliable. We estimate that the infection fatality rate (IFR) from serology studies in nine different sampling locations in the United State and Europe is on average 0.38 percent. We analyze the data from New York City in-depth to estimate that the IFR for all ages and genders in New York City was 0.85 percent. New York City is a preferable location to estimate IFR because it has one of the highest infection rates in the world. Thus, random sampling is less prone to an upward bias in false positives. In addition, New York City’s official counts are less likely to understate deaths than in other locations in the United States. We find that the infection fatality rates from New York vary a great deal by age and gender. Females ages 0 to 17 can expect infection fatality rates of 0.001 percent while males of age 75 and over can expect infection fatality rates of 9.127 percent."
Dr. Linus Wilson[1]
Associate Professor of Finance
Department of Economics & Finance
B.I. Moody III College of Business
University of Louisiana at Lafayette
Moody Hall, Room 253
P.O. Box 43709
Lafayette, LA 70504
(337) 482-6209
linus [dot] wilson {at} louisiana [dot] edu
Apr 19, 2020
Apr 19, 2020
59 min
Dr. Wilson reads and discusses his new paper:
Wilson, Linus, Estimating the Life Expectancy and Value of Statistical Life (VSL) Losses from COVID-19 Infections in the United States (April 19, 2020). Available at SSRN: https://ssrn.com/abstract=3580414
His the "Download this paper" button to get a free copy.
Estimating the Life Expectancy and Value of Statistical Life (VSL) Losses from COVID-19 Infections in the United States
By
Dr. Linus Wilson
Associate Professor of Finance
Department of Economics & Finance
B.I. Moody III College of Business
University of Louisiana at Lafayette
Theses are the views of the author alone.
"
Abstract
Americans aged sixty or older stand to lose 153 to 222 days of life expectancy from contracting COVID-19. Over 90 percent of the U.S. population was under stay at home orders by April 2020. These social distancing measures to slow the spread of the SARS-CoV-2 or novel coronavirus have led to over 20 million new applications for unemployment benefits. Are these economic losses justified? We find the value of statistical lives lost (VSL) from an unconstrained spread of the virus which hypothetically infected 81 percent of the population would amount to $8 to $60 trillion.
Journal of Economic Literature Codes: G22, I1, I18, J31, J65, K32
Keywords: actuarial tables, mortality, death rates, CFR, COVID-19, IFR, life expectancy, SARS-CoV-2, school closures, social distancing, stay at home orders, VSL
1. Introduction
This paper attempts to open the discussion of how to model the benefits of social distancing measures in terms of the value of statistical lives (VSL) saved in the SARS-CoV-2 or COVID-19 pandemic. To do this we compare the infection fatality rates IFR’s of Ferguson et al. (2020) to the VSL from several studies. We find in figure 3 panels A and B that the costs of 50 percent of the U.S. population being infected with COVID-19 in lives lost and VSL are, respectively, between 0.659 million and 2.305 million lives lost and between $5 trillion and $37 trillion in VSL losses. The Gross Domestic Product (GDP) was only $21.7 trillion at the end of 2019 according to Mataloni and Aversa (2020).
We use U.S. Census data to control for the age and gender of the population and show how a COVID-19 infection affects an individual’s life expectancy and compares to a typical year’s mortality. Life expectancy losses of between 153 and 222 days can be expected for Americans over 60 with a novel coronavirus infection, according to figure 2, panel C. Americans younger than forty can expect to lose less than two weeks of life expectancy from contracting the virus. In figure 1, panel C, persons over fifty can expect COVID-19 to be about as deadly or up to 70 percent more deadly than a year’s mortality risks. Persons younger than forty-years-old can expect less than half a year’s mortality risk in a COVID-19 infection.
To argue against the social distancing measures, either the IFR of COVID-19 must be nearer to the low end of Ferguson et al. (2020)’s 95 percent confidence interval or the social distancing measure must be very ineffective in reducing the reproductive number, R0, of the SARS-CoV-2 virus. As to the former, a few studies suggest a much lower mean IFR than the 0.9 percent from Ferguson et al. (2020). Ioannidis (2020) argues that, after adjusting for the age of the infected on the Diamond Princess cruise ship, the IFR for the U.S. population should be 0.3 percent, which is below the lower bound of the 95 percent confidence interval calculated in Ferguson et al. (2020) and used here. Likewise, a population-weighted study, Bendavid et al. (2020), recruited people to be tested in Santa Clara County, California regardless of symptoms for COVID-19. It found infection rates were severely under-reported. They calculated an IFR between 0.12 and 0.2 percent.
On the other hand, social distancing may be effective in reducing the spread of COVID-19. R0 is the number of additional persons that an infected person goes on to infect on average. Anecdotal evidence indicates that social distancing in late March and early April 2020 has been effective. Governor Andrew Cuomo of New York State, which has had the highest number of deaths and confirmed cases of COVID-19 of the U.S. states on April 16, 2020, argued in his daily briefing in CNBC (2020) that his modeling teams believed that R0 fell from a median of 2.5 in Wuhan before social distancing and 2.2 on the Diamond Princess Cruise ship to 0.9 in New York State after the mitigation efforts. Governor Cuomo argued that his advisors’ projected hospitals in the state would be overwhelmed with COVID-19 patients if R0 was persistently above 1.2.[1] Rocklöv et al. (2020) estimate that uncontrolled R0 for COVID-19 on the Diamond Princess cruise ship was 14.8 before social isolation and 1.8 afterward. Chowell et al. (2011) argued that school closures in Mexico reduced the R0 of the H1N1 outbreak by more than 30 percent.
This paper will not attempt to measure the costs of social distancing which, no doubt, number in the many trillions of dollars in the United States alone. By April 7, 2020, Secon and Woodward (2020) reported that 95 percent of the U.S. population was under a stay at home order that meant all but “essential” businesses were shuddered. Morath and Chaney (2020) report that by April 16, 2020, 13 percent of the U.S. workforce or 22 million workers had filed unemployment insurance claims. The COVID-19 multi-state stay at home orders, and associated non-essential business shutdowns, began with California, on March 19, 2020, according to Mervosh et al. (2020). Before the SARS-CoV-2 disruptions, the U.S. unemployment rate stood at a record low 3.5 percent in February 2020 according to the Bureau of Labor Statistics.
Eichenbaum et al. (2020) estimate containing COVID-19 “optimally” with social distancing will lead to consumption dropping by 22 percent versus 7 percent without containment of the virus. Since consumption is about 68.1 percent of GDP, according to the St. Louis Fed, and 2019 GDP was $21.7 trillion, they are arguing macroeconomic consumption losses are about (0.22 – .07)*$21.7 trillion = $3.26 trillion. The loss of freedom cannot just be measured in just macroeconomic statistics. Aggregate consumption does not measure the loss of consumer and producer surplus. There may also be long-term effects to school-age children or households facing bankruptcy that may not be captured fully in Eichenbaum et al. (2020). People losing their jobs also lose their health insurance and may be more likely to die as a result. Certainly, more empirical work can be done to estimate the actual costs of social distancing measures relative to their R0 benefits.
One press release, Yale News (2020), from the Yale Tobin Center for Economic Policy estimated the daily losses of shutdowns at $19 billion per day or about $7 trillion per year. Nevertheless, $3 to $7 trillion annually is less than this paper’s low-end estimates of the VSL losses for anything resembling the unconstrained spread of the virus infecting 81 percent of the population that Ferguson et. al (2020) project. Our low-end VSL losses are $8 trillion for an 81 percent infection rate. For the high-end estimate, the value of statistical lives lost is about $60 trillion.
Overall, the results of this paper point to sizable personal risks for individuals over sixty becoming infected with COVID-19 in terms of the increased chance of death and reduced life expectancy. We also find that the number of deaths and value of statistical life (VSL) losses are extremely high from high rates of COVID-19 infection. Thus, major economic disruptions from social distancing, stay at home orders, and school closures will be justified if the infection fatality rate estimates are reasonable. Measurement error of IFR, pharmaceutical treatments reducing the fatality rates, or a vaccine could make social distancing economic disruptions not worth the cost.
[1] See the discussion around the 24:45 minute mark of CNBC (2020)."